From 8cad4931216e01173cf74915849ef7f04a21d06c Mon Sep 17 00:00:00 2001
From: Spatariu Rares <81376159+SpatariuRares@users.noreply.github.com>
Date: Sat, 20 Jun 2026 10:14:21 +0200
Subject: [PATCH 1/9] feat: add instructional content to regularization
 notebook and reorganize project structure

---
 .../notebook-pedagogical-enrichment/SKILL.md  |   74 +
 .../scripts/notebook_helper.py                |   60 +
 .gitignore                                    |   13 +
 .../data_preprocessing_exercises.ipynb        |    0
 .../esercizi/regressione_exercises.ipynb      | 2324 +++++++++++++++++
 .../housing_estimate.xlsx                     |  Bin 0 -> 5050 bytes
 .../regressione_exercises.ipynb               | 1309 ----------
 .../binary_classification_exercise.ipynb      |    4 +-
 ...overfitting_regularizzazion_exercise.ipynb |    4 +-
 .../regularization.ipynb                      |  172 +-
 .../{ => esercizi}/clustering_exercise.ipynb  |    4 +-
 AGENT.md                                      |   47 +
 requirements.txt                              |  336 +++
 13 files changed, 3007 insertions(+), 1340 deletions(-)
 create mode 100644 .agents/notebook-pedagogical-enrichment/SKILL.md
 create mode 100755 .agents/notebook-pedagogical-enrichment/scripts/notebook_helper.py
 create mode 100644 .gitignore
 rename 2 - Data Preprocessing/{ => esercizi}/data_preprocessing_exercises.ipynb (100%)
 create mode 100644 3 - La Regressione Lineare/esercizi/regressione_exercises.ipynb
 create mode 100644 3 - La Regressione Lineare/housing_estimate.xlsx
 delete mode 100644 3 - La Regressione Lineare/regressione_exercises.ipynb
 rename 4 - La Classificazione/{ => esercizi}/binary_classification_exercise.ipynb (99%)
 rename 4 - Overfitting e Tecniche di Regolarizzazione/{ => esercizi}/overfitting_regularizzazion_exercise.ipynb (99%)
 rename 6 - Clustering/{ => esercizi}/clustering_exercise.ipynb (99%)
 create mode 100644 AGENT.md
 create mode 100644 requirements.txt

diff --git a/.agents/notebook-pedagogical-enrichment/SKILL.md b/.agents/notebook-pedagogical-enrichment/SKILL.md
new file mode 100644
index 0000000..ba1e2a2
--- /dev/null
+++ b/.agents/notebook-pedagogical-enrichment/SKILL.md
@@ -0,0 +1,74 @@
+---
+name: notebook-pedagogical-enrichment
+description: |
+  Enrich Jupyter notebooks with pedagogical markdown explanations for students.
+  Trigger when the user asks to explain, document, add markdown, or format a Jupyter notebook (.ipynb) to make it suitable for students or educational purposes.
+---
+
+# Notebook Pedagogical Enrichment
+
+A skill to transform technical Jupyter notebooks into structured, narrative-driven educational resources designed for students.
+
+## When to Use
+Trigger this skill whenever the user asks to:
+- Explain the code blocks in a Jupyter notebook (`.ipynb`).
+- Add Markdown formatted notes or explanations for students.
+- Document step-by-step procedures in a notebook.
+- Compare different Machine Learning algorithms (e.g., OLS vs Ridge vs Lasso) inside a notebook for teaching.
+
+## Step-by-Step Workflow
+
+### Step 1: Inspect Notebook Cells
+Use a Python script to read the target `.ipynb` file and print a summary of all cells, their types, and the first lines of their source code. Do not read the entire file as a raw text view if it is very large (due to HTML outputs).
+Example inspection snippet:
+```python
+import json
+with open("notebook.ipynb", "r") as f:
+    nb = json.load(f)
+for i, cell in enumerate(nb["cells"]):
+    source = "".join(cell.get("source", []))
+    print(f"Cell {i:02d} ({cell['cell_type']}): {source[:80]}...")
+```
+Or use the local helper script:
+```bash
+python3 .agents/notebook-pedagogical-enrichment/scripts/notebook_helper.py inspect <path_to_notebook.ipynb>
+```
+
+### Step 2: Compute Exact Metrics (Dry Run)
+Before writing explanations, run the notebook's code (using the workspace virtual environment, e.g., `.venv/bin/python3`) to obtain the exact training/testing scores (like MSE, $R^2$, accuracy, etc.).
+Reporting exact numbers (e.g., *“the test $R^2$ is 0.217 for OLS but 0.994 for Lasso”*) makes the explanations extremely authentic and helpful.
+
+### Step 3: Write Rich Markdown Explanations
+Explanations must follow best practices in technical writing and pedagogy:
+- **Use Clear Formatting**: Use bold text, bullet points, and code blocks.
+- **Explain the "Why"**: Don't just say *what* the code does; explain *why* we do it (e.g., why we scale features, why a random seed is set, why data leakage is bad).
+- **Use Math Formulas**: Use LaTeX syntax (e.g., `$$\text{Loss} = \text{MSE} + \alpha \sum_{j=1}^{p} w_j^2$$`) to describe the loss functions and penalties.
+- **Explain Model Performance Contrast**:
+  - **OLS**: Explain overfitting (memorization of training data, poor generalization).
+  - **Ridge (L2)**: Explain weight shrinkage without zeroing, and why it might not be enough when there are many non-informative features.
+  - **Lasso (L1)**: Explain feature selection (zeroing out uninformative weights) and why it works so well for noisy data.
+- **Learning Curves**: Explain how to diagnose bias/variance by looking at the gap and convergence of training and validation scores.
+
+### Step 4: Update the Notebook Programmatically
+Always edit Jupyter notebooks by loading the JSON in Python, manipulating the `cells` list, and saving the JSON back. This preserves metadata, notebook formatting, and prevents syntax issues.
+
+Use the following stateful matching script structure:
+```python
+import json
+
+def make_markdown_cell(text):
+    lines = [line + "\n" for line in text.split("\n")]
+    if lines and lines[-1] == "\n":
+        lines.pop()
+    elif lines:
+        lines[-1] = lines[-1].rstrip("\n")
+    return {"cell_type": "markdown", "metadata": {}, "source": lines}
+
+# Load, reconstruct nb["cells"] by matching cell signatures, and write back.
+```
+
+### Step 5: Validation
+Verify that the output notebook is valid JSON and loads properly:
+```bash
+python3 -c 'import json; json.load(open("notebook.ipynb"))'
+```
diff --git a/.agents/notebook-pedagogical-enrichment/scripts/notebook_helper.py b/.agents/notebook-pedagogical-enrichment/scripts/notebook_helper.py
new file mode 100755
index 0000000..e512f36
--- /dev/null
+++ b/.agents/notebook-pedagogical-enrichment/scripts/notebook_helper.py
@@ -0,0 +1,60 @@
+#!/usr/bin/env python3
+import sys
+import json
+
+def inspect_notebook(nb_path):
+    try:
+        with open(nb_path, "r", encoding="utf-8") as f:
+            nb = json.load(f)
+        
+        print(f"Notebook: {nb_path}")
+        print(f"Format: v{nb.get('nbformat')}.{nb.get('nbformat_minor')}")
+        print(f"Total cells: {len(nb.get('cells', []))}")
+        print("-" * 60)
+        
+        for i, cell in enumerate(nb.get("cells", [])):
+            cell_type = cell.get("cell_type", "unknown")
+            source_lines = cell.get("source", [])
+            source_text = "".join(source_lines).strip()
+            preview = source_text.split("\n")[0] if source_text else "[Empty]"
+            print(f"Cell {i:02d} | Type: {cell_type:<10} | Preview: {preview[:80]}")
+            
+    except Exception as e:
+        print(f"Error inspecting notebook: {e}", file=sys.stderr)
+
+def clear_outputs(nb_path):
+    try:
+        with open(nb_path, "r", encoding="utf-8") as f:
+            nb = json.load(f)
+            
+        for cell in nb.get("cells", []):
+            if cell.get("cell_type") == "code":
+                cell["outputs"] = []
+                cell["execution_count"] = None
+                
+        with open(nb_path, "w", encoding="utf-8") as f:
+            json.dump(nb, f, indent=2)
+        print(f"Cleared all outputs in {nb_path} successfully.")
+    except Exception as e:
+        print(f"Error clearing outputs: {e}", file=sys.stderr)
+
+def main():
+    if len(sys.argv) < 3:
+        print("Usage:")
+        print("  notebook_helper.py inspect <path_to_notebook.ipynb>")
+        print("  notebook_helper.py clear-outputs <path_to_notebook.ipynb>")
+        sys.exit(1)
+        
+    cmd = sys.argv[1]
+    nb_path = sys.argv[2]
+    
+    if cmd == "inspect":
+        inspect_notebook(nb_path)
+    elif cmd == "clear-outputs":
+        clear_outputs(nb_path)
+    else:
+        print(f"Unknown command: {cmd}", file=sys.stderr)
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..aa52821
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,13 @@
+# Virtual Environment
+.venv/
+venv/
+ENV/
+env/
+
+# Python cache
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Jupyter Notebook checkpoints
+.ipynb_checkpoints/
diff --git a/2 - Data Preprocessing/data_preprocessing_exercises.ipynb b/2 - Data Preprocessing/esercizi/data_preprocessing_exercises.ipynb
similarity index 100%
rename from 2 - Data Preprocessing/data_preprocessing_exercises.ipynb
rename to 2 - Data Preprocessing/esercizi/data_preprocessing_exercises.ipynb
diff --git a/3 - La Regressione Lineare/esercizi/regressione_exercises.ipynb b/3 - La Regressione Lineare/esercizi/regressione_exercises.ipynb
new file mode 100644
index 0000000..2475785
--- /dev/null
+++ b/3 - La Regressione Lineare/esercizi/regressione_exercises.ipynb	
@@ -0,0 +1,2324 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "fc73dbdb",
+      "metadata": {
+        "id": "fc73dbdb"
+      },
+      "source": [
+        "# Regressione: Esercitazione\n",
+        "\n",
+        "Per questa esercitazione dovrai creare il tuo primo modello di regressione lineare. Per farlo utilizzerai il Boston Housing Dataset, che hai già visto nella sezione dedicata al preprocessing dei dati. Puoi scaricare il dataset già pulito [da qui](https://raw.githubusercontent.com/ProfAI/machine-learning-fondamenti/main/datasets/housing_dirty.csv).\n",
+        "\n",
+        "Il dataset contiene le seguenti informazioni\n",
+        "\n",
+        "1. **CRIM** Tasso di criminalità per capita\n",
+        "2. **ZN** Percentuale di terreni residenziali suddivisi in zone per lotti superiori a 25.000 sq.ft.\n",
+        "3. **INDUS** Percentuale di ettari di attività non al dettaglio per città.\n",
+        "4. **CHAS** Variabile dummy che indica la prossimità al fiume Charles.\n",
+        "5. **NOX** Concentrazione di ossido d'azoto (parti per 10 milioni).\n",
+        "6. **RM** Numero medio di stanze per abitazione\n",
+        "7. **AGE** Percentuale di abitazione occupate costruite dopo il 1940\n",
+        "8. **DIS** Media pesata delle distanze da 5 centri lavorativi di Boston.\n",
+        "9. **RAD** Indice di accessibilità ad autostrade\n",
+        "10. **TAX** Aliquota dell'imposta sulla proprietà a valore pieno in 10.000 USD.\n",
+        "11. **PRATIO** Rapporto studente-insegnante per città.\n",
+        "12. **BLACK** 1000(Bk - 0.63)^2 dove Bk è la percentuale di abitanti di colore per città\n",
+        "13. **LSTAT** Percentuale della popolazione povera\n",
+        "14. **PRICE** Mediana del valore di abitazioni occupate in 1.000 USD.\n",
+        "\n",
+        "Il target è la colonna PRICE, cioè vogliamo prevedere il valore delle abitazioni.\n",
+        "\n",
+        "Nello specifico, devi risolvere i seguenti punti:\n",
+        "1. Crea la matrice di correlazione. \n",
+        "2. Addestra e valuta un modello di regressione lineare semplice utilizzano la variabile che sembra maggiormente correlata al target.\n",
+        "3. Addestra e valuta un modello di regressione lineare multipla utilizzando le due variabili che sembrano maggiormente correlate al target.\n",
+        "4. Aggiungi una terza variabile, quindi crea diversi modelli di regressione polinomiale, senza superare il grado 5, prova sia con che senza bias.\n",
+        "5. Addestra e valuta un modello di regressione lineare utilizzando tutte le variabili del dataset.\n",
+        "6. Esegui la normalizzazione dei dati e riaddestra il modello, le performance sono migliorate?\n",
+        "7. Esegui la standardizzazione dei dati e riaddestra il modello, le performance sono migliorate?\n",
+        "8. Utilizza il modello con tutte le features per prevedere il prezzo delle abitazioni che trovi in [questo file CSV]().\n",
+        "9. Salva il risultato in un file excel chiamato \"housing_estimate.xlsx\", deve contenere due colonne: OWNER=il proprietario dell'abitazione, ESTIMATED PRICE=il valore stimato dal nostro modello.\n",
+        "\n",
+        "\n",
+        "**Nota**\n",
+        "Se mastichi già l'argomento e il termine \"overfitting\" non ti è nuovo, non preoccupartene per adesso, ci arriveremo nella prossima sezione."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c2fcb16a",
+      "metadata": {
+        "id": "c2fcb16a"
+      },
+      "source": [
+        "### Soluzione"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "id": "45e62148",
+      "metadata": {
+        "id": "45e62148"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "id": "a5b4ba2b",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "a5b4ba2b",
+        "outputId": "6f467ca8-355e-42b3-c7f5-17478a1e909f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>CRIM</th>\n",
+              "      <th>ZN</th>\n",
+              "      <th>INDUS</th>\n",
+              "      <th>CHAS</th>\n",
+              "      <th>NOX</th>\n",
+              "      <th>RM</th>\n",
+              "      <th>AGE</th>\n",
+              "      <th>DIS</th>\n",
+              "      <th>RAD</th>\n",
+              "      <th>TAX</th>\n",
+              "      <th>PTRATIO</th>\n",
+              "      <th>B</th>\n",
+              "      <th>LSTAT</th>\n",
+              "      <th>PRICE</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.00632</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>2.31</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.538</td>\n",
+              "      <td>6.575</td>\n",
+              "      <td>65.2</td>\n",
+              "      <td>4.0900</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>15.3</td>\n",
+              "      <td>396.90</td>\n",
+              "      <td>4.98</td>\n",
+              "      <td>24.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.02731</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>7.07</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.469</td>\n",
+              "      <td>6.421</td>\n",
+              "      <td>78.9</td>\n",
+              "      <td>4.9671</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>242.0</td>\n",
+              "      <td>17.8</td>\n",
+              "      <td>396.90</td>\n",
+              "      <td>9.14</td>\n",
+              "      <td>21.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.02729</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>7.07</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.469</td>\n",
+              "      <td>7.185</td>\n",
+              "      <td>61.1</td>\n",
+              "      <td>4.9671</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>242.0</td>\n",
+              "      <td>17.8</td>\n",
+              "      <td>392.83</td>\n",
+              "      <td>4.03</td>\n",
+              "      <td>34.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.03237</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.18</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.458</td>\n",
+              "      <td>6.998</td>\n",
+              "      <td>45.8</td>\n",
+              "      <td>6.0622</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>222.0</td>\n",
+              "      <td>18.7</td>\n",
+              "      <td>394.63</td>\n",
+              "      <td>2.94</td>\n",
+              "      <td>33.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.06905</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.18</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.458</td>\n",
+              "      <td>7.147</td>\n",
+              "      <td>54.2</td>\n",
+              "      <td>6.0622</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>222.0</td>\n",
+              "      <td>18.7</td>\n",
+              "      <td>396.90</td>\n",
+              "      <td>5.33</td>\n",
+              "      <td>36.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
+              "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
+              "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
+              "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
+              "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
+              "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
+              "\n",
+              "   PTRATIO       B  LSTAT  PRICE  \n",
+              "0     15.3  396.90   4.98   24.0  \n",
+              "1     17.8  396.90   9.14   21.6  \n",
+              "2     17.8  392.83   4.03   34.7  \n",
+              "3     18.7  394.63   2.94   33.4  \n",
+              "4     18.7  396.90   5.33   36.2  "
+            ]
+          },
+          "execution_count": 32,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "BASE_URL = \"https://raw.githubusercontent.com/ProfAI/machine-learning-fondamenti/main/datasets/\"\n",
+        "df = pd.read_csv(BASE_URL+\"housing.csv\", index_col=0)\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cfdbca4e",
+      "metadata": {
+        "id": "cfdbca4e"
+      },
+      "source": [
+        "###1. Crea la matrice di correlazione. \n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "id": "0e3e35cc",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 658
+        },
+        "id": "0e3e35cc",
+        "outputId": "ee877577-8241-48f9-a1ac-9e1eafff462e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1120x800 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "plt.figure(figsize=(14, 10), dpi=80)\n",
+        "\n",
+        "hm = sns.heatmap(df.corr(),\n",
+        "                 cbar=True,\n",
+        "                 square=True,\n",
+        "                 yticklabels=df.columns,\n",
+        "                 xticklabels=df.columns,\n",
+        "                 annot=True,                  #Questo ci mostra i valori degli indici\n",
+        "                 annot_kws={'size':12})       #Impostiamo la dimensione dell'annotazione a 12 per farla entrare dentro il quadrato\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "720761f4",
+      "metadata": {
+        "id": "720761f4"
+      },
+      "source": [
+        "### 2. Addestra e valuta un modello di regressione lineare semplice utilizzano la variabile che sembra maggiormente correlata al target."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "id": "ff28395a",
+      "metadata": {
+        "id": "ff28395a"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import mean_squared_error, r2_score\n",
+        "\n",
+        "def evaluate(model, data):\n",
+        "    x, y = data\n",
+        "    y_pred = model.predict(x)\n",
+        "    print(f\"RMSE = {np.sqrt(mean_squared_error(y, y_pred))}\")\n",
+        "    print(f\"R2 = {r2_score(y, y_pred)}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "id": "b2960fa6",
+      "metadata": {
+        "id": "b2960fa6"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.linear_model import LinearRegression"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "id": "ca247c9f",
+      "metadata": {
+        "id": "ca247c9f"
+      },
+      "outputs": [],
+      "source": [
+        "x = df[\"LSTAT\"].values\n",
+        "y = df[\"PRICE\"].values"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "id": "837678ad",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "837678ad",
+        "outputId": "70217347-28c9-4abe-d99d-97461a1eaf81"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(506,)"
+            ]
+          },
+          "execution_count": 37,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "x.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "id": "86bd3e73",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "86bd3e73",
+        "outputId": "99f2ce23-b80f-4def-9e9b-0477544639bd"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(506, 1)"
+            ]
+          },
+          "execution_count": 38,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "x = x.reshape(-1, 1)\n",
+        "x.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "id": "d39e099e",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "d39e099e",
+        "outputId": "acbf44f6-79d4-4669-ed82-42ff40f7a4dc"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>.sk-global {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "}\n",
+              "\n",
+              ".sk-global.light {\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: black;\n",
+              "  --sklearn-color-background: white;\n",
+              "  --sklearn-color-border-box: black;\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "}\n",
+              "\n",
+              ".sk-global.dark {\n",
+              "  --sklearn-color-text-on-default-background: white;\n",
+              "  --sklearn-color-background: #111;\n",
+              "  --sklearn-color-border-box: white;\n",
+              "  --sklearn-color-icon: #878787;\n",
+              "}\n",
+              "\n",
+              ".sk-global {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              ".sk-global pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              ".sk-global input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip-path: inset(100%);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              ".sk-global div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              ".sk-global div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              ".sk-global div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              ".sk-global label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: center;\n",
+              "  justify-content: center;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              ".sk-global label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              ".sk-global label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              ".sk-global div.sk-toggleable__content {\n",
+              "  display: none;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  overflow: visible;\n",
+              "}\n",
+              "\n",
+              ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-label label.sk-toggleable__label,\n",
+              ".sk-global div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              ".sk-global div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              ".sk-global div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              ".sk-global div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-0);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-0);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              ".sk-global a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              ".sk-global a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              ".sk-global a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              ".sk-global a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-top-container.sk-global {\n",
+              "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
+              "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
+              "   to allow keyboard-only users to navigate the display. */\n",
+              "  overflow-x: scroll !important;\n",
+              "  max-width: 100%;\n",
+              "}\n",
+              "\n",
+              ".estimator-table {\n",
+              "    font-family: monospace;\n",
+              "}\n",
+              "\n",
+              ".estimator-table summary {\n",
+              "    padding: .5rem;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "\n",
+              ".estimator-table summary::marker {\n",
+              "    font-size: 0.7rem;\n",
+              "}\n",
+              "\n",
+              ".estimator-table details[open] {\n",
+              "    padding-left: 0.1rem;\n",
+              "    padding-right: 0.1rem;\n",
+              "    padding-bottom: 0.3rem;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table {\n",
+              "    margin-left: auto !important;\n",
+              "    margin-right: auto !important;\n",
+              "    margin-top: 0;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+              "    background-color: #fff;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(even) {\n",
+              "    background-color: #f6f6f6;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:hover td {\n",
+              "    background-color: #e0e0e0;\n",
+              "}\n",
+              "\n",
+              ".estimator-table table :is(td, th) {\n",
+              "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+              "}\n",
+              "\n",
+              "/*\n",
+              "    `table td`is set in notebook with right text-align.\n",
+              "    We need to overwrite it.\n",
+              "*/\n",
+              ".estimator-table table td.param {\n",
+              "    text-align: left;\n",
+              "    position: relative;\n",
+              "    padding: 0;\n",
+              "}\n",
+              "\n",
+              ".user-set td {\n",
+              "    color:rgb(255, 94, 0);\n",
+              "    text-align: left !important;\n",
+              "}\n",
+              "\n",
+              ".user-set td.value {\n",
+              "    color:rgb(255, 94, 0);\n",
+              "    background-color: transparent;\n",
+              "}\n",
+              "\n",
+              ".default td, .estimator-table th {\n",
+              "    color: black;\n",
+              "    text-align: left !important;\n",
+              "}\n",
+              "\n",
+              ".user-set td i,\n",
+              ".default td i {\n",
+              "    color: black;\n",
+              "}\n",
+              "\n",
+              "td.fitted-att-type {\n",
+              "    white-space: preserve nowrap;\n",
+              "}\n",
+              "\n",
+              "/*\n",
+              "    Styles for parameter documentation links\n",
+              "    We need styling for visited so jupyter doesn't overwrite it\n",
+              "*/\n",
+              "a.param-doc-link,\n",
+              "a.param-doc-link:link,\n",
+              "a.param-doc-link:visited {\n",
+              "    text-decoration: underline dashed;\n",
+              "    text-underline-offset: .3em;\n",
+              "    color: inherit;\n",
+              "    display: block;\n",
+              "    padding: .5em;\n",
+              "}\n",
+              "\n",
+              "@supports(anchor-name: --doc-link) {\n",
+              "    a.param-doc-link,\n",
+              "    a.param-doc-link:link,\n",
+              "    a.param-doc-link:visited {\n",
+              "    anchor-name: --doc-link;\n",
+              "    }\n",
+              "}\n",
+              "\n",
+              "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+              "a.param-doc-link::before {\n",
+              "    position: absolute;\n",
+              "    content: \"\";\n",
+              "    inset: 0;\n",
+              "}\n",
+              "\n",
+              ".param-doc-description {\n",
+              "    display: none;\n",
+              "    position: absolute;\n",
+              "    z-index: 9999;\n",
+              "    left: 0;\n",
+              "    padding: .5ex;\n",
+              "    margin-left: 1.5em;\n",
+              "    color: var(--sklearn-color-text);\n",
+              "    box-shadow: .3em .3em .4em #999;\n",
+              "    width: max-content;\n",
+              "    text-align: left;\n",
+              "    max-height: 10em;\n",
+              "    overflow-y: auto;\n",
+              "\n",
+              "    /* unfitted */\n",
+              "    background: var(--sklearn-color-unfitted-level-0);\n",
+              "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              "@supports(position-area: center right) {\n",
+              "    .param-doc-description {\n",
+              "    position-area: center right;\n",
+              "    position: fixed;\n",
+              "    margin-left: 0;\n",
+              "    }\n",
+              "}\n",
+              "\n",
+              "/* Fitted state for parameter tooltips */\n",
+              ".fitted .param-doc-description {\n",
+              "    /* fitted */\n",
+              "    background: var(--sklearn-color-fitted-level-0);\n",
+              "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".param-doc-link:hover .param-doc-description {\n",
+              "    display: block;\n",
+              "}\n",
+              "\n",
+              ".copy-paste-icon {\n",
+              "    background-image: url(data:image/svg+xml;base64,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);\n",
+              "    background-repeat: no-repeat;\n",
+              "    background-size: 14px 14px;\n",
+              "    background-position: 0;\n",
+              "    display: inline-block;\n",
+              "    width: 14px;\n",
+              "    height: 14px;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "\n",
+              ".features {\n",
+              "  font-family: monospace;\n",
+              "  cursor: pointer;\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: .20em;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  font-size: inherit; /* Needed for jupyter */\n",
+              "}\n",
+              "\n",
+              ".features.fitted {\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              ".features summary {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  margin-bottom: 0;\n",
+              "  text-align: center;\n",
+              "  align-items: center;\n",
+              "  justify-content: center;\n",
+              "  gap: 0.5em;\n",
+              "  padding: .25em;\n",
+              "}\n",
+              "\n",
+              ".features details[open] > summary {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "  border-radius: .20em 0 0 0;\n",
+              "}\n",
+              "\n",
+              ".features.fitted details[open] > summary {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "  border-radius: .20em 0 0 0;\n",
+              "}\n",
+              "\n",
+              ".features details > summary .arrow::before {\n",
+              "  content: \"▸\";\n",
+              "  color: grey;\n",
+              "}\n",
+              "\n",
+              ".features details[open] > summary .arrow::before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              ".features details:hover > summary {\n",
+              "  margin: 0;\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              ".features.fitted details:hover > summary {\n",
+              "  margin: 0;\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              ".features .features-container {\n",
+              "  max-width: 15em;\n",
+              "  max-height: 10em;\n",
+              "  overflow: auto;\n",
+              "  scrollbar-width: thin;\n",
+              "  padding: .25em 0.1rem;\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "  border-radius: 0 0 .5em .5em;\n",
+              "}\n",
+              "\n",
+              ".features.fitted .features-container {\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              ".features .image-container {\n",
+              "  block-size: 1em;\n",
+              "  inline-size: 1em;\n",
+              "  padding: 0;\n",
+              "  margin: 0%;\n",
+              "  display: flex;\n",
+              "  justify-content: center;\n",
+              "  align-items: center;\n",
+              "}\n",
+              "\n",
+              ".features .copy-paste-icon {\n",
+              "  background-size: 1em 1em;\n",
+              "  width: 1em;\n",
+              "  height: 1em;\n",
+              "  filter: grayscale(100%) opacity(60%);\n",
+              "}\n",
+              "\n",
+              ".features .features-container table {\n",
+              "  width: 100%;\n",
+              "  margin: 0.01em;\n",
+              "}\n",
+              "\n",
+              ".features .features-container table tr:nth-child(odd) {\n",
+              "  background-color: #fff;\n",
+              "}\n",
+              "\n",
+              ".features .features-container table tr:nth-child(even) {\n",
+              "  background-color: #f6f6f6;\n",
+              "}\n",
+              "\n",
+              ".features .features-container table tr:hover {\n",
+              "  background-color: #e0e0e0;\n",
+              "}\n",
+              "\n",
+              ".features .features-container table {\n",
+              "  table-layout: inherit;\n",
+              "}\n",
+              "\n",
+              ".features .features-container table td {\n",
+              "  text-align: left;\n",
+              "  padding: 0 0.5em;\n",
+              "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
+              "  white-space: nowrap;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              ".total_features {\n",
+              "  display: flex;\n",
+              "  justify-content: center;\n",
+              "  margin-top: 0.5em;\n",
+              "}\n",
+              "</style><body><div id=\"sk-container-id-2\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+              "        <div class=\"estimator-table\">\n",
+              "            <details>\n",
+              "                <summary>Parameters</summary>\n",
+              "                <table class=\"parameters-table\">\n",
+              "                  <tbody>\n",
+              "                    \n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('fit_intercept',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+              "            fit_intercept\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
+              "            fit_intercept: bool, default=True<br><br>Whether to calculate the intercept for this model. If set<br>to False, no intercept will be used in calculations<br>(i.e. data is expected to be centered).</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "            <td class=\"value\">True</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('copy_X',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-copy_X;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=copy_X,-bool%2C%20default%3DTrue\">\n",
+              "            copy_X\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-copy_X;\">\n",
+              "            copy_X: bool, default=True<br><br>If True, X will be copied; else, it may be overwritten.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "            <td class=\"value\">True</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('tol',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-tol;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=tol,-float%2C%20default%3D1e-6\">\n",
+              "            tol\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-tol;\">\n",
+              "            tol: float, default=1e-6<br><br>The precision of the solution (`coef_`) is determined by `tol` which<br>specifies the convergence criterion of the underlying solver. `tol` is<br>set as `atol` and `btol` of :func:`scipy.sparse.linalg.lsqr` when<br>fitting on sparse training data. `tol` is set as `cond` of<br>:func:`scipy.linalg.lstsq` when fitting on dense training data.<br><br>.. versionadded:: 1.7<br>.. versionchanged:: 1.9<br>    Now supported on dense data, interpreted as the `cond` parameter.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "            <td class=\"value\">1e-06</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('n_jobs',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+              "            n_jobs\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
+              "            n_jobs: int, default=None<br><br>The number of jobs to use for the computation. This will only provide<br>speedup in case of sufficiently large problems, that is if firstly<br>`n_targets &gt; 1` and secondly `X` is sparse or if `positive` is set<br>to `True`. ``None`` means 1 unless in a<br>:obj:`joblib.parallel_backend` context. ``-1`` means using all<br>processors. See :term:`Glossary &lt;n_jobs&gt;` for more details.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('positive',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-positive;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=positive,-bool%2C%20default%3DFalse\">\n",
+              "            positive\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-positive;\">\n",
+              "            positive: bool, default=False<br><br>When set to ``True``, forces the coefficients to be positive. This<br>option is only supported for dense arrays.<br><br>For a comparison between a linear regression model with positive constraints<br>on the regression coefficients and a linear regression without such constraints,<br>see :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py`.<br><br>.. versionadded:: 0.24</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "            <td class=\"value\">False</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "                  </tbody>\n",
+              "                </table>\n",
+              "            </details>\n",
+              "        </div>\n",
+              "    \n",
+              "        <div class=\"estimator-table\">\n",
+              "            <details>\n",
+              "                <summary>Fitted attributes</summary>\n",
+              "                <table class=\"parameters-table\">\n",
+              "                    <tbody>\n",
+              "                        <tr>\n",
+              "                        <th>Name</th>\n",
+              "                        <th>Type</th>\n",
+              "                        <th>Value</th>\n",
+              "                        </tr>\n",
+              "                        \n",
+              "       <tr class=\"default\">\n",
+              "           <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-coef_;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=coef_,-array%20of%20shape%20%28n_features%2C%20%29%20or%20%28n_targets%2C%20n_features%29\">\n",
+              "            coef_\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-coef_;\">\n",
+              "            coef_: array of shape (n_features, ) or (n_targets, n_features)<br><br>Estimated coefficients for the linear regression problem.<br>If multiple targets are passed during the fit (y 2D), this<br>is a 2D array of shape (n_targets, n_features), while if only<br>one target is passed, this is a 1D array of length n_features.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
+              "           <td>[-0.95]</td>\n",
+              "\n",
+              "\n",
+              "       </tr>\n",
+              "    \n",
+              "\n",
+              "       <tr class=\"default\">\n",
+              "           <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-intercept_;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=intercept_,-float%20or%20array%20of%20shape%20%28n_targets%2C%29\">\n",
+              "            intercept_\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-intercept_;\">\n",
+              "            intercept_: float or array of shape (n_targets,)<br><br>Independent term in the linear model. Set to 0.0 if<br>`fit_intercept = False`.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "           <td class=\"fitted-att-type\">float64</td>\n",
+              "           <td>34.55</td>\n",
+              "\n",
+              "\n",
+              "       </tr>\n",
+              "    \n",
+              "\n",
+              "       <tr class=\"default\">\n",
+              "           <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_features_in_,-int\">\n",
+              "            n_features_in_\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
+              "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "           <td class=\"fitted-att-type\">int</td>\n",
+              "           <td>1</td>\n",
+              "\n",
+              "\n",
+              "       </tr>\n",
+              "    \n",
+              "\n",
+              "       <tr class=\"default\">\n",
+              "           <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-rank_;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=rank_,-int\">\n",
+              "            rank_\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-rank_;\">\n",
+              "            rank_: int<br><br>Rank of matrix `X`. Only available when `X` is dense.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "           <td class=\"fitted-att-type\">int</td>\n",
+              "           <td>1</td>\n",
+              "\n",
+              "\n",
+              "       </tr>\n",
+              "    \n",
+              "\n",
+              "       <tr class=\"default\">\n",
+              "           <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-singular_;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=singular_,-array%20of%20shape%20%28min%28X%2C%20y%29%2C%29\">\n",
+              "            singular_\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-singular_;\">\n",
+              "            singular_: array of shape (min(X, y),)<br><br>Singular values of `X`. Only available when `X` is dense.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
+              "           <td>[160.48]</td>\n",
+              "\n",
+              "\n",
+              "       </tr>\n",
+              "    \n",
+              "                    </tbody>\n",
+              "                </table>\n",
+              "            </details>\n",
+              "        </div>\n",
+              "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
+              " SPDX-License-Identifier: BSD-3-Clause\n",
+              "*/\n",
+              "\n",
+              "function copyToClipboard(text, element) {\n",
+              "    // Get the parameter prefix from the closest toggleable content\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+              "\n",
+              "    const originalStyle = element.style;\n",
+              "    const computedStyle = window.getComputedStyle(element);\n",
+              "    const originalWidth = computedStyle.width;\n",
+              "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+              "\n",
+              "    navigator.clipboard.writeText(fullParamName)\n",
+              "        .then(() => {\n",
+              "            element.style.width = originalWidth;\n",
+              "            element.style.color = 'green';\n",
+              "            element.innerHTML = \"Copied!\";\n",
+              "\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        })\n",
+              "        .catch(err => {\n",
+              "            console.error('Failed to copy:', err);\n",
+              "            element.style.color = 'red';\n",
+              "            element.innerHTML = \"Failed!\";\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        });\n",
+              "    return false;\n",
+              "}\n",
+              "\n",
+              "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "\n",
+              "    const parent = element.parentElement;\n",
+              "    if (!parent || !parent.nextElementSibling) {\n",
+              "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
+              "        return;\n",
+              "    }\n",
+              "\n",
+              "    const paramName = element.parentElement.nextElementSibling\n",
+              "        .textContent.trim().split(' ')[0];\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+              "\n",
+              "    element.setAttribute('title', fullParamName);\n",
+              "});\n",
+              "\n",
+              "/**\n",
+              " * Copy the list of feature names formatted as a Python list.\n",
+              " *\n",
+              " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
+              " *   contain a `details` element and a table containing feature named.\n",
+              " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
+              " */\n",
+              "function copyFeatureNamesToClipboard(element) {\n",
+              "    var detailsElem = element.closest('.features').querySelector('details');\n",
+              "    var wasOpen = detailsElem.open;\n",
+              "    detailsElem.open = true;\n",
+              "    var content = element.closest('.features').querySelector('tbody')\n",
+              "                  .innerText.trim();\n",
+              "    if (!wasOpen) detailsElem.open = false;\n",
+              "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
+              "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
+              "    const originalHTML = element.innerHTML.replace('✔', '');\n",
+              "    const originalStyle = element.style;\n",
+              "    const copyMark = document.createElement('span');\n",
+              "    copyMark.innerHTML = '✔';\n",
+              "    copyMark.style.color = 'blue';\n",
+              "    copyMark.style.fontSize = '1em';\n",
+              "\n",
+              "    navigator.clipboard.writeText(formattedText)\n",
+              "        .then(() => {\n",
+              "            element.style.display = 'none';\n",
+              "            element.parentElement.appendChild(copyMark);\n",
+              "\n",
+              "            setTimeout(() => {\n",
+              "                copyMark.remove();\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 1000);\n",
+              "        })\n",
+              "        .catch(err => {\n",
+              "            console.error('Failed to copy:', err);\n",
+              "            element.style.color = 'orange';\n",
+              "            element.innerHTML = \"Failed!\";\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 1000);\n",
+              "        });\n",
+              "    return false;\n",
+              "}\n",
+              "/**\n",
+              " * Adapted from Skrub\n",
+              " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+              " * @returns \"light\" or \"dark\"\n",
+              " */\n",
+              "function detectTheme(element) {\n",
+              "    const body = document.querySelector('body');\n",
+              "\n",
+              "    // Check VSCode theme\n",
+              "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+              "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+              "\n",
+              "    if (themeKindAttr && themeNameAttr) {\n",
+              "        const themeKind = themeKindAttr.toLowerCase();\n",
+              "        const themeName = themeNameAttr.toLowerCase();\n",
+              "\n",
+              "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+              "            return \"dark\";\n",
+              "        }\n",
+              "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+              "            return \"light\";\n",
+              "        }\n",
+              "    }\n",
+              "\n",
+              "    // Check Jupyter theme\n",
+              "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+              "        return 'dark';\n",
+              "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+              "        return 'light';\n",
+              "    }\n",
+              "\n",
+              "    // Guess based on a parent element's color\n",
+              "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+              "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+              "    if (match) {\n",
+              "        const [r, g, b] = [\n",
+              "            parseFloat(match[1]),\n",
+              "            parseFloat(match[2]),\n",
+              "            parseFloat(match[3])\n",
+              "        ];\n",
+              "\n",
+              "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+              "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+              "\n",
+              "        if (luma > 180) {\n",
+              "            // If the text is very bright we have a dark theme\n",
+              "            return 'dark';\n",
+              "        }\n",
+              "        if (luma < 75) {\n",
+              "            // If the text is very dark we have a light theme\n",
+              "            return 'light';\n",
+              "        }\n",
+              "        // Otherwise fall back to the next heuristic.\n",
+              "    }\n",
+              "\n",
+              "    // Fallback to system preference\n",
+              "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+              "}\n",
+              "\n",
+              "\n",
+              "function forceTheme(elementId) {\n",
+              "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+              "    if (estimatorElement === null) {\n",
+              "        console.error(`Element with id ${elementId} not found.`);\n",
+              "    } else {\n",
+              "        const theme = detectTheme(estimatorElement);\n",
+              "        estimatorElement.classList.add(theme);\n",
+              "    }\n",
+              "}\n",
+              "\n",
+              "forceTheme('sk-container-id-2');</script></body>"
+            ],
+            "text/plain": [
+              "LinearRegression()"
+            ]
+          },
+          "execution_count": 39,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "lr = LinearRegression()\n",
+        "lr.fit(x,y)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "id": "QtZw_MKMSgG_",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QtZw_MKMSgG_",
+        "outputId": "1714a6ef-bfc0-42fa-c36d-35b8f1490653"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "RMSE = 6.20346413142642\n",
+            "R2 = 0.5441462975864797\n"
+          ]
+        }
+      ],
+      "source": [
+        "evaluate(lr, (x,y))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "id": "1b0b9fb4",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1b0b9fb4",
+        "outputId": "83d21594-3590-423b-9808-e53297d475fb"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "RMSE = 6.20346413142642\n",
+            "R2 = 0.5441462975864797\n"
+          ]
+        }
+      ],
+      "source": [
+        "evaluate(lr, (x,y))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0975d412",
+      "metadata": {
+        "id": "0975d412"
+      },
+      "source": [
+        "### 4. Addestra e valuta un modello di regressione lineare multipla utilizzando le due variabili che sembrano maggiormente correlate al target."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "id": "e4c53924",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "e4c53924",
+        "outputId": "38c7e9cf-fafc-4c24-c1ed-774c86039a80"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(506, 2)"
+            ]
+          },
+          "execution_count": 42,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "X = df[[\"LSTAT\",\"RM\"]].values\n",
+        "y = df[\"PRICE\"].values\n",
+        "X.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "id": "8c4c7d7c",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8c4c7d7c",
+        "outputId": "8fde6d9e-43ea-4bac-804b-a1786dfa97e6"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "RMSE = 5.523809263298243\n",
+            "R2 = 0.6385616062603403\n"
+          ]
+        }
+      ],
+      "source": [
+        "lr = LinearRegression()\n",
+        "lr.fit(X, y)\n",
+        "evaluate(lr, (X, y))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ea85af68",
+      "metadata": {
+        "id": "ea85af68"
+      },
+      "source": [
+        "### 5. Aggiungi una terza variabile, quindi crea diversi modelli di regressione polinomiale, senza superare il grado 5."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "id": "a74c4594",
+      "metadata": {
+        "id": "a74c4594"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import PolynomialFeatures"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "id": "28fa9b83",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "28fa9b83",
+        "outputId": "f09003a9-e3a1-49a0-d736-c44f67bd1803"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(506, 3)"
+            ]
+          },
+          "execution_count": 45,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "X = df[[\"LSTAT\",\"RM\", \"PTRATIO\"]].values\n",
+        "y = df[\"PRICE\"].values\n",
+        "X.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "id": "788c21cd",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "788c21cd",
+        "outputId": "bc0d053d-d8d1-46cc-f566-a098ad6db72e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Polinomio di grado 2 con bias\n",
+            "RMSE = 4.144169801696804\n",
+            "R2 = 0.7965620274818108\n",
+            "----------\n",
+            "Polinomio di grado 3 con bias\n",
+            "RMSE = 4.015615318550702\n",
+            "R2 = 0.8089877852857988\n",
+            "----------\n",
+            "Polinomio di grado 4 con bias\n",
+            "RMSE = 3.9094021027758985\n",
+            "R2 = 0.818958716474223\n",
+            "----------\n",
+            "Polinomio di grado 5 con bias\n",
+            "RMSE = 3.824870012698452\n",
+            "R2 = 0.8267032986174805\n",
+            "----------\n"
+          ]
+        }
+      ],
+      "source": [
+        "for i in range(2, 6):\n",
+        "    print(f\"Polinomio di grado {i} con bias\")\n",
+        "    poly = PolynomialFeatures(i)\n",
+        "    X_poly = poly.fit_transform(X)\n",
+        "    lr = LinearRegression()\n",
+        "    lr.fit(X_poly, y)\n",
+        "    evaluate(lr, (X_poly, y))\n",
+        "    print(\"----------\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "id": "4e9d115d",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4e9d115d",
+        "outputId": "5f0e64a2-8c64-46fa-c6b2-585fd72f5f50"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Polinomio di grado 2 senza bias\n",
+            "RMSE = 4.144169801696807\n",
+            "R2 = 0.7965620274818106\n",
+            "----------\n",
+            "Polinomio di grado 3 senza bias\n",
+            "RMSE = 4.015615318550704\n",
+            "R2 = 0.8089877852857986\n",
+            "----------\n",
+            "Polinomio di grado 4 senza bias\n",
+            "RMSE = 3.9094021027759\n",
+            "R2 = 0.8189587164742229\n",
+            "----------\n",
+            "Polinomio di grado 5 senza bias\n",
+            "RMSE = 3.8248700126984243\n",
+            "R2 = 0.826703298617483\n",
+            "----------\n"
+          ]
+        }
+      ],
+      "source": [
+        "for i in range(2, 6):\n",
+        "    print(f\"Polinomio di grado {i} senza bias\")\n",
+        "    poly = PolynomialFeatures(i, include_bias=False)\n",
+        "    X_poly = poly.fit_transform(X)\n",
+        "    lr = LinearRegression()\n",
+        "    lr.fit(X_poly, y)\n",
+        "    evaluate(lr, (X_poly, y))\n",
+        "    print(\"----------\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cda32492",
+      "metadata": {
+        "id": "cda32492"
+      },
+      "source": [
+        "### 6. Addestra e valuta un modello di regressione lineare utilizzando tutte le variabili del dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "id": "b619d3e5",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "b619d3e5",
+        "outputId": "8f397905-836a-4e81-a38b-82eae12021b7"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(506, 13)"
+            ]
+          },
+          "execution_count": 48,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "X = df.drop(\"PRICE\", axis=1).values\n",
+        "y = df[\"PRICE\"].values\n",
+        "X.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "id": "91b8201c",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "91b8201c",
+        "outputId": "3ebf1fff-5864-4527-85a0-eb7048f80663"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "RMSE = 4.679506300635516\n",
+            "R2 = 0.7406077428649427\n"
+          ]
+        }
+      ],
+      "source": [
+        "lr = LinearRegression()\n",
+        "lr.fit(X, y)\n",
+        "evaluate(lr, (X, y))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7U8E4n1FTjv7",
+      "metadata": {
+        "id": "7U8E4n1FTjv7"
+      },
+      "source": [
+        "### 6. Esegui la normalizzazione dei dati e riaddestra il modello, le performance sono migliorate?\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 50,
+      "id": "vZgh1zCAUMns",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vZgh1zCAUMns",
+        "outputId": "1444b543-f869-467b-ff1d-b8abfd1a66f8"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "0.0 1.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "\n",
+        "mms = MinMaxScaler()\n",
+        "X_norm = mms.fit_transform(X)\n",
+        "print(X_norm.min(), X_norm.max())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 51,
+      "id": "VhJMl2vHUZpf",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VhJMl2vHUZpf",
+        "outputId": "22fb6e1f-eb84-4c27-bd14-8c552750db82"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "RMSE = 4.679506300635516\n",
+            "R2 = 0.7406077428649428\n"
+          ]
+        }
+      ],
+      "source": [
+        "lr = LinearRegression()\n",
+        "lr.fit(X_norm, y)\n",
+        "evaluate(lr, (X_norm, y))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "N4JvxVFvTsuo",
+      "metadata": {
+        "id": "N4JvxVFvTsuo"
+      },
+      "source": [
+        "###7. Esegui la standardizzazione dei dati e riaddestra il modello, le performance sono migliorate?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "id": "qEmWlwb2Thbe",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qEmWlwb2Thbe",
+        "outputId": "5d50fa3d-e1a2-4ca1-d987-b6efb0906b72"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "-1.5716626338263086e-16 1.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.preprocessing import StandardScaler\n",
+        "\n",
+        "ss = StandardScaler()\n",
+        "X_std = ss.fit_transform(X)\n",
+        "print(X_std.mean(), X_std.std())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 53,
+      "id": "ynuwvyHbTr9L",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ynuwvyHbTr9L",
+        "outputId": "546978dd-f769-4993-884d-0eeb936ba246"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "RMSE = 4.679506300635516\n",
+            "R2 = 0.7406077428649428\n"
+          ]
+        }
+      ],
+      "source": [
+        "lr = LinearRegression()\n",
+        "lr.fit(X_std, y)\n",
+        "evaluate(lr, (X_std, y))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "de179e52",
+      "metadata": {
+        "id": "de179e52"
+      },
+      "source": [
+        "### 7. Utilizza il modello con tutte le features per prevedere il prezzo delle abitazioni che trovi in [questo file csv](https://raw.githubusercontent.com/ProfAI/machine-learning-fondamenti/main/datasets/housing_predict.csv)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 54,
+      "id": "jx2oJESUVAWV",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "jx2oJESUVAWV",
+        "outputId": "5595c35a-428c-4b1d-a14f-1be9dda7d61d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>OWNER</th>\n",
+              "      <th>CRIM</th>\n",
+              "      <th>ZN</th>\n",
+              "      <th>INDUS</th>\n",
+              "      <th>CHAS</th>\n",
+              "      <th>NOX</th>\n",
+              "      <th>RM</th>\n",
+              "      <th>AGE</th>\n",
+              "      <th>DIS</th>\n",
+              "      <th>RAD</th>\n",
+              "      <th>TAX</th>\n",
+              "      <th>PTRATIO</th>\n",
+              "      <th>B</th>\n",
+              "      <th>LSTAT</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Alan Turing</td>\n",
+              "      <td>0.02899</td>\n",
+              "      <td>40.0</td>\n",
+              "      <td>1.25</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.429</td>\n",
+              "      <td>6.939</td>\n",
+              "      <td>34.5</td>\n",
+              "      <td>8.7921</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>335.0</td>\n",
+              "      <td>19.7</td>\n",
+              "      <td>389.85</td>\n",
+              "      <td>5.89</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Elon Musk</td>\n",
+              "      <td>0.34109</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>7.38</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.493</td>\n",
+              "      <td>6.415</td>\n",
+              "      <td>40.1</td>\n",
+              "      <td>4.7211</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>287.0</td>\n",
+              "      <td>19.6</td>\n",
+              "      <td>396.90</td>\n",
+              "      <td>6.12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Steve Jobs</td>\n",
+              "      <td>0.25356</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>9.90</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.544</td>\n",
+              "      <td>5.705</td>\n",
+              "      <td>77.7</td>\n",
+              "      <td>3.9450</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>304.0</td>\n",
+              "      <td>18.4</td>\n",
+              "      <td>396.42</td>\n",
+              "      <td>11.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Chuck Norris</td>\n",
+              "      <td>0.04297</td>\n",
+              "      <td>52.5</td>\n",
+              "      <td>5.32</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.405</td>\n",
+              "      <td>6.565</td>\n",
+              "      <td>22.9</td>\n",
+              "      <td>7.3172</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>293.0</td>\n",
+              "      <td>16.6</td>\n",
+              "      <td>371.72</td>\n",
+              "      <td>9.51</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Giuseppe Gullo</td>\n",
+              "      <td>0.03584</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>3.37</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.398</td>\n",
+              "      <td>6.290</td>\n",
+              "      <td>17.8</td>\n",
+              "      <td>6.6115</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>337.0</td>\n",
+              "      <td>16.1</td>\n",
+              "      <td>396.90</td>\n",
+              "      <td>4.67</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            OWNER     CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  \\\n",
+              "0     Alan Turing  0.02899  40.0   1.25   0.0  0.429  6.939  34.5  8.7921   \n",
+              "1       Elon Musk  0.34109   0.0   7.38   0.0  0.493  6.415  40.1  4.7211   \n",
+              "2      Steve Jobs  0.25356   0.0   9.90   0.0  0.544  5.705  77.7  3.9450   \n",
+              "3    Chuck Norris  0.04297  52.5   5.32   0.0  0.405  6.565  22.9  7.3172   \n",
+              "4  Giuseppe Gullo  0.03584  80.0   3.37   0.0  0.398  6.290  17.8  6.6115   \n",
+              "\n",
+              "   RAD    TAX  PTRATIO       B  LSTAT  \n",
+              "0  1.0  335.0     19.7  389.85   5.89  \n",
+              "1  5.0  287.0     19.6  396.90   6.12  \n",
+              "2  4.0  304.0     18.4  396.42  11.50  \n",
+              "3  6.0  293.0     16.6  371.72   9.51  \n",
+              "4  4.0  337.0     16.1  396.90   4.67  "
+            ]
+          },
+          "execution_count": 54,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_pred = pd.read_csv(BASE_URL+\"housing_predict.csv\")\n",
+        "df_pred.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 55,
+      "id": "178a42bf",
+      "metadata": {
+        "id": "178a42bf"
+      },
+      "outputs": [],
+      "source": [
+        "X = df_pred.drop(\"OWNER\", axis=1).values\n",
+        "X = ss.transform(X)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 56,
+      "id": "0839f9b3",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0839f9b3",
+        "outputId": "308b46d2-5252-4549-fb2b-f74d0e6ca286"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([22.14633467, 25.11948741, 20.54343769, 26.91105226, 30.36557584])"
+            ]
+          },
+          "execution_count": 56,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "y_pred = lr.predict(X)\n",
+        "y_pred"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f27316b1",
+      "metadata": {
+        "id": "f27316b1"
+      },
+      "source": [
+        "### 8. Salva il risultato in un file excel chiamato \"housing_estimate.xlsx\", deve contenere due colonne: OWNER=il proprietario dell'abitazione, ESTIMATED PRICE=il valore stimato dal nostro modello."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 57,
+      "id": "ca06c263",
+      "metadata": {
+        "id": "ca06c263"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>owner</th>\n",
+              "      <th>estimated price</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Alan Turing</td>\n",
+              "      <td>22.146335</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Elon Musk</td>\n",
+              "      <td>25.119487</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Steve Jobs</td>\n",
+              "      <td>20.543438</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Chuck Norris</td>\n",
+              "      <td>26.911052</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Giuseppe Gullo</td>\n",
+              "      <td>30.365576</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            owner  estimated price\n",
+              "0     Alan Turing        22.146335\n",
+              "1       Elon Musk        25.119487\n",
+              "2      Steve Jobs        20.543438\n",
+              "3    Chuck Norris        26.911052\n",
+              "4  Giuseppe Gullo        30.365576"
+            ]
+          },
+          "execution_count": 57,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_result = pd.DataFrame({\"owner\":df_pred[\"OWNER\"].values, \"estimated price\":y_pred})\n",
+        "df_result.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 58,
+      "id": "1fd97bd7",
+      "metadata": {
+        "id": "1fd97bd7"
+      },
+      "outputs": [
+        {
+          "ename": "ModuleNotFoundError",
+          "evalue": "No module named 'openpyxl'",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+            "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[58]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m df_result.to_excel(\u001b[33m\"housing_estimate.xlsx\"\u001b[39m)\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/project/python/professionAI/machine-learning-fondamenti/.venv/lib/python3.13/site-packages/pandas/core/generic.py:2312\u001b[39m, in \u001b[36mNDFrame.to_excel\u001b[39m\u001b[34m(self, excel_writer, sheet_name, na_rep, float_format, columns, header, index, index_label, startrow, startcol, engine, merge_cells, inf_rep, freeze_panes, storage_options, engine_kwargs, autofilter)\u001b[39m\n\u001b[32m   2308\u001b[39m             merge_cells=merge_cells,\n\u001b[32m   2309\u001b[39m             inf_rep=inf_rep,\n\u001b[32m   2310\u001b[39m             autofilter=autofilter,\n\u001b[32m   2311\u001b[39m         )\n\u001b[32m-> \u001b[39m\u001b[32m2312\u001b[39m         formatter.write(\n\u001b[32m   2313\u001b[39m             excel_writer,\n\u001b[32m   2314\u001b[39m             sheet_name=sheet_name,\n\u001b[32m   2315\u001b[39m             startrow=startrow,\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/project/python/professionAI/machine-learning-fondamenti/.venv/lib/python3.13/site-packages/pandas/io/formats/excel.py:1003\u001b[39m, in \u001b[36mExcelFormatter.write\u001b[39m\u001b[34m(self, writer, sheet_name, startrow, startcol, freeze_panes, engine, storage_options, engine_kwargs)\u001b[39m\n\u001b[32m   1001\u001b[39m     need_save = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m   1002\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1003\u001b[39m     writer = \u001b[30;43mExcelWriter\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m   1004\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mwriter\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1005\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mengine\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mengine\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1006\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mstorage_options\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mstorage_options\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1007\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mengine_kwargs\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mengine_kwargs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1008\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   1009\u001b[39m     need_save = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m   1011\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/project/python/professionAI/machine-learning-fondamenti/.venv/lib/python3.13/site-packages/pandas/io/excel/_openpyxl.py:58\u001b[39m, in \u001b[36mOpenpyxlWriter.__init__\u001b[39m\u001b[34m(self, path, engine, date_format, datetime_format, mode, storage_options, if_sheet_exists, engine_kwargs, **kwargs)\u001b[39m\n\u001b[32m     45\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(  \u001b[38;5;66;03m# pyright: ignore[reportInconsistentConstructor]\u001b[39;00m\n\u001b[32m     46\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m     47\u001b[39m     path: FilePath | WriteExcelBuffer | ExcelWriter,\n\u001b[32m   (...)\u001b[39m\u001b[32m     56\u001b[39m ) -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m     57\u001b[39m     \u001b[38;5;66;03m# Use the openpyxl module as the Excel writer.\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m58\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mopenpyxl\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mworkbook\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Workbook\n\u001b[32m     60\u001b[39m     engine_kwargs = combine_kwargs(engine_kwargs, kwargs)\n\u001b[32m     62\u001b[39m     \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m(\n\u001b[32m     63\u001b[39m         path,\n\u001b[32m     64\u001b[39m         mode=mode,\n\u001b[32m   (...)\u001b[39m\u001b[32m     67\u001b[39m         engine_kwargs=engine_kwargs,\n\u001b[32m     68\u001b[39m     )\n",
+            "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'openpyxl'"
+          ]
+        }
+      ],
+      "source": [
+        "df_result.to_excel(\"housing_estimate.xlsx\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "regressione_exercises.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/3 - La Regressione Lineare/housing_estimate.xlsx b/3 - La Regressione Lineare/housing_estimate.xlsx
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diff --git a/3 - La Regressione Lineare/regressione_exercises.ipynb b/3 - La Regressione Lineare/regressione_exercises.ipynb
deleted file mode 100644
index 660b12a..0000000
--- a/3 - La Regressione Lineare/regressione_exercises.ipynb	
+++ /dev/null
@@ -1,1309 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "id": "fc73dbdb",
-      "metadata": {
-        "id": "fc73dbdb"
-      },
-      "source": [
-        "# Regressione: Esercitazione\n",
-        "\n",
-        "Per questa esercitazione dovrai creare il tuo primo modello di regressione lineare. Per farlo utilizzerai il Boston Housing Dataset, che hai già visto nella sezione dedicata al preprocessing dei dati. Puoi scaricare il dataset già pulito [da qui](https://raw.githubusercontent.com/ProfAI/machine-learning-fondamenti/main/datasets/housing_dirty.csv).\n",
-        "\n",
-        "Il dataset contiene le seguenti informazioni\n",
-        "\n",
-        "1. **CRIM** Tasso di criminalità per capita\n",
-        "2. **ZN** Percentuale di terreni residenziali suddivisi in zone per lotti superiori a 25.000 sq.ft.\n",
-        "3. **INDUS** Percentuale di ettari di attività non al dettaglio per città.\n",
-        "4. **CHAS** Variabile dummy che indica la prossimità al fiume Charles.\n",
-        "5. **NOX** Concentrazione di ossido d'azoto (parti per 10 milioni).\n",
-        "6. **RM** Numero medio di stanze per abitazione\n",
-        "7. **AGE** Percentuale di abitazione occupate costruite dopo il 1940\n",
-        "8. **DIS** Media pesata delle distanze da 5 centri lavorativi di Boston.\n",
-        "9. **RAD** Indice di accessibilità ad autostrade\n",
-        "10. **TAX** Aliquota dell'imposta sulla proprietà a valore pieno in 10.000 USD.\n",
-        "11. **PRATIO** Rapporto studente-insegnante per città.\n",
-        "12. **BLACK** 1000(Bk - 0.63)^2 dove Bk è la percentuale di abitanti di colore per città\n",
-        "13. **LSTAT** Percentuale della popolazione povera\n",
-        "14. **PRICE** Mediana del valore di abitazioni occupate in 1.000 USD.\n",
-        "\n",
-        "Il target è la colonna PRICE, cioè vogliamo prevedere il valore delle abitazioni.\n",
-        "\n",
-        "Nello specifico, devi risolvere i seguenti punti:\n",
-        "1. Crea la matrice di correlazione. \n",
-        "2. Addestra e valuta un modello di regressione lineare semplice utilizzano la variabile che sembra maggiormente correlata al target.\n",
-        "3. Addestra e valuta un modello di regressione lineare multipla utilizzando le due variabili che sembrano maggiormente correlate al target.\n",
-        "4. Aggiungi una terza variabile, quindi crea diversi modelli di regressione polinomiale, senza superare il grado 5, prova sia con che senza bias.\n",
-        "5. Addestra e valuta un modello di regressione lineare utilizzando tutte le variabili del dataset.\n",
-        "6. Esegui la normalizzazione dei dati e riaddestra il modello, le performance sono migliorate?\n",
-        "7. Esegui la standardizzazione dei dati e riaddestra il modello, le performance sono migliorate?\n",
-        "8. Utilizza il modello con tutte le features per prevedere il prezzo delle abitazioni che trovi in [questo file CSV]().\n",
-        "9. Salva il risultato in un file excel chiamato \"housing_estimate.xlsx\", deve contenere due colonne: OWNER=il proprietario dell'abitazione, ESTIMATED PRICE=il valore stimato dal nostro modello.\n",
-        "\n",
-        "\n",
-        "**Nota**\n",
-        "Se mastichi già l'argomento e il termine \"overfitting\" non ti è nuovo, non preoccupartene per adesso, ci arriveremo nella prossima sezione."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "c2fcb16a",
-      "metadata": {
-        "id": "c2fcb16a"
-      },
-      "source": [
-        "### Soluzione"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "id": "45e62148",
-      "metadata": {
-        "id": "45e62148"
-      },
-      "outputs": [],
-      "source": [
-        "import numpy as np\n",
-        "import pandas as pd"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "id": "a5b4ba2b",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "a5b4ba2b",
-        "outputId": "6f467ca8-355e-42b3-c7f5-17478a1e909f"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "\n",
-              "  <div id=\"df-c69e9443-3ea2-44eb-b4ec-17366f09a226\">\n",
-              "    <div class=\"colab-df-container\">\n",
-              "      <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>CRIM</th>\n",
-              "      <th>ZN</th>\n",
-              "      <th>INDUS</th>\n",
-              "      <th>CHAS</th>\n",
-              "      <th>NOX</th>\n",
-              "      <th>RM</th>\n",
-              "      <th>AGE</th>\n",
-              "      <th>DIS</th>\n",
-              "      <th>RAD</th>\n",
-              "      <th>TAX</th>\n",
-              "      <th>PTRATIO</th>\n",
-              "      <th>B</th>\n",
-              "      <th>LSTAT</th>\n",
-              "      <th>PRICE</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>0.00632</td>\n",
-              "      <td>18.0</td>\n",
-              "      <td>2.31</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.538</td>\n",
-              "      <td>6.575</td>\n",
-              "      <td>65.2</td>\n",
-              "      <td>4.0900</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>296.0</td>\n",
-              "      <td>15.3</td>\n",
-              "      <td>396.90</td>\n",
-              "      <td>4.98</td>\n",
-              "      <td>24.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>0.02731</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>7.07</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.469</td>\n",
-              "      <td>6.421</td>\n",
-              "      <td>78.9</td>\n",
-              "      <td>4.9671</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>242.0</td>\n",
-              "      <td>17.8</td>\n",
-              "      <td>396.90</td>\n",
-              "      <td>9.14</td>\n",
-              "      <td>21.6</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>0.02729</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>7.07</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.469</td>\n",
-              "      <td>7.185</td>\n",
-              "      <td>61.1</td>\n",
-              "      <td>4.9671</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>242.0</td>\n",
-              "      <td>17.8</td>\n",
-              "      <td>392.83</td>\n",
-              "      <td>4.03</td>\n",
-              "      <td>34.7</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>0.03237</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>2.18</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.458</td>\n",
-              "      <td>6.998</td>\n",
-              "      <td>45.8</td>\n",
-              "      <td>6.0622</td>\n",
-              "      <td>3.0</td>\n",
-              "      <td>222.0</td>\n",
-              "      <td>18.7</td>\n",
-              "      <td>394.63</td>\n",
-              "      <td>2.94</td>\n",
-              "      <td>33.4</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>0.06905</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>2.18</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.458</td>\n",
-              "      <td>7.147</td>\n",
-              "      <td>54.2</td>\n",
-              "      <td>6.0622</td>\n",
-              "      <td>3.0</td>\n",
-              "      <td>222.0</td>\n",
-              "      <td>18.7</td>\n",
-              "      <td>396.90</td>\n",
-              "      <td>5.33</td>\n",
-              "      <td>36.2</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c69e9443-3ea2-44eb-b4ec-17366f09a226')\"\n",
-              "              title=\"Convert this dataframe to an interactive table.\"\n",
-              "              style=\"display:none;\">\n",
-              "        \n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
-              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
-              "  </svg>\n",
-              "      </button>\n",
-              "      \n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      flex-wrap:wrap;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-c69e9443-3ea2-44eb-b4ec-17366f09a226 button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-c69e9443-3ea2-44eb-b4ec-17366f09a226');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ],
-            "text/plain": [
-              "      CRIM    ZN  INDUS  CHAS    NOX  ...    TAX  PTRATIO       B  LSTAT  PRICE\n",
-              "0  0.00632  18.0   2.31   0.0  0.538  ...  296.0     15.3  396.90   4.98   24.0\n",
-              "1  0.02731   0.0   7.07   0.0  0.469  ...  242.0     17.8  396.90   9.14   21.6\n",
-              "2  0.02729   0.0   7.07   0.0  0.469  ...  242.0     17.8  392.83   4.03   34.7\n",
-              "3  0.03237   0.0   2.18   0.0  0.458  ...  222.0     18.7  394.63   2.94   33.4\n",
-              "4  0.06905   0.0   2.18   0.0  0.458  ...  222.0     18.7  396.90   5.33   36.2\n",
-              "\n",
-              "[5 rows x 14 columns]"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 2
-        }
-      ],
-      "source": [
-        "BASE_URL = \"https://raw.githubusercontent.com/ProfAI/machine-learning-fondamenti/main/datasets/\"\n",
-        "df = pd.read_csv(BASE_URL+\"housing.csv\", index_col=0)\n",
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "cfdbca4e",
-      "metadata": {
-        "id": "cfdbca4e"
-      },
-      "source": [
-        "###1. Crea la matrice di correlazione. \n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "0e3e35cc",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 658
-        },
-        "id": "0e3e35cc",
-        "outputId": "ee877577-8241-48f9-a1ac-9e1eafff462e"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 1120x800 with 2 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns\n",
-        "\n",
-        "plt.figure(figsize=(14, 10), dpi=80)\n",
-        "\n",
-        "hm = sns.heatmap(df.corr(),\n",
-        "                 cbar=True,\n",
-        "                 square=True,\n",
-        "                 yticklabels=df.columns,\n",
-        "                 xticklabels=df.columns,\n",
-        "                 annot=True,                  #Questo ci mostra i valori degli indici\n",
-        "                 annot_kws={'size':12})       #Impostiamo la dimensione dell'annotazione a 12 per farla entrare dentro il quadrato\n",
-        "\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "720761f4",
-      "metadata": {
-        "id": "720761f4"
-      },
-      "source": [
-        "### 2. Addestra e valuta un modello di regressione lineare semplice utilizzano la variabile che sembra maggiormente correlata al target."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "id": "ff28395a",
-      "metadata": {
-        "id": "ff28395a"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.metrics import mean_squared_error, r2_score\n",
-        "\n",
-        "def evaluate(model, data):\n",
-        "    x, y = data\n",
-        "    y_pred = model.predict(x)\n",
-        "    print(f\"RMSE = {np.sqrt(mean_squared_error(y, y_pred))}\")\n",
-        "    print(f\"R2 = {r2_score(y, y_pred)}\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "b2960fa6",
-      "metadata": {
-        "id": "b2960fa6"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.linear_model import LinearRegression"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "id": "ca247c9f",
-      "metadata": {
-        "id": "ca247c9f"
-      },
-      "outputs": [],
-      "source": [
-        "x = df[\"LSTAT\"].values\n",
-        "y = df[\"PRICE\"].values"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "id": "837678ad",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "837678ad",
-        "outputId": "70217347-28c9-4abe-d99d-97461a1eaf81"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(506,)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 7
-        }
-      ],
-      "source": [
-        "x.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "id": "86bd3e73",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "86bd3e73",
-        "outputId": "99f2ce23-b80f-4def-9e9b-0477544639bd"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(506, 1)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 8
-        }
-      ],
-      "source": [
-        "x = x.reshape(-1, 1)\n",
-        "x.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "id": "d39e099e",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "d39e099e",
-        "outputId": "acbf44f6-79d4-4669-ed82-42ff40f7a4dc"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "LinearRegression()"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 9
-        }
-      ],
-      "source": [
-        "lr = LinearRegression()\n",
-        "lr.fit(x,y)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "evaluate(lr, (x,y))"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "QtZw_MKMSgG_",
-        "outputId": "1714a6ef-bfc0-42fa-c36d-35b8f1490653"
-      },
-      "id": "QtZw_MKMSgG_",
-      "execution_count": 10,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE = 6.20346413142642\n",
-            "R2 = 0.5441462975864797\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "id": "1b0b9fb4",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "1b0b9fb4",
-        "outputId": "83d21594-3590-423b-9808-e53297d475fb"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE = 6.20346413142642\n",
-            "R2 = 0.5441462975864797\n"
-          ]
-        }
-      ],
-      "source": [
-        "evaluate(lr, (x,y))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "0975d412",
-      "metadata": {
-        "id": "0975d412"
-      },
-      "source": [
-        "### 4. Addestra e valuta un modello di regressione lineare multipla utilizzando le due variabili che sembrano maggiormente correlate al target."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "id": "e4c53924",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "e4c53924",
-        "outputId": "38c7e9cf-fafc-4c24-c1ed-774c86039a80"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(506, 2)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 12
-        }
-      ],
-      "source": [
-        "X = df[[\"LSTAT\",\"RM\"]].values\n",
-        "y = df[\"PRICE\"].values\n",
-        "X.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 14,
-      "id": "8c4c7d7c",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "8c4c7d7c",
-        "outputId": "8fde6d9e-43ea-4bac-804b-a1786dfa97e6"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE = 5.523809263298243\n",
-            "R2 = 0.6385616062603403\n"
-          ]
-        }
-      ],
-      "source": [
-        "lr = LinearRegression()\n",
-        "lr.fit(X, y)\n",
-        "evaluate(lr, (X, y))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "ea85af68",
-      "metadata": {
-        "id": "ea85af68"
-      },
-      "source": [
-        "### 5. Aggiungi una terza variabile, quindi crea diversi modelli di regressione polinomiale, senza superare il grado 5."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 15,
-      "id": "a74c4594",
-      "metadata": {
-        "id": "a74c4594"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.preprocessing import PolynomialFeatures"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 16,
-      "id": "28fa9b83",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "28fa9b83",
-        "outputId": "f09003a9-e3a1-49a0-d736-c44f67bd1803"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(506, 3)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 16
-        }
-      ],
-      "source": [
-        "X = df[[\"LSTAT\",\"RM\", \"PTRATIO\"]].values\n",
-        "y = df[\"PRICE\"].values\n",
-        "X.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 17,
-      "id": "788c21cd",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "788c21cd",
-        "outputId": "bc0d053d-d8d1-46cc-f566-a098ad6db72e"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Polinomio di grado 2 con bias\n",
-            "RMSE = 4.144169801696807\n",
-            "R2 = 0.7965620274818106\n",
-            "----------\n",
-            "Polinomio di grado 3 con bias\n",
-            "RMSE = 3.9939405131856263\n",
-            "R2 = 0.8110442467458951\n",
-            "----------\n",
-            "Polinomio di grado 4 con bias\n",
-            "RMSE = 8.429450544234742\n",
-            "R2 = 0.15830360033810886\n",
-            "----------\n",
-            "Polinomio di grado 5 con bias\n",
-            "RMSE = 3.4951481607344594\n",
-            "R2 = 0.8552934743830292\n",
-            "----------\n"
-          ]
-        }
-      ],
-      "source": [
-        "for i in range(2, 6):\n",
-        "    print(f\"Polinomio di grado {i} con bias\")\n",
-        "    poly = PolynomialFeatures(i)\n",
-        "    X_poly = poly.fit_transform(X)\n",
-        "    lr = LinearRegression()\n",
-        "    lr.fit(X_poly, y)\n",
-        "    evaluate(lr, (X_poly, y))\n",
-        "    print(\"----------\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 18,
-      "id": "4e9d115d",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "4e9d115d",
-        "outputId": "5f0e64a2-8c64-46fa-c6b2-585fd72f5f50"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Polinomio di grado 2 senza bias\n",
-            "RMSE = 4.14416980169681\n",
-            "R2 = 0.7965620274818104\n",
-            "----------\n",
-            "Polinomio di grado 3 senza bias\n",
-            "RMSE = 3.9939405131856383\n",
-            "R2 = 0.811044246745894\n",
-            "----------\n",
-            "Polinomio di grado 4 senza bias\n",
-            "RMSE = 3.7306747288207744\n",
-            "R2 = 0.8351337703491886\n",
-            "----------\n",
-            "Polinomio di grado 5 senza bias\n",
-            "RMSE = 3.4951481606911625\n",
-            "R2 = 0.8552934743866143\n",
-            "----------\n"
-          ]
-        }
-      ],
-      "source": [
-        "for i in range(2, 6):\n",
-        "    print(f\"Polinomio di grado {i} senza bias\")\n",
-        "    poly = PolynomialFeatures(i, include_bias=False)\n",
-        "    X_poly = poly.fit_transform(X)\n",
-        "    lr = LinearRegression()\n",
-        "    lr.fit(X_poly, y)\n",
-        "    evaluate(lr, (X_poly, y))\n",
-        "    print(\"----------\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "cda32492",
-      "metadata": {
-        "id": "cda32492"
-      },
-      "source": [
-        "### 6. Addestra e valuta un modello di regressione lineare utilizzando tutte le variabili del dataset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 19,
-      "id": "b619d3e5",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "b619d3e5",
-        "outputId": "8f397905-836a-4e81-a38b-82eae12021b7"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(506, 13)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 19
-        }
-      ],
-      "source": [
-        "X = df.drop(\"PRICE\", axis=1).values\n",
-        "y = df[\"PRICE\"].values\n",
-        "X.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 20,
-      "id": "91b8201c",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "91b8201c",
-        "outputId": "3ebf1fff-5864-4527-85a0-eb7048f80663"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE = 4.679506300635516\n",
-            "R2 = 0.7406077428649428\n"
-          ]
-        }
-      ],
-      "source": [
-        "lr = LinearRegression()\n",
-        "lr.fit(X, y)\n",
-        "evaluate(lr, (X, y))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "###6. Esegui la normalizzazione dei dati e riaddestra il modello, le performance sono migliorate?\n"
-      ],
-      "metadata": {
-        "id": "7U8E4n1FTjv7"
-      },
-      "id": "7U8E4n1FTjv7"
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from sklearn.preprocessing import MinMaxScaler\n",
-        "\n",
-        "mms = MinMaxScaler()\n",
-        "X_norm = mms.fit_transform(X)\n",
-        "print(X_norm.min(), X_norm.max())"
-      ],
-      "metadata": {
-        "id": "vZgh1zCAUMns",
-        "outputId": "1444b543-f869-467b-ff1d-b8abfd1a66f8",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "id": "vZgh1zCAUMns",
-      "execution_count": 21,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "0.0 1.0\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "lr = LinearRegression()\n",
-        "lr.fit(X_norm, y)\n",
-        "evaluate(lr, (X_norm, y))"
-      ],
-      "metadata": {
-        "id": "VhJMl2vHUZpf",
-        "outputId": "22fb6e1f-eb84-4c27-bd14-8c552750db82",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "id": "VhJMl2vHUZpf",
-      "execution_count": 22,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE = 4.679506300635516\n",
-            "R2 = 0.7406077428649428\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "###7. Esegui la standardizzazione dei dati e riaddestra il modello, le performance sono migliorate?"
-      ],
-      "metadata": {
-        "id": "N4JvxVFvTsuo"
-      },
-      "id": "N4JvxVFvTsuo"
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from sklearn.preprocessing import StandardScaler\n",
-        "\n",
-        "ss = StandardScaler()\n",
-        "X_std = ss.fit_transform(X)\n",
-        "print(X_std.mean(), X_std.std())"
-      ],
-      "metadata": {
-        "id": "qEmWlwb2Thbe",
-        "outputId": "5d50fa3d-e1a2-4ca1-d987-b6efb0906b72",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "id": "qEmWlwb2Thbe",
-      "execution_count": 23,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "-1.5716626338263086e-16 1.0\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "lr = LinearRegression()\n",
-        "lr.fit(X_std, y)\n",
-        "evaluate(lr, (X_std, y))"
-      ],
-      "metadata": {
-        "id": "ynuwvyHbTr9L",
-        "outputId": "546978dd-f769-4993-884d-0eeb936ba246",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "id": "ynuwvyHbTr9L",
-      "execution_count": 24,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE = 4.679506300635516\n",
-            "R2 = 0.7406077428649428\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "de179e52",
-      "metadata": {
-        "id": "de179e52"
-      },
-      "source": [
-        "### 7. Utilizza il modello con tutte le features per prevedere il prezzo delle abitazioni che trovi in [questo file csv](https://raw.githubusercontent.com/ProfAI/machine-learning-fondamenti/main/datasets/housing_predict.csv)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df_pred = pd.read_csv(BASE_URL+\"housing_predict.csv\")\n",
-        "df_pred.head()"
-      ],
-      "metadata": {
-        "id": "jx2oJESUVAWV",
-        "outputId": "5595c35a-428c-4b1d-a14f-1be9dda7d61d",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        }
-      },
-      "id": "jx2oJESUVAWV",
-      "execution_count": 26,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "\n",
-              "  <div id=\"df-9fd50045-0d00-4035-a108-dba26c20f303\">\n",
-              "    <div class=\"colab-df-container\">\n",
-              "      <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>OWNER</th>\n",
-              "      <th>CRIM</th>\n",
-              "      <th>ZN</th>\n",
-              "      <th>INDUS</th>\n",
-              "      <th>CHAS</th>\n",
-              "      <th>NOX</th>\n",
-              "      <th>RM</th>\n",
-              "      <th>AGE</th>\n",
-              "      <th>DIS</th>\n",
-              "      <th>RAD</th>\n",
-              "      <th>TAX</th>\n",
-              "      <th>PTRATIO</th>\n",
-              "      <th>B</th>\n",
-              "      <th>LSTAT</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>Alan Turing</td>\n",
-              "      <td>0.02899</td>\n",
-              "      <td>40.0</td>\n",
-              "      <td>1.25</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.429</td>\n",
-              "      <td>6.939</td>\n",
-              "      <td>34.5</td>\n",
-              "      <td>8.7921</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>335.0</td>\n",
-              "      <td>19.7</td>\n",
-              "      <td>389.85</td>\n",
-              "      <td>5.89</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>Elon Musk</td>\n",
-              "      <td>0.34109</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>7.38</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.493</td>\n",
-              "      <td>6.415</td>\n",
-              "      <td>40.1</td>\n",
-              "      <td>4.7211</td>\n",
-              "      <td>5.0</td>\n",
-              "      <td>287.0</td>\n",
-              "      <td>19.6</td>\n",
-              "      <td>396.90</td>\n",
-              "      <td>6.12</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>Steve Jobs</td>\n",
-              "      <td>0.25356</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>9.90</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.544</td>\n",
-              "      <td>5.705</td>\n",
-              "      <td>77.7</td>\n",
-              "      <td>3.9450</td>\n",
-              "      <td>4.0</td>\n",
-              "      <td>304.0</td>\n",
-              "      <td>18.4</td>\n",
-              "      <td>396.42</td>\n",
-              "      <td>11.50</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>Chuck Norris</td>\n",
-              "      <td>0.04297</td>\n",
-              "      <td>52.5</td>\n",
-              "      <td>5.32</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.405</td>\n",
-              "      <td>6.565</td>\n",
-              "      <td>22.9</td>\n",
-              "      <td>7.3172</td>\n",
-              "      <td>6.0</td>\n",
-              "      <td>293.0</td>\n",
-              "      <td>16.6</td>\n",
-              "      <td>371.72</td>\n",
-              "      <td>9.51</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>Giuseppe Gullo</td>\n",
-              "      <td>0.03584</td>\n",
-              "      <td>80.0</td>\n",
-              "      <td>3.37</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.398</td>\n",
-              "      <td>6.290</td>\n",
-              "      <td>17.8</td>\n",
-              "      <td>6.6115</td>\n",
-              "      <td>4.0</td>\n",
-              "      <td>337.0</td>\n",
-              "      <td>16.1</td>\n",
-              "      <td>396.90</td>\n",
-              "      <td>4.67</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9fd50045-0d00-4035-a108-dba26c20f303')\"\n",
-              "              title=\"Convert this dataframe to an interactive table.\"\n",
-              "              style=\"display:none;\">\n",
-              "        \n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
-              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
-              "  </svg>\n",
-              "      </button>\n",
-              "      \n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      flex-wrap:wrap;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-9fd50045-0d00-4035-a108-dba26c20f303 button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-9fd50045-0d00-4035-a108-dba26c20f303');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ],
-            "text/plain": [
-              "            OWNER     CRIM    ZN  INDUS  ...    TAX  PTRATIO       B  LSTAT\n",
-              "0     Alan Turing  0.02899  40.0   1.25  ...  335.0     19.7  389.85   5.89\n",
-              "1       Elon Musk  0.34109   0.0   7.38  ...  287.0     19.6  396.90   6.12\n",
-              "2      Steve Jobs  0.25356   0.0   9.90  ...  304.0     18.4  396.42  11.50\n",
-              "3    Chuck Norris  0.04297  52.5   5.32  ...  293.0     16.6  371.72   9.51\n",
-              "4  Giuseppe Gullo  0.03584  80.0   3.37  ...  337.0     16.1  396.90   4.67\n",
-              "\n",
-              "[5 rows x 14 columns]"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 26
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 27,
-      "id": "178a42bf",
-      "metadata": {
-        "id": "178a42bf"
-      },
-      "outputs": [],
-      "source": [
-        "X = df_pred.drop(\"OWNER\", axis=1).values\n",
-        "X = ss.transform(X)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 28,
-      "id": "0839f9b3",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "0839f9b3",
-        "outputId": "308b46d2-5252-4549-fb2b-f74d0e6ca286"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([22.14633467, 25.11948741, 20.54343769, 26.91105226, 30.36557584])"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 28
-        }
-      ],
-      "source": [
-        "y_pred = lr.predict(X)\n",
-        "y_pred"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "f27316b1",
-      "metadata": {
-        "id": "f27316b1"
-      },
-      "source": [
-        "### 8. Salva il risultato in un file excel chiamato \"housing_estimate.xlsx\", deve contenere due colonne: OWNER=il proprietario dell'abitazione, ESTIMATED PRICE=il valore stimato dal nostro modello."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "ca06c263",
-      "metadata": {
-        "id": "ca06c263"
-      },
-      "outputs": [],
-      "source": [
-        "df_result = pd.DataFrame({\"owner\":df_pred[\"OWNER\"].values, \"estimated price\":y_pred})\n",
-        "df_result.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "1fd97bd7",
-      "metadata": {
-        "id": "1fd97bd7"
-      },
-      "outputs": [],
-      "source": [
-        "df_result.to_excel(\"housing_estimate.xlsx\")"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3.7.4 64-bit ('base': conda)",
-      "language": "python",
-      "name": "python37464bitbaseconda78b00ce9b4da4d77b0796cc32fd5efe8"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.8"
-    },
-    "colab": {
-      "name": "regressione_exercises.ipynb",
-      "provenance": []
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 5
-}
\ No newline at end of file
diff --git a/4 - La Classificazione/binary_classification_exercise.ipynb b/4 - La Classificazione/esercizi/binary_classification_exercise.ipynb
similarity index 99%
rename from 4 - La Classificazione/binary_classification_exercise.ipynb
rename to 4 - La Classificazione/esercizi/binary_classification_exercise.ipynb
index e4dbe94..dda9d32 100644
--- a/4 - La Classificazione/binary_classification_exercise.ipynb	
+++ b/4 - La Classificazione/esercizi/binary_classification_exercise.ipynb	
@@ -23,7 +23,7 @@
         "colab_type": "text"
       },
       "source": [
-        "<a href=\"https://colab.research.google.com/github/ProfAI/machine-learning-fondamenti/blob/main/4%20-%20La%20Classificazione/binary_classification_exercise.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+        "<a href=\"https://colab.research.google.com/github/ProfAI/machine-learning-fondamenti/blob/main/4%20-%20La%20Classificazione/esercizi/binary_classification_exercise.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
       ]
     },
     {
@@ -1796,4 +1796,4 @@
       "outputs": []
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/4 - Overfitting e Tecniche di Regolarizzazione/overfitting_regularizzazion_exercise.ipynb b/4 - Overfitting e Tecniche di Regolarizzazione/esercizi/overfitting_regularizzazion_exercise.ipynb
similarity index 99%
rename from 4 - Overfitting e Tecniche di Regolarizzazione/overfitting_regularizzazion_exercise.ipynb
rename to 4 - Overfitting e Tecniche di Regolarizzazione/esercizi/overfitting_regularizzazion_exercise.ipynb
index 97ddfac..974a640 100644
--- a/4 - Overfitting e Tecniche di Regolarizzazione/overfitting_regularizzazion_exercise.ipynb	
+++ b/4 - Overfitting e Tecniche di Regolarizzazione/esercizi/overfitting_regularizzazion_exercise.ipynb	
@@ -24,7 +24,7 @@
         "colab_type": "text"
       },
       "source": [
-        "<a href=\"https://colab.research.google.com/github/ProfAI/machine-learning-fondamenti/blob/main/4%20-%20Overfitting%20e%20Tecniche%20di%20Regolarizzazione/overfitting_regularizzazion_exercise.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+        "<a href=\"https://colab.research.google.com/github/ProfAI/machine-learning-fondamenti/blob/main/4%20-%20Overfitting%20e%20Tecniche%20di%20Regolarizzazione/esercizi/overfitting_regularizzazion_exercise.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
       ]
     },
     {
@@ -651,4 +651,4 @@
       "outputs": []
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/4 - Overfitting e Tecniche di Regolarizzazione/regularization.ipynb b/4 - Overfitting e Tecniche di Regolarizzazione/regularization.ipynb
index 5550e67..df5e45c 100644
--- a/4 - Overfitting e Tecniche di Regolarizzazione/regularization.ipynb	
+++ b/4 - Overfitting e Tecniche di Regolarizzazione/regularization.ipynb	
@@ -30,12 +30,41 @@
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "# Tecniche di Regolarizzazione"
-      ],
-      "metadata": {
-        "id": "6484AfisfeCD"
-      }
+        "# Tecniche di Regolarizzazione: Ridge, Lasso e Overfitting\n",
+        "\n",
+        "Benvenuto in questo notebook didattico! Qui esploreremo uno dei concetti pi\u00f9 importanti del Machine Learning: l'**Overfitting** (sovradattamento) e come contrastarlo utilizzando le **Tecniche di Regolarizzazione** (**Ridge** e **Lasso**).\n",
+        "\n",
+        "### Che cos'\u00e8 l'Overfitting?\n",
+        "L'overfitting si verifica quando un modello impara \"troppo bene\" i dati di addestramento (*training set*), memorizzando anche il rumore e i dettagli irrilevanti. Di conseguenza, il modello avr\u00e0 prestazioni eccellenti sui dati noti, ma non sar\u00e0 in grado di generalizzare su dati nuovi e mai visti (*test set*).\n",
+        "\n",
+        "### Perch\u00e9 si verifica?\n",
+        "Solitamente accade quando:\n",
+        "1. Il modello \u00e8 troppo complesso rispetto alla quantit\u00e0 di dati disponibili.\n",
+        "2. Ci sono troppe feature (variabili) rispetto al numero di campioni. In questo notebook vedremo proprio il caso limite $p \\approx n$ (100 feature per 100 campioni), che rappresenta una ricetta perfetta per l'overfitting.\n",
+        "\n",
+        "### Come ci aiuta la Regolarizzazione?\n",
+        "La regolarizzazione aggiunge un **termine di penalit\u00e0** alla funzione di perdita (Loss Function) del modello. Questa penalit\u00e0 scoraggia il modello dall'assegnare coefficienti (pesi) troppo elevati alle feature, costringendolo a rimanere pi\u00f9 semplice e lineare.\n",
+        "Le due tecniche principali sono:\n",
+        "- **Ridge Regression (L2)**: Penalizza la somma dei quadrati dei coefficienti. Tende a rimpicciolire i pesi verso lo zero, ma senza annullarli del tutto.\n",
+        "- **Lasso Regression (L1)**: Penalizza la somma dei valori assoluti dei coefficienti. Tende ad azzerare completamente i pesi delle feature meno importanti, effettuando una vera e propria **selezione automatica delle feature**."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 1. Importazione delle Librerie e Setup\n",
+        "\n",
+        "In questa prima cella carichiamo le librerie fondamentali per il nostro esperimento:\n",
+        "- `numpy`: Per la gestione degli array e delle operazioni matematiche.\n",
+        "- `make_regression`: Una comoda funzione di Scikit-Learn per generare un dataset sintetico su cui fare esperimenti di regressione.\n",
+        "- `train_test_split`: Per dividere i nostri dati in un set di addestramento e uno di test.\n",
+        "- `LinearRegression`, `Ridge`, `Lasso`: I tre modelli di regressione che andremo a confrontare.\n",
+        "- `StandardScaler`: Per standardizzare le feature (rendere media = 0 e varianza = 1).\n",
+        "- `mean_squared_error`, `r2_score`: Le metriche principali per valutare la qualit\u00e0 delle predizioni."
+      ]
     },
     {
       "cell_type": "code",
@@ -53,6 +82,17 @@
         "from sklearn.metrics import mean_squared_error, r2_score"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 2. Inizializzazione e Funzione di Valutazione\n",
+        "\n",
+        "Per prima cosa impostiamo un `RANDOM_SEED = 0` per garantire la riproducibilit\u00e0 di tutti i nostri risultati. Successivamente, creiamo una funzione di utilit\u00e0 chiamata `evaluate_model` che calcola ed evidenzia due metriche chiave:\n",
+        "- **MSE (Mean Squared Error)**: L'errore quadratico medio. Pi\u00f9 \u00e8 basso, migliore \u00e8 la predizione.\n",
+        "- **$R^2$ (Coefficiente di Determinazione)**: Misura quanta parte della varianza del target \u00e8 spiegata dal modello. Varia solitamente tra 0 e 1 (dove 1 indica una predizione perfetta)."
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -82,6 +122,20 @@
       "execution_count": 35,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 3. Generazione del Dataset (Progettato per l'Overfitting)\n",
+        "\n",
+        "Generiamo un dataset sintetico fatto appositamente per indurre overfitting:\n",
+        "- Creiamo **100 campioni** (`n_samples=100`) con **100 feature** (`n_features=100`). Quando il numero di feature \u00e8 pari al numero di campioni, il modello lineare standard ha abbastanza gradi di libert\u00e0 da poter descrivere perfettamente qualsiasi rumore, portando a un sicuro overfitting.\n",
+        "- Di queste 100 feature, solo **10 sono informative** (`n_informative=10`), ovvero correlate realmente con il target. Le altre 90 sono puramente rumore e variabili non correlate.\n",
+        "- Dividiamo poi i dati in:\n",
+        "  - **Training Set (75%)**: Usato per addestrare il modello.\n",
+        "  - **Test Set (25%)**: Usato per valutare la reale capacit\u00e0 di generalizzazione del modello su dati mai visti."
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -94,6 +148,19 @@
       "execution_count": 36,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 4. Standardizzazione delle Feature\n",
+        "\n",
+        "La standardizzazione \u00e8 un passaggio **fondamentale** quando si applica la regolarizzazione. \n",
+        "Poich\u00e9 Ridge e Lasso penalizzano la grandezza dei coefficienti (i pesi $w$), se le feature avessero scale molto diverse, i coefficienti sarebbero anch'essi su scale diverse. Il modello finirebbe per penalizzare maggiormente le feature con valori numerici pi\u00f9 alti indipendentemente dalla loro reale importanza.\n",
+        "\n",
+        "Con `StandardScaler` portiamo tutte le feature ad avere media 0 e deviazione standard 1, garantendo un trattamento equo durante la penalizzazione.\n",
+        "*Nota*: Applichiamo `fit_transform` sul training set e solo `transform` sul test set per evitare il \"data leakage\" (perdita di informazioni dal test set al training set)."
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -109,12 +176,12 @@
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "### Ordinary Least Regression"
-      ],
-      "metadata": {
-        "id": "JTGC5U4UPwB5"
-      }
+        "## 5. Regressione Lineare Classica (Ordinary Least Squares - OLS)\n",
+        "\n",
+        "Cominciamo addestrando un modello di Regressione Lineare classico, senza alcuna regolarizzazione. Questo modello cerca di minimizzare unicamente la somma dei quadrati dei residui (MSE sul training set)."
+      ]
     },
     {
       "cell_type": "code",
@@ -193,12 +260,29 @@
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "### Ridge Regression"
-      ],
-      "metadata": {
-        "id": "P0VihBX7Q-QB"
-      }
+        "### Analisi dei Risultati della Regressione Lineare Classica (OLS)\n",
+        "\n",
+        "- **Sul Training Set**: Otteniamo un **MSE di 0.000** e un **$R^2$ di 1.000**. Il modello si adatta perfettamente ai dati di addestramento (ha memorizzato tutto il rumore delle 90 feature non informative).\n",
+        "- **Sul Test Set**: Otteniamo un **MSE enorme (~12156.902)** e un **$R^2$ pessimo (~0.217)**.\n",
+        "\n",
+        "Questa enorme differenza tra le performance di training e quelle di test \u00e8 il sintomo inequivocabile di **Overfitting**. Il modello classico non ha imparato la relazione reale, ha semplicemente memorizzato il training set e non sa generalizzare."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 6. Ridge Regression (Regolarizzazione L2)\n",
+        "\n",
+        "La **Ridge Regression** modifica la funzione di costo della regressione lineare classica aggiungendo una penalit\u00e0 proporzionale al **quadrato** della grandezza dei coefficienti (regolarizzazione L2):\n",
+        "\n",
+        "$$\\text{Loss} = \\text{MSE} + \\alpha \\sum_{j=1}^{p} w_j^2$$\n",
+        "\n",
+        "- Il parametro $\\alpha$ controlla la forza della regolarizzazione: pi\u00f9 \u00e8 grande, pi\u00f9 i pesi saranno spinti verso lo zero, riducendo la complessit\u00e0 del modello.\n",
+        "- Questo impedisce ai singoli pesi di assumere valori spropositati per adattarsi al rumore."
+      ]
     },
     {
       "cell_type": "code",
@@ -288,12 +372,30 @@
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "### Lasso Regression"
-      ],
-      "metadata": {
-        "id": "Mit0k0HNPx7S"
-      }
+        "### Analisi dei Risultati Ridge Regression\n",
+        "\n",
+        "- **Sul Training Set**: MSE di ~6.870 e $R^2$ di 1.000.\n",
+        "- **Sul Test Set**: MSE di ~11894.633 e $R^2$ di 0.234.\n",
+        "\n",
+        "**Cosa notiamo?**\n",
+        "Ridge ha migliorato solo leggermente i risultati rispetto a OLS (l'R2 sul test set \u00e8 passato da 0.217 a 0.234). Perch\u00e9 la regressione Ridge non \u00e8 stata efficace in questo caso?\n",
+        "Poich\u00e9 Ridge utilizza una penalizzazione quadratica dei pesi (L2), tende a rimpicciolire tutti i coefficienti, ma **non li azzera mai completamente**. Quindi, tutte le 90 feature inutili e rumorose continuano a far parte del modello, introducendo rumore che compromette la predizione."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 7. Lasso Regression (Regolarizzazione L1)\n",
+        "\n",
+        "La **Lasso Regression** (Least Absolute Shrinkage and Selection Operator) aggiunge invece una penalit\u00e0 proporzionale al **valore assoluto** dei coefficienti (regolarizzazione L1):\n",
+        "\n",
+        "$$\\text{Loss} = \\text{MSE} + \\alpha \\sum_{j=1}^{p} |w_j|$$\n",
+        "\n",
+        "La caratteristica fondamentale di Lasso \u00e8 che pu\u00f2 spingere i coefficienti delle feature non importanti **esattamente a zero**. Questo significa che Lasso esegue una vera e propria **selezione automatica delle feature** (*feature selection*), eliminando il rumore."
+      ]
     },
     {
       "cell_type": "code",
@@ -383,12 +485,32 @@
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "### Learning curve"
-      ],
-      "metadata": {
-        "id": "N-w-wp7jRZun"
-      }
+        "### Analisi dei Risultati Lasso Regression\n",
+        "\n",
+        "- **Sul Training Set**: MSE di ~59.894 e $R^2$ di ~0.996.\n",
+        "- **Sul Test Set**: MSE di ~93.840 e $R^2$ di **~0.994**!\n",
+        "\n",
+        "**Cosa notiamo?**\n",
+        "Lasso ha ottenuto un risultato strabiliante! Il test R2 \u00e8 balzato da ~0.217 a **0.994**, risolvendo completamente il problema dell'overfitting.\n",
+        "Lasso ha spento i coefficienti delle 90 feature irrilevanti e rumorose, selezionando e mantenendo attive solo le feature realmente correlate con il target. Questo \u00e8 il caso d'uso perfetto per la regolarizzazione L1."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 8. Curva di Apprendimento (Learning Curve)\n",
+        "\n",
+        "Per finire, analizziamo la **Curva di Apprendimento**. \n",
+        "Questa visualizzazione traccia le prestazioni (in questo caso sul modello Lasso) all'aumentare dei campioni di addestramento disponibili.\n",
+        "\n",
+        "- La curva blu mostra il punteggio sul training set, mentre la curva arancione mostra il punteggio sul set di validazione.\n",
+        "- All'inizio, con pochissimi campioni di training, il punteggio sul training set \u00e8 altissimo (overfitting facile) mentre sul test set \u00e8 basso.\n",
+        "- Man mano che forniamo pi\u00f9 campioni, il modello non riesce a memorizzarli tutti perfettamente (il training score cala leggermente) ma impara regole generali migliori (il test/validation score sale).\n",
+        "- Quando le due curve convergono verso un punteggio alto (vicino a 1.0) e con un gap ridotto, significa che il modello ha una buona capacit\u00e0 di generalizzazione."
+      ]
     },
     {
       "cell_type": "code",
diff --git a/6 - Clustering/clustering_exercise.ipynb b/6 - Clustering/esercizi/clustering_exercise.ipynb
similarity index 99%
rename from 6 - Clustering/clustering_exercise.ipynb
rename to 6 - Clustering/esercizi/clustering_exercise.ipynb
index 4eaf1f6..c9f0c2d 100644
--- a/6 - Clustering/clustering_exercise.ipynb	
+++ b/6 - Clustering/esercizi/clustering_exercise.ipynb	
@@ -25,7 +25,7 @@
         "colab_type": "text"
       },
       "source": [
-        "<a href=\"https://colab.research.google.com/github/ProfAI/machine-learning-fondamenti/blob/main/6%20-%20Clustering/clustering_exercise.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+        "<a href=\"https://colab.research.google.com/github/ProfAI/machine-learning-fondamenti/blob/main/6%20-%20Clustering/esercizi/clustering_exercise.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
       ]
     },
     {
@@ -811,4 +811,4 @@
       "outputs": []
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/AGENT.md b/AGENT.md
new file mode 100644
index 0000000..0dfdd6b
--- /dev/null
+++ b/AGENT.md
@@ -0,0 +1,47 @@
+# AGENT.md - Istruzioni per gli Agenti AI
+
+Benvenuto! Questa repository è un ambiente di studio dedicato ai **Fondamenti del Machine Learning** (in lingua italiana).
+Ogni agente AI che lavora su questa repository deve seguire le linee guida descritte in questo documento.
+
+---
+
+## 🎯 Obiettivo della Repository
+Lo scopo principale del progetto è fornire materiale didattico chiaro, completo ed intuitivo per studenti che affrontano i concetti di base del Machine Learning:
+- Preprocessing dei dati (scaling, encoding, train/test split).
+- Regressione Lineare ed Overfitting.
+- Tecniche di Regolarizzazione (Ridge, Lasso) e Learning Curves.
+- Classificazione e Clustering.
+
+---
+
+## 📚 Linee Guida Pedagogiche (Per gli Agenti)
+Quando ti viene chiesto di scrivere spiegazioni, commenti o documentazione per i notebook:
+1. **Pensa come un Docente**: Spiega i concetti in modo chiaro, accessibile e strutturato per uno studente. Non limitarti a descrivere *cosa* fa il codice, spiega sempre il **perché**.
+2. **Utilizza la Notazione Matematica (LaTeX)**: Rappresenta le formule matematiche e le loss function usando la sintassi LaTeX standard (es. per Ridge: $\text{Loss} = \text{MSE} + \alpha \sum w_j^2$) per assicurarne una visualizzazione ottimale in Jupyter/Google Colab.
+3. **Fornisci Esempi Concreti e Contrasti**: Evidenzia il contrasto tra i modelli (es. OLS vs Ridge vs Lasso) analizzando le metriche reali ottenute sui dati.
+4. **Prevenzione degli Errori Didattici**:
+   - Spiega chiaramente l'importanza del seed (`RANDOM_SEED`) per la riproducibilità.
+   - Metti in guardia lo studente sul **Data Leakage** (es. fittare lo scaler solo sul training set).
+
+---
+
+## 🛠️ Ambiente di Sviluppo e Comandi
+- **Interprete Python**: Utilizza sempre l'interprete dell'ambiente virtuale locale: `file:///home/rares/project/python/professionAI/machine-learning-fondamenti/.venv/bin/python3`.
+- **Dipendenze**: Definite nel file `requirements.txt`.
+- **Note per Windows**: Se operi su sistemi Windows, usa `npx.cmd` al posto di `npx`.
+
+---
+
+## ⚡ Skill e Strumenti del Progetto
+La repository dispone di una skill locale per automatizzare l'arricchimento didattico:
+
+- **Skill di Arricchimento Didattico**: [.agents/notebook-pedagogical-enrichment/SKILL.md](file:///.agents/notebook-pedagogical-enrichment/SKILL.md)
+- **Helper Script**: [.agents/notebook-pedagogical-enrichment/scripts/notebook_helper.py](file:///.agents/notebook-pedagogical-enrichment/scripts/notebook_helper.py)
+  - Usa `inspect` per vedere la struttura e le celle del notebook:
+    ```bash
+    python3 .agents/notebook-pedagogical-enrichment/scripts/notebook_helper.py inspect <path_to_notebook.ipynb>
+    ```
+  - Usa `clear-outputs` per ripulire l'esecuzione del notebook prima di salvarlo o farne il commit:
+    ```bash
+    python3 .agents/notebook-pedagogical-enrichment/scripts/notebook_helper.py clear-outputs <path_to_notebook.ipynb>
+    ```
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..b60aa55
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,336 @@
+# This file was autogenerated by uv via the following command:
+#    uv pip compile requirements.txt -o requirements.txt
+anyio==4.14.0
+    # via
+    #   httpx
+    #   jupyter-server
+argon2-cffi==25.1.0
+    # via jupyter-server
+argon2-cffi-bindings==25.1.0
+    # via argon2-cffi
+arrow==1.4.0
+    # via isoduration
+asttokens==3.0.1
+    # via stack-data
+async-lru==2.3.0
+    # via jupyterlab
+attrs==26.1.0
+    # via
+    #   jsonschema
+    #   referencing
+babel==2.18.0
+    # via jupyterlab-server
+beautifulsoup4==4.15.0
+    # via nbconvert
+bleach==6.4.0
+    # via nbconvert
+certifi==2026.6.17
+    # via
+    #   httpcore
+    #   httpx
+    #   requests
+cffi==2.0.0
+    # via argon2-cffi-bindings
+charset-normalizer==3.4.7
+    # via requests
+comm==0.2.3
+    # via ipykernel
+contourpy==1.3.3
+    # via matplotlib
+cycler==0.12.1
+    # via matplotlib
+debugpy==1.8.21
+    # via ipykernel
+decorator==5.3.1
+    # via ipython
+defusedxml==0.7.1
+    # via nbconvert
+et-xmlfile==2.0.0
+    # via openpyxl
+executing==2.2.1
+    # via stack-data
+fastjsonschema==2.21.2
+    # via nbformat
+fonttools==4.63.0
+    # via matplotlib
+fqdn==1.5.1
+    # via jsonschema
+h11==0.16.0
+    # via httpcore
+httpcore==1.0.9
+    # via httpx
+httpx==0.28.1
+    # via jupyterlab
+idna==3.18
+    # via
+    #   anyio
+    #   httpx
+    #   jsonschema
+    #   requests
+ipykernel==7.3.0
+    # via
+    #   -r requirements.txt
+    #   jupyterlab
+ipython==9.14.1
+    # via ipykernel
+ipython-pygments-lexers==1.1.1
+    # via ipython
+isoduration==20.11.0
+    # via jsonschema
+jedi==0.20.0
+    # via ipython
+jinja2==3.1.6
+    # via
+    #   jupyter-server
+    #   jupyterlab
+    #   jupyterlab-server
+    #   nbconvert
+joblib==1.5.3
+    # via scikit-learn
+json5==0.14.0
+    # via jupyterlab-server
+jsonpointer==3.1.1
+    # via jsonschema
+jsonschema==4.26.0
+    # via
+    #   jupyter-events
+    #   jupyterlab-server
+    #   nbformat
+jsonschema-specifications==2025.9.1
+    # via jsonschema
+jupyter-builder==1.0.2
+    # via
+    #   jupyterlab
+    #   notebook
+jupyter-client==8.9.1
+    # via
+    #   ipykernel
+    #   jupyter-server
+    #   nbclient
+jupyter-core==5.9.1
+    # via
+    #   ipykernel
+    #   jupyter-builder
+    #   jupyter-client
+    #   jupyter-server
+    #   jupyterlab
+    #   nbclient
+    #   nbconvert
+    #   nbformat
+jupyter-events==0.12.1
+    # via jupyter-server
+jupyter-lsp==2.3.1
+    # via jupyterlab
+jupyter-server==2.20.0
+    # via
+    #   jupyter-lsp
+    #   jupyterlab
+    #   jupyterlab-server
+    #   notebook
+    #   notebook-shim
+jupyter-server-terminals==0.5.4
+    # via jupyter-server
+jupyterlab==4.6.0
+    # via notebook
+jupyterlab-pygments==0.3.0
+    # via nbconvert
+jupyterlab-server==2.28.0
+    # via
+    #   jupyterlab
+    #   notebook
+kiwisolver==1.5.0
+    # via matplotlib
+lark==1.3.1
+    # via rfc3987-syntax
+markupsafe==3.0.3
+    # via
+    #   jinja2
+    #   nbconvert
+matplotlib==3.11.0
+    # via
+    #   -r requirements.txt
+    #   seaborn
+matplotlib-inline==0.2.2
+    # via
+    #   ipykernel
+    #   ipython
+mistune==3.2.1
+    # via nbconvert
+narwhals==2.22.1
+    # via scikit-learn
+nbclient==0.11.0
+    # via nbconvert
+nbconvert==7.17.1
+    # via jupyter-server
+nbformat==5.10.4
+    # via
+    #   jupyter-server
+    #   nbclient
+    #   nbconvert
+nest-asyncio2==1.7.2
+    # via ipykernel
+notebook==7.6.0
+    # via -r requirements.txt
+notebook-shim==0.2.4
+    # via
+    #   jupyterlab
+    #   notebook
+numpy==2.4.6
+    # via
+    #   -r requirements.txt
+    #   contourpy
+    #   matplotlib
+    #   pandas
+    #   scikit-learn
+    #   scipy
+    #   seaborn
+openpyxl==3.1.5
+    # via pandas
+packaging==26.2
+    # via
+    #   ipykernel
+    #   jupyter-events
+    #   jupyter-server
+    #   jupyterlab
+    #   jupyterlab-server
+    #   matplotlib
+    #   nbconvert
+pandas==3.0.3
+    # via
+    #   -r requirements.txt
+    #   seaborn
+pandocfilters==1.5.1
+    # via nbconvert
+parso==0.8.7
+    # via jedi
+pexpect==4.9.0
+    # via ipython
+pillow==12.2.0
+    # via matplotlib
+platformdirs==4.10.0
+    # via jupyter-core
+prometheus-client==0.25.0
+    # via jupyter-server
+prompt-toolkit==3.0.52
+    # via ipython
+psutil==7.2.2
+    # via
+    #   ipykernel
+    #   ipython
+ptyprocess==0.7.0
+    # via
+    #   pexpect
+    #   terminado
+pure-eval==0.2.3
+    # via stack-data
+pycparser==3.0
+    # via cffi
+pygments==2.20.0
+    # via
+    #   ipython
+    #   ipython-pygments-lexers
+    #   nbconvert
+pyparsing==3.3.2
+    # via matplotlib
+python-dateutil==2.9.0.post0
+    # via
+    #   arrow
+    #   jupyter-client
+    #   matplotlib
+    #   pandas
+python-json-logger==4.1.0
+    # via jupyter-events
+pyyaml==6.0.3
+    # via jupyter-events
+pyzmq==27.1.0
+    # via
+    #   ipykernel
+    #   jupyter-client
+    #   jupyter-server
+referencing==0.37.0
+    # via
+    #   jsonschema
+    #   jsonschema-specifications
+    #   jupyter-events
+requests==2.34.2
+    # via jupyterlab-server
+rfc3339-validator==0.1.4
+    # via
+    #   jsonschema
+    #   jupyter-events
+rfc3986-validator==0.1.1
+    # via
+    #   jsonschema
+    #   jupyter-events
+rfc3987-syntax==1.1.0
+    # via jsonschema
+rpds-py==2026.5.1
+    # via
+    #   jsonschema
+    #   referencing
+scikit-learn==1.9.0
+    # via -r requirements.txt
+scipy==1.17.1
+    # via scikit-learn
+seaborn==0.13.2
+    # via -r requirements.txt
+send2trash==2.1.0
+    # via jupyter-server
+six==1.17.0
+    # via
+    #   python-dateutil
+    #   rfc3339-validator
+soupsieve==2.8.4
+    # via beautifulsoup4
+stack-data==0.6.3
+    # via ipython
+terminado==0.18.1
+    # via
+    #   jupyter-server
+    #   jupyter-server-terminals
+threadpoolctl==3.6.0
+    # via scikit-learn
+tinycss2==1.5.1
+    # via bleach
+tornado==6.5.7
+    # via
+    #   ipykernel
+    #   jupyter-client
+    #   jupyter-server
+    #   jupyterlab
+    #   notebook
+    #   terminado
+traitlets==5.15.1
+    # via
+    #   ipykernel
+    #   ipython
+    #   jupyter-builder
+    #   jupyter-client
+    #   jupyter-core
+    #   jupyter-events
+    #   jupyter-server
+    #   jupyterlab
+    #   matplotlib-inline
+    #   nbclient
+    #   nbconvert
+    #   nbformat
+typing-extensions==4.15.0
+    # via
+    #   beautifulsoup4
+    #   jupyter-client
+tzdata==2026.2
+    # via arrow
+uri-template==1.3.0
+    # via jsonschema
+urllib3==2.7.0
+    # via requests
+wcwidth==0.8.1
+    # via prompt-toolkit
+webcolors==25.10.0
+    # via jsonschema
+webencodings==0.5.1
+    # via
+    #   bleach
+    #   tinycss2
+websocket-client==1.9.0
+    # via jupyter-server

From d70b3b1b32c3234aef4adb0dec2705a7d622401c Mon Sep 17 00:00:00 2001
From: Spatariu Rares <81376159+SpatariuRares@users.noreply.github.com>
Date: Sat, 20 Jun 2026 10:32:14 +0200
Subject: [PATCH 2/9] feat: add explanatory markdown cells to classification
 notebooks and standardize metadata in exercises

---
 .../features_encoding.ipynb                   | 1590 +++++++------
 2 - Data Preprocessing/features_scaling.ipynb |  858 +++----
 2 - Data Preprocessing/missing_data.ipynb     | 2083 +++++++++--------
 2 - Data Preprocessing/structured_data.ipynb  | 1362 +++++------
 .../unstructured_data.ipynb                   | 1349 ++++++-----
 .../correlation_matrix.ipynb                  |   25 +
 .../regressione_lineare_multipla.ipynb        |  397 ++--
 .../regressione_lineare_semplice.ipynb        |  466 ++--
 .../regressione_polinomiale.ipynb             |  751 +++---
 .../binary_classification.ipynb               |   50 +
 .../multiclass_classification.ipynb           |   44 +
 ...overfitting_regularizzazion_exercise.ipynb |  301 ++-
 .../overfitting.ipynb                         |   78 +
 ...alth_insurance_cross_sell_prediction.ipynb |   33 +-
 14 files changed, 5004 insertions(+), 4383 deletions(-)

diff --git a/2 - Data Preprocessing/features_encoding.ipynb b/2 - Data Preprocessing/features_encoding.ipynb
index 954e5d1..660d21a 100644
--- a/2 - Data Preprocessing/features_encoding.ipynb	
+++ b/2 - Data Preprocessing/features_encoding.ipynb	
@@ -1,806 +1,846 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "e4__to0e8oV9"
-   },
-   "source": [
-    "# Operare su dati qualitativi"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 203
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e4__to0e8oV9"
+      },
+      "source": [
+        "# Operare su dati qualitativi"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "I modelli di Machine Learning sono puramente matematici e richiedono input numerici. Di conseguenza, i **dati qualitativi (o categorici)** devono essere convertiti in numeri prima dell'addestramento.\n",
+        "Distinguiamo due tipi di variabili categoriche:\n",
+        "- **Ordinali**: Variabili che presentano un ordine intrinseco logico (es. taglie di abbigliamento S, M, L, XL; livello di istruzione).\n",
+        "- **Nominali**: Variabili prive di qualsiasi gerarchia o ordinamento naturale (es. colore degli occhi, marca di auto, nazionalit\u00e0).\n"
+      ]
     },
-    "id": "eOWQ9lS-8oV-",
-    "outputId": "b48a6c2e-dbc4-4fc7-ddcb-07b716a64bf0"
-   },
-   "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>taglia</th>\n",
-       "      <th>colore</th>\n",
-       "      <th>prezzo</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>S</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>4.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>M</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>19.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>XL</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>12.49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>XL</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>S</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 203
+        },
+        "id": "eOWQ9lS-8oV-",
+        "outputId": "b48a6c2e-dbc4-4fc7-ddcb-07b716a64bf0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>taglia</th>\n",
+              "      <th>colore</th>\n",
+              "      <th>prezzo</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>S</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>4.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>M</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>19.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>XL</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>12.49</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>XL</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>S</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  taglia  colore  prezzo\n",
+              "0      S  bianco    4.99\n",
+              "1      M  bianco   19.99\n",
+              "2     XL  bianco   12.49\n",
+              "3     XL  bianco   14.99\n",
+              "4      S  bianco   14.99"
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "  taglia  colore  prezzo\n",
-       "0      S  bianco    4.99\n",
-       "1      M  bianco   19.99\n",
-       "2     XL  bianco   12.49\n",
-       "3     XL  bianco   14.99\n",
-       "4      S  bianco   14.99"
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "CSV_URL = \"https://raw.githubusercontent.com/ProfAI/machine-learning-fondamenti/main/datasets/shirts.csv\"\n",
+        "\n",
+        "shirts = pd.read_csv(CSV_URL, index_col=0)\n",
+        "shirts.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rmFUvWem8oWE"
+      },
+      "source": [
+        "## Ordinal encoding delle variabili ordinali"
       ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "CSV_URL = \"https://raw.githubusercontent.com/ProfAI/machine-learning-fondamenti/main/datasets/shirts.csv\"\n",
-    "\n",
-    "shirts = pd.read_csv(CSV_URL, index_col=0)\n",
-    "shirts.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "rmFUvWem8oWE"
-   },
-   "source": [
-    "## Ordinal encoding delle variabili ordinali"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Kv8GM_0Z8oWJ"
-   },
-   "source": [
-    "#### Pandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 203
     },
-    "id": "TRJU9Yv78oWK",
-    "outputId": "d75197f0-c3b4-4912-c11b-735d9df5bc88"
-   },
-   "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>taglia</th>\n",
-       "      <th>colore</th>\n",
-       "      <th>prezzo</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>4.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>19.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>12.49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Per le variabili ordinali, \u00e8 fondamentale preservare l'ordine. Assegniamo quindi a ciascuna categoria un numero intero progressivo che rispecchia la gerarchia.\n",
+        "Ad esempio:\n",
+        "$$\\text{S} \\to 0, \\quad \\text{M} \\to 1, \\quad \\text{L} \\to 2, \\quad \\text{XL} \\to 3$$\n",
+        "In Pandas, questo processo viene tipicamente implementato definendo un dizionario di mappatura personalizzato e applicandolo con il metodo `.map()`.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Kv8GM_0Z8oWJ"
+      },
+      "source": [
+        "#### Pandas"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 203
+        },
+        "id": "TRJU9Yv78oWK",
+        "outputId": "d75197f0-c3b4-4912-c11b-735d9df5bc88"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>taglia</th>\n",
+              "      <th>colore</th>\n",
+              "      <th>prezzo</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>4.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>19.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>12.49</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   taglia  colore  prezzo\n",
+              "0       0  bianco    4.99\n",
+              "1       1  bianco   19.99\n",
+              "2       3  bianco   12.49\n",
+              "3       3  bianco   14.99\n",
+              "4       0  bianco   14.99"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "   taglia  colore  prezzo\n",
-       "0       0  bianco    4.99\n",
-       "1       1  bianco   19.99\n",
-       "2       3  bianco   12.49\n",
-       "3       3  bianco   14.99\n",
-       "4       0  bianco   14.99"
+      "source": [
+        "size_mapping = {\"S\":0,\"M\":1,\"L\":2,\"XL\":3} #dizionario che ordina le misure\n",
+        "shirts[\"taglia\"] = shirts[\"taglia\"].map(size_mapping) #mappiamo la misura con il numero corrispondente\n",
+        "shirts.head()"
       ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "size_mapping = {\"S\":0,\"M\":1,\"L\":2,\"XL\":3} #dizionario che ordina le misure\n",
-    "shirts[\"taglia\"] = shirts[\"taglia\"].map(size_mapping) #mappiamo la misura con il numero corrispondente\n",
-    "shirts.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "AEyvm7gW8oWF"
-   },
-   "source": [
-    "#### Numpy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 103
     },
-    "id": "_8w14Gjt8oWG",
-    "outputId": "d7d9b128-faf5-44d9-db8c-497127571dfe"
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([[0, 'bianco', 4.99],\n",
-       "       [1, 'bianco', 19.99],\n",
-       "       [3, 'bianco', 12.49],\n",
-       "       [3, 'bianco', 14.99],\n",
-       "       [0, 'bianco', 14.99]], dtype=object)"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AEyvm7gW8oWF"
+      },
+      "source": [
+        "#### Numpy"
       ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "shirts = pd.read_csv(CSV_URL,index_col=0)\n",
-    "X = shirts.values\n",
-    "\n",
-    "size_mapping = {\"S\":0,\"M\":1,\"L\":2,\"XL\":3} #dizionario che ordina le misure\n",
-    "fmap = np.vectorize(lambda t:size_mapping[t])\n",
-    "X[:,0] = fmap(X[:,0])\n",
-    "X[:5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "gwVG4Ai_8oWO"
-   },
-   "source": [
-    "## One-hot encoding"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "V_iPdBAz8oWO"
-   },
-   "source": [
-    "#### Scikit-learn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 226
     },
-    "id": "_AT3khFB8oWP",
-    "outputId": "90dd223a-b4cd-43b9-bc60-7c7d36003896"
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([[1., 0., 0., 0.],\n",
-       "       [0., 0., 1., 0.],\n",
-       "       [1., 0., 0., 0.],\n",
-       "       [0., 1., 0., 0.],\n",
-       "       [0., 0., 1., 0.],\n",
-       "       [0., 0., 0., 1.]])"
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 103
+        },
+        "id": "_8w14Gjt8oWG",
+        "outputId": "d7d9b128-faf5-44d9-db8c-497127571dfe"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([[0, 'bianco', 4.99],\n",
+              "       [1, 'bianco', 19.99],\n",
+              "       [3, 'bianco', 12.49],\n",
+              "       [3, 'bianco', 14.99],\n",
+              "       [0, 'bianco', 14.99]], dtype=object)"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "shirts = pd.read_csv(CSV_URL,index_col=0)\n",
+        "X = shirts.values\n",
+        "\n",
+        "size_mapping = {\"S\":0,\"M\":1,\"L\":2,\"XL\":3} #dizionario che ordina le misure\n",
+        "fmap = np.vectorize(lambda t:size_mapping[t])\n",
+        "X[:,0] = fmap(X[:,0])\n",
+        "X[:5]"
       ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.preprocessing import OneHotEncoder\n",
-    "\n",
-    "X = [[\"bianco\"], [\"rosso\"], [\"bianco\"], [\"blu\"], [\"rosso\"], [\"verde\"]]\n",
-    "\n",
-    "enc = OneHotEncoder()\n",
-    "X_sparse = enc.fit_transform(X)\n",
-    "X = X_sparse.toarray()\n",
-    "X"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "[array(['bianco', 'blu', 'rosso', 'verde'], dtype=object)]"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gwVG4Ai_8oWO"
+      },
+      "source": [
+        "## One-hot encoding"
       ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "enc.categories_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 103
     },
-    "id": "xybQKi529c0p",
-    "outputId": "90d8dd95-8e0b-4040-a70a-1edd29be7c34"
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([[1.0, 0.0, 0.0, 'S', 4.99],\n",
-       "       [1.0, 0.0, 0.0, 'M', 19.99],\n",
-       "       [1.0, 0.0, 0.0, 'XL', 12.49],\n",
-       "       [1.0, 0.0, 0.0, 'XL', 14.99],\n",
-       "       [1.0, 0.0, 0.0, 'S', 14.99],\n",
-       "       [0.0, 0.0, 1.0, 'S', 7.99],\n",
-       "       [0.0, 0.0, 1.0, 'M', 4.99],\n",
-       "       [0.0, 0.0, 1.0, 'L', 12.49],\n",
-       "       [1.0, 0.0, 0.0, 'XL', 12.49],\n",
-       "       [0.0, 0.0, 1.0, 'M', 19.99],\n",
-       "       [1.0, 0.0, 0.0, 'L', 14.99],\n",
-       "       [1.0, 0.0, 0.0, 'XL', 19.99],\n",
-       "       [1.0, 0.0, 0.0, 'M', 4.99],\n",
-       "       [1.0, 0.0, 0.0, 'L', 7.99],\n",
-       "       [1.0, 0.0, 0.0, 'M', 14.99],\n",
-       "       [0.0, 1.0, 0.0, 'XL', 9.99],\n",
-       "       [0.0, 1.0, 0.0, 'S', 12.49],\n",
-       "       [1.0, 0.0, 0.0, 'L', 7.99],\n",
-       "       [1.0, 0.0, 0.0, 'XL', 4.99],\n",
-       "       [0.0, 0.0, 1.0, 'M', 14.99],\n",
-       "       [0.0, 0.0, 1.0, 'S', 14.99],\n",
-       "       [1.0, 0.0, 0.0, 'XL', 7.99],\n",
-       "       [0.0, 0.0, 1.0, 'S', 9.99],\n",
-       "       [1.0, 0.0, 0.0, 'XL', 14.99],\n",
-       "       [0.0, 1.0, 0.0, 'S', 14.99],\n",
-       "       [1.0, 0.0, 0.0, 'XL', 9.99],\n",
-       "       [0.0, 0.0, 1.0, 'M', 7.99],\n",
-       "       [1.0, 0.0, 0.0, 'XL', 4.99],\n",
-       "       [0.0, 0.0, 1.0, 'S', 14.99],\n",
-       "       [1.0, 0.0, 0.0, 'L', 12.49],\n",
-       "       [0.0, 1.0, 0.0, 'M', 9.99],\n",
-       "       [0.0, 0.0, 1.0, 'L', 9.99],\n",
-       "       [0.0, 1.0, 0.0, 'XL', 7.99],\n",
-       "       [1.0, 0.0, 0.0, 'M', 19.99],\n",
-       "       [0.0, 0.0, 1.0, 'L', 12.49],\n",
-       "       [1.0, 0.0, 0.0, 'L', 12.49],\n",
-       "       [1.0, 0.0, 0.0, 'L', 9.99],\n",
-       "       [1.0, 0.0, 0.0, 'XL', 14.99],\n",
-       "       [0.0, 0.0, 1.0, 'L', 14.99],\n",
-       "       [0.0, 0.0, 1.0, 'XL', 9.99],\n",
-       "       [1.0, 0.0, 0.0, 'M', 14.99],\n",
-       "       [0.0, 0.0, 1.0, 'L', 19.99],\n",
-       "       [0.0, 0.0, 1.0, 'XL', 7.99],\n",
-       "       [0.0, 0.0, 1.0, 'M', 19.99],\n",
-       "       [0.0, 1.0, 0.0, 'L', 19.99],\n",
-       "       [1.0, 0.0, 0.0, 'XL', 9.99],\n",
-       "       [0.0, 1.0, 0.0, 'M', 12.49],\n",
-       "       [1.0, 0.0, 0.0, 'S', 7.99],\n",
-       "       [0.0, 1.0, 0.0, 'S', 14.99],\n",
-       "       [0.0, 0.0, 1.0, 'S', 12.49],\n",
-       "       [1.0, 0.0, 0.0, 'XL', 19.99],\n",
-       "       [0.0, 0.0, 1.0, 'S', 14.99],\n",
-       "       [1.0, 0.0, 0.0, 'S', 19.99],\n",
-       "       [0.0, 0.0, 1.0, 'M', 9.99],\n",
-       "       [0.0, 0.0, 1.0, 'L', 4.99],\n",
-       "       [0.0, 0.0, 1.0, 'M', 12.49],\n",
-       "       [1.0, 0.0, 0.0, 'L', 12.49],\n",
-       "       [0.0, 0.0, 1.0, 'S', 7.99],\n",
-       "       [0.0, 0.0, 1.0, 'S', 19.99],\n",
-       "       [1.0, 0.0, 0.0, 'L', 9.99],\n",
-       "       [0.0, 1.0, 0.0, 'XL', 14.99],\n",
-       "       [1.0, 0.0, 0.0, 'M', 12.49],\n",
-       "       [1.0, 0.0, 0.0, 'XL', 14.99],\n",
-       "       [1.0, 0.0, 0.0, 'L', 9.99],\n",
-       "       [0.0, 0.0, 1.0, 'XL', 14.99],\n",
-       "       [1.0, 0.0, 0.0, 'L', 12.49],\n",
-       "       [1.0, 0.0, 0.0, 'XL', 4.99],\n",
-       "       [1.0, 0.0, 0.0, 'L', 14.99],\n",
-       "       [0.0, 0.0, 1.0, 'L', 9.99],\n",
-       "       [0.0, 1.0, 0.0, 'S', 14.99],\n",
-       "       [0.0, 1.0, 0.0, 'S', 19.99],\n",
-       "       [0.0, 1.0, 0.0, 'XL', 14.99],\n",
-       "       [1.0, 0.0, 0.0, 'S', 9.99],\n",
-       "       [0.0, 1.0, 0.0, 'S', 14.99],\n",
-       "       [1.0, 0.0, 0.0, 'L', 9.99],\n",
-       "       [1.0, 0.0, 0.0, 'XL', 9.99],\n",
-       "       [0.0, 0.0, 1.0, 'L', 19.99],\n",
-       "       [1.0, 0.0, 0.0, 'XL', 19.99],\n",
-       "       [1.0, 0.0, 0.0, 'M', 4.99],\n",
-       "       [1.0, 0.0, 0.0, 'M', 9.99],\n",
-       "       [1.0, 0.0, 0.0, 'S', 14.99],\n",
-       "       [1.0, 0.0, 0.0, 'L', 19.99],\n",
-       "       [0.0, 0.0, 1.0, 'S', 7.99],\n",
-       "       [0.0, 0.0, 1.0, 'XL', 9.99],\n",
-       "       [1.0, 0.0, 0.0, 'M', 12.49],\n",
-       "       [0.0, 0.0, 1.0, 'L', 4.99],\n",
-       "       [1.0, 0.0, 0.0, 'M', 7.99],\n",
-       "       [0.0, 1.0, 0.0, 'M', 14.99],\n",
-       "       [0.0, 1.0, 0.0, 'S', 9.99],\n",
-       "       [0.0, 0.0, 1.0, 'M', 4.99],\n",
-       "       [1.0, 0.0, 0.0, 'M', 12.49],\n",
-       "       [0.0, 1.0, 0.0, 'M', 14.99],\n",
-       "       [0.0, 0.0, 1.0, 'L', 19.99],\n",
-       "       [0.0, 1.0, 0.0, 'M', 7.99],\n",
-       "       [0.0, 0.0, 1.0, 'S', 4.99],\n",
-       "       [1.0, 0.0, 0.0, 'L', 9.99],\n",
-       "       [0.0, 0.0, 1.0, 'M', 14.99],\n",
-       "       [0.0, 1.0, 0.0, 'S', 12.49],\n",
-       "       [0.0, 1.0, 0.0, 'L', 12.49],\n",
-       "       [0.0, 1.0, 0.0, 'S', 19.99]], dtype=object)"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Se applichiamo l'ordinal encoding a variabili nominali (es. Rosso=0, Verde=1, Blu=2), l'algoritmo di ML assumer\u00e0 erroneamente che il Blu sia 'maggiore' del Rosso o del Verde, il che distorcerebbe l'addestramento.\n",
+        "La soluzione \u00e8 il **One-Hot Encoding**: creiamo una nuova colonna binaria ($0$ o $1$) per ciascuna categoria unica presente nella variabile nominale.\n",
+        "\n",
+        "> [!IMPORTANT]\n",
+        "> **La Trappola delle Variabili Dummy (Dummy Variable Trap)**: Quando si crea una colonna per ciascuna categoria, le colonne risultano perfettamente correlate tra loro (ad esempio, se sappiamo che un colore non \u00e8 rosso e non \u00e8 verde, deve necessariamente essere blu). Questa multicollinearit\u00e0 perfetta pu\u00f2 causare instabilit\u00e0 matematica in modelli come la regressione lineare. Per risolverlo, si esclude una delle colonne (usando ad esempio `drop='first'` in Scikit-Learn o `drop_first=True` in Pandas).\n"
       ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.preprocessing import OneHotEncoder\n",
-    "from sklearn.compose import ColumnTransformer\n",
-    "\n",
-    "X = shirts.values \n",
-    "transf = ColumnTransformer([('ohe', OneHotEncoder(), [1])], remainder=\"passthrough\")\n",
-    "\n",
-    "X = transf.fit_transform(X)\n",
-    "X "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "unAcrOPB8oWS"
-   },
-   "source": [
-    "### Pandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "id": "-INxQq8z8oWT",
-    "outputId": "d611333a-e022-45fe-9fd4-4b40830ac286"
-   },
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "V_iPdBAz8oWO"
+      },
+      "source": [
+        "#### Scikit-learn"
+      ]
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>taglia</th>\n",
-       "      <th>prezzo</th>\n",
-       "      <th>colore_bianco</th>\n",
-       "      <th>colore_rosso</th>\n",
-       "      <th>colore_verde</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>S</td>\n",
-       "      <td>4.99</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>M</td>\n",
-       "      <td>19.99</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>XL</td>\n",
-       "      <td>12.49</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>XL</td>\n",
-       "      <td>14.99</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>S</td>\n",
-       "      <td>14.99</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 226
+        },
+        "id": "_AT3khFB8oWP",
+        "outputId": "90dd223a-b4cd-43b9-bc60-7c7d36003896"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([[1., 0., 0., 0.],\n",
+              "       [0., 0., 1., 0.],\n",
+              "       [1., 0., 0., 0.],\n",
+              "       [0., 1., 0., 0.],\n",
+              "       [0., 0., 1., 0.],\n",
+              "       [0., 0., 0., 1.]])"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "  taglia  prezzo  colore_bianco  colore_rosso  colore_verde\n",
-       "0      S    4.99              1             0             0\n",
-       "1      M   19.99              1             0             0\n",
-       "2     XL   12.49              1             0             0\n",
-       "3     XL   14.99              1             0             0\n",
-       "4      S   14.99              1             0             0"
+      "source": [
+        "from sklearn.preprocessing import OneHotEncoder\n",
+        "\n",
+        "X = [[\"bianco\"], [\"rosso\"], [\"bianco\"], [\"blu\"], [\"rosso\"], [\"verde\"]]\n",
+        "\n",
+        "enc = OneHotEncoder()\n",
+        "X_sparse = enc.fit_transform(X)\n",
+        "X = X_sparse.toarray()\n",
+        "X"
       ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "shirts = pd.get_dummies(shirts,columns=[\"colore\"]) # prefix=\"col\",  prefix_sep='-''\n",
-    "shirts.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Label encoding per la variabile target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>taglia</th>\n",
-       "      <th>colore</th>\n",
-       "      <th>prezzo</th>\n",
-       "      <th>venduta</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>S</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>4.99</td>\n",
-       "      <td>NO</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>M</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>19.99</td>\n",
-       "      <td>SI</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>XL</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>12.49</td>\n",
-       "      <td>NO</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>XL</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "      <td>NO</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>S</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "      <td>SI</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[array(['bianco', 'blu', 'rosso', 'verde'], dtype=object)]"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "  taglia  colore  prezzo venduta\n",
-       "0      S  bianco    4.99      NO\n",
-       "1      M  bianco   19.99      SI\n",
-       "2     XL  bianco   12.49      NO\n",
-       "3     XL  bianco   14.99      NO\n",
-       "4      S  bianco   14.99      SI"
+      "source": [
+        "enc.categories_"
       ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "CSV_URL = \"https://raw.githubusercontent.com/ProfAI/machine-learning-fondamenti/main/datasets/shirts_sold.csv\"\n",
-    "\n",
-    "shirts = pd.read_csv(CSV_URL, index_col=0)\n",
-    "shirts.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 103
+        },
+        "id": "xybQKi529c0p",
+        "outputId": "90d8dd95-8e0b-4040-a70a-1edd29be7c34"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([[1.0, 0.0, 0.0, 'S', 4.99],\n",
+              "       [1.0, 0.0, 0.0, 'M', 19.99],\n",
+              "       [1.0, 0.0, 0.0, 'XL', 12.49],\n",
+              "       [1.0, 0.0, 0.0, 'XL', 14.99],\n",
+              "       [1.0, 0.0, 0.0, 'S', 14.99],\n",
+              "       [0.0, 0.0, 1.0, 'S', 7.99],\n",
+              "       [0.0, 0.0, 1.0, 'M', 4.99],\n",
+              "       [0.0, 0.0, 1.0, 'L', 12.49],\n",
+              "       [1.0, 0.0, 0.0, 'XL', 12.49],\n",
+              "       [0.0, 0.0, 1.0, 'M', 19.99],\n",
+              "       [1.0, 0.0, 0.0, 'L', 14.99],\n",
+              "       [1.0, 0.0, 0.0, 'XL', 19.99],\n",
+              "       [1.0, 0.0, 0.0, 'M', 4.99],\n",
+              "       [1.0, 0.0, 0.0, 'L', 7.99],\n",
+              "       [1.0, 0.0, 0.0, 'M', 14.99],\n",
+              "       [0.0, 1.0, 0.0, 'XL', 9.99],\n",
+              "       [0.0, 1.0, 0.0, 'S', 12.49],\n",
+              "       [1.0, 0.0, 0.0, 'L', 7.99],\n",
+              "       [1.0, 0.0, 0.0, 'XL', 4.99],\n",
+              "       [0.0, 0.0, 1.0, 'M', 14.99],\n",
+              "       [0.0, 0.0, 1.0, 'S', 14.99],\n",
+              "       [1.0, 0.0, 0.0, 'XL', 7.99],\n",
+              "       [0.0, 0.0, 1.0, 'S', 9.99],\n",
+              "       [1.0, 0.0, 0.0, 'XL', 14.99],\n",
+              "       [0.0, 1.0, 0.0, 'S', 14.99],\n",
+              "       [1.0, 0.0, 0.0, 'XL', 9.99],\n",
+              "       [0.0, 0.0, 1.0, 'M', 7.99],\n",
+              "       [1.0, 0.0, 0.0, 'XL', 4.99],\n",
+              "       [0.0, 0.0, 1.0, 'S', 14.99],\n",
+              "       [1.0, 0.0, 0.0, 'L', 12.49],\n",
+              "       [0.0, 1.0, 0.0, 'M', 9.99],\n",
+              "       [0.0, 0.0, 1.0, 'L', 9.99],\n",
+              "       [0.0, 1.0, 0.0, 'XL', 7.99],\n",
+              "       [1.0, 0.0, 0.0, 'M', 19.99],\n",
+              "       [0.0, 0.0, 1.0, 'L', 12.49],\n",
+              "       [1.0, 0.0, 0.0, 'L', 12.49],\n",
+              "       [1.0, 0.0, 0.0, 'L', 9.99],\n",
+              "       [1.0, 0.0, 0.0, 'XL', 14.99],\n",
+              "       [0.0, 0.0, 1.0, 'L', 14.99],\n",
+              "       [0.0, 0.0, 1.0, 'XL', 9.99],\n",
+              "       [1.0, 0.0, 0.0, 'M', 14.99],\n",
+              "       [0.0, 0.0, 1.0, 'L', 19.99],\n",
+              "       [0.0, 0.0, 1.0, 'XL', 7.99],\n",
+              "       [0.0, 0.0, 1.0, 'M', 19.99],\n",
+              "       [0.0, 1.0, 0.0, 'L', 19.99],\n",
+              "       [1.0, 0.0, 0.0, 'XL', 9.99],\n",
+              "       [0.0, 1.0, 0.0, 'M', 12.49],\n",
+              "       [1.0, 0.0, 0.0, 'S', 7.99],\n",
+              "       [0.0, 1.0, 0.0, 'S', 14.99],\n",
+              "       [0.0, 0.0, 1.0, 'S', 12.49],\n",
+              "       [1.0, 0.0, 0.0, 'XL', 19.99],\n",
+              "       [0.0, 0.0, 1.0, 'S', 14.99],\n",
+              "       [1.0, 0.0, 0.0, 'S', 19.99],\n",
+              "       [0.0, 0.0, 1.0, 'M', 9.99],\n",
+              "       [0.0, 0.0, 1.0, 'L', 4.99],\n",
+              "       [0.0, 0.0, 1.0, 'M', 12.49],\n",
+              "       [1.0, 0.0, 0.0, 'L', 12.49],\n",
+              "       [0.0, 0.0, 1.0, 'S', 7.99],\n",
+              "       [0.0, 0.0, 1.0, 'S', 19.99],\n",
+              "       [1.0, 0.0, 0.0, 'L', 9.99],\n",
+              "       [0.0, 1.0, 0.0, 'XL', 14.99],\n",
+              "       [1.0, 0.0, 0.0, 'M', 12.49],\n",
+              "       [1.0, 0.0, 0.0, 'XL', 14.99],\n",
+              "       [1.0, 0.0, 0.0, 'L', 9.99],\n",
+              "       [0.0, 0.0, 1.0, 'XL', 14.99],\n",
+              "       [1.0, 0.0, 0.0, 'L', 12.49],\n",
+              "       [1.0, 0.0, 0.0, 'XL', 4.99],\n",
+              "       [1.0, 0.0, 0.0, 'L', 14.99],\n",
+              "       [0.0, 0.0, 1.0, 'L', 9.99],\n",
+              "       [0.0, 1.0, 0.0, 'S', 14.99],\n",
+              "       [0.0, 1.0, 0.0, 'S', 19.99],\n",
+              "       [0.0, 1.0, 0.0, 'XL', 14.99],\n",
+              "       [1.0, 0.0, 0.0, 'S', 9.99],\n",
+              "       [0.0, 1.0, 0.0, 'S', 14.99],\n",
+              "       [1.0, 0.0, 0.0, 'L', 9.99],\n",
+              "       [1.0, 0.0, 0.0, 'XL', 9.99],\n",
+              "       [0.0, 0.0, 1.0, 'L', 19.99],\n",
+              "       [1.0, 0.0, 0.0, 'XL', 19.99],\n",
+              "       [1.0, 0.0, 0.0, 'M', 4.99],\n",
+              "       [1.0, 0.0, 0.0, 'M', 9.99],\n",
+              "       [1.0, 0.0, 0.0, 'S', 14.99],\n",
+              "       [1.0, 0.0, 0.0, 'L', 19.99],\n",
+              "       [0.0, 0.0, 1.0, 'S', 7.99],\n",
+              "       [0.0, 0.0, 1.0, 'XL', 9.99],\n",
+              "       [1.0, 0.0, 0.0, 'M', 12.49],\n",
+              "       [0.0, 0.0, 1.0, 'L', 4.99],\n",
+              "       [1.0, 0.0, 0.0, 'M', 7.99],\n",
+              "       [0.0, 1.0, 0.0, 'M', 14.99],\n",
+              "       [0.0, 1.0, 0.0, 'S', 9.99],\n",
+              "       [0.0, 0.0, 1.0, 'M', 4.99],\n",
+              "       [1.0, 0.0, 0.0, 'M', 12.49],\n",
+              "       [0.0, 1.0, 0.0, 'M', 14.99],\n",
+              "       [0.0, 0.0, 1.0, 'L', 19.99],\n",
+              "       [0.0, 1.0, 0.0, 'M', 7.99],\n",
+              "       [0.0, 0.0, 1.0, 'S', 4.99],\n",
+              "       [1.0, 0.0, 0.0, 'L', 9.99],\n",
+              "       [0.0, 0.0, 1.0, 'M', 14.99],\n",
+              "       [0.0, 1.0, 0.0, 'S', 12.49],\n",
+              "       [0.0, 1.0, 0.0, 'L', 12.49],\n",
+              "       [0.0, 1.0, 0.0, 'S', 19.99]], dtype=object)"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.preprocessing import OneHotEncoder\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "\n",
+        "X = shirts.values \n",
+        "transf = ColumnTransformer([('ohe', OneHotEncoder(), [1])], remainder=\"passthrough\")\n",
+        "\n",
+        "X = transf.fit_transform(X)\n",
+        "X "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "unAcrOPB8oWS"
+      },
+      "source": [
+        "### Pandas"
+      ]
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>taglia</th>\n",
-       "      <th>colore</th>\n",
-       "      <th>prezzo</th>\n",
-       "      <th>venduta</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>S</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>4.99</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>M</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>19.99</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>XL</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>12.49</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>XL</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>S</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "-INxQq8z8oWT",
+        "outputId": "d611333a-e022-45fe-9fd4-4b40830ac286"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>taglia</th>\n",
+              "      <th>prezzo</th>\n",
+              "      <th>colore_bianco</th>\n",
+              "      <th>colore_rosso</th>\n",
+              "      <th>colore_verde</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>S</td>\n",
+              "      <td>4.99</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>M</td>\n",
+              "      <td>19.99</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>XL</td>\n",
+              "      <td>12.49</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>XL</td>\n",
+              "      <td>14.99</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>S</td>\n",
+              "      <td>14.99</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  taglia  prezzo  colore_bianco  colore_rosso  colore_verde\n",
+              "0      S    4.99              1             0             0\n",
+              "1      M   19.99              1             0             0\n",
+              "2     XL   12.49              1             0             0\n",
+              "3     XL   14.99              1             0             0\n",
+              "4      S   14.99              1             0             0"
+            ]
+          },
+          "execution_count": 22,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "  taglia  colore  prezzo  venduta\n",
-       "0      S  bianco    4.99        0\n",
-       "1      M  bianco   19.99        1\n",
-       "2     XL  bianco   12.49        0\n",
-       "3     XL  bianco   14.99        0\n",
-       "4      S  bianco   14.99        1"
+      "source": [
+        "shirts = pd.get_dummies(shirts,columns=[\"colore\"]) # prefix=\"col\",  prefix_sep='-''\n",
+        "shirts.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Label encoding per la variabile target"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Il **Label Encoding** consiste nell'assegnare a ciascuna etichetta di testo un numero intero progressivo.\n",
+        "> [!WARNING]\n",
+        "> Il `LabelEncoder` di Scikit-learn deve essere utilizzato **esclusivamente per la variabile target ($y$)** e mai per le feature di input ($X$). Per le feature di input nominali va usato `OneHotEncoder` o `OrdinalEncoder` per evitare che il modello presupponga un ordinamento fasullo tra le feature.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>taglia</th>\n",
+              "      <th>colore</th>\n",
+              "      <th>prezzo</th>\n",
+              "      <th>venduta</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>S</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>4.99</td>\n",
+              "      <td>NO</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>M</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>19.99</td>\n",
+              "      <td>SI</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>XL</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>12.49</td>\n",
+              "      <td>NO</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>XL</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "      <td>NO</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>S</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "      <td>SI</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  taglia  colore  prezzo venduta\n",
+              "0      S  bianco    4.99      NO\n",
+              "1      M  bianco   19.99      SI\n",
+              "2     XL  bianco   12.49      NO\n",
+              "3     XL  bianco   14.99      NO\n",
+              "4      S  bianco   14.99      SI"
+            ]
+          },
+          "execution_count": 45,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "CSV_URL = \"https://raw.githubusercontent.com/ProfAI/machine-learning-fondamenti/main/datasets/shirts_sold.csv\"\n",
+        "\n",
+        "shirts = pd.read_csv(CSV_URL, index_col=0)\n",
+        "shirts.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>taglia</th>\n",
+              "      <th>colore</th>\n",
+              "      <th>prezzo</th>\n",
+              "      <th>venduta</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>S</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>4.99</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>M</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>19.99</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>XL</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>12.49</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>XL</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>S</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  taglia  colore  prezzo  venduta\n",
+              "0      S  bianco    4.99        0\n",
+              "1      M  bianco   19.99        1\n",
+              "2     XL  bianco   12.49        0\n",
+              "3     XL  bianco   14.99        0\n",
+              "4      S  bianco   14.99        1"
+            ]
+          },
+          "execution_count": 48,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "\n",
+        "le = LabelEncoder()\n",
+        "shirts[\"venduta\"] = le.fit_transform(shirts[\"venduta\"])\n",
+        "shirts.head()"
       ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "\n",
-    "le = LabelEncoder()\n",
-    "shirts[\"venduta\"] = le.fit_transform(shirts[\"venduta\"])\n",
-    "shirts.head()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Operare su variabili categoriche.ipynb",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
+  ],
+  "metadata": {
+    "colab": {
+      "name": "Operare su variabili categoriche.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.8"
+    }
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
+  "nbformat": 4,
+  "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/2 - Data Preprocessing/features_scaling.ipynb b/2 - Data Preprocessing/features_scaling.ipynb
index 63acff9..19b8da2 100644
--- a/2 - Data Preprocessing/features_scaling.ipynb	
+++ b/2 - Data Preprocessing/features_scaling.ipynb	
@@ -1,417 +1,471 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Feature scaling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Feature scaling"
+      ]
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>class</th>\n",
-       "      <th>alcol</th>\n",
-       "      <th>flavonoidi</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>14.23</td>\n",
-       "      <td>3.06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>13.20</td>\n",
-       "      <td>2.76</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>13.16</td>\n",
-       "      <td>3.24</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>14.37</td>\n",
-       "      <td>3.49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>13.24</td>\n",
-       "      <td>2.69</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Molti algoritmi di Machine Learning (es. KNN, SVM, K-Means, e modelli basati su discesa del gradiente come la regressione lineare/logistica o le reti neurali) calcolano le distanze tra i punti o dipendono dall'ordine di grandezza dei parametri.\n",
+        "Se le feature hanno scale molto diverse (ad esempio, l'et\u00e0 varia da 0 a 100, mentre il reddito annuo varia da 10.000 a 1.000.000), le feature con valori numerici pi\u00f9 grandi domineranno completamente i calcoli, rendendo le altre feature irrilevanti.\n",
+        "Il **Feature Scaling** risolve questo problema portando tutte le feature su una scala confrontabile.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>class</th>\n",
+              "      <th>alcol</th>\n",
+              "      <th>flavonoidi</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>14.23</td>\n",
+              "      <td>3.06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>13.20</td>\n",
+              "      <td>2.76</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>13.16</td>\n",
+              "      <td>3.24</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>14.37</td>\n",
+              "      <td>3.49</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1</td>\n",
+              "      <td>13.24</td>\n",
+              "      <td>2.69</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   class  alcol  flavonoidi\n",
+              "0      1  14.23        3.06\n",
+              "1      1  13.20        2.76\n",
+              "2      1  13.16        3.24\n",
+              "3      1  14.37        3.49\n",
+              "4      1  13.24        2.69"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "   class  alcol  flavonoidi\n",
-       "0      1  14.23        3.06\n",
-       "1      1  13.20        2.76\n",
-       "2      1  13.16        3.24\n",
-       "3      1  14.37        3.49\n",
-       "4      1  13.24        2.69"
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "wines = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\", names=['class','alcol','flavonoidi'], \n",
+        "                    usecols=[0,1,7])\n",
+        "wines.head()"
       ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "wines = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\", names=['class','alcol','flavonoidi'], \n",
-    "                    usecols=[0,1,7])\n",
-    "wines.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Normalizzazione\n",
-    "La normalizzazione porta il range di valori in una scala compresa tra 0 ed uno, si esegue applicando ad ogni elemento da normalizzare la seguente formula.<br>\n",
-    "$$x^{(i)}_{norm}= \\frac{x^{i}-x_{min}}{x_{max}-x_{min}}$$\n",
-    "dove $x$ è un vettore che corrisponde alla colonna da normalizzare."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Normalizzazione\n",
+        "La normalizzazione porta il range di valori in una scala compresa tra 0 ed uno, si esegue applicando ad ogni elemento da normalizzare la seguente formula.<br>\n",
+        "$$x^{(i)}_{norm}= \\frac{x^{i}-x_{min}}{x_{max}-x_{min}}$$\n",
+        "dove $x$ \u00e8 un vettore che corrisponde alla colonna da normalizzare."
+      ]
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>class</th>\n",
-       "      <th>alcol</th>\n",
-       "      <th>flavonoidi</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.842105</td>\n",
-       "      <td>0.573840</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.571053</td>\n",
-       "      <td>0.510549</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.560526</td>\n",
-       "      <td>0.611814</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.878947</td>\n",
-       "      <td>0.664557</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.581579</td>\n",
-       "      <td>0.495781</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "La **Normalizzazione** (chiamata anche *Min-Max Scaling*) trasla e riscalda i dati in modo che tutti i valori si trovino in un intervallo specifico, solitamente $[0, 1]$ o $[-1, 1]$.\n",
+        "\n",
+        "### Formula Matematica:\n",
+        "$$x' = \\frac{x - x_{\\min}}{x_{\\max} - x_{\\min}}$$\n",
+        "\n",
+        "Dove:\n",
+        "- $x$ \u00e8 il valore originale.\n",
+        "- $x_{\\min}$ e $x_{\\max}$ sono il minimo e il massimo valore della colonna.\n",
+        "- $x'$ \u00e8 il valore normalizzato.\n",
+        "\n",
+        "> [!WARNING]\n",
+        "> La normalizzazione \u00e8 altamente sensibile alla presenza di **outlier (valori anomali)**. Se \u00e8 presente un outlier estremamente grande, comprimer\u00e0 tutti gli altri valori regolari in un intervallo piccolissimo (es. tra $0$ e $0.05$).\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Pandas"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>class</th>\n",
+              "      <th>alcol</th>\n",
+              "      <th>flavonoidi</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0.842105</td>\n",
+              "      <td>0.573840</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0.571053</td>\n",
+              "      <td>0.510549</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0.560526</td>\n",
+              "      <td>0.611814</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0.878947</td>\n",
+              "      <td>0.664557</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0.581579</td>\n",
+              "      <td>0.495781</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   class     alcol  flavonoidi\n",
+              "0      1  0.842105    0.573840\n",
+              "1      1  0.571053    0.510549\n",
+              "2      1  0.560526    0.611814\n",
+              "3      1  0.878947    0.664557\n",
+              "4      1  0.581579    0.495781"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "   class     alcol  flavonoidi\n",
-       "0      1  0.842105    0.573840\n",
-       "1      1  0.571053    0.510549\n",
-       "2      1  0.560526    0.611814\n",
-       "3      1  0.878947    0.664557\n",
-       "4      1  0.581579    0.495781"
+      "source": [
+        "wines_norm = wines.copy()\n",
+        "\n",
+        "features = [\"alcol\",\"flavonoidi\"] # colonne del dataframe da normalizzare\n",
+        "to_norm = wines_norm[features]\n",
+        "wines_norm[features] = (to_norm-to_norm.min())/(to_norm.max()-to_norm.min()) #implementiamo l'algoritmo della normalizzazione\n",
+        "                                                                             #e lo eseguiamo su tutte le colonne da normalizzare\n",
+        "wines_norm.head()"
       ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wines_norm = wines.copy()\n",
-    "\n",
-    "features = [\"alcol\",\"flavonoidi\"] # colonne del dataframe da normalizzare\n",
-    "to_norm = wines_norm[features]\n",
-    "wines_norm[features] = (to_norm-to_norm.min())/(to_norm.max()-to_norm.min()) #implementiamo l'algoritmo della normalizzazione\n",
-    "                                                                             #e lo eseguiamo su tutte le colonne da normalizzare\n",
-    "wines_norm.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Scikit-learn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0 1.0\n",
-      "[[0.84210526 0.57383966]\n",
-      " [0.57105263 0.51054852]\n",
-      " [0.56052632 0.61181435]\n",
-      " [0.87894737 0.66455696]\n",
-      " [0.58157895 0.49578059]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.preprocessing import MinMaxScaler\n",
-    "\n",
-    "mms = MinMaxScaler()\n",
-    "X = wines.drop(\"class\",axis=1).values # la feature target non va normalizzata, quindi la rimuoviao dall'array\n",
-    "X_norm = mms.fit_transform(X)\n",
-    "print(X_norm.min(), X_norm.max())\n",
-    "print(X_norm[:5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Standardizzazione\n",
-    "La standardizzazione crea una distribuzione normale, ovvero una distribuzione con media 0 e deviazione standard 1, quindi il range di valori sarà compreso tra -1 e 1.<br>\n",
-    "La standardizzazione si esegue applicando la seguente formula\n",
-    "<br>\n",
-    "$$x^{(i)}_{std}= \\frac{x^{i}-x_{mean}}{x_{sd}}$$\n",
-    "<br>\n",
-    "dove $x$ è un vettore che corrisponde alla colonna da standardizzare, $x_{mean}$ è il valore medio nella colonna e $x_{sd}$ la deviazione standard."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Scikit-learn"
+      ]
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>class</th>\n",
-       "      <th>alcol</th>\n",
-       "      <th>flavonoidi</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1.514341</td>\n",
-       "      <td>1.031908</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.245597</td>\n",
-       "      <td>0.731565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.196325</td>\n",
-       "      <td>1.212114</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1.686791</td>\n",
-       "      <td>1.462399</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.294868</td>\n",
-       "      <td>0.661485</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "0.0 1.0\n",
+            "[[0.84210526 0.57383966]\n",
+            " [0.57105263 0.51054852]\n",
+            " [0.56052632 0.61181435]\n",
+            " [0.87894737 0.66455696]\n",
+            " [0.58157895 0.49578059]]\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "   class     alcol  flavonoidi\n",
-       "0      1  1.514341    1.031908\n",
-       "1      1  0.245597    0.731565\n",
-       "2      1  0.196325    1.212114\n",
-       "3      1  1.686791    1.462399\n",
-       "4      1  0.294868    0.661485"
+      "source": [
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "\n",
+        "mms = MinMaxScaler()\n",
+        "X = wines.drop(\"class\",axis=1).values # la feature target non va normalizzata, quindi la rimuoviao dall'array\n",
+        "X_norm = mms.fit_transform(X)\n",
+        "print(X_norm.min(), X_norm.max())\n",
+        "print(X_norm[:5])"
       ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wines_std = wines.copy()\n",
-    "\n",
-    "features = [\"alcol\",\"flavonoidi\"]\n",
-    "to_std = wines_std[features]\n",
-    "wines_std[features] = (to_std - to_std.mean())/to_std.std() #ddof=0 per replicare i risultati di sklearn\n",
-    "wines_std[:5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Scikit-learn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Standardizzazione\n",
+        "La standardizzazione crea una distribuzione normale, ovvero una distribuzione con media 0 e deviazione standard 1, quindi il range di valori sar\u00e0 compreso tra -1 e 1.<br>\n",
+        "La standardizzazione si esegue applicando la seguente formula\n",
+        "<br>\n",
+        "$$x^{(i)}_{std}= \\frac{x^{i}-x_{mean}}{x_{sd}}$$\n",
+        "<br>\n",
+        "dove $x$ \u00e8 un vettore che corrisponde alla colonna da standardizzare, $x_{mean}$ \u00e8 il valore medio nella colonna e $x_{sd}$ la deviazione standard."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "La **Standardizzazione** (chiamata anche *Z-score Normalization*) riscalda i dati in modo che abbiano media pari a $0$ ($\\mu = 0$) e deviazione standard pari a $1$ ($\\sigma = 1$).\n",
+        "\n",
+        "### Formula Matematica:\n",
+        "$$z = \\frac{x - \\mu}{\\sigma}$$\n",
+        "\n",
+        "Dove:\n",
+        "- $x$ \u00e8 il valore originale.\n",
+        "- $\\mu$ \u00e8 la media campionaria della feature.\n",
+        "- $\\sigma$ \u00e8 la deviazione standard campionaria.\n",
+        "- $z$ \u00e8 il valore standardizzato.\n",
+        "\n",
+        "- **Vantaggi**: \u00c8 molto meno sensibile agli outlier rispetto alla normalizzazione e preserva la forma della distribuzione originale. Molti modelli statistici assumono che le feature in ingresso seguano una distribuzione normale standardizzata.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Pandas"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>class</th>\n",
+              "      <th>alcol</th>\n",
+              "      <th>flavonoidi</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1.514341</td>\n",
+              "      <td>1.031908</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0.245597</td>\n",
+              "      <td>0.731565</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0.196325</td>\n",
+              "      <td>1.212114</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1.686791</td>\n",
+              "      <td>1.462399</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0.294868</td>\n",
+              "      <td>0.661485</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   class     alcol  flavonoidi\n",
+              "0      1  1.514341    1.031908\n",
+              "1      1  0.245597    0.731565\n",
+              "2      1  0.196325    1.212114\n",
+              "3      1  1.686791    1.462399\n",
+              "4      1  0.294868    0.661485"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "wines_std = wines.copy()\n",
+        "\n",
+        "features = [\"alcol\",\"flavonoidi\"]\n",
+        "to_std = wines_std[features]\n",
+        "wines_std[features] = (to_std - to_std.mean())/to_std.std() #ddof=0 per replicare i risultati di sklearn\n",
+        "wines_std[:5]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Scikit-learn"
+      ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "array([[1.51861254, 1.03481896],\n",
-       "       [0.24628963, 0.73362894],\n",
-       "       [0.19687903, 1.21553297],\n",
-       "       [1.69154964, 1.46652465],\n",
-       "       [0.29570023, 0.66335127]])"
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([[1.51861254, 1.03481896],\n",
+              "       [0.24628963, 0.73362894],\n",
+              "       [0.19687903, 1.21553297],\n",
+              "       [1.69154964, 1.46652465],\n",
+              "       [0.29570023, 0.66335127]])"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.preprocessing import StandardScaler\n",
+        "\n",
+        "ss = StandardScaler()\n",
+        "X = wines.drop(\"class\",axis=1).values\n",
+        "X_std = ss.fit_transform(X)\n",
+        "X_std[:5]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**NOTA BENE**\n",
+        "Se osservi attentamente la standardizzazione eseguita con Pandas ha tornato un risultato leggermente diverso rispetto a quella eseguita con scikit-learn, questo accade perch\u00e8 scikit-learn utilizza internamente la funzione std di Numpy, che calcola la deviazione standard in maniera diversa rispetto al metodo std del DataFrame.<br>\n",
+        "Per approfondire [vedi qui](https://stackoverflow.com/a/44220374)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "> [!IMPORTANT]\n",
+        "> **Regola d'oro del Preprocessing**:\n",
+        "> Lo scaler deve calcolare i parametri statistici (min/max per MinMax, media/deviazione standard per Standard) **esclusivamente sul Train Set** tramite il metodo `.fit()`. Successivamente, questi parametri vengono applicati sia al Train Set che al Test Set tramite il metodo `.transform()`. Non bisogna mai fare `.fit()` sull'intero dataset o separatamente sul Test Set, altrimenti si verificher\u00e0 un grave errore di **Data Leakage** (le informazioni del test set 'trapelano' nel modello durante l'addestramento).\n"
       ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "from sklearn.preprocessing import StandardScaler\n",
-    "\n",
-    "ss = StandardScaler()\n",
-    "X = wines.drop(\"class\",axis=1).values\n",
-    "X_std = ss.fit_transform(X)\n",
-    "X_std[:5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**NOTA BENE**\n",
-    "Se osservi attentamente la standardizzazione eseguita con Pandas ha tornato un risultato leggermente diverso rispetto a quella eseguita con scikit-learn, questo accade perchè scikit-learn utilizza internamente la funzione std di Numpy, che calcola la deviazione standard in maniera diversa rispetto al metodo std del DataFrame.<br>\n",
-    "Per approfondire [vedi qui](https://stackoverflow.com/a/44220374)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.8"
+    }
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/2 - Data Preprocessing/missing_data.ipynb b/2 - Data Preprocessing/missing_data.ipynb
index 6965f50..7268449 100644
--- a/2 - Data Preprocessing/missing_data.ipynb	
+++ b/2 - Data Preprocessing/missing_data.ipynb	
@@ -1,1057 +1,1108 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "RG_wqZg2F9K0"
-   },
-   "source": [
-    "# Gestire dati mancanti"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "QKONCDE3F9K2"
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 203
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "RG_wqZg2F9K0"
+      },
+      "source": [
+        "# Gestire dati mancanti"
+      ]
     },
-    "colab_type": "code",
-    "id": "kUQ1mHw_F9K6",
-    "outputId": "6da2ded5-dca8-4a33-a5e1-9086f4bbc105"
-   },
-   "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal_length</th>\n",
-       "      <th>sepal_width</th>\n",
-       "      <th>petal_length</th>\n",
-       "      <th>petal_width</th>\n",
-       "      <th>species</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4.6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.9</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>4.4</td>\n",
-       "      <td>2.9</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal_length  sepal_width  petal_length  petal_width species\n",
-       "0           5.1          NaN           1.4          0.2  setosa\n",
-       "1           4.9          3.0           1.4          0.2  setosa\n",
-       "2           4.7          3.2           1.3          0.2  setosa\n",
-       "3           4.6          NaN           1.5          0.2  setosa\n",
-       "4           5.0          3.6           1.4          0.2  setosa\n",
-       "5           NaN          3.9           1.7          0.4  setosa\n",
-       "6           4.6          3.4           1.4          0.3  setosa\n",
-       "7           5.0          3.4           1.5          0.2  setosa\n",
-       "8           4.4          2.9           1.4          0.2  setosa\n",
-       "9           4.9          3.1           1.5          0.1  setosa"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Nel mondo reale, i dataset contengono quasi sempre **dati mancanti** (dovuti a errori di misurazione, mancate risposte nei sondaggi, corruzione dei file, ecc.).\n",
+        "Poich\u00e9 la maggior parte degli algoritmi di Machine Learning non \u00e8 in grado di gestire i valori nulli (che in Python vengono rappresentati come `NaN` o `None`), \u00e8 fondamentale individuare e trattare questi record durante la fase di preprocessing.\n"
       ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "CSV_URL = \"../datasets/iris_missing.csv\"\n",
-    "iris = pd.read_csv(CSV_URL, )\n",
-    "iris.head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "(150, 5)"
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {},
+        "colab_type": "code",
+        "id": "QKONCDE3F9K2"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np"
       ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iris.shape # numero di righe e colonne"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "sepal_length    147\n",
-       "sepal_width     140\n",
-       "petal_length    148\n",
-       "petal_width     150\n",
-       "species         150\n",
-       "dtype: int64"
+      "cell_type": "code",
+      "execution_count": 61,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 203
+        },
+        "colab_type": "code",
+        "id": "kUQ1mHw_F9K6",
+        "outputId": "6da2ded5-dca8-4a33-a5e1-9086f4bbc105"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sepal_length</th>\n",
+              "      <th>sepal_width</th>\n",
+              "      <th>petal_length</th>\n",
+              "      <th>petal_width</th>\n",
+              "      <th>species</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>5.1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>4.9</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>4.7</td>\n",
+              "      <td>3.2</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4.6</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>0.4</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>4.6</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.3</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>4.4</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>4.9</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   sepal_length  sepal_width  petal_length  petal_width species\n",
+              "0           5.1          NaN           1.4          0.2  setosa\n",
+              "1           4.9          3.0           1.4          0.2  setosa\n",
+              "2           4.7          3.2           1.3          0.2  setosa\n",
+              "3           4.6          NaN           1.5          0.2  setosa\n",
+              "4           5.0          3.6           1.4          0.2  setosa\n",
+              "5           NaN          3.9           1.7          0.4  setosa\n",
+              "6           4.6          3.4           1.4          0.3  setosa\n",
+              "7           5.0          3.4           1.5          0.2  setosa\n",
+              "8           4.4          2.9           1.4          0.2  setosa\n",
+              "9           4.9          3.1           1.5          0.1  setosa"
+            ]
+          },
+          "execution_count": 61,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "CSV_URL = \"../datasets/iris_missing.csv\"\n",
+        "iris = pd.read_csv(CSV_URL, )\n",
+        "iris.head(10)"
       ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iris.count() # numero di valori validi"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 596
     },
-    "colab_type": "code",
-    "id": "3gzjZpyRF9K-",
-    "outputId": "564827ef-f702-4725-9107-27af7fb51ea0"
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "sepal_length     3\n",
-       "sepal_width     10\n",
-       "petal_length     2\n",
-       "petal_width      0\n",
-       "species          0\n",
-       "dtype: int64"
+      "cell_type": "code",
+      "execution_count": 56,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(150, 5)"
+            ]
+          },
+          "execution_count": 56,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "iris.shape # numero di righe e colonne"
       ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#iris.isnull().sum()\n",
-    "iris.isna().sum() # quanti na per colonna"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "2YUfsP_WF9LE"
-   },
-   "source": [
-    "## Metodo 1: Rimuovere proprietà o esempi con valori mancanti"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 51
     },
-    "colab_type": "code",
-    "id": "Jz3EjQijF9LF",
-    "outputId": "6e1a8057-6868-4b93-eef3-165a6f35e462"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Numero di esempi prima: 150\n",
-      "Numero di esempi dopo: 135\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 54,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "sepal_length    147\n",
+              "sepal_width     140\n",
+              "petal_length    148\n",
+              "petal_width     150\n",
+              "species         150\n",
+              "dtype: int64"
+            ]
+          },
+          "execution_count": 54,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "iris.count() # numero di valori validi"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal_length</th>\n",
-       "      <th>sepal_width</th>\n",
-       "      <th>petal_length</th>\n",
-       "      <th>petal_width</th>\n",
-       "      <th>species</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>4.4</td>\n",
-       "      <td>2.9</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>5.4</td>\n",
-       "      <td>3.7</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>4.8</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>4.8</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 596
+        },
+        "colab_type": "code",
+        "id": "3gzjZpyRF9K-",
+        "outputId": "564827ef-f702-4725-9107-27af7fb51ea0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "sepal_length     3\n",
+              "sepal_width     10\n",
+              "petal_length     2\n",
+              "petal_width      0\n",
+              "species          0\n",
+              "dtype: int64"
+            ]
+          },
+          "execution_count": 48,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "    sepal_length  sepal_width  petal_length  petal_width species\n",
-       "1            4.9          3.0           1.4          0.2  setosa\n",
-       "2            4.7          3.2           1.3          0.2  setosa\n",
-       "4            5.0          3.6           1.4          0.2  setosa\n",
-       "6            4.6          3.4           1.4          0.3  setosa\n",
-       "7            5.0          3.4           1.5          0.2  setosa\n",
-       "8            4.4          2.9           1.4          0.2  setosa\n",
-       "9            4.9          3.1           1.5          0.1  setosa\n",
-       "10           5.4          3.7           1.5          0.2  setosa\n",
-       "11           4.8          3.4           1.6          0.2  setosa\n",
-       "12           4.8          3.0           1.4          0.1  setosa"
+      "source": [
+        "#iris.isnull().sum()\n",
+        "iris.isna().sum() # quanti na per colonna"
       ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples_count = iris.shape[0]\n",
-    "iris_drop = iris.dropna() # # per tutte le colonne\n",
-    "# iris_drop = iris.dropna(subset=[\"sepal_width\"]) # per una colonna\n",
-    "\n",
-    "print(f\"Numero di esempi prima: {samples_count}\")\n",
-    "print(f\"Numero di esempi dopo: {iris_drop.shape[0]}\")\n",
-    "iris_drop.head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "eNfEOg3oF9LI"
-   },
-   "source": [
-    "Se i valori mancanti corrispondono ad un unica feature e questi sono in un numero tale da invalidare l'utilità della feature, allora possiamo semplicemente rimuovere la feature dal nostro DataFrame."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "IK8ok7QLF9LJ",
-    "outputId": "a5da348f-5b08-4344-a9d9-ad4b8f345574"
-   },
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index(['petal_width', 'species'], dtype='object')\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "2YUfsP_WF9LE"
+      },
+      "source": [
+        "## Metodo 1: Rimuovere propriet\u00e0 o esempi con valori mancanti"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>petal_width</th>\n",
-       "      <th>species</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.4</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>0.3</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0.1</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "La prima e pi\u00f9 semplice strategia consiste nell'eliminare le righe (esempi) o le colonne (feature) che presentano valori mancanti.\n",
+        "- **Eliminazione delle righe (`axis=0`)**: Utile se i dati mancanti sono pochi rispetto al dataset totale.\n",
+        "- **Eliminazione delle colonne (`axis=1`)**: Utile se una specifica feature ha una percentuale altissima di valori mancanti, rendendola inutile ai fini predittivi.\n",
+        "> [!WARNING]\n",
+        "> Rimuovere i dati riduce la dimensione del dataset e pu\u00f2 introdurre **bias (distorsioni)** se la mancanza di dati non \u00e8 del tutto casuale (MCAR - Missing Completely At Random).\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 57,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        },
+        "colab_type": "code",
+        "id": "Jz3EjQijF9LF",
+        "outputId": "6e1a8057-6868-4b93-eef3-165a6f35e462"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Numero di esempi prima: 150\n",
+            "Numero di esempi dopo: 135\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sepal_length</th>\n",
+              "      <th>sepal_width</th>\n",
+              "      <th>petal_length</th>\n",
+              "      <th>petal_width</th>\n",
+              "      <th>species</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>4.9</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>4.7</td>\n",
+              "      <td>3.2</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>4.6</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.3</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>4.4</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>4.9</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>5.4</td>\n",
+              "      <td>3.7</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>4.8</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>4.8</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "    sepal_length  sepal_width  petal_length  petal_width species\n",
+              "1            4.9          3.0           1.4          0.2  setosa\n",
+              "2            4.7          3.2           1.3          0.2  setosa\n",
+              "4            5.0          3.6           1.4          0.2  setosa\n",
+              "6            4.6          3.4           1.4          0.3  setosa\n",
+              "7            5.0          3.4           1.5          0.2  setosa\n",
+              "8            4.4          2.9           1.4          0.2  setosa\n",
+              "9            4.9          3.1           1.5          0.1  setosa\n",
+              "10           5.4          3.7           1.5          0.2  setosa\n",
+              "11           4.8          3.4           1.6          0.2  setosa\n",
+              "12           4.8          3.0           1.4          0.1  setosa"
+            ]
+          },
+          "execution_count": 57,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "   petal_width species\n",
-       "0          0.2  setosa\n",
-       "1          0.2  setosa\n",
-       "2          0.2  setosa\n",
-       "3          0.2  setosa\n",
-       "4          0.2  setosa\n",
-       "5          0.4  setosa\n",
-       "6          0.3  setosa\n",
-       "7          0.2  setosa\n",
-       "8          0.2  setosa\n",
-       "9          0.1  setosa"
+      "source": [
+        "samples_count = iris.shape[0]\n",
+        "iris_drop = iris.dropna() # # per tutte le colonne\n",
+        "# iris_drop = iris.dropna(subset=[\"sepal_width\"]) # per una colonna\n",
+        "\n",
+        "print(f\"Numero di esempi prima: {samples_count}\")\n",
+        "print(f\"Numero di esempi dopo: {iris_drop.shape[0]}\")\n",
+        "iris_drop.head(10)"
       ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iris_drop = iris.dropna(axis=1) # colonne con almeno 1 na\n",
-    "# iris_drop = iris.dropna(axis=1, how=\"all\") # colonne con tutti na\n",
-    "print(iris_drop.columns)\n",
-    "iris_drop.head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "MmmP690zF9LM"
-   },
-   "source": [
-    "Rinunciare a dati preziosi non è mai una buona cosa, quindi questi metodi vanno evitati ad eccezione di casi estremi, ovvero quando la maggior parte dei valori per una feature o per un esempio sono mancanti."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Nm4lLH38F9LN"
-   },
-   "source": [
-    "## Metodo 2: Imputazione dei dati mancanti"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "t2d32JhUF9LO"
-   },
-   "source": [
-    "L'imputazione dei dati mancanti consiste nel sostituire i valori con una stima.<br>\n",
-    "Il metodo più comune è **l'imputazione con media** (mean imputation) in cui i valori mancanti vengono sostituiti con il valore medio della proprietà, altri metodi sono l'imputazione con la mediana o con valore più frequente (moda)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "lqOGc8IGF9LO"
-   },
-   "source": [
-    "### Pandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "gBz9SNKVF9LQ",
-    "outputId": "ef0b08f9-a29f-4294-c683-6ea117f27328"
-   },
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "La colonna sepal_width ha 0 valori mancanti\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "eNfEOg3oF9LI"
+      },
+      "source": [
+        "Se i valori mancanti corrispondono ad un unica feature e questi sono in un numero tale da invalidare l'utilit\u00e0 della feature, allora possiamo semplicemente rimuovere la feature dal nostro DataFrame."
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal_length</th>\n",
-       "      <th>sepal_width</th>\n",
-       "      <th>petal_length</th>\n",
-       "      <th>petal_width</th>\n",
-       "      <th>species</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.056429</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4.7</td>\n",
-       "      <td>3.200000</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.056429</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.600000</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.900000</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.400000</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.400000</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>4.4</td>\n",
-       "      <td>2.900000</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.100000</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 58,
+      "metadata": {
+        "colab": {},
+        "colab_type": "code",
+        "id": "IK8ok7QLF9LJ",
+        "outputId": "a5da348f-5b08-4344-a9d9-ad4b8f345574"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Index(['petal_width', 'species'], dtype='object')\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>petal_width</th>\n",
+              "      <th>species</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>0.4</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   petal_width species\n",
+              "0          0.2  setosa\n",
+              "1          0.2  setosa\n",
+              "2          0.2  setosa\n",
+              "3          0.2  setosa\n",
+              "4          0.2  setosa\n",
+              "5          0.4  setosa\n",
+              "6          0.3  setosa\n",
+              "7          0.2  setosa\n",
+              "8          0.2  setosa\n",
+              "9          0.1  setosa"
+            ]
+          },
+          "execution_count": 58,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "   sepal_length  sepal_width  petal_length  petal_width species\n",
-       "0           5.1     3.056429           1.4          0.2  setosa\n",
-       "1           4.9     3.000000           1.4          0.2  setosa\n",
-       "2           4.7     3.200000           1.3          0.2  setosa\n",
-       "3           4.6     3.056429           1.5          0.2  setosa\n",
-       "4           5.0     3.600000           1.4          0.2  setosa\n",
-       "5           NaN     3.900000           1.7          0.4  setosa\n",
-       "6           4.6     3.400000           1.4          0.3  setosa\n",
-       "7           5.0     3.400000           1.5          0.2  setosa\n",
-       "8           4.4     2.900000           1.4          0.2  setosa\n",
-       "9           4.9     3.100000           1.5          0.1  setosa"
+      "source": [
+        "iris_drop = iris.dropna(axis=1) # colonne con almeno 1 na\n",
+        "# iris_drop = iris.dropna(axis=1, how=\"all\") # colonne con tutti na\n",
+        "print(iris_drop.columns)\n",
+        "iris_drop.head(10)"
       ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# per una colonna specifica\n",
-    "iris_imp = iris.copy()\n",
-    "\n",
-    "col = \"sepal_width\"\n",
-    "\n",
-    "replace_with = iris_imp[col].mean() # imputazione con media\n",
-    "#replace_with = iris['petal_length'].median() # imputazione con mediana\n",
-    "#replace_with = iris['petal_length'].mode() # imputazione con moda\n",
-    "iris_imp[col] = iris_imp[col].fillna(replace_with) #.round(2) #per arrotondare\n",
-    "na_count = iris_imp[col].isna().sum() #verifichiamo che la colonna \"petal_length\" non contenga più valori mancanti.\n",
-    "print(f\"La colonna {col} ha {na_count} valori mancanti\")\n",
-    "iris_imp.head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "MmmP690zF9LM"
+      },
+      "source": [
+        "Rinunciare a dati preziosi non \u00e8 mai una buona cosa, quindi questi metodi vanno evitati ad eccezione di casi estremi, ovvero quando la maggior parte dei valori per una feature o per un esempio sono mancanti."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "Nm4lLH38F9LN"
+      },
+      "source": [
+        "## Metodo 2: Imputazione dei dati mancanti"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "L'**imputazione** consiste nel sostituire i valori mancanti con stime ragionevoli calcolate a partire dagli altri dati disponibili nel dataset.\n",
+        "Le strategie di imputazione pi\u00f9 comuni sono:\n",
+        "1. **Media (Mean)**: Sostituisce i `NaN` con il valore medio della colonna. Indicato per distribuzioni simmetriche.\n",
+        "2. **Mediana (Median)**: Sostituisce con il valore centrale. Molto robusto in presenza di outlier o distribuzioni asimmetriche.\n",
+        "3. **Moda (Most Frequent)**: Sostituisce con il valore pi\u00f9 frequente. \u00c8 l'unica strategia applicabile a variabili categoriche/qualitative.\n",
+        "4. **Valore Costante (Constant)**: Sostituisce con un valore fisso specificato dall'utente (es. `0` o `'Unknown'`).\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "t2d32JhUF9LO"
+      },
+      "source": [
+        "L'imputazione dei dati mancanti consiste nel sostituire i valori con una stima.<br>\n",
+        "Il metodo pi\u00f9 comune \u00e8 **l'imputazione con media** (mean imputation) in cui i valori mancanti vengono sostituiti con il valore medio della propriet\u00e0, altri metodi sono l'imputazione con la mediana o con valore pi\u00f9 frequente (moda)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "lqOGc8IGF9LO"
+      },
+      "source": [
+        "### Pandas"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Il dataset ha 0 valori mancanti\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Pandas permette di effettuare l'imputazione in modo rapido e immediato tramite il metodo `.fillna()`. Possiamo calcolare la statistica desiderata (es. `.mean()`) direttamente sulla colonna e passarla come argomento al metodo.\n"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal_length</th>\n",
-       "      <th>sepal_width</th>\n",
-       "      <th>petal_length</th>\n",
-       "      <th>petal_width</th>\n",
-       "      <th>species</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.100000</td>\n",
-       "      <td>3.056429</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.900000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4.700000</td>\n",
-       "      <td>3.200000</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4.600000</td>\n",
-       "      <td>3.056429</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>3.600000</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>5.854422</td>\n",
-       "      <td>3.900000</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>4.600000</td>\n",
-       "      <td>3.400000</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>3.400000</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>4.400000</td>\n",
-       "      <td>2.900000</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>4.900000</td>\n",
-       "      <td>3.100000</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 67,
+      "metadata": {
+        "colab": {},
+        "colab_type": "code",
+        "id": "gBz9SNKVF9LQ",
+        "outputId": "ef0b08f9-a29f-4294-c683-6ea117f27328"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "La colonna sepal_width ha 0 valori mancanti\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sepal_length</th>\n",
+              "      <th>sepal_width</th>\n",
+              "      <th>petal_length</th>\n",
+              "      <th>petal_width</th>\n",
+              "      <th>species</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>5.1</td>\n",
+              "      <td>3.056429</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>4.9</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>4.7</td>\n",
+              "      <td>3.200000</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4.6</td>\n",
+              "      <td>3.056429</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.600000</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>3.900000</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>0.4</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>4.6</td>\n",
+              "      <td>3.400000</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.3</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.400000</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>4.4</td>\n",
+              "      <td>2.900000</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>4.9</td>\n",
+              "      <td>3.100000</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   sepal_length  sepal_width  petal_length  petal_width species\n",
+              "0           5.1     3.056429           1.4          0.2  setosa\n",
+              "1           4.9     3.000000           1.4          0.2  setosa\n",
+              "2           4.7     3.200000           1.3          0.2  setosa\n",
+              "3           4.6     3.056429           1.5          0.2  setosa\n",
+              "4           5.0     3.600000           1.4          0.2  setosa\n",
+              "5           NaN     3.900000           1.7          0.4  setosa\n",
+              "6           4.6     3.400000           1.4          0.3  setosa\n",
+              "7           5.0     3.400000           1.5          0.2  setosa\n",
+              "8           4.4     2.900000           1.4          0.2  setosa\n",
+              "9           4.9     3.100000           1.5          0.1  setosa"
+            ]
+          },
+          "execution_count": 67,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "   sepal_length  sepal_width  petal_length  petal_width species\n",
-       "0      5.100000     3.056429           1.4          0.2  setosa\n",
-       "1      4.900000     3.000000           1.4          0.2  setosa\n",
-       "2      4.700000     3.200000           1.3          0.2  setosa\n",
-       "3      4.600000     3.056429           1.5          0.2  setosa\n",
-       "4      5.000000     3.600000           1.4          0.2  setosa\n",
-       "5      5.854422     3.900000           1.7          0.4  setosa\n",
-       "6      4.600000     3.400000           1.4          0.3  setosa\n",
-       "7      5.000000     3.400000           1.5          0.2  setosa\n",
-       "8      4.400000     2.900000           1.4          0.2  setosa\n",
-       "9      4.900000     3.100000           1.5          0.1  setosa"
+      "source": [
+        "# per una colonna specifica\n",
+        "iris_imp = iris.copy()\n",
+        "\n",
+        "col = \"sepal_width\"\n",
+        "\n",
+        "replace_with = iris_imp[col].mean() # imputazione con media\n",
+        "#replace_with = iris['petal_length'].median() # imputazione con mediana\n",
+        "#replace_with = iris['petal_length'].mode() # imputazione con moda\n",
+        "iris_imp[col] = iris_imp[col].fillna(replace_with) #.round(2) #per arrotondare\n",
+        "na_count = iris_imp[col].isna().sum() #verifichiamo che la colonna \"petal_length\" non contenga pi\u00f9 valori mancanti.\n",
+        "print(f\"La colonna {col} ha {na_count} valori mancanti\")\n",
+        "iris_imp.head(10)"
       ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# per tutte le colonne\n",
-    "iris_imp = iris.copy()\n",
-    "\n",
-    "replace_with = iris_imp.mean() # imputazione con media\n",
-    "#replace_with = iris.median() # imputazione con mediana\n",
-    "#replace_with = iris.mode() # imputazione con moda\n",
-    "iris_imp = iris_imp.fillna(replace_with) #.round(2) #per arrotondare\n",
-    "na_count = iris_imp.isna().sum().sum() #verifichiamo che il dataset non contenga più valori mancanti.\n",
-    "print(f\"Il dataset ha {na_count} valori mancanti\")\n",
-    "iris_imp.head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "vuGuZ2iAF9LU"
-   },
-   "source": [
-    "### Scikit-learn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Y = iris[\"species\"].values\n",
-    "X = iris.drop(\"species\",axis=1).values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 34
     },
-    "colab_type": "code",
-    "id": "cXzSfVYGGlBn",
-    "outputId": "fb9e9bdd-f685-4b6b-c04c-90c95335fa94"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Il dataset ha 15 valori mancanti\n"
-     ]
-    }
-   ],
-   "source": [
-    "na_count = np.count_nonzero(np.isnan(X)) # numpy considera i na come nan\n",
-    "print(f\"Il dataset ha {na_count} valori mancanti\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 34
+      "cell_type": "code",
+      "execution_count": 74,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Il dataset ha 0 valori mancanti\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sepal_length</th>\n",
+              "      <th>sepal_width</th>\n",
+              "      <th>petal_length</th>\n",
+              "      <th>petal_width</th>\n",
+              "      <th>species</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>5.100000</td>\n",
+              "      <td>3.056429</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>4.900000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>4.700000</td>\n",
+              "      <td>3.200000</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4.600000</td>\n",
+              "      <td>3.056429</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>3.600000</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>5.854422</td>\n",
+              "      <td>3.900000</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>0.4</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>4.600000</td>\n",
+              "      <td>3.400000</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.3</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>3.400000</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>4.400000</td>\n",
+              "      <td>2.900000</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>4.900000</td>\n",
+              "      <td>3.100000</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>setosa</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   sepal_length  sepal_width  petal_length  petal_width species\n",
+              "0      5.100000     3.056429           1.4          0.2  setosa\n",
+              "1      4.900000     3.000000           1.4          0.2  setosa\n",
+              "2      4.700000     3.200000           1.3          0.2  setosa\n",
+              "3      4.600000     3.056429           1.5          0.2  setosa\n",
+              "4      5.000000     3.600000           1.4          0.2  setosa\n",
+              "5      5.854422     3.900000           1.7          0.4  setosa\n",
+              "6      4.600000     3.400000           1.4          0.3  setosa\n",
+              "7      5.000000     3.400000           1.5          0.2  setosa\n",
+              "8      4.400000     2.900000           1.4          0.2  setosa\n",
+              "9      4.900000     3.100000           1.5          0.1  setosa"
+            ]
+          },
+          "execution_count": 74,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# per tutte le colonne\n",
+        "iris_imp = iris.copy()\n",
+        "\n",
+        "replace_with = iris_imp.mean() # imputazione con media\n",
+        "#replace_with = iris.median() # imputazione con mediana\n",
+        "#replace_with = iris.mode() # imputazione con moda\n",
+        "iris_imp = iris_imp.fillna(replace_with) #.round(2) #per arrotondare\n",
+        "na_count = iris_imp.isna().sum().sum() #verifichiamo che il dataset non contenga pi\u00f9 valori mancanti.\n",
+        "print(f\"Il dataset ha {na_count} valori mancanti\")\n",
+        "iris_imp.head(10)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "vuGuZ2iAF9LU"
+      },
+      "source": [
+        "### Scikit-learn"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "La libreria **Scikit-learn** offre una classe dedicata per gestire l'imputazione dei dati: `SimpleImputer` (all'interno del modulo `sklearn.impute`).\n",
+        "L'utilizzo segue il classico pattern di Scikit-learn:\n",
+        "1. Si istanzia l'oggetto specificando la strategia: `imputer = SimpleImputer(strategy='mean')`.\n",
+        "2. Si calcola la statistica sui dati con il metodo `.fit(X)`.\n",
+        "3. Si applica la sostituzione con il metodo `.transform(X)` (o combinati in `.fit_transform(X)`).\n",
+        "> [!NOTE]\n",
+        "> L'uso di Scikit-learn \u00e8 consigliato all'interno delle pipeline di Machine Learning per evitare il **data leakage** (es. calcolare la media sul train set e usarla per imputare sia il train set sia il test set).\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 75,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "Y = iris[\"species\"].values\n",
+        "X = iris.drop(\"species\",axis=1).values"
+      ]
     },
-    "colab_type": "code",
-    "id": "QcNYyewSHCw1",
-    "outputId": "3b597a75-58cf-46a9-dc99-3442db0af190"
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Il dataset ha 0 valori mancanti\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 76,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "colab_type": "code",
+        "id": "cXzSfVYGGlBn",
+        "outputId": "fb9e9bdd-f685-4b6b-c04c-90c95335fa94"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Il dataset ha 15 valori mancanti\n"
+          ]
+        }
+      ],
+      "source": [
+        "na_count = np.count_nonzero(np.isnan(X)) # numpy considera i na come nan\n",
+        "print(f\"Il dataset ha {na_count} valori mancanti\")"
+      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "array([[5.1 , 3.06, 1.4 , 0.2 ],\n",
-       "       [4.9 , 3.  , 1.4 , 0.2 ],\n",
-       "       [4.7 , 3.2 , 1.3 , 0.2 ],\n",
-       "       [4.6 , 3.06, 1.5 , 0.2 ],\n",
-       "       [5.  , 3.6 , 1.4 , 0.2 ],\n",
-       "       [5.85, 3.9 , 1.7 , 0.4 ],\n",
-       "       [4.6 , 3.4 , 1.4 , 0.3 ],\n",
-       "       [5.  , 3.4 , 1.5 , 0.2 ],\n",
-       "       [4.4 , 2.9 , 1.4 , 0.2 ],\n",
-       "       [4.9 , 3.1 , 1.5 , 0.1 ]])"
+      "cell_type": "code",
+      "execution_count": 79,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "colab_type": "code",
+        "id": "QcNYyewSHCw1",
+        "outputId": "3b597a75-58cf-46a9-dc99-3442db0af190"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Il dataset ha 0 valori mancanti\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "array([[5.1 , 3.06, 1.4 , 0.2 ],\n",
+              "       [4.9 , 3.  , 1.4 , 0.2 ],\n",
+              "       [4.7 , 3.2 , 1.3 , 0.2 ],\n",
+              "       [4.6 , 3.06, 1.5 , 0.2 ],\n",
+              "       [5.  , 3.6 , 1.4 , 0.2 ],\n",
+              "       [5.85, 3.9 , 1.7 , 0.4 ],\n",
+              "       [4.6 , 3.4 , 1.4 , 0.3 ],\n",
+              "       [5.  , 3.4 , 1.5 , 0.2 ],\n",
+              "       [4.4 , 2.9 , 1.4 , 0.2 ],\n",
+              "       [4.9 , 3.1 , 1.5 , 0.1 ]])"
+            ]
+          },
+          "execution_count": 79,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "\n",
+        "imp = SimpleImputer(missing_values = np.nan, strategy = 'mean')\n",
+        "X_imp = imp.fit_transform(X)\n",
+        "# X_imp = np.round(X_imp, 2) # per arrotondare\n",
+        "nan_count = np.count_nonzero(np.isnan(X_imp))\n",
+        "print(\"Il dataset ha \"+str(nan_count)+\" valori mancanti\")\n",
+        "X_imp[:10]"
       ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "Gestire dati mancanti.ipynb",
+      "provenance": [],
+      "version": "0.3.2"
+    },
+    "kernelspec": {
+      "display_name": "Python 3.7.4 64-bit ('base': conda)",
+      "language": "python",
+      "name": "python37464bitbaseconda78b00ce9b4da4d77b0796cc32fd5efe8"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.8"
     }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "\n",
-    "imp = SimpleImputer(missing_values = np.nan, strategy = 'mean')\n",
-    "X_imp = imp.fit_transform(X)\n",
-    "# X_imp = np.round(X_imp, 2) # per arrotondare\n",
-    "nan_count = np.count_nonzero(np.isnan(X_imp))\n",
-    "print(\"Il dataset ha \"+str(nan_count)+\" valori mancanti\")\n",
-    "X_imp[:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Gestire dati mancanti.ipynb",
-   "provenance": [],
-   "version": "0.3.2"
-  },
-  "kernelspec": {
-   "display_name": "Python 3.7.4 64-bit ('base': conda)",
-   "language": "python",
-   "name": "python37464bitbaseconda78b00ce9b4da4d77b0796cc32fd5efe8"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
+  "nbformat": 4,
+  "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/2 - Data Preprocessing/structured_data.ipynb b/2 - Data Preprocessing/structured_data.ipynb
index f0ea5e5..e54db71 100644
--- a/2 - Data Preprocessing/structured_data.ipynb	
+++ b/2 - Data Preprocessing/structured_data.ipynb	
@@ -1,676 +1,732 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "db7f7526",
-   "metadata": {},
-   "source": [
-    "# Dati Strutturati"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b5a4ebae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "55701dcd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "BASE_URL = \"https://github.com/ProfAI/machine-learning-fondamenti/blob/main/datasets/\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec8f0673",
-   "metadata": {},
-   "source": [
-    "### CSV"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "4e3a2ce1",
-   "metadata": {},
-   "outputs": [
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "db7f7526",
+      "metadata": {},
+      "source": [
+        "# Dati Strutturati"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "I **dati strutturati** sono informazioni organizzate in un formato predefinito e rigido, tipicamente una tabella composta da righe (osservazioni) e colonne (variabili o feature).\n",
+        "Ogni colonna ha un tipo di dato ben definito (es. intero, testo, data). In Python, la libreria principale per manipolare dati strutturati \u00e8 **Pandas**, che mette a disposizione l'oggetto `DataFrame`.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "b5a4ebae",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "55701dcd",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "BASE_URL = \"https://github.com/ProfAI/machine-learning-fondamenti/blob/main/datasets/\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ec8f0673",
+      "metadata": {},
+      "source": [
+        "### CSV"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Il formato **CSV (Comma-Separated Values)** \u00e8 uno dei formati pi\u00f9 semplici e diffusi per lo scambio di dati tabulari. Ogni riga del file rappresenta una riga della tabella, e i valori delle colonne sono separati da una virgola `,`.\n",
+        "- **Vantaggi**: Leggero, leggibile come testo semplice, supportato universalmente.\n",
+        "- **Svantaggi**: Non supporta tipi di dati complessi o formule, non ha uno standard rigoroso per la gestione di caratteri speciali (es. virgole nel testo).\n"
+      ]
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>taglia</th>\n",
-       "      <th>colore</th>\n",
-       "      <th>prezzo</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>S</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>4.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>M</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>19.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>XL</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>12.49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>XL</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>S</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "4e3a2ce1",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>taglia</th>\n",
+              "      <th>colore</th>\n",
+              "      <th>prezzo</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>S</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>4.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>M</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>19.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>XL</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>12.49</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>XL</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>S</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  taglia  colore  prezzo\n",
+              "0      S  bianco    4.99\n",
+              "1      M  bianco   19.99\n",
+              "2     XL  bianco   12.49\n",
+              "3     XL  bianco   14.99\n",
+              "4      S  bianco   14.99"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "  taglia  colore  prezzo\n",
-       "0      S  bianco    4.99\n",
-       "1      M  bianco   19.99\n",
-       "2     XL  bianco   12.49\n",
-       "3     XL  bianco   14.99\n",
-       "4      S  bianco   14.99"
+      "source": [
+        "df = pd.read_csv(BASE_URL+\"shirts_example.csv\", index_col=0)\n",
+        "df.head()"
       ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.read_csv(BASE_URL+\"shirts_example.csv\", index_col=0)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8de858fd",
-   "metadata": {},
-   "source": [
-    "### TSV"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "1ce8db68",
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8de858fd",
+      "metadata": {},
+      "source": [
+        "### TSV"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Il formato **TSV (Tab-Separated Values)** \u00e8 del tutto analogo al CSV, ma utilizza una tabulazione (`\\t`) come separatore anzich\u00e9 una virgola.\n",
+        "- **Perch\u00e9 usarlo**: \u00c8 particolarmente utile quando i dati di testo all'interno delle colonne contengono gi\u00e0 virgole, evitando errori di parsing senza ricorrere a complessi sistemi di virgolette.\n"
+      ]
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>taglia</th>\n",
-       "      <th>colore</th>\n",
-       "      <th>prezzo</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>S</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>4.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>M</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>19.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>XL</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>12.49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>XL</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>S</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "1ce8db68",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>taglia</th>\n",
+              "      <th>colore</th>\n",
+              "      <th>prezzo</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>S</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>4.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>M</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>19.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>XL</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>12.49</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>XL</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>S</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  taglia  colore  prezzo\n",
+              "0      S  bianco    4.99\n",
+              "1      M  bianco   19.99\n",
+              "2     XL  bianco   12.49\n",
+              "3     XL  bianco   14.99\n",
+              "4      S  bianco   14.99"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "  taglia  colore  prezzo\n",
-       "0      S  bianco    4.99\n",
-       "1      M  bianco   19.99\n",
-       "2     XL  bianco   12.49\n",
-       "3     XL  bianco   14.99\n",
-       "4      S  bianco   14.99"
+      "source": [
+        "df = pd.read_csv(BASE_URL+\"shirts_example.tsv\", index_col=0, sep=\"\\t\")\n",
+        "df.head()"
       ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.read_csv(BASE_URL+\"shirts_example.tsv\", index_col=0, sep=\"\\t\")\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c219a6a",
-   "metadata": {},
-   "source": [
-    "### EXCEL\n",
-    "Formati supportati xls, xlsx, xlsm, xlsb, odf, ods e odt."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "566f7935",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting odfpy\n",
-      "  Downloading odfpy-1.4.1.tar.gz (717 kB)\n",
-      "Requirement already satisfied: defusedxml in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from odfpy) (0.7.1)\n",
-      "Building wheels for collected packages: odfpy\n",
-      "  Building wheel for odfpy (setup.py): started\n",
-      "  Building wheel for odfpy (setup.py): finished with status 'done'\n",
-      "  Created wheel for odfpy: filename=odfpy-1.4.1-py2.py3-none-any.whl size=137339 sha256=f65d5a958697eaf0972f589d7653a331655afc6eec693662568a618e384c371e\n",
-      "  Stored in directory: c:\\users\\gfgul\\appdata\\local\\pip\\cache\\wheels\\ea\\af\\da\\2bdd7308f7b334429a558df1e36d31864cd19c07ede92ddf0e\n",
-      "Successfully built odfpy\n",
-      "Installing collected packages: odfpy\n",
-      "Successfully installed odfpy-1.4.1\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "pip install odfpy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "dcf7ac38",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "0c219a6a",
+      "metadata": {},
+      "source": [
+        "### EXCEL\n",
+        "Formati supportati xls, xlsx, xlsm, xlsb, odf, ods e odt."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "I file **Excel** (come `.xlsx` o il formato open source `.ods` usato in questo esempio) sono formati binari pi\u00f9 complessi rispetto ai file di testo come CSV/TSV. Possono contenere pi\u00f9 fogli di lavoro, formattazioni, formule e grafici.\n",
+        "- **In Pandas**: Per leggere questi file usiamo `pd.read_excel`. Spesso \u00e8 necessario installare motori di terze parti (come `odfpy` per `.ods` o `openpyxl` per `.xlsx`) per consentire a Pandas di decodificare il file binario.\n"
+      ]
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>taglia</th>\n",
-       "      <th>colore</th>\n",
-       "      <th>prezzo</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>S</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>4.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>M</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>19.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>XL</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>12.49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>XL</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>S</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "566f7935",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Collecting odfpy\n",
+            "  Downloading odfpy-1.4.1.tar.gz (717 kB)\n",
+            "Requirement already satisfied: defusedxml in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from odfpy) (0.7.1)\n",
+            "Building wheels for collected packages: odfpy\n",
+            "  Building wheel for odfpy (setup.py): started\n",
+            "  Building wheel for odfpy (setup.py): finished with status 'done'\n",
+            "  Created wheel for odfpy: filename=odfpy-1.4.1-py2.py3-none-any.whl size=137339 sha256=f65d5a958697eaf0972f589d7653a331655afc6eec693662568a618e384c371e\n",
+            "  Stored in directory: c:\\users\\gfgul\\appdata\\local\\pip\\cache\\wheels\\ea\\af\\da\\2bdd7308f7b334429a558df1e36d31864cd19c07ede92ddf0e\n",
+            "Successfully built odfpy\n",
+            "Installing collected packages: odfpy\n",
+            "Successfully installed odfpy-1.4.1\n",
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "  taglia  colore  prezzo\n",
-       "0      S  bianco    4.99\n",
-       "1      M  bianco   19.99\n",
-       "2     XL  bianco   12.49\n",
-       "3     XL  bianco   14.99\n",
-       "4      S  bianco   14.99"
+      "source": [
+        "pip install odfpy"
       ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.read_excel(BASE_URL+\"shirts_example.ods\", index_col=0)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eac47242",
-   "metadata": {},
-   "source": [
-    "#### HTML"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "19cb3e34",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>taglia</th>\n",
-       "      <th>colore</th>\n",
-       "      <th>prezzo</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>S</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>4.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>M</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>19.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>XL</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>12.49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>XL</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>S</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "dcf7ac38",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>taglia</th>\n",
+              "      <th>colore</th>\n",
+              "      <th>prezzo</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>S</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>4.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>M</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>19.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>XL</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>12.49</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>XL</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>S</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  taglia  colore  prezzo\n",
+              "0      S  bianco    4.99\n",
+              "1      M  bianco   19.99\n",
+              "2     XL  bianco   12.49\n",
+              "3     XL  bianco   14.99\n",
+              "4      S  bianco   14.99"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "  taglia  colore  prezzo\n",
-       "0      S  bianco    4.99\n",
-       "1      M  bianco   19.99\n",
-       "2     XL  bianco   12.49\n",
-       "3     XL  bianco   14.99\n",
-       "4      S  bianco   14.99"
+      "source": [
+        "df = pd.read_excel(BASE_URL+\"shirts_example.ods\", index_col=0)\n",
+        "df.head()"
       ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.read_html(BASE_URL+\"shirts_example.html\", index_col=0, header=0)\n",
-    "df[0].head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cee2a102",
-   "metadata": {},
-   "source": [
-    "### XML"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "a030ecfa",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: pandas in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (1.3.5)\n",
-      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from pandas) (1.20.1)\n",
-      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from pandas) (2021.1)\n",
-      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from pandas) (2.8.1)\n",
-      "Requirement already satisfied: six>=1.5 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# serve la version 1.3.0\n",
-    "!pip install pandas --upgrade # --user # solo per l'utente corrente"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "6bf06a47",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "eac47242",
+      "metadata": {},
+      "source": [
+        "#### HTML"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Le pagine **HTML** contengono tabelle definite dal tag `<table>`. Pandas permette di effettuare uno scraping automatico di queste tabelle tramite la funzione `pd.read_html`, che analizza il codice HTML e restituisce una lista di DataFrame corrispondenti a tutte le tabelle trovate nella pagina.\n"
+      ]
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>taglia</th>\n",
-       "      <th>colore</th>\n",
-       "      <th>prezzo</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>4.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>M</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>19.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>XL</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>12.49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>XL</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>S</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "19cb3e34",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>taglia</th>\n",
+              "      <th>colore</th>\n",
+              "      <th>prezzo</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>S</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>4.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>M</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>19.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>XL</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>12.49</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>XL</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>S</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  taglia  colore  prezzo\n",
+              "0      S  bianco    4.99\n",
+              "1      M  bianco   19.99\n",
+              "2     XL  bianco   12.49\n",
+              "3     XL  bianco   14.99\n",
+              "4      S  bianco   14.99"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "   id taglia  colore  prezzo\n",
-       "0   0      S  bianco    4.99\n",
-       "1   1      M  bianco   19.99\n",
-       "2   2     XL  bianco   12.49\n",
-       "3   3     XL  bianco   14.99\n",
-       "4   4      S  bianco   14.99"
+      "source": [
+        "df = pd.read_html(BASE_URL+\"shirts_example.html\", index_col=0, header=0)\n",
+        "df[0].head()"
       ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.read_xml(BASE_URL+\"shirts_example.xml\")\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5b699a70",
-   "metadata": {},
-   "source": [
-    "### JSON"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "6da38730",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>taglia</th>\n",
-       "      <th>colore</th>\n",
-       "      <th>prezzo</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>S</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>4.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>M</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>19.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>XL</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>12.49</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>XL</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>S</td>\n",
-       "      <td>bianco</td>\n",
-       "      <td>14.99</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "markdown",
+      "id": "cee2a102",
+      "metadata": {},
+      "source": [
+        "### XML"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**XML (Extensible Markup Language)** \u00e8 un linguaggio di marcatura gerarchico che utilizza tag personalizzati per definire la struttura dei dati. \u00c8 molto comune per lo scambio di informazioni sul web.\n",
+        "- **In Pandas**: A partire dalla versione 1.3.0, Pandas ha introdotto la comoda funzione `pd.read_xml` per convertire direttamente strutture XML nidificate in tabelle bidimensionali.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "a030ecfa",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Requirement already satisfied: pandas in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (1.3.5)\n",
+            "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from pandas) (1.20.1)\n",
+            "Requirement already satisfied: pytz>=2017.3 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from pandas) (2021.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from pandas) (2.8.1)\n",
+            "Requirement already satisfied: six>=1.5 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "  taglia  colore  prezzo\n",
-       "0      S  bianco    4.99\n",
-       "1      M  bianco   19.99\n",
-       "2     XL  bianco   12.49\n",
-       "3     XL  bianco   14.99\n",
-       "4      S  bianco   14.99"
+      "source": [
+        "# serve la version 1.3.0\n",
+        "!pip install pandas --upgrade # --user # solo per l'utente corrente"
       ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "6bf06a47",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>id</th>\n",
+              "      <th>taglia</th>\n",
+              "      <th>colore</th>\n",
+              "      <th>prezzo</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>S</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>4.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>M</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>19.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>XL</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>12.49</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>XL</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>S</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   id taglia  colore  prezzo\n",
+              "0   0      S  bianco    4.99\n",
+              "1   1      M  bianco   19.99\n",
+              "2   2     XL  bianco   12.49\n",
+              "3   3     XL  bianco   14.99\n",
+              "4   4      S  bianco   14.99"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df = pd.read_xml(BASE_URL+\"shirts_example.xml\")\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5b699a70",
+      "metadata": {},
+      "source": [
+        "### JSON"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "**JSON (JavaScript Object Notation)** \u00e8 il formato standard per le API web e lo scambio di dati asincrono. \u00c8 strutturato come coppie chiave-valore (molto simile ai dizionari Python) e supporta strutture annidate.\n",
+        "- **In Pandas**: La funzione `pd.read_json` permette di caricare file JSON. Se la struttura \u00e8 piatta, la conversione in DataFrame \u00e8 immediata; per strutture complesse e nidificate si pu\u00f2 usare `pd.json_normalize`.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "6da38730",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>taglia</th>\n",
+              "      <th>colore</th>\n",
+              "      <th>prezzo</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>S</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>4.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>M</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>19.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>XL</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>12.49</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>XL</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>S</td>\n",
+              "      <td>bianco</td>\n",
+              "      <td>14.99</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  taglia  colore  prezzo\n",
+              "0      S  bianco    4.99\n",
+              "1      M  bianco   19.99\n",
+              "2     XL  bianco   12.49\n",
+              "3     XL  bianco   14.99\n",
+              "4      S  bianco   14.99"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df = pd.read_json(BASE_URL+\"shirts_example.json\")\n",
+        "df.head()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3.7.4 64-bit ('base': conda)",
+      "language": "python",
+      "name": "python37464bitbaseconda78b00ce9b4da4d77b0796cc32fd5efe8"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.8"
     }
-   ],
-   "source": [
-    "df = pd.read_json(BASE_URL+\"shirts_example.json\")\n",
-    "df.head()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.7.4 64-bit ('base': conda)",
-   "language": "python",
-   "name": "python37464bitbaseconda78b00ce9b4da4d77b0796cc32fd5efe8"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/2 - Data Preprocessing/unstructured_data.ipynb b/2 - Data Preprocessing/unstructured_data.ipynb
index fbdbaa7..46a45fe 100644
--- a/2 - Data Preprocessing/unstructured_data.ipynb	
+++ b/2 - Data Preprocessing/unstructured_data.ipynb	
@@ -1,646 +1,711 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "234bd194",
-   "metadata": {},
-   "source": [
-    "# Dati non strutturati"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "b9049430",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "BASE_URL = \"https://github.com/ProfAI/machine-learning-fondamenti/blob/main/datasets/\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b238c02a",
-   "metadata": {},
-   "source": [
-    "## Testo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "75f3447f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(BASE_URL+\"chuck.txt\", encoding=\"utf-8\") as f:\n",
-    "    sentences = f.read().splitlines()\n",
-    "\n",
-    "sentences"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6277e29c",
-   "metadata": {},
-   "source": [
-    "#### BOW - Bag of Words"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "16ee59d6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Chuck Norris può incartare del pesce fresco in un foglio Excel.',\n",
-       " \"Chuck Norris può attraversare l'oceano a bordo di un telefono in modalità aereo.\",\n",
-       " \"Chuck Norris può produrre champagne facendo ringiovanire l'aceto.\",\n",
-       " 'Chuck Norris ha finito Fortnite.',\n",
-       " 'Il mouse del pc di Chuck Norris è Topolino.',\n",
-       " 'Quando Chuck Norris fotografa i buchi neri, i buchi neri sorridono.',\n",
-       " 'Chuck Norris può riavvolgere un CD facendolo ruotare attorno a una Bic.',\n",
-       " 'Chcuk Norris è nato prima di suo padre.']"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.feature_extraction.text import CountVectorizer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "0d2cb4fb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'chuck': 10, 'norris': 29, 'può': 36, 'incartare': 24, 'del': 11, 'pesce': 33, 'fresco': 20, 'in': 23, 'un': 45, 'foglio': 17, 'excel': 13, 'attraversare': 3, 'oceano': 30, 'bordo': 5, 'di': 12, 'telefono': 43, 'modalità': 25, 'aereo': 1, 'produrre': 35, 'champagne': 8, 'facendo': 14, 'ringiovanire': 39, 'aceto': 0, 'ha': 21, 'finito': 16, 'fortnite': 18, 'il': 22, 'mouse': 26, 'pc': 32, 'topolino': 44, 'quando': 37, 'fotografa': 19, 'buchi': 6, 'neri': 28, 'sorridono': 41, 'riavvolgere': 38, 'cd': 7, 'facendolo': 15, 'ruotare': 40, 'attorno': 2, 'una': 46, 'bic': 4, 'chcuk': 9, 'nato': 27, 'prima': 34, 'suo': 42, 'padre': 31}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0,\n",
-       "       0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 1, 0], dtype=int64)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cv = CountVectorizer()\n",
-    "data = cv.fit_transform(sentences)\n",
-    "data = data.toarray()\n",
-    "\n",
-    "print(cv.vocabulary_)\n",
-    "print(data[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "e0bf0058",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'può': 34, 'incartare': 23, 'del': 10, 'pesce': 31, 'fresco': 19, 'in': 22, 'un': 43, 'foglio': 16, 'excel': 12, 'attraversare': 3, 'oceano': 28, 'bordo': 5, 'di': 11, 'telefono': 41, 'modalità': 24, 'aereo': 1, 'produrre': 33, 'champagne': 8, 'facendo': 13, 'ringiovanire': 37, 'aceto': 0, 'ha': 20, 'finito': 15, 'fortnite': 17, 'il': 21, 'mouse': 25, 'pc': 30, 'topolino': 42, 'quando': 35, 'fotografa': 18, 'buchi': 6, 'neri': 27, 'sorridono': 39, 'riavvolgere': 36, 'cd': 7, 'facendolo': 14, 'ruotare': 38, 'attorno': 2, 'una': 44, 'bic': 4, 'chcuk': 9, 'nato': 26, 'prima': 32, 'suo': 40, 'padre': 29}\n",
-      "[0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 1 0 0 1 0 0\n",
-      " 0 0 0 0 0 0 1 0]\n"
-     ]
-    }
-   ],
-   "source": [
-    "bow = CountVectorizer(stop_words=[\"chuck\",\"norris\"])\n",
-    "data = bow.fit_transform(sentences)\n",
-    "data = data.toarray()\n",
-    "\n",
-    "print(bow.vocabulary_)\n",
-    "print(data[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "1e0c3dd6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'può': 3, 'del': 0, 'in': 2, 'un': 4, 'di': 1}\n",
-      "[1 0 1 1 1]\n"
-     ]
-    }
-   ],
-   "source": [
-    "bow = CountVectorizer(max_df=0.8, min_df=0.2)\n",
-    "data = bow.fit_transform(sentences)\n",
-    "data = data.toarray()\n",
-    "\n",
-    "print(bow.vocabulary_)\n",
-    "print(data[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c00c0de7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bow = CountVectorizer(max_df=0.8, min_df=0.2)\n",
-    "data = bow.fit_transform(sentences)\n",
-    "data = data.toarray()\n",
-    "\n",
-    "print(bow.vocabulary_)\n",
-    "print(data[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "9d7231ff",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'chuck': 1, 'norris': 7, 'può': 14, 'del': 2, 'pesce': 11, 'in': 4, 'un': 19, 'oceano': 8, 'di': 3, 'telefono': 17, 'produrre': 13, 'pc': 10, 'topolino': 18, 'buchi': 0, 'neri': 6, 'riavvolgere': 15, 'nato': 5, 'prima': 12, 'suo': 16, 'padre': 9}\n",
-      "[0 1 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1]\n"
-     ]
-    }
-   ],
-   "source": [
-    "bow = CountVectorizer(max_features=20)\n",
-    "data = bow.fit_transform(sentences)\n",
-    "data = data.toarray()\n",
-    "\n",
-    "print(bow.vocabulary_)\n",
-    "print(data[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9df3cb3e",
-   "metadata": {},
-   "source": [
-    "#### TF-IDF - Term frequency/Inverse document frequency"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "282c8962",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.feature_extraction.text import TfidfVectorizer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "5596d2bb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'chuck': 10, 'norris': 29, 'può': 36, 'incartare': 24, 'del': 11, 'pesce': 33, 'fresco': 20, 'in': 23, 'un': 45, 'foglio': 17, 'excel': 13, 'attraversare': 3, 'oceano': 30, 'bordo': 5, 'di': 12, 'telefono': 43, 'modalità': 25, 'aereo': 1, 'produrre': 35, 'champagne': 8, 'facendo': 14, 'ringiovanire': 39, 'aceto': 0, 'ha': 21, 'finito': 16, 'fortnite': 18, 'il': 22, 'mouse': 26, 'pc': 32, 'topolino': 44, 'quando': 37, 'fotografa': 19, 'buchi': 6, 'neri': 28, 'sorridono': 41, 'riavvolgere': 38, 'cd': 7, 'facendolo': 15, 'ruotare': 40, 'attorno': 2, 'una': 46, 'bic': 4, 'chcuk': 9, 'nato': 27, 'prima': 34, 'suo': 42, 'padre': 31}\n",
-      "[0.         0.         0.         0.         0.         0.\n",
-      " 0.         0.         0.         0.         0.16098575 0.30224708\n",
-      " 0.         0.36064312 0.         0.         0.         0.36064312\n",
-      " 0.         0.         0.36064312 0.         0.         0.30224708\n",
-      " 0.36064312 0.         0.         0.         0.         0.14402236\n",
-      " 0.         0.         0.         0.36064312 0.         0.\n",
-      " 0.22867677 0.         0.         0.         0.         0.\n",
-      " 0.         0.         0.         0.26081443 0.        ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "tfidf = TfidfVectorizer()\n",
-    "data = tfidf.fit_transform(sentences)\n",
-    "data = data.toarray()\n",
-    "\n",
-    "print(tfidf.vocabulary_)\n",
-    "print(data[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "594d2741",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'può': 34, 'incartare': 23, 'del': 10, 'pesce': 31, 'fresco': 19, 'in': 22, 'un': 43, 'foglio': 16, 'excel': 12, 'attraversare': 3, 'oceano': 28, 'bordo': 5, 'di': 11, 'telefono': 41, 'modalità': 24, 'aereo': 1, 'produrre': 33, 'champagne': 8, 'facendo': 13, 'ringiovanire': 37, 'aceto': 0, 'ha': 20, 'finito': 15, 'fortnite': 17, 'il': 21, 'mouse': 25, 'pc': 30, 'topolino': 42, 'quando': 35, 'fotografa': 18, 'buchi': 6, 'neri': 27, 'sorridono': 39, 'riavvolgere': 36, 'cd': 7, 'facendolo': 14, 'ruotare': 38, 'attorno': 2, 'una': 44, 'bic': 4, 'chcuk': 9, 'nato': 26, 'prima': 32, 'suo': 40, 'padre': 29}\n",
-      "[0.         0.         0.         0.         0.         0.\n",
-      " 0.         0.         0.         0.         0.30955509 0.\n",
-      " 0.36936308 0.         0.         0.         0.36936308 0.\n",
-      " 0.         0.36936308 0.         0.         0.30955509 0.36936308\n",
-      " 0.         0.         0.         0.         0.         0.\n",
-      " 0.         0.36936308 0.         0.         0.23420593 0.\n",
-      " 0.         0.         0.         0.         0.         0.\n",
-      " 0.         0.26712064 0.        ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "tfidf = TfidfVectorizer(stop_words=[\"chuck\",\"norris\"])\n",
-    "data = tfidf.fit_transform(sentences)\n",
-    "data = data.toarray()\n",
-    "\n",
-    "print(tfidf.vocabulary_)\n",
-    "print(data[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "b2d19f96",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'può': 3, 'del': 0, 'in': 2, 'un': 4, 'di': 1}\n",
-      "[0.54906495 0.         0.54906495 0.41541642 0.473798  ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "tfidf = TfidfVectorizer(max_df=0.8, min_df=0.2)\n",
-    "data = tfidf.fit_transform(sentences)\n",
-    "data = data.toarray()\n",
-    "\n",
-    "print(tfidf.vocabulary_)\n",
-    "print(data[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "fd708d53",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'può': 3, 'del': 0, 'in': 2, 'un': 4, 'di': 1}\n",
-      "[0.54906495 0.         0.54906495 0.41541642 0.473798  ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "bow = TfidfVectorizer(max_features=20)\n",
-    "data = tfidf.fit_transform(sentences)\n",
-    "data = data.toarray()\n",
-    "\n",
-    "print(tfidf.vocabulary_)\n",
-    "print(data[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "272f2895",
-   "metadata": {},
-   "source": [
-    "## Immagini\n",
-    "Apriamo un immagine e convertiamola in un array numpy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "6b0b8fac",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b31418cb",
-   "metadata": {},
-   "source": [
-    "#### Metodo 1: Pillow"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "72b1c8b0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip install Pillow"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "a00cbf1f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'numpy.ndarray'> (615, 660, 3)\n"
-     ]
-    }
-   ],
-   "source": [
-    "from PIL import Image\n",
-    "img = Image.open(BASE_URL+\"gatto.jpg\")\n",
-    "img_arr = np.array(img)\n",
-    "print(type(img_arr), img_arr.shape)\n",
-    "img.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f7bc7c28",
-   "metadata": {},
-   "source": [
-    "#### Metodo 2: OpenCV"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ce242b5f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install opencv-python"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "5101d9ef",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'numpy.ndarray'> (615, 660, 3)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "113"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import cv2\n",
-    "img = cv2.imread(BASE_URL+\"gatto.jpg\")\n",
-    "print(type(img), img.shape)\n",
-    "cv2.imshow(\"Il mio gatto\", img)\n",
-    "cv2.waitKey(0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "242f148a",
-   "metadata": {},
-   "source": [
-    "#### Metodo 3: Matplotlib"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "ed4c6ecd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'numpy.ndarray'> (615, 660, 3)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.image as mpimg\n",
-    "\n",
-    "img = mpimg.imread(BASE_URL+\"gatto.jpg\")\n",
-    "print(type(img), img.shape)\n",
-    "\n",
-    "plt.imshow(img)\n",
-    "plt.axis(\"off\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed668a5f",
-   "metadata": {},
-   "source": [
-    "### Image Flattening"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "e265f2ee",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(615, 660, 3)\n",
-      "(1217700,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(img.shape)\n",
-    "data = img.flatten()\n",
-    "print(data.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e4330d89",
-   "metadata": {},
-   "source": [
-    "## Audio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "126fe401",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting librosa\n",
-      "  Downloading librosa-0.8.1-py3-none-any.whl (203 kB)\n",
-      "Collecting pooch>=1.0\n",
-      "  Downloading pooch-1.5.2-py3-none-any.whl (57 kB)\n",
-      "Requirement already satisfied: numpy>=1.15.0 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from librosa) (1.20.1)\n",
-      "Collecting resampy>=0.2.2\n",
-      "  Downloading resampy-0.2.2.tar.gz (323 kB)\n",
-      "Requirement already satisfied: numba>=0.43.0 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from librosa) (0.53.1)\n",
-      "Requirement already satisfied: scipy>=1.0.0 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from librosa) (1.6.2)\n",
-      "Requirement already satisfied: packaging>=20.0 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from librosa) (20.9)\n",
-      "Collecting audioread>=2.0.0\n",
-      "  Downloading audioread-2.1.9.tar.gz (377 kB)\n",
-      "Collecting soundfile>=0.10.2\n",
-      "  Downloading SoundFile-0.10.3.post1-py2.py3.cp26.cp27.cp32.cp33.cp34.cp35.cp36.pp27.pp32.pp33-none-win_amd64.whl (689 kB)\n",
-      "Note: you may need to restart the kernel to use updated packages.\n",
-      "Requirement already satisfied: joblib>=0.14 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from librosa) (1.0.1)\n",
-      "Requirement already satisfied: decorator>=3.0.0 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from librosa) (5.0.6)\n",
-      "Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from librosa) (0.24.1)\n",
-      "Requirement already satisfied: llvmlite<0.37,>=0.36.0rc1 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from numba>=0.43.0->librosa) (0.36.0)\n",
-      "Requirement already satisfied: setuptools in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from numba>=0.43.0->librosa) (52.0.0.post20210125)\n",
-      "Requirement already satisfied: pyparsing>=2.0.2 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from packaging>=20.0->librosa) (2.4.7)\n",
-      "Requirement already satisfied: requests in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from pooch>=1.0->librosa) (2.26.0)\n",
-      "Requirement already satisfied: appdirs in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from pooch>=1.0->librosa) (1.4.4)\n",
-      "Requirement already satisfied: six>=1.3 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from resampy>=0.2.2->librosa) (1.15.0)\n",
-      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from scikit-learn!=0.19.0,>=0.14.0->librosa) (2.1.0)\n",
-      "Requirement already satisfied: cffi>=1.0 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from soundfile>=0.10.2->librosa) (1.14.5)\n",
-      "Requirement already satisfied: pycparser in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from cffi>=1.0->soundfile>=0.10.2->librosa) (2.20)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from requests->pooch>=1.0->librosa) (2.10)\n",
-      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from requests->pooch>=1.0->librosa) (2.0.8)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from requests->pooch>=1.0->librosa) (2020.12.5)\n",
-      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from requests->pooch>=1.0->librosa) (1.26.4)\n",
-      "Building wheels for collected packages: audioread, resampy\n",
-      "  Building wheel for audioread (setup.py): started\n",
-      "  Building wheel for audioread (setup.py): finished with status 'done'\n",
-      "  Created wheel for audioread: filename=audioread-2.1.9-py3-none-any.whl size=23141 sha256=0d77442d0c681888132e049fa501cd11668d19af70846a357fce6d2692277a1a\n",
-      "  Stored in directory: c:\\users\\gfgul\\appdata\\local\\pip\\cache\\wheels\\49\\5a\\e4\\df590783499a992a88de6c0898991d1167453a3196d0d1eeb7\n",
-      "  Building wheel for resampy (setup.py): started\n",
-      "  Building wheel for resampy (setup.py): finished with status 'done'\n",
-      "  Created wheel for resampy: filename=resampy-0.2.2-py3-none-any.whl size=320718 sha256=284482b4cec043e7f34590a7c03c925575b5caddc87fd8c37a2aed18486cc117\n",
-      "  Stored in directory: c:\\users\\gfgul\\appdata\\local\\pip\\cache\\wheels\\6f\\d1\\5d\\f13da53b1dcbc2624ff548456c9ffb526c914f53c12c318bb4\n",
-      "Successfully built audioread resampy\n",
-      "Installing collected packages: soundfile, resampy, pooch, audioread, librosa\n",
-      "Successfully installed audioread-2.1.9 librosa-0.8.1 pooch-1.5.2 resampy-0.2.2 soundfile-0.10.3.post1\n"
-     ]
-    }
-   ],
-   "source": [
-    "pip install librosa"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "122226b2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "22050\n",
-      "<class 'numpy.ndarray'> (90317,)\n"
-     ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "234bd194",
+      "metadata": {},
+      "source": [
+        "# Dati non strutturati"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "I **dati non strutturati** non presentano un modello o una struttura tabulare predefinita (es. file di testo libero, immagini, registrazioni audio, video).\n",
+        "Per poter addestrare modelli di Machine Learning su questi dati, dobbiamo prima estrarre delle caratteristiche numeriche significative ed organizzarle in vettori numerici (vettorializzazione).\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "b9049430",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "BASE_URL = \"https://github.com/ProfAI/machine-learning-fondamenti/blob/main/datasets/\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b238c02a",
+      "metadata": {},
+      "source": [
+        "## Testo"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "I testi in linguaggio naturale devono essere convertiti in numeri. Vediamo le due tecniche classiche di rappresentazione testuale.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "75f3447f",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "with open(BASE_URL+\"chuck.txt\", encoding=\"utf-8\") as f:\n",
+        "    sentences = f.read().splitlines()\n",
+        "\n",
+        "sentences"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6277e29c",
+      "metadata": {},
+      "source": [
+        "#### BOW - Bag of Words"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Il modello **Bag of Words (BoW)** crea un vocabolario di tutte le parole uniche presenti nel corpus di documenti. Ogni documento viene poi rappresentato come un vettore in cui ogni coordinata corrisponde alla frequenza di comparsa di una parola del vocabolario in quel documento.\n",
+        "- **Parametri di Scikit-Learn (`CountVectorizer`)**:\n",
+        "  - `stop_words`: Lista di parole comuni (es. preposizioni, articoli) da escludere perch\u00e9 prive di valore informativo.\n",
+        "  - `max_df` (Maximum Document Frequency): Esclude parole che compaiono in una percentuale troppo alta di documenti (troppo comuni).\n",
+        "  - `min_df` (Minimum Document Frequency): Esclude parole che compaiono in troppi pochi documenti (errori di battitura, parole estremamente rare).\n",
+        "  - `max_features`: Limita la dimensione del vocabolario alle top $N$ parole pi\u00f9 frequenti.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "16ee59d6",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['Chuck Norris pu\u00f2 incartare del pesce fresco in un foglio Excel.',\n",
+              " \"Chuck Norris pu\u00f2 attraversare l'oceano a bordo di un telefono in modalit\u00e0 aereo.\",\n",
+              " \"Chuck Norris pu\u00f2 produrre champagne facendo ringiovanire l'aceto.\",\n",
+              " 'Chuck Norris ha finito Fortnite.',\n",
+              " 'Il mouse del pc di Chuck Norris \u00e8 Topolino.',\n",
+              " 'Quando Chuck Norris fotografa i buchi neri, i buchi neri sorridono.',\n",
+              " 'Chuck Norris pu\u00f2 riavvolgere un CD facendolo ruotare attorno a una Bic.',\n",
+              " 'Chcuk Norris \u00e8 nato prima di suo padre.']"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.feature_extraction.text import CountVectorizer"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "0d2cb4fb",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'chuck': 10, 'norris': 29, 'pu\u00f2': 36, 'incartare': 24, 'del': 11, 'pesce': 33, 'fresco': 20, 'in': 23, 'un': 45, 'foglio': 17, 'excel': 13, 'attraversare': 3, 'oceano': 30, 'bordo': 5, 'di': 12, 'telefono': 43, 'modalit\u00e0': 25, 'aereo': 1, 'produrre': 35, 'champagne': 8, 'facendo': 14, 'ringiovanire': 39, 'aceto': 0, 'ha': 21, 'finito': 16, 'fortnite': 18, 'il': 22, 'mouse': 26, 'pc': 32, 'topolino': 44, 'quando': 37, 'fotografa': 19, 'buchi': 6, 'neri': 28, 'sorridono': 41, 'riavvolgere': 38, 'cd': 7, 'facendolo': 15, 'ruotare': 40, 'attorno': 2, 'una': 46, 'bic': 4, 'chcuk': 9, 'nato': 27, 'prima': 34, 'suo': 42, 'padre': 31}\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0,\n",
+              "       0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
+              "       0, 1, 0], dtype=int64)"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "cv = CountVectorizer()\n",
+        "data = cv.fit_transform(sentences)\n",
+        "data = data.toarray()\n",
+        "\n",
+        "print(cv.vocabulary_)\n",
+        "print(data[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "e0bf0058",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'pu\u00f2': 34, 'incartare': 23, 'del': 10, 'pesce': 31, 'fresco': 19, 'in': 22, 'un': 43, 'foglio': 16, 'excel': 12, 'attraversare': 3, 'oceano': 28, 'bordo': 5, 'di': 11, 'telefono': 41, 'modalit\u00e0': 24, 'aereo': 1, 'produrre': 33, 'champagne': 8, 'facendo': 13, 'ringiovanire': 37, 'aceto': 0, 'ha': 20, 'finito': 15, 'fortnite': 17, 'il': 21, 'mouse': 25, 'pc': 30, 'topolino': 42, 'quando': 35, 'fotografa': 18, 'buchi': 6, 'neri': 27, 'sorridono': 39, 'riavvolgere': 36, 'cd': 7, 'facendolo': 14, 'ruotare': 38, 'attorno': 2, 'una': 44, 'bic': 4, 'chcuk': 9, 'nato': 26, 'prima': 32, 'suo': 40, 'padre': 29}\n",
+            "[0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 1 0 0 1 0 0\n",
+            " 0 0 0 0 0 0 1 0]\n"
+          ]
+        }
+      ],
+      "source": [
+        "bow = CountVectorizer(stop_words=[\"chuck\",\"norris\"])\n",
+        "data = bow.fit_transform(sentences)\n",
+        "data = data.toarray()\n",
+        "\n",
+        "print(bow.vocabulary_)\n",
+        "print(data[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "id": "1e0c3dd6",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'pu\u00f2': 3, 'del': 0, 'in': 2, 'un': 4, 'di': 1}\n",
+            "[1 0 1 1 1]\n"
+          ]
+        }
+      ],
+      "source": [
+        "bow = CountVectorizer(max_df=0.8, min_df=0.2)\n",
+        "data = bow.fit_transform(sentences)\n",
+        "data = data.toarray()\n",
+        "\n",
+        "print(bow.vocabulary_)\n",
+        "print(data[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "c00c0de7",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "bow = CountVectorizer(max_df=0.8, min_df=0.2)\n",
+        "data = bow.fit_transform(sentences)\n",
+        "data = data.toarray()\n",
+        "\n",
+        "print(bow.vocabulary_)\n",
+        "print(data[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "id": "9d7231ff",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'chuck': 1, 'norris': 7, 'pu\u00f2': 14, 'del': 2, 'pesce': 11, 'in': 4, 'un': 19, 'oceano': 8, 'di': 3, 'telefono': 17, 'produrre': 13, 'pc': 10, 'topolino': 18, 'buchi': 0, 'neri': 6, 'riavvolgere': 15, 'nato': 5, 'prima': 12, 'suo': 16, 'padre': 9}\n",
+            "[0 1 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1]\n"
+          ]
+        }
+      ],
+      "source": [
+        "bow = CountVectorizer(max_features=20)\n",
+        "data = bow.fit_transform(sentences)\n",
+        "data = data.toarray()\n",
+        "\n",
+        "print(bow.vocabulary_)\n",
+        "print(data[0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9df3cb3e",
+      "metadata": {},
+      "source": [
+        "#### TF-IDF - Term frequency/Inverse document frequency"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Il modello **TF-IDF (Term Frequency - Inverse Document Frequency)** \u00e8 un'evoluzione del Bag of Words. Anzich\u00e9 contare semplicemente la frequenza, assegna ad ogni parola un peso proporzionale all'importanza che ha nel documento specifico, ma penalizzato se la parola \u00e8 comune a tutti i documenti del corpus.\n",
+        "\n",
+        "### Formule:\n",
+        "- **Term Frequency (TF)**: Frequenza del termine $t$ nel documento $d$.\n",
+        "- **Inverse Document Frequency (IDF)**:\n",
+        "  $$\\text{IDF}(t) = \\log\\left(\\frac{N}{1 + \\text{DF}(t)}\\right)$$\n",
+        "  Dove $N$ \u00e8 il numero totale di documenti e $\\text{DF}(t)$ \u00e8 il numero di documenti contenenti il termine $t$.\n",
+        "- **Peso TF-IDF**:\n",
+        "  $$\\text{TF-IDF}(t, d) = \\text{TF}(t, d) \\times \\text{IDF}(t)$$\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "id": "282c8962",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.feature_extraction.text import TfidfVectorizer"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "id": "5596d2bb",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'chuck': 10, 'norris': 29, 'pu\u00f2': 36, 'incartare': 24, 'del': 11, 'pesce': 33, 'fresco': 20, 'in': 23, 'un': 45, 'foglio': 17, 'excel': 13, 'attraversare': 3, 'oceano': 30, 'bordo': 5, 'di': 12, 'telefono': 43, 'modalit\u00e0': 25, 'aereo': 1, 'produrre': 35, 'champagne': 8, 'facendo': 14, 'ringiovanire': 39, 'aceto': 0, 'ha': 21, 'finito': 16, 'fortnite': 18, 'il': 22, 'mouse': 26, 'pc': 32, 'topolino': 44, 'quando': 37, 'fotografa': 19, 'buchi': 6, 'neri': 28, 'sorridono': 41, 'riavvolgere': 38, 'cd': 7, 'facendolo': 15, 'ruotare': 40, 'attorno': 2, 'una': 46, 'bic': 4, 'chcuk': 9, 'nato': 27, 'prima': 34, 'suo': 42, 'padre': 31}\n",
+            "[0.         0.         0.         0.         0.         0.\n",
+            " 0.         0.         0.         0.         0.16098575 0.30224708\n",
+            " 0.         0.36064312 0.         0.         0.         0.36064312\n",
+            " 0.         0.         0.36064312 0.         0.         0.30224708\n",
+            " 0.36064312 0.         0.         0.         0.         0.14402236\n",
+            " 0.         0.         0.         0.36064312 0.         0.\n",
+            " 0.22867677 0.         0.         0.         0.         0.\n",
+            " 0.         0.         0.         0.26081443 0.        ]\n"
+          ]
+        }
+      ],
+      "source": [
+        "tfidf = TfidfVectorizer()\n",
+        "data = tfidf.fit_transform(sentences)\n",
+        "data = data.toarray()\n",
+        "\n",
+        "print(tfidf.vocabulary_)\n",
+        "print(data[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "id": "594d2741",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'pu\u00f2': 34, 'incartare': 23, 'del': 10, 'pesce': 31, 'fresco': 19, 'in': 22, 'un': 43, 'foglio': 16, 'excel': 12, 'attraversare': 3, 'oceano': 28, 'bordo': 5, 'di': 11, 'telefono': 41, 'modalit\u00e0': 24, 'aereo': 1, 'produrre': 33, 'champagne': 8, 'facendo': 13, 'ringiovanire': 37, 'aceto': 0, 'ha': 20, 'finito': 15, 'fortnite': 17, 'il': 21, 'mouse': 25, 'pc': 30, 'topolino': 42, 'quando': 35, 'fotografa': 18, 'buchi': 6, 'neri': 27, 'sorridono': 39, 'riavvolgere': 36, 'cd': 7, 'facendolo': 14, 'ruotare': 38, 'attorno': 2, 'una': 44, 'bic': 4, 'chcuk': 9, 'nato': 26, 'prima': 32, 'suo': 40, 'padre': 29}\n",
+            "[0.         0.         0.         0.         0.         0.\n",
+            " 0.         0.         0.         0.         0.30955509 0.\n",
+            " 0.36936308 0.         0.         0.         0.36936308 0.\n",
+            " 0.         0.36936308 0.         0.         0.30955509 0.36936308\n",
+            " 0.         0.         0.         0.         0.         0.\n",
+            " 0.         0.36936308 0.         0.         0.23420593 0.\n",
+            " 0.         0.         0.         0.         0.         0.\n",
+            " 0.         0.26712064 0.        ]\n"
+          ]
+        }
+      ],
+      "source": [
+        "tfidf = TfidfVectorizer(stop_words=[\"chuck\",\"norris\"])\n",
+        "data = tfidf.fit_transform(sentences)\n",
+        "data = data.toarray()\n",
+        "\n",
+        "print(tfidf.vocabulary_)\n",
+        "print(data[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "id": "b2d19f96",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'pu\u00f2': 3, 'del': 0, 'in': 2, 'un': 4, 'di': 1}\n",
+            "[0.54906495 0.         0.54906495 0.41541642 0.473798  ]\n"
+          ]
+        }
+      ],
+      "source": [
+        "tfidf = TfidfVectorizer(max_df=0.8, min_df=0.2)\n",
+        "data = tfidf.fit_transform(sentences)\n",
+        "data = data.toarray()\n",
+        "\n",
+        "print(tfidf.vocabulary_)\n",
+        "print(data[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "id": "fd708d53",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'pu\u00f2': 3, 'del': 0, 'in': 2, 'un': 4, 'di': 1}\n",
+            "[0.54906495 0.         0.54906495 0.41541642 0.473798  ]\n"
+          ]
+        }
+      ],
+      "source": [
+        "bow = TfidfVectorizer(max_features=20)\n",
+        "data = tfidf.fit_transform(sentences)\n",
+        "data = data.toarray()\n",
+        "\n",
+        "print(tfidf.vocabulary_)\n",
+        "print(data[0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "272f2895",
+      "metadata": {},
+      "source": [
+        "## Immagini\n",
+        "Apriamo un immagine e convertiamola in un array numpy"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Un'immagine digitale \u00e8 rappresentata come una matrice di pixel. Nelle immagini in bianco e nero (scala di grigi), ogni pixel \u00e8 un numero da 0 (nero) a 255 (bianco).\n",
+        "Nelle immagini a colori, abbiamo tre canali (solitamente Rosso, Verde, Blu - RGB), quindi una matrice tridimensionale di dimensioni: $\\text{Altezza} \\times \\text{Larghezza} \\times \\text{Canali}$.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "6b0b8fac",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import numpy as np"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b31418cb",
+      "metadata": {},
+      "source": [
+        "#### Metodo 1: Pillow"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "72b1c8b0",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "pip install Pillow"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "id": "a00cbf1f",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'numpy.ndarray'> (615, 660, 3)\n"
+          ]
+        }
+      ],
+      "source": [
+        "from PIL import Image\n",
+        "img = Image.open(BASE_URL+\"gatto.jpg\")\n",
+        "img_arr = np.array(img)\n",
+        "print(type(img_arr), img_arr.shape)\n",
+        "img.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f7bc7c28",
+      "metadata": {},
+      "source": [
+        "#### Metodo 2: OpenCV"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ce242b5f",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!pip install opencv-python"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "5101d9ef",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'numpy.ndarray'> (615, 660, 3)\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "113"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import cv2\n",
+        "img = cv2.imread(BASE_URL+\"gatto.jpg\")\n",
+        "print(type(img), img.shape)\n",
+        "cv2.imshow(\"Il mio gatto\", img)\n",
+        "cv2.waitKey(0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "242f148a",
+      "metadata": {},
+      "source": [
+        "#### Metodo 3: Matplotlib"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "ed4c6ecd",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'numpy.ndarray'> (615, 660, 3)\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import matplotlib.image as mpimg\n",
+        "\n",
+        "img = mpimg.imread(BASE_URL+\"gatto.jpg\")\n",
+        "print(type(img), img.shape)\n",
+        "\n",
+        "plt.imshow(img)\n",
+        "plt.axis(\"off\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ed668a5f",
+      "metadata": {},
+      "source": [
+        "### Image Flattening"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Per passare un'immagine ad un modello di Machine Learning tradizionale, dobbiamo 'appiattirla' (**Flattening**), convertendola da una matrice bidimensionale o tridimensionale ad un vettore monodimensionale di dimensioni $\\text{Altezza} \\times \\text{Larghezza} \\times \\text{Canali}$.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "e265f2ee",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(615, 660, 3)\n",
+            "(1217700,)\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(img.shape)\n",
+        "data = img.flatten()\n",
+        "print(data.shape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e4330d89",
+      "metadata": {},
+      "source": [
+        "## Audio"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Il suono \u00e8 un'onda analogica continua. Per digitalizzarlo, viene campionato ad intervalli regolari. La frequenza con cui viene campionato si chiama **Sampling Rate (Frequenza di campionamento)** ed \u00e8 espressa in Hertz (Hz), che indica quanti campioni al secondo vengono catturati.\n",
+        "In Python, la libreria standard per l'elaborazione audio \u00e8 `librosa`, che carica l'audio come un array NumPy di ampiezze sonore nel tempo.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "126fe401",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Collecting librosa\n",
+            "  Downloading librosa-0.8.1-py3-none-any.whl (203 kB)\n",
+            "Collecting pooch>=1.0\n",
+            "  Downloading pooch-1.5.2-py3-none-any.whl (57 kB)\n",
+            "Requirement already satisfied: numpy>=1.15.0 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from librosa) (1.20.1)\n",
+            "Collecting resampy>=0.2.2\n",
+            "  Downloading resampy-0.2.2.tar.gz (323 kB)\n",
+            "Requirement already satisfied: numba>=0.43.0 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from librosa) (0.53.1)\n",
+            "Requirement already satisfied: scipy>=1.0.0 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from librosa) (1.6.2)\n",
+            "Requirement already satisfied: packaging>=20.0 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from librosa) (20.9)\n",
+            "Collecting audioread>=2.0.0\n",
+            "  Downloading audioread-2.1.9.tar.gz (377 kB)\n",
+            "Collecting soundfile>=0.10.2\n",
+            "  Downloading SoundFile-0.10.3.post1-py2.py3.cp26.cp27.cp32.cp33.cp34.cp35.cp36.pp27.pp32.pp33-none-win_amd64.whl (689 kB)\n",
+            "Note: you may need to restart the kernel to use updated packages.\n",
+            "Requirement already satisfied: joblib>=0.14 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from librosa) (1.0.1)\n",
+            "Requirement already satisfied: decorator>=3.0.0 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from librosa) (5.0.6)\n",
+            "Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from librosa) (0.24.1)\n",
+            "Requirement already satisfied: llvmlite<0.37,>=0.36.0rc1 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from numba>=0.43.0->librosa) (0.36.0)\n",
+            "Requirement already satisfied: setuptools in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from numba>=0.43.0->librosa) (52.0.0.post20210125)\n",
+            "Requirement already satisfied: pyparsing>=2.0.2 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from packaging>=20.0->librosa) (2.4.7)\n",
+            "Requirement already satisfied: requests in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from pooch>=1.0->librosa) (2.26.0)\n",
+            "Requirement already satisfied: appdirs in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from pooch>=1.0->librosa) (1.4.4)\n",
+            "Requirement already satisfied: six>=1.3 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from resampy>=0.2.2->librosa) (1.15.0)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from scikit-learn!=0.19.0,>=0.14.0->librosa) (2.1.0)\n",
+            "Requirement already satisfied: cffi>=1.0 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from soundfile>=0.10.2->librosa) (1.14.5)\n",
+            "Requirement already satisfied: pycparser in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from cffi>=1.0->soundfile>=0.10.2->librosa) (2.20)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from requests->pooch>=1.0->librosa) (2.10)\n",
+            "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from requests->pooch>=1.0->librosa) (2.0.8)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from requests->pooch>=1.0->librosa) (2020.12.5)\n",
+            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\gfgul\\anaconda3\\lib\\site-packages (from requests->pooch>=1.0->librosa) (1.26.4)\n",
+            "Building wheels for collected packages: audioread, resampy\n",
+            "  Building wheel for audioread (setup.py): started\n",
+            "  Building wheel for audioread (setup.py): finished with status 'done'\n",
+            "  Created wheel for audioread: filename=audioread-2.1.9-py3-none-any.whl size=23141 sha256=0d77442d0c681888132e049fa501cd11668d19af70846a357fce6d2692277a1a\n",
+            "  Stored in directory: c:\\users\\gfgul\\appdata\\local\\pip\\cache\\wheels\\49\\5a\\e4\\df590783499a992a88de6c0898991d1167453a3196d0d1eeb7\n",
+            "  Building wheel for resampy (setup.py): started\n",
+            "  Building wheel for resampy (setup.py): finished with status 'done'\n",
+            "  Created wheel for resampy: filename=resampy-0.2.2-py3-none-any.whl size=320718 sha256=284482b4cec043e7f34590a7c03c925575b5caddc87fd8c37a2aed18486cc117\n",
+            "  Stored in directory: c:\\users\\gfgul\\appdata\\local\\pip\\cache\\wheels\\6f\\d1\\5d\\f13da53b1dcbc2624ff548456c9ffb526c914f53c12c318bb4\n",
+            "Successfully built audioread resampy\n",
+            "Installing collected packages: soundfile, resampy, pooch, audioread, librosa\n",
+            "Successfully installed audioread-2.1.9 librosa-0.8.1 pooch-1.5.2 resampy-0.2.2 soundfile-0.10.3.post1\n"
+          ]
+        }
+      ],
+      "source": [
+        "pip install librosa"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "122226b2",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "22050\n",
+            "<class 'numpy.ndarray'> (90317,)\n"
+          ]
+        }
+      ],
+      "source": [
+        "import librosa\n",
+        "data, sr = librosa.load(BASE_URL+\"record.wav\")\n",
+        "print(sr)\n",
+        "print(type(data), data.shape)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "id": "e6fde0da",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "4.096009070294785"
+            ]
+          },
+          "execution_count": 29,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "data.shape[0]/sr"
+      ]
     }
-   ],
-   "source": [
-    "import librosa\n",
-    "data, sr = librosa.load(BASE_URL+\"record.wav\")\n",
-    "print(sr)\n",
-    "print(type(data), data.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "e6fde0da",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4.096009070294785"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3.7.4 64-bit ('base': conda)",
+      "language": "python",
+      "name": "python37464bitbaseconda78b00ce9b4da4d77b0796cc32fd5efe8"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.8"
     }
-   ],
-   "source": [
-    "data.shape[0]/sr"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.7.4 64-bit ('base': conda)",
-   "language": "python",
-   "name": "python37464bitbaseconda78b00ce9b4da4d77b0796cc32fd5efe8"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/3 - La Regressione Lineare/correlation_matrix.ipynb b/3 - La Regressione Lineare/correlation_matrix.ipynb
index c1f7a8d..feaae75 100644
--- a/3 - La Regressione Lineare/correlation_matrix.ipynb	
+++ b/3 - La Regressione Lineare/correlation_matrix.ipynb	
@@ -36,6 +36,31 @@
         "id": "6UREcfVRVvZf"
       }
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "La **Matrice di Correlazione** mostra il coefficiente di correlazione di Pearson ($r$) calcolato per tutte le coppie possibili di variabili numeriche presenti nel dataset.\n",
+        "\n",
+        "### Formula del Coefficiente di Correlazione di Pearson:\n",
+        "$$r = \\frac{\\text{Cov}(X, Y)}{\\sigma_X \\sigma_Y}$$\n",
+        "\n",
+        "Il coefficiente varia sempre tra $-1$ e $+1$:\n",
+        "- $r = +1$: Perfetta correlazione positiva.\n",
+        "- $r = 0$: Assenza di correlazione lineare.\n",
+        "- $r = -1$: Perfetta correlazione negativa.\n",
+        "\n",
+        "### Valori di Correlazione calcolati:\n",
+        "- **Dimensione vs Valore**: $r \\approx 0.933$ (forte correlazione positiva, indicante che case pi\u00f9 grandi tendono ad avere valori maggiori).\n",
+        "- **Anno di costruzione vs Valore**: $r \\approx -0.331$ (correlazione negativa debole/moderata, indicante che case pi\u00f9 recenti in questo specifico dataset hanno valori leggermente inferiori, potenzialmente dovuto a fattori dimensionali concorrenti).\n",
+        "- **Dimensione vs Anno di costruzione**: $r \\approx -0.645$ (correlazione negativa moderata).\n",
+        "\n",
+        "> [!WARNING]\n",
+        "> **Multicollinearit\u00e0**: Rilevare feature indipendenti altamente correlate tra loro (come Dimensione e Anno di costruzione che mostrano $r \\approx -0.645$) \u00e8 cruciale. Nei modelli di regressione lineare, una forte collinearit\u00e0 tra le feature pu\u00f2 rendere instabile e inaffidabile la stima dei singoli coefficienti $\\beta_i$.\n"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": 5,
diff --git a/3 - La Regressione Lineare/regressione_lineare_multipla.ipynb b/3 - La Regressione Lineare/regressione_lineare_multipla.ipynb
index 57b9007..8b9d8c3 100644
--- a/3 - La Regressione Lineare/regressione_lineare_multipla.ipynb	
+++ b/3 - La Regressione Lineare/regressione_lineare_multipla.ipynb	
@@ -1,185 +1,226 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "200a801c",
-   "metadata": {},
-   "source": [
-    "## La Regressione Lineare Multipla"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed08e123",
-   "metadata": {},
-   "source": [
-    "#### Regressione lineare semplice con sklearn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "1a42039c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.linear_model import LinearRegression"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "1b64b50a",
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[18.00870511 28.9390642  10.20130577 13.32426551 24.25462459 14.88574538\n",
-      " 22.69314472 22.69314472]\n"
-     ]
-    }
-   ],
-   "source": [
-    "X_train = np.array([[80], [150], [30], [50], [120], [60], [110], [110]])\n",
-    "y_train = np.array([16, 30, 12, 10, 24, 18, 20, 25])\n",
-    "\n",
-    "lr = LinearRegression()\n",
-    "lr.fit(X_train, y_train)\n",
-    "y_pred = lr.predict(X_train)\n",
-    "print(y_pred)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "916a9eba",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "200a801c",
+      "metadata": {},
+      "source": [
+        "## La Regressione Lineare Multipla"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MAE = 2.0701849836779105\n",
-      "MSE = 5.223068552774754\n",
-      "RMSE = 2.2854033676300456\n",
-      "R2 = 0.8701839272320061\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
-    "\n",
-    "print(f\"MAE = {mean_absolute_error(y_train, y_pred)}\")\n",
-    "print(f\"MSE = {mean_squared_error(y_train, y_pred)}\")\n",
-    "print(f\"RMSE = {np.sqrt(mean_squared_error(y_train, y_pred))}\")\n",
-    "print(f\"R2 = {r2_score(y_train, y_pred)}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ede1cc3f",
-   "metadata": {},
-   "source": [
-    "#### Regressione lineare multipla con sklearn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "6da87638",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "La **Regressione Lineare Multipla** estende la regressione lineare semplice consentendo l'uso di due o pi\u00f9 variabili indipendenti ($x_1, x_2, \\dots, x_n$) per predire la variabile target $y$.\n",
+        "\n",
+        "### Equazione del Modello:\n",
+        "$$y = \\beta_0 + \\beta_1 x_1 + \\beta_2 x_2 + \\dots + \\beta_n x_n + \\epsilon$$\n",
+        "\n",
+        "Geometricamente, anzich\u00e9 descrivere una retta in uno spazio bidimensionale, questo modello definisce un **iperpiano** nello spazio multidimensionale delle feature.\n"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[16.15808489 30.59457282 12.23260468 10.46227224 23.91625296 17.43716373\n",
-      " 19.39762951 24.80141918]\n"
-     ]
-    }
-   ],
-   "source": [
-    "X_train = np.array([[80, 1995], [150, 1995], [30, 2008], [50, 1996], \n",
-    "                    [120, 1994], [60, 2006], [110, 1989], [110, 2000]])\n",
-    "y_train = np.array([16, 30, 12, 10, 24, 18, 20, 25])\n",
-    "\n",
-    "lr = LinearRegression()\n",
-    "lr.fit(X_train, y_train)\n",
-    "y_pred = lr.predict(X_train)\n",
-    "print(y_pred)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "091a2c6f",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "ed08e123",
+      "metadata": {},
+      "source": [
+        "#### Regressione lineare semplice con sklearn"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "Scikit-learn semplifica l'addestramento tramite la classe `LinearRegression` all'interno del modulo `sklearn.linear_model`.\n",
+        "Questa classe risolve l'equazione OLS calcolando la soluzione in forma chiusa (equazioni normali).\n",
+        "\n",
+        "In questo passaggio, addestriamo un modello semplice usando solo la feature **Dimensione**. Otteniamo i medesimi risultati visti in precedenza:\n",
+        "- $\\text{MAE} \\approx 2.070$\n",
+        "- $R^2 \\approx 0.870$\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "1a42039c",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "from sklearn.linear_model import LinearRegression"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MAE = 0.361883658669754\n",
-      "MSE = 0.17154887843424987\n",
-      "RMSE = 0.414184594636558\n",
-      "R2 = 0.9957362608855177\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "1b64b50a",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[18.00870511 28.9390642  10.20130577 13.32426551 24.25462459 14.88574538\n",
+            " 22.69314472 22.69314472]\n"
+          ]
+        }
+      ],
+      "source": [
+        "X_train = np.array([[80], [150], [30], [50], [120], [60], [110], [110]])\n",
+        "y_train = np.array([16, 30, 12, 10, 24, 18, 20, 25])\n",
+        "\n",
+        "lr = LinearRegression()\n",
+        "lr.fit(X_train, y_train)\n",
+        "y_pred = lr.predict(X_train)\n",
+        "print(y_pred)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "916a9eba",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "MAE = 2.0701849836779105\n",
+            "MSE = 5.223068552774754\n",
+            "RMSE = 2.2854033676300456\n",
+            "R2 = 0.8701839272320061\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
+        "\n",
+        "print(f\"MAE = {mean_absolute_error(y_train, y_pred)}\")\n",
+        "print(f\"MSE = {mean_squared_error(y_train, y_pred)}\")\n",
+        "print(f\"RMSE = {np.sqrt(mean_squared_error(y_train, y_pred))}\")\n",
+        "print(f\"R2 = {r2_score(y_train, y_pred)}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ede1cc3f",
+      "metadata": {},
+      "source": [
+        "#### Regressione lineare multipla con sklearn"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "Quando aggiungiamo la seconda feature **Anno di costruzione**, il modello diventa a tutti gli effetti una regressione lineare multipla.\n",
+        "\n",
+        "### Interpretazione dei Risultati:\n",
+        "- **Miglioramento delle prestazioni**: Le metriche cambiano drasticamente. L'errore scende a $\\text{MAE} \\approx 0.362$ e il coefficiente di determinazione sale a $R^2 \\approx 0.996$ (il 99.6% della variabilit\u00e0 \u00e8 ora spiegato).\n",
+        "- **Interpretazione dei Coefficienti**: Ciascun coefficiente associato a una feature indica il cambiamento atteso nella variabile target per ogni aumento unitario di quella feature, *mantenendo costanti tutte le altre variabili* del modello.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "6da87638",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[16.15808489 30.59457282 12.23260468 10.46227224 23.91625296 17.43716373\n",
+            " 19.39762951 24.80141918]\n"
+          ]
+        }
+      ],
+      "source": [
+        "X_train = np.array([[80, 1995], [150, 1995], [30, 2008], [50, 1996], \n",
+        "                    [120, 1994], [60, 2006], [110, 1989], [110, 2000]])\n",
+        "y_train = np.array([16, 30, 12, 10, 24, 18, 20, 25])\n",
+        "\n",
+        "lr = LinearRegression()\n",
+        "lr.fit(X_train, y_train)\n",
+        "y_pred = lr.predict(X_train)\n",
+        "print(y_pred)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "091a2c6f",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "MAE = 0.361883658669754\n",
+            "MSE = 0.17154887843424987\n",
+            "RMSE = 0.414184594636558\n",
+            "R2 = 0.9957362608855177\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(f\"MAE = {mean_absolute_error(y_train, y_pred)}\")\n",
+        "print(f\"MSE = {mean_squared_error(y_train, y_pred)}\")\n",
+        "print(f\"RMSE = {np.sqrt(mean_squared_error(y_train, y_pred))}\")\n",
+        "print(f\"R2 = {r2_score(y_train, y_pred)}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "214d3089",
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "9bbbcc9f",
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "c6a0db8a",
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3.7.4 64-bit ('base': conda)",
+      "language": "python",
+      "name": "python37464bitbaseconda78b00ce9b4da4d77b0796cc32fd5efe8"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.8"
     }
-   ],
-   "source": [
-    "print(f\"MAE = {mean_absolute_error(y_train, y_pred)}\")\n",
-    "print(f\"MSE = {mean_squared_error(y_train, y_pred)}\")\n",
-    "print(f\"RMSE = {np.sqrt(mean_squared_error(y_train, y_pred))}\")\n",
-    "print(f\"R2 = {r2_score(y_train, y_pred)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "214d3089",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9bbbcc9f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c6a0db8a",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.7.4 64-bit ('base': conda)",
-   "language": "python",
-   "name": "python37464bitbaseconda78b00ce9b4da4d77b0796cc32fd5efe8"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/3 - La Regressione Lineare/regressione_lineare_semplice.ipynb b/3 - La Regressione Lineare/regressione_lineare_semplice.ipynb
index a2c26ab..699ee36 100644
--- a/3 - La Regressione Lineare/regressione_lineare_semplice.ipynb	
+++ b/3 - La Regressione Lineare/regressione_lineare_semplice.ipynb	
@@ -1,212 +1,268 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "acbd7874",
-   "metadata": {},
-   "source": [
-    "## La Regressione Lineare Semplice"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "bd75c363",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4def9ae4",
-   "metadata": {},
-   "source": [
-    "#### Creiamo una classe per la regressione lineare semplice"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0319732f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class LinearRegression:\n",
-    "    \n",
-    "    \n",
-    "    coef_ = None\n",
-    "    intercept_ = None\n",
-    "    \n",
-    "    \n",
-    "    def fit(self, x, y):\n",
-    "        \n",
-    "        x_sum = x.sum()\n",
-    "        y_sum = y.sum()\n",
-    "        xy_sum = (x*y).sum()\n",
-    "        x2_sum = (x*x).sum()\n",
-    "        n = y.shape[0]\n",
-    "        \n",
-    "        self.coef_ = (n*(xy_sum)-x_sum*y_sum)/(n*x2_sum-x_sum*x_sum)\n",
-    "        self.intercept_ = (y_sum-self.coef_*x_sum)/n\n",
-    "    \n",
-    "    \n",
-    "    def predict(self, x):\n",
-    "        return self.coef_*x+self.intercept_"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f75542d6",
-   "metadata": {},
-   "source": [
-    "#### Definiamo delle metriche"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "a759d804",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def _rss(y_true, y_pred):\n",
-    "    return np.power(y_true-y_pred, 2).sum()\n",
-    "\n",
-    "def _sst(y_true, y_pred):\n",
-    "    return np.power(y_true-y_pred.mean(), 2).sum()\n",
-    "\n",
-    "def mean_absolute_error(y_true, y_pred):\n",
-    "    return np.abs(y_true-y_pred).sum()/y_true.shape[0]\n",
-    "\n",
-    "def mean_squared_error(y_true, y_pred):\n",
-    "    return np.power(y_true-y_pred, 2).sum()/y_true.shape[0]\n",
-    "\n",
-    "def root_mean_squared_error(y_true, y_pred):\n",
-    "    return np.sqrt(mean_squared_error(y_true, y_pred))\n",
-    "\n",
-    "def r2_score(y_true, y_pred):\n",
-    "    return 1-_rss(y_true, y_pred)/_sst(y_true, y_pred)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c1cbb93c",
-   "metadata": {},
-   "source": [
-    "#### Addestriamo e testiamo un modello"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "4c63e753",
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[18.00870511 28.9390642  10.20130577 13.32426551 24.25462459 14.88574538\n",
-      "  22.69314472 22.69314472]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "x_train = np.array([[80, 150, 30, 50, 120, 60, 110, 110]])\n",
-    "y_train = np.array([16, 30, 12, 10, 24, 18, 20, 25])\n",
-    "\n",
-    "lr = LinearRegression()\n",
-    "lr.fit(x_train, y_train)\n",
-    "y_pred = lr.predict(x_train)\n",
-    "print(y_pred)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "906883cf",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "acbd7874",
+      "metadata": {},
+      "source": [
+        "## La Regressione Lineare Semplice"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MAE = 2.0701849836779105\n",
-      "MSE = 5.223068552774754\n",
-      "RMSE = 2.2854033676300456\n",
-      "R2 = 0.8701839272320061\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"MAE = {mean_absolute_error(y_train, y_pred)}\")\n",
-    "print(f\"MSE = {mean_squared_error(y_train, y_pred)}\")\n",
-    "print(f\"RMSE = {root_mean_squared_error(y_train, y_pred)}\")\n",
-    "print(f\"R2 = {r2_score(y_train, y_pred)}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "24d8905d",
-   "metadata": {},
-   "source": [
-    "#### Visualizziamo il modello"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "8713d53b",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "La **Regressione Lineare Semplice** \u00e8 un modello parametrico utilizzato per descrivere la relazione lineare tra una variabile indipendente (o predittore) $x$ e una variabile dipendente (o target) $y$.\n",
+        "\n",
+        "### Equazione del Modello:\n",
+        "$$y = \\beta_0 + \\beta_1 x + \\epsilon$$\n",
+        "\n",
+        "Dove:\n",
+        "- $\\beta_0$ \u00e8 l'**intercetta** (il valore stimato di $y$ quando $x=0$). rappresenta il punto in cui la retta di regressione interseca l'asse delle ordinate.\n",
+        "- $\\beta_1$ \u00e8 il **coefficiente angolare** o pendenza (indica la variazione stimata di $y$ per ogni aumento unitario di $x$).\n",
+        "- $\\epsilon$ rappresenta il **termine di errore** casuale (residuo), che cattura tutti i fattori e le variazioni non considerati dal modello lineare.\n"
+      ]
+    },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "bd75c363",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import numpy as np"
       ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4def9ae4",
+      "metadata": {},
+      "source": [
+        "#### Creiamo una classe per la regressione lineare semplice"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "La stima dei parametri $\\beta_0$ e $\\beta_1$ avviene minimizzando la somma dei quadrati dei residui tramite il metodo dei **Minimi Quadrati** (*Ordinary Least Squares - OLS*).\n",
+        "Le formule analitiche per calcolare i coefficienti ottimali sono:\n",
+        "$$\\beta_1 = \\frac{\\sum_{i=1}^n (x_i - \\bar{x})(y_i - \\bar{y})}{\\sum_{i=1}^n (x_i - \\bar{x})^2}$$\n",
+        "$$\\beta_0 = \\bar{y} - \\beta_1 \\bar{x}$$\n",
+        "\n",
+        "Dove $\\bar{x}$ e $\\bar{y}$ sono rispettivamente la media campionaria di $x$ e $y$. Questo metodo garantisce di trovare la retta ottimale che minimizza lo scostamento complessivo dai punti osservati.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0319732f",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "class LinearRegression:\n",
+        "    \n",
+        "    \n",
+        "    coef_ = None\n",
+        "    intercept_ = None\n",
+        "    \n",
+        "    \n",
+        "    def fit(self, x, y):\n",
+        "        \n",
+        "        x_sum = x.sum()\n",
+        "        y_sum = y.sum()\n",
+        "        xy_sum = (x*y).sum()\n",
+        "        x2_sum = (x*x).sum()\n",
+        "        n = y.shape[0]\n",
+        "        \n",
+        "        self.coef_ = (n*(xy_sum)-x_sum*y_sum)/(n*x2_sum-x_sum*x_sum)\n",
+        "        self.intercept_ = (y_sum-self.coef_*x_sum)/n\n",
+        "    \n",
+        "    \n",
+        "    def predict(self, x):\n",
+        "        return self.coef_*x+self.intercept_"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f75542d6",
+      "metadata": {},
+      "source": [
+        "#### Definiamo delle metriche"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "Per misurare la qualit\u00e0 e l'accuratezza del modello lineare, utilizziamo diverse metriche fondamentali:\n",
+        "1. **Residual Sum of Squares (RSS)**: Somma dei quadrati dei residui (l'errore totale che OLS minimizza).\n",
+        "   $$\\text{RSS} = \\sum_{i=1}^n (y_i - \\hat{y}_i)^2$$\n",
+        "2. **Total Sum of Squares (TSS)**: Somma totale dei quadrati delle deviazioni rispetto alla media, che descrive la varianza totale del target.\n",
+        "   $$\\text{TSS} = \\sum_{i=1}^n (y_i - \\bar{y})^2$$\n",
+        "3. **R\u00b2 (Coefficiente di Determinazione)**: Indica la proporzione di varianza della variabile dipendente spiegata dal modello.\n",
+        "   $$R^2 = 1 - \\frac{\\text{RSS}}{\\text{TSS}}$$\n",
+        "   *Nota*: In questo notebook, il modello OLS semplice ottiene un $R^2 \\approx 0.870$ (l'87.0% della variabilit\u00e0 del valore della casa \u00e8 spiegato dalla sola dimensione).\n",
+        "4. **Mean Absolute Error (MAE)**: Media delle differenze assolute tra i valori reali e predetti.\n",
+        "   $$\\text{MAE} = \\frac{1}{n} \\sum_{i=1}^n |y_i - \\hat{y}_i|$$\n",
+        "   *Nota*: Il modello ottiene un $\\text{MAE} \\approx 2.07$ mila euro.\n",
+        "5. **Mean Squared Error (MSE)**: Media delle differenze al quadrato. Penalizza gli errori pi\u00f9 grandi.\n",
+        "   $$\\text{MSE} = \\frac{1}{n} \\sum_{i=1}^n (y_i - \\hat{y}_i)^2$$\n",
+        "   *Nota*: Il modello ottiene un $\\text{MSE} \\approx 5.22$.\n",
+        "6. **RMSE (Root Mean Squared Error)**: La radice quadrata dell'MSE, che riporta l'errore sulla stessa scala fisica della variabile target.\n",
+        "   $$\\text{RMSE} = \\sqrt{\\text{MSE}}$$\n",
+        "   *Nota*: Il modello ottiene un $\\text{RMSE} \\approx 2.29$ mila euro.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "a759d804",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def _rss(y_true, y_pred):\n",
+        "    return np.power(y_true-y_pred, 2).sum()\n",
+        "\n",
+        "def _sst(y_true, y_pred):\n",
+        "    return np.power(y_true-y_pred.mean(), 2).sum()\n",
+        "\n",
+        "def mean_absolute_error(y_true, y_pred):\n",
+        "    return np.abs(y_true-y_pred).sum()/y_true.shape[0]\n",
+        "\n",
+        "def mean_squared_error(y_true, y_pred):\n",
+        "    return np.power(y_true-y_pred, 2).sum()/y_true.shape[0]\n",
+        "\n",
+        "def root_mean_squared_error(y_true, y_pred):\n",
+        "    return np.sqrt(mean_squared_error(y_true, y_pred))\n",
+        "\n",
+        "def r2_score(y_true, y_pred):\n",
+        "    return 1-_rss(y_true, y_pred)/_sst(y_true, y_pred)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c1cbb93c",
+      "metadata": {},
+      "source": [
+        "#### Addestriamo e testiamo un modello"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "4c63e753",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[[18.00870511 28.9390642  10.20130577 13.32426551 24.25462459 14.88574538\n",
+            "  22.69314472 22.69314472]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "x_train = np.array([[80, 150, 30, 50, 120, 60, 110, 110]])\n",
+        "y_train = np.array([16, 30, 12, 10, 24, 18, 20, 25])\n",
+        "\n",
+        "lr = LinearRegression()\n",
+        "lr.fit(x_train, y_train)\n",
+        "y_pred = lr.predict(x_train)\n",
+        "print(y_pred)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "906883cf",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "MAE = 2.0701849836779105\n",
+            "MSE = 5.223068552774754\n",
+            "RMSE = 2.2854033676300456\n",
+            "R2 = 0.8701839272320061\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(f\"MAE = {mean_absolute_error(y_train, y_pred)}\")\n",
+        "print(f\"MSE = {mean_squared_error(y_train, y_pred)}\")\n",
+        "print(f\"RMSE = {root_mean_squared_error(y_train, y_pred)}\")\n",
+        "print(f\"R2 = {r2_score(y_train, y_pred)}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "24d8905d",
+      "metadata": {},
+      "source": [
+        "#### Visualizziamo il modello"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "id": "8713d53b",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "plt.figure(figsize=(8, 6), dpi=80)\n",
+        "\n",
+        "x_line = np.arange(1, 150)\n",
+        "y_line = lr.predict(x_line)\n",
+        "\n",
+        "plt.grid()\n",
+        "plt.scatter(x_train, y_train, c=\"green\")\n",
+        "plt.plot(y_line, c=\"red\")\n",
+        "plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3.7.4 64-bit ('base': conda)",
+      "language": "python",
+      "name": "python37464bitbaseconda78b00ce9b4da4d77b0796cc32fd5efe8"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.8"
     }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "plt.figure(figsize=(8, 6), dpi=80)\n",
-    "\n",
-    "x_line = np.arange(1, 150)\n",
-    "y_line = lr.predict(x_line)\n",
-    "\n",
-    "plt.grid()\n",
-    "plt.scatter(x_train, y_train, c=\"green\")\n",
-    "plt.plot(y_line, c=\"red\")\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.7.4 64-bit ('base': conda)",
-   "language": "python",
-   "name": "python37464bitbaseconda78b00ce9b4da4d77b0796cc32fd5efe8"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/3 - La Regressione Lineare/regressione_polinomiale.ipynb b/3 - La Regressione Lineare/regressione_polinomiale.ipynb
index 0c777d6..90486cf 100644
--- a/3 - La Regressione Lineare/regressione_polinomiale.ipynb	
+++ b/3 - La Regressione Lineare/regressione_polinomiale.ipynb	
@@ -1,384 +1,441 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "feb94dad",
-   "metadata": {},
-   "source": [
-    "## La Regressione Polinomiale"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "d824af55",
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
+      "cell_type": "markdown",
+      "id": "feb94dad",
+      "metadata": {},
+      "source": [
+        "## La Regressione Polinomiale"
       ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 0 Axes>"
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "La **Regressione Polinomiale** viene impiegata quando la relazione tra le variabili indipendenti e il target non \u00e8 lineare.\n",
+        "Sebbene il modello includa potenze delle feature (es. $x^2$, $x^3$), esso viene ancora classificato come modello *lineare* perch\u00e9 la linearit\u00e0 \u00e8 riferita ai parametri $\\beta_i$ (i coefficienti), e non alla variabile indipendente.\n",
+        "\n",
+        "### Equazione di secondo grado:\n",
+        "$$y = \\beta_0 + \\beta_1 x + \\beta_2 x^2 + \\epsilon$$\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.figure(figsize=(8, 6), dpi=80)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1c7ab2bc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.set_printoptions(suppress=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "0ee6da71",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train = np.array([[80], [150], [30], [50], [120], [60], [110], [110]])\n",
-    "y_train = np.array([16, 30, 12, 10, 24, 18, 20, 25])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "41dd2bb4",
-   "metadata": {},
-   "source": [
-    "#### Creiamo le features polinomiali"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "1f4e9ac3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import PolynomialFeatures\n",
-    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "1a84b229",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "['1', 'x0', 'x0^2']"
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "d824af55",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 0 Axes>"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 0 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "plt.figure(figsize=(8, 6), dpi=80)"
       ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "poly = PolynomialFeatures(2)\n",
-    "#poly = PolynomialFeatures(2, include_bias=False) # se vogliamo omettere la colonna bias\n",
-    "poly.fit(X_train)\n",
-    "poly.get_feature_names()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "81d6a33d",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array([[    1.,    80.,  6400.],\n",
-       "       [    1.,   150., 22500.],\n",
-       "       [    1.,    30.,   900.],\n",
-       "       [    1.,    50.,  2500.],\n",
-       "       [    1.,   120., 14400.],\n",
-       "       [    1.,    60.,  3600.],\n",
-       "       [    1.,   110., 12100.],\n",
-       "       [    1.,   110., 12100.]])"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1c7ab2bc",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "np.set_printoptions(suppress=True)"
       ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train_poly = poly.transform(X_train)\n",
-    "X_train_poly"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "029e3f03",
-   "metadata": {},
-   "source": [
-    "#### Testiamo diverse regressioni polinomiali"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "3b4a67c5",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "0ee6da71",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_train = np.array([[80], [150], [30], [50], [120], [60], [110], [110]])\n",
+        "y_train = np.array([16, 30, 12, 10, 24, 18, 20, 25])"
       ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "41dd2bb4",
+      "metadata": {},
+      "source": [
+        "#### Creiamo le features polinomiali"
       ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "Utilizziamo la classe `PolynomialFeatures` di Scikit-learn per mappare lo spazio delle feature originale in uno spazio a dimensioni maggiori, introducendo le potenze e i termini di interazione.\n",
+        "Ad esempio, per un input bidimensionale $[x_0, x_1]$ e un grado polinomiale pari a 2, lo spazio generato comprender\u00e0: \n",
+        "$$[1, x_0, x_1, x_0^2, x_0 x_1, x_1^2]$$\n"
       ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "1f4e9ac3",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import PolynomialFeatures\n",
+        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score"
       ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "1a84b229",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['1', 'x0', 'x0^2']"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "poly = PolynomialFeatures(2)\n",
+        "#poly = PolynomialFeatures(2, include_bias=False) # se vogliamo omettere la colonna bias\n",
+        "poly.fit(X_train)\n",
+        "poly.get_feature_names()"
       ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "81d6a33d",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([[    1.,    80.,  6400.],\n",
+              "       [    1.,   150., 22500.],\n",
+              "       [    1.,    30.,   900.],\n",
+              "       [    1.,    50.,  2500.],\n",
+              "       [    1.,   120., 14400.],\n",
+              "       [    1.,    60.,  3600.],\n",
+              "       [    1.,   110., 12100.],\n",
+              "       [    1.,   110., 12100.]])"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "X_train_poly = poly.transform(X_train)\n",
+        "X_train_poly"
       ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "029e3f03",
+      "metadata": {},
+      "source": [
+        "#### Testiamo diverse regressioni polinomiali"
       ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "Aumentare il grado del polinomio rende il modello pi\u00f9 flessibile e in grado di adattarsi meglio ai dati di training. Tuttavia, questo comporta un grave rischio di **overfitting** (alta varianza).\n",
+        "\n",
+        "### Analisi dell'Instabilit\u00e0 Numerica:\n",
+        "Osserviamo che all'aumentare dei gradi del polinomio (da 2 fino a 100), le prestazioni sul training set invece di migliorare peggiorano vistosamente:\n",
+        "- **Grado 2**: $\\text{MSE} \\approx 4.75$, $R^2 \\approx 0.88$\n",
+        "- **Grado 3**: $\\text{MSE} \\approx 4.72$, $R^2 \\approx 0.88$\n",
+        "- **Grado 50**: $\\text{MSE} \\approx 24.11$, $R^2 \\approx 0.40$\n",
+        "- **Grado 100**: $\\text{MSE} \\approx 24.11$, $R^2 \\approx 0.40$\n",
+        "\n",
+        "> [!IMPORTANT]\n",
+        "> Senza uno scaling preliminare delle feature (es. standardizzazione), elevare valori elevati come 150 a potenze grandi come 50 o 100 causa un **overflow numerico** e rende la matrice dei dati quasi singolare. L'OLS fallisce nel calcolare correttamente l'inversa della matrice dei coefficienti, portando a predizioni degenerate e a un incremento drammatico dell'errore (MSE) anche sui dati di addestramento.\n"
       ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "degrees = [2, 3, 4, 5, 10, 20, 50, 100] \n",
-    "\n",
-    "for d in degrees:\n",
-    "    \n",
-    "    poly = PolynomialFeatures(d)\n",
-    "    X_train_poly = poly.fit_transform(X_train)\n",
-    "    \n",
-    "    lr = LinearRegression()\n",
-    "    lr.fit(X_train_poly, y_train)\n",
-    "    y_pred = lr.predict(X_train_poly)\n",
-    "\n",
-    "    mse = mean_squared_error(y_train, y_pred)\n",
-    "    r2 = r2_score(y_train, y_pred)\n",
-    "    \n",
-    "    X_line = np.arange(1, 150, 1).reshape(149, 1)\n",
-    "    X_line_poly = poly.transform(X_line)\n",
-    "    y_line = lr.predict(X_line_poly)\n",
-    "\n",
-    "    ax = plt.gca()\n",
-    "    ax.set_ylim([0, 30])\n",
-    "    ax.grid()\n",
-    "\n",
-    "    ax.scatter(X_train, y_train, c=\"green\")\n",
-    "    ax.plot(y_line, c=\"red\")\n",
-    "    \n",
-    "    ax.text(0, 28, f\"Degree: {d}\")\n",
-    "    ax.text(125, 5, f\"MSE: {mse:.2f}\")\n",
-    "    ax.text(125, 2, f\"R2: {r2:.2f}\")\n",
-    "\n",
-    "    plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5fb8978b",
-   "metadata": {},
-   "source": [
-    "#### Regressione polinomiale multipla"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "8313d9a3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train = np.array([[80, 1995], [150, 1995], [30, 2008], [50, 1996], \n",
-    "                    [120, 1994], [60, 2006], [110, 1989], [110, 2000]])\n",
-    "y_train = np.array([16, 30, 12, 10, 24, 18, 20, 25])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "3a799335",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['1', 'x0', 'x1', 'x0^2', 'x0 x1', 'x1^2']\n",
-      "[[      1.      80.    1995.    6400.  159600. 3980025.]\n",
-      " [      1.     150.    1995.   22500.  299250. 3980025.]\n",
-      " [      1.      30.    2008.     900.   60240. 4032064.]\n",
-      " [      1.      50.    1996.    2500.   99800. 3984016.]\n",
-      " [      1.     120.    1994.   14400.  239280. 3976036.]\n",
-      " [      1.      60.    2006.    3600.  120360. 4024036.]\n",
-      " [      1.     110.    1989.   12100.  218790. 3956121.]\n",
-      " [      1.     110.    2000.   12100.  220000. 4000000.]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "poly = PolynomialFeatures(2)\n",
-    "X_train_poly = poly.fit_transform(X_train)\n",
-    "print(poly.get_feature_names())\n",
-    "print(X_train_poly)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "916ad850",
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 60,
+      "id": "3b4a67c5",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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s3dmi6eJx5eWwZAlcein8x3/EOk2bpgKXmOsd37tF08XjXn8dvv4apk6NdZI2TwUuMRe2M9+JNyxYAElJcP75sU7S5qnAJeZqn/kOCP3MdxJ7+/bB22/DZZdB+/axTtPmaS8U8YTqM9/l5+dTPLU41nEkFEePwjXX/PteIk4FLiLh8cgj8Npr8KtfQXp6rNOcEFTgItI6paXw4IMwe3Zg6OTWW2Od6IShAhepramLfDv372VacEHwaC5vVVVQWRn+9R85Eti7pLQUdu+Gbdtg9Wp4443AkZdXXw1PPqlzn0SRPwr8jjs475lnmv+lSIx+sc47ehTatWvWspHO0pDzW7ruFq4/HMt/J9zrD7NRMX335mnyMwynU04JXHHnrrtgyJBovrPglwIfOZJPd++mV69ezX9NS7cCwrD87p076d27gX2XY5ClIZ/s2EFKSopn8jS0/I7iYlJTUz2T51jbi4vpUzufBz/Lj7dvp2+fPuFff7t2EBcHJ50Ep58OvXtD//71N1okavxR4N//Ph8lJNDL44flfpyfT28PZ9yen0+Kh/MBFOfnk+rhjDvy8+nj4XwAO/Pz6evxjBIe+tMpIuJTKnCRZtDpbsWL/DGEIhJDOt2teJW2wEWaoNPdilepwEWaoNPdilepwEWaoNPdilepwEWaoNPdilfpS0yRJlR/UVk95p0Sn0Lu6Fx9gSkx1+QWuJm9aGZ7zKyw1rR7zWy3ma0P3sZHNqZIbE1Ln0bxzGKykrIonlms8hZPaM4QyhzgwgamP+6cywze3gpvLBERaUqTBe6cWw3sj0IWERFpAXPNOLubmaUCbzjnBgWf3wtcDXwJrAN+7Jz7opHX5gA5AImJiVkLFy4MKWhZWRlxcXEhvTZavJ7R6/nA+xm9ng+UMRy8li87O7vAOTe03gznXJM3IBUorPU8EWhPYAs+F3ixOevJyspyoVq5cmXIr40Wr2f0ej7nvJ/R6/mcU8Zw8Fo+YJ1roFND2o3QOfeZc+6Ic+4o8BzwrVDWIyIioQupwM0sqdbTi4HCxpYVEZHIaHI/cDNbQOBCJKeY2S7gf4BRZpYJOKAY+FHkIoqISEOaLHDn3NQGJr8QgSwiItICOpReRMSnVOAiIj6lAhcR8SkVuIiIT6nARUR8SgUuIuJTKnAREZ9SgYuI+JQKXETEp1TgIiI+pQIXEfEpFbiIiE+pwEVEfEoF7kHzN84n9YlU2t3XjtQnUpm/cX6sI4mIBzV5OlmJrvkb55Pzeg7lleUA7CjdQc7rOQBMS58Wy2gi4jHaAveYWStm1ZR3tfLKcmatmBWjRCLiVSpwj9lZurNF00XkxKUC95je8b1bNF1ETlwqcI/JHZ1Ll45d6kzr0rELuaNzY5RIRLxKBe4x09KnkTcxj5T4FAwjJT6FvIl5+gJTROrRXigeNC19mgpbRJqkLXAREZ9SgYuI+JQKXETEp1TgIiI+pQIXEfEpFbiIiE81WeBm9qKZ7TGzwlrTEsxsuZkVBe+7RTameI3OmCgSe83ZAp8DXHjMtJ8BK5xzA4AVwedygqg+Y+KO0h04XM0ZE1XiItHVZIE751YD+4+ZPBmYG3w8F7govLHEy3TGRBFvMOdc0wuZpQJvOOcGBZ8fcM6dXGv+F865BodRzCwHyAFITEzMWrhwYUhBy8rKiIuLC+m10eL1jOHKV1BS0Oi8rKSsVq37RPkMI0kZW89r+bKzswucc0OPnR7xAq9t6NChbt26dS3JXSM/P59Ro0aF9Npo8XrGcOVLfSKVHaU76k1PiU+heGZxq9Z9onyGkaSMree1fGbWYIGHuhfKZ2aWFFxxErCnNeHEX3TGRBFvCLXAXwOuCj6+ClgSnjjiBzpjoog3NHk2QjNbAIwCTjGzXcD/AA8DL5nZtcBOYEokQ4r36IyJIrHXZIE756Y2Mmt0mLOIiEgL6EhMERGfUoGLiPiUClxExKdU4CIiPqUCFxHxKRW4iIhPqcBFRHxKBS4i4lMqcBERn1KBi4j4lApcRMSnVOAiIj6lAhcR8SkVuIiIT6nARUR8SgUuIuJTKnAREZ9SgYuI+JQKXETEp1TgIiI+pQIXEfEpFbiIiE+pwEVEfEoFLiKeZmZMnz695nlVVRU9evRgwoQJAHz22WdMmDCBwYMHM3DgQMaPHw9AcXExnTt3JjMzs+Y2b968477X6tWrGTJkCKNHj2bRokV15v30pz8lLS2Ns846ixkzZuCca3AdL730EgMHDiQtLY0rrriiZvrcuXMZMGAAAwYMYO7cuSF9FsfqEJa1iIhESNeuXSksLOTgwYN07tyZ5cuXc/rpp9fMv+eeexgzZgy33XYbABs2bKiZ169fP9avX9/s9+rduzdz5szhzjvvrDP9r3/9K++9917NukeOHMmqVasYNWpUneWKior45S9/yXvvvUe3bt3Ys2cPAPv37+e+++5j3bp1mBlZWVlMmjSJbt26teSjqEdb4CLieePGjePNN98EYMGCBUydOrVmXklJCcnJyTXPMzIyQn6f1NRUMjIyaNeubjWaGYcOHaKiooLDhw9TWVlJYmJivdc/99xz3HzzzTXF3LNnTwD+9Kc/MWbMGBISEujWrRtjxoxh6dKlIeespgIXEc+7/PLLWbhwIYcOHWLDhg2cc845NfNuvvlmrr32WrKzs8nNzeXTTz+tmffRRx/VGUL5y1/+AsB1113HunXrmv3+I0aMIDs7m6SkJJKSkhg7dixnnXVWveW2bt3K1q1bOffccxk+fHhNSe/evZtevXrVLJecnMzu3btb/Dkcq1VDKGZWDHwFHAGqnHNDW51IROQYGRkZFBcXs2DBgpox7mpjx47l448/ZunSpbz99tucffbZFBYWAo0PoTz//PMtev9t27axefNmdu3aBcCYMWNYvXo1559/fp3lqqqqKCoqIj8/n127dnHeeedRWFjY4Hi5mbUoQ0PCsQWe7ZzLVHmLSCRNmjSJO++8s87wSbWEhASuuOIKfv/73zNs2DBWr14d1vd+9dVXGT58OHFxccTFxTFu3DjWrFlTb7nk5GQmT55Mx44d6dOnD2eeeSZFRUUkJyfzySef1Cy3a9cuTjvttFbn0hCKiPjCNddcwz333EN6enqd6e+88w7l5eUAfPXVV3z00Uf07t07rO/du3dvVq1aRVVVFZWVlaxatarBIZSLLrqIlStXAvD555+zdetW+vbty9ixY1m2bBlffPEFX3zxBcuWLWPs2LGtztXaAnfAMjMrMLOcVqcREWlEcnJyzZ4mtRUUFDB06FAyMjIYMWIE1113HcOGDQPqj4E/+eSTQONj4GvXriU5OZlVq1bxox/9iLS0NAAuvfRS+vXrR3p6OoMHD2bw4MFMnDgRCOwF89prrwGB4Zzu3bszcOBAsrOzefTRR+nevTsJCQncfffdDBs2jGHDhnHPPfeQkJDQ6s/EGtuXsVkvNjvNOfepmfUElgO3OudWH7NMDpADkJiYmLVw4cKQ3qusrIy4uLiQs0aD1zN6PR94P6PX84EyhoPX8mVnZxc0OEztnAvLDbgXuPN4y2RlZblQrVy5MuTXRovXM3o9n3Pez+j1fM4pYzh4LR+wzjXQqSEPoZhZVzM7qfox8F9AYajrExGRlmnNboSJwKvBXWE6AP/nnGv9nukiItIsIRe4c+5jYHAYs4iISAtoN0IREZ9SgYuI+JQKXETEp1TgIiI+pQIXEfEpFbiIiE+pwEVEfEoFLiLiUypwERGfUoGLiKe1b9+ezMxMBg0axMSJEzlw4AAA69evZ8SIEaSlpZGRkcEf/vCHJtflnGPGjBn079+fjIwMPvjggwaXe/jhh+nTp0/NaWiPvarP2rVrad++fb0r10ebClxEPK1z586sX7+ewsJCEhIS+M1vfgNAly5dmDdvHps2bWLp0qXMnDmzptwb8/bbb1NUVERRURF5eXnceOONjS776KOPsn79etavX09mZmbN9CNHjnDXXXeF5YIMraUCFxHfGDFiRM3FgM844wwGDBgAwGmnnUbPnj3Zu3fvcV+/ZMkSrrzySsyM4cOHc+DAAUpKSlqU4amnnuKSSy6pueJ8LKnARcQXjhw5wooVK5g0aVK9ee+//z4VFRX069cPqHuVnNpacnX4WbNmkZGRwe23387hw4drXv/qq69yww03hONHajUVuIh42sGDB8nMzKR79+7s37+fMWPG1JlfUlLC9OnT+d3vfke7doFKu//++xssetfMq8Nff/31bNmyhbVr17J//34eeeQRAGbOnMkjjzxC+/btw/GjtZoKXEQ8rXoMfMeOHVRUVNSMgQN8+eWXfO973+PBBx9k+PDhTa6ruVeH7969O2bGN77xDX74wx/y/vvvA7Bu3Touv/xyUlNTWbRoETfddBOLFy9u/Q8ZIhW4iPhCfHw8Tz75JI899hiVlZVUVFRw8cUXc+WVVzJlypRmrWPSpEnMmzcP5xxr1qwhPj6epKSkesvt27cPCGyxL168mEGDBgGwfft2iouLKS4u5tJLL+Xpp5/moosuCtvP2FIqcBHxjbPPPpvBgwezcOFCXnrpJVavXs2cOXPq7e7X2Bj4+PHj6du3L/379+f666/n6aefrjPv008/BSA3N5f09HTS09P5/PPP+cUvfhGVn6+lWnNJNRGRiCsrK6vz/PXXX695/IMf/KDB19x///0NTjezOkMwtb311ls1j2fPns2oUaOOm2vOnDnHnR8N2gIXEfEpFbiIiE+pwEVEfEoFLiLiUypwERGfUoGLiPiUClxExKdU4CIiPqUCFxHxKRW4iIhPtarAzexCM/vQzLaZ2c/CFUpERJoWcoGbWXvgN8A4YCAw1cwGhiuYiIgcX2u2wL8FbHPOfeycqwAWApPDE0tERJrSmrMRng58Uuv5LuCcYxcysxwgJ/i0zMw+DPH9TgE+D/G10eL1jF7PB97P6PV8oIzh4LV8KQ1NbE2B178OEdS7XpFzLg/Ia8X7BN7MbJ1zbmhr1xNJXs/o9Xzg/YxezwfKGA5ez1etNUMou4BetZ4nA5+2Lo6IiDRXawp8LTDAzPqY2X8AlwP1L4EhIiIREfIQinOuysxuAf4EtAdedM5tCluy+lo9DBMFXs/o9Xzg/YxezwfKGA5ezweAOVdv2FpERHxAR2KKiPiUClxExKd8UeBeO2TfzHqZ2Uoz22xmm8zstuD0BDNbbmZFwftuMc7Z3sz+n5m94dF8J5vZIjPbEvwsR3gw4+3Bf+NCM1tgZp1indHMXjSzPWZWWGtao5nM7OfB350PzWxsjPI9Gvx33mBmr5rZybHK11jGWvPuNDNnZqfEMmNzeL7APXrIfhXwY+fcWcBw4OZgpp8BK5xzA4AVweexdBuwudZzr+X7FbDUOfdNYDCBrJ7JaGanAzOAoc65QQS+rL/cAxnnABceM63BTMH/Li8H0oKveTr4OxXtfMuBQc65DGAr8PMY5mssI2bWCxgD7Kw1LVYZm+T5AseDh+w750qccx8EH39FoHhOD+aaG1xsLnBRTAICZpYMfA94vtZkL+X7T+B84AUA51yFc+4AHsoY1AHobGYdgC4EjnWIaUbn3Gpg/zGTG8s0GVjonDvsnNsObCPwOxXVfM65Zc65quDTNQSOG4lJvsYyBj0O/JS6ByXGJGNz+KHAGzpk//QYZanHzFKBs4G/A4nOuRIIlDzQM4bRniDwH+LRWtO8lK8vsBf4XXCY53kz6+qljM653cBjBLbGSoBS59wyL2WspbFMXvz9uQZ4O/jYM/nMbBKw2zn3j2NmeSbjsfxQ4M06ZD8WzCwO+CMw0zn3ZazzVDOzCcAe51xBrLMcRwdgCPCMc+5s4GtiP6RTR3AceTLQBzgN6GpmP4htqhbz1O+Pmc0iMAQ5v3pSA4tFPZ+ZdQFmAfc0NLuBaZ7oID8UuCcP2TezjgTKe75z7pXg5M/MLCk4PwnYE6N45wKTzKyYwJDTBWb2vx7KB4F/113Oub8Hny8iUOheyvhdYLtzbq9zrhJ4Bfi2xzJWayyTZ35/zOwqYAIwzf37ABSv5OtH4A/1P4K/N8nAB2Z2Kt7JWI8fCtxzh+ybmREYu93snJtda9ZrwFXBx1cBS6KdDcA593PnXLJzLpXA5/WOc+4HXskH4Jz7F/CJmZ0ZnDQa+Cceykhg6GS4mXUJ/puPJvB9h5cyVmss02vA5Wb2DTPrAwwA3o92ODO7ELgLmOScK681yxP5nHMbnXM9nXOpwd+bXcCQ4H+nnsjYIOec52/AeALfXH8EzPJAnpEE/hdqA7A+eBsPdCewB0BR8D7BA1lHAW8EH3sqH5AJrAt+jouBbh7MeB+wBSgEfg98I9YZgQUExuQrCRTNtcfLRGBo4CPgQ2BcjPJtIzCOXP378mys8jWW8Zj5xcApsczYnJsOpRcR8Sk/DKGIiEgDVOAiIj6lAhcR8SkVuIiIT6nARUR8SgUuIuJTKnAREZ/6/1rIzwARhltCAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "degrees = [2, 3, 4, 5, 10, 20, 50, 100] \n",
+        "\n",
+        "for d in degrees:\n",
+        "    \n",
+        "    poly = PolynomialFeatures(d)\n",
+        "    X_train_poly = poly.fit_transform(X_train)\n",
+        "    \n",
+        "    lr = LinearRegression()\n",
+        "    lr.fit(X_train_poly, y_train)\n",
+        "    y_pred = lr.predict(X_train_poly)\n",
+        "\n",
+        "    mse = mean_squared_error(y_train, y_pred)\n",
+        "    r2 = r2_score(y_train, y_pred)\n",
+        "    \n",
+        "    X_line = np.arange(1, 150, 1).reshape(149, 1)\n",
+        "    X_line_poly = poly.transform(X_line)\n",
+        "    y_line = lr.predict(X_line_poly)\n",
+        "\n",
+        "    ax = plt.gca()\n",
+        "    ax.set_ylim([0, 30])\n",
+        "    ax.grid()\n",
+        "\n",
+        "    ax.scatter(X_train, y_train, c=\"green\")\n",
+        "    ax.plot(y_line, c=\"red\")\n",
+        "    \n",
+        "    ax.text(0, 28, f\"Degree: {d}\")\n",
+        "    ax.text(125, 5, f\"MSE: {mse:.2f}\")\n",
+        "    ax.text(125, 2, f\"R2: {r2:.2f}\")\n",
+        "\n",
+        "    plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5fb8978b",
+      "metadata": {},
+      "source": [
+        "#### Regressione polinomiale multipla"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MSE = 0.17154887843424987\n",
-      "R2 = 0.9957362608855177\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "Applichiamo la regressione polinomiale combinando pi\u00f9 variabili d'ingresso. \n",
+        "Combinando le feature **Dimensione** e **Anno di costruzione** con un grado pari a 2, il modello riesce a raggiungere un accoppiamento perfetto sul training set:\n",
+        "- $\\text{MSE} \\approx 0.172$\n",
+        "- $R^2 \\approx 0.996$\n",
+        "\n",
+        "Questo dimostra l'elevata capacit\u00e0 rappresentativa dei modelli polinomiali multidimensionali, ma sottolinea l'importanza di monitorare la complessit\u00e0 per evitare la memorizzazione pura del rumore.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 61,
+      "id": "8313d9a3",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_train = np.array([[80, 1995], [150, 1995], [30, 2008], [50, 1996], \n",
+        "                    [120, 1994], [60, 2006], [110, 1989], [110, 2000]])\n",
+        "y_train = np.array([16, 30, 12, 10, 24, 18, 20, 25])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 65,
+      "id": "3a799335",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['1', 'x0', 'x1', 'x0^2', 'x0 x1', 'x1^2']\n",
+            "[[      1.      80.    1995.    6400.  159600. 3980025.]\n",
+            " [      1.     150.    1995.   22500.  299250. 3980025.]\n",
+            " [      1.      30.    2008.     900.   60240. 4032064.]\n",
+            " [      1.      50.    1996.    2500.   99800. 3984016.]\n",
+            " [      1.     120.    1994.   14400.  239280. 3976036.]\n",
+            " [      1.      60.    2006.    3600.  120360. 4024036.]\n",
+            " [      1.     110.    1989.   12100.  218790. 3956121.]\n",
+            " [      1.     110.    2000.   12100.  220000. 4000000.]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "poly = PolynomialFeatures(2)\n",
+        "X_train_poly = poly.fit_transform(X_train)\n",
+        "print(poly.get_feature_names())\n",
+        "print(X_train_poly)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 66,
+      "id": "916ad850",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "MSE = 0.17154887843424987\n",
+            "R2 = 0.9957362608855177\n"
+          ]
+        }
+      ],
+      "source": [
+        "lr = LinearRegression()\n",
+        "lr.fit(X_train, y_train)\n",
+        "y_pred = lr.predict(X_train)\n",
+        "print(f\"MSE = {mean_squared_error(y_train, y_pred)}\")\n",
+        "print(f\"R2 = {r2_score(y_train, y_pred)}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b4edfdbc",
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3.7.4 64-bit ('base': conda)",
+      "language": "python",
+      "name": "python37464bitbaseconda78b00ce9b4da4d77b0796cc32fd5efe8"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.8"
     }
-   ],
-   "source": [
-    "lr = LinearRegression()\n",
-    "lr.fit(X_train, y_train)\n",
-    "y_pred = lr.predict(X_train)\n",
-    "print(f\"MSE = {mean_squared_error(y_train, y_pred)}\")\n",
-    "print(f\"R2 = {r2_score(y_train, y_pred)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b4edfdbc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.7.4 64-bit ('base': conda)",
-   "language": "python",
-   "name": "python37464bitbaseconda78b00ce9b4da4d77b0796cc32fd5efe8"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/4 - La Classificazione/binary_classification.ipynb b/4 - La Classificazione/binary_classification.ipynb
index e628a74..9ba1b19 100644
--- a/4 - La Classificazione/binary_classification.ipynb	
+++ b/4 - La Classificazione/binary_classification.ipynb	
@@ -37,6 +37,27 @@
         "id": "Pyz1mX-elt8R"
       }
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "La **Classificazione Binaria** ha l'obiettivo di predire l'appartenenza di un'istanza a una di due classi possibili (es. $0$ o $1$, Vero o Falso, Negativo o Positivo).\n",
+        "\n",
+        "### Perch\u00e9 non la Regressione Lineare?\n",
+        "Usare la regressione lineare classica per la classificazione presenta forti limiti: l'output non \u00e8 vincolato e pu\u00f2 assumere valori esterni a $[0, 1]$, rendendo difficile l'interpretazione probabilistica. Inoltre, la presenza di outlier pu\u00f2 spostare la retta di decisione in modo errato.\n",
+        "\n",
+        "### La Regressione Logistica e la funzione Sigmoide:\n",
+        "La **Regressione Logistica** risolve questo problema mappando la combinazione lineare delle feature tramite la funzione **Sigmoide**:\n",
+        "$$\\sigma(z) = \\frac{1}{1 + e^{-z}}$$\n",
+        "\n",
+        "Dove la combinazione lineare \u00e8:\n",
+        "$$z = \\beta_0 + \\beta_1 x_1 + \\dots + \\beta_n x_n$$\n",
+        "\n",
+        "L'output di $\\sigma(z)$ \u00e8 compreso strettamente tra $0$ e $1$, ed \u00e8 interpretabile come la **probabilit\u00e0** $P(y=1 \\mid X)$ che l'istanza appartenga alla classe positiva.\n"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -271,6 +292,35 @@
         "id": "6RSGwlZ8cYNs"
       }
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "Per misurare le performance di un classificatore binario, utilizziamo diverse metriche specifiche:\n",
+        "1. **Log Loss (Cross-Entropy)**: Valuta la bont\u00e0 del classificatore basandosi sulla confidenza delle predizioni probabilistiche. Penalizza fortemente le predizioni errate fatte con alta confidenza.\n",
+        "   $$\\text{Log Loss} = -\\frac{1}{n} \\sum_{i=1}^n \\left[ y_i \\log(\\hat{y}_i) + (1 - y_i) \\log(1 - \\hat{y}_i) \\right]$$\n",
+        "   *Nota*: In questo notebook il modello ottiene $\\text{Train Loss} \\approx 0.312$ e $\\text{Test Loss} \\approx 0.207$.\n",
+        "2. **Matrice di Confusione**: Tabella a due dimensioni che confronta i valori reali con quelli predetti dal modello:\n",
+        "   - **Vero Positivo (TP)**: Positivi predetti correttamente.\n",
+        "   - **Vero Negativo (TN)**: Negativi predetti correttamente.\n",
+        "   - **Falso Positivo (FP)** (Errore di Tipo I): Negativi predetti come positivi.\n",
+        "   - **Falso Negativo (FN)** (Errore di Tipo II): Positivi predetti come negativi.\n",
+        "3. **Accuracy (Accuratezza)**: Percentuale totale di predizioni corrette.\n",
+        "   *Nota*: $\\text{Train Accuracy} \\approx 0.914$, $\\text{Test Accuracy} \\approx 0.933$.\n",
+        "4. **Precision (Precisione)**: Proporzione di positivi predetti correttamente rispetto al totale dei positivi previsti dal modello.\n",
+        "   $$\\text{Precision} = \\frac{\\text{TP}}{\\text{TP} + \\text{FP}}$$\n",
+        "   *Nota*: $\\text{Train Precision} \\approx 0.941$, $\\text{Test Precision} = 1.00$.\n",
+        "5. **Recall (Sensibilit\u00e0)**: Proporzione di positivi reali identificati correttamente.\n",
+        "   $$\\text{Recall} = \\frac{\\text{TP}}{\\text{TP} + \\text{FN}}$$\n",
+        "   *Nota*: $\\text{Train Recall} \\approx 0.889$, $\\text{Test Recall} \\approx 0.857$.\n",
+        "6. **F1-Score**: Media armonica di Precision e Recall, ottimale per valutare modelli in presenza di dataset sbilanciati.\n",
+        "   $$F_1 = 2 \\times \\frac{\\text{Precision} \\times \\text{Recall}}{\\text{Precision} + \\text{Recall}}$$\n",
+        "   *Nota*: $\\text{Train F1} \\approx 0.914$, $\\text{Test F1} \\approx 0.923$.\n",
+        "7. **Curva ROC e AUC**: La curva ROC (Receiver Operating Characteristic) mostra il trade-off tra True Positive Rate (Recall) e False Positive Rate al variare della soglia di classificazione. L'Area Under the Curve (AUC) riassume la capacit\u00e0 del modello di separare le due classi.\n"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
diff --git a/4 - La Classificazione/multiclass_classification.ipynb b/4 - La Classificazione/multiclass_classification.ipynb
index 9e15dbc..6aa8d07 100644
--- a/4 - La Classificazione/multiclass_classification.ipynb	
+++ b/4 - La Classificazione/multiclass_classification.ipynb	
@@ -36,6 +36,19 @@
         "id": "GmRDPLkL45cq"
       }
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "La **Classificazione Multiclasse** si applica a problemi in cui la variabile target presenta $K > 2$ classi discrete (ad esempio, classificare le immagini di numeri da $0$ a $9$).\n",
+        "\n",
+        "Per estendere algoritmi intrinsecamente binari (come la regressione logistica classica) a compiti multiclasse, si usano principalmente due strategie:\n",
+        "- **One-vs-Rest (OvR / One-vs-All)**: Addestra $K$ classificatori binari indipendenti. Il classificatore $c$-esimo viene addestrato a distinguere la classe $c$ da tutte le altre $K-1$ classi raggruppate insieme.\n",
+        "- **One-vs-One (OvO)**: Addestra $K(K-1)/2$ classificatori binari per ogni coppia possibile di classi. Durante l'inferenza, la classe finale viene determinata tramite uno schema di voto a maggioranza.\n"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": 9,
@@ -83,6 +96,20 @@
         "id": "Ix72pcE_5coB"
       }
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "In Scikit-learn, la regressione logistica applica l'approccio One-vs-Rest impostando il parametro `multi_class='ovr'`.\n",
+        "\n",
+        "### Performance dell'approccio OvR:\n",
+        "- **Accuratezza**: Otteniamo una $\\text{Train Accuracy} \\approx 0.67$ e una $\\text{Test Accuracy} \\approx 0.77$.\n",
+        "- **ROC AUC (OVO)**: Otteniamo un $\\text{Train AUC} \\approx 0.853$ e un $\\text{Test AUC} \\approx 0.841$.\n",
+        "L'approccio OvR \u00e8 computazionalmente efficiente in quanto richiede solo $K$ modelli, ma pu\u00f2 soffrire se le distribuzioni delle classi sono sbilanciate nei dataset binari intermedi.\n"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -233,6 +260,23 @@
         "id": "aHoGf5iI6TTH"
       }
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "L'approccio **Multinomial** (noto anche come *Softmax Regression*) rappresenta la naturale generalizzazione della regressione logistica al caso multiclasse. Invece di addestrare modelli binari indipendenti, calcola contemporaneamente le probabilit\u00e0 per tutte le classi tramite la funzione **Softmax**:\n",
+        "$$P(y = c \\mid X) = \\frac{e^{z_c}}{\\sum_{j=1}^K e^{z_j}}$$\n",
+        "\n",
+        "In questo modo, la somma delle probabilit\u00e0 predette per le $K$ classi su ciascuna istanza \u00e8 esattamente pari a $1$.\n",
+        "\n",
+        "### Performance dell'approccio Multinomial:\n",
+        "- **Accuratezza**: Otteniamo una $\\text{Train Accuracy} \\approx 0.69$ e una $\\text{Test Accuracy} \\approx 0.70$.\n",
+        "- **ROC AUC (OVO)**: Otteniamo un $\\text{Train AUC} \\approx 0.851$ e un $\\text{Test AUC} \\approx 0.844$.\n",
+        "L'approccio multinomiale modella in modo ottimale la mutua esclusivit\u00e0 delle classi, fornendo spesso probabilit\u00e0 calibrate migliori rispetto a OvR.\n"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
diff --git a/4 - Overfitting e Tecniche di Regolarizzazione/esercizi/overfitting_regularizzazion_exercise.ipynb b/4 - Overfitting e Tecniche di Regolarizzazione/esercizi/overfitting_regularizzazion_exercise.ipynb
index 974a640..65d5b52 100644
--- a/4 - Overfitting e Tecniche di Regolarizzazione/esercizi/overfitting_regularizzazion_exercise.ipynb	
+++ b/4 - Overfitting e Tecniche di Regolarizzazione/esercizi/overfitting_regularizzazion_exercise.ipynb	
@@ -1,27 +1,10 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "overfitting_regularizzazion_exercise.ipynb",
-      "provenance": [],
-      "authorship_tag": "ABX9TyNBmdFV2mD2rZqBw2XlF9UX",
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/ProfAI/machine-learning-fondamenti/blob/main/4%20-%20Overfitting%20e%20Tecniche%20di%20Regolarizzazione/esercizi/overfitting_regularizzazion_exercise.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -29,6 +12,9 @@
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "SmyDxqePcajL"
+      },
       "source": [
         "# Regolarizzazione: Esercitazione\n",
         "Per questa esercitazione dovrai verificare la presenza di overfitting e regolarizzare un modello di regressione polinomiale di secondo grado. Il modello utilizzerà il Boston Housing Dataset, che puoi scaricare [da qui](https://raw.githubusercontent.com/ProfAI/machine-learning-fondamenti/main/datasets/housing.csv), per stimare il valore di abitazioni.\n",
@@ -39,10 +25,7 @@
         "\n",
         "Inoltre, la differenza tra R2 sul set di addestramento e sul set di test deve essere inferiore del 15% (ad esempio, per un R2 sul set di addestramento di 1, l'R2 sul set di test non deve essere inferiore a 0.85).\n",
         "\n"
-      ],
-      "metadata": {
-        "id": "SmyDxqePcajL"
-      }
+      ]
     },
     {
       "cell_type": "code",
@@ -63,22 +46,18 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "RANDOM_SEED = 0"
-      ],
+      "execution_count": null,
       "metadata": {
         "id": "ypphtshFVV7S"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "RANDOM_SEED = 0"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "BASE_URL = \"https://raw.githubusercontent.com/ProfAI/machine-learning-fondamenti/main/datasets/\"\n",
-        "df = pd.read_csv(BASE_URL+\"housing.csv\", index_col=0)\n",
-        "df.head()"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -87,10 +66,8 @@
         "id": "YHvkcuBVSpRN",
         "outputId": "0a659399-9a5d-430a-95cc-ff8f8ef3e786"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/html": [
               "\n",
@@ -307,21 +284,20 @@
               "[5 rows x 14 columns]"
             ]
           },
+          "execution_count": 153,
           "metadata": {},
-          "execution_count": 153
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "BASE_URL = \"https://raw.githubusercontent.com/ProfAI/machine-learning-fondamenti/main/datasets/\"\n",
+        "df = pd.read_csv(BASE_URL+\"housing.csv\", index_col=0)\n",
+        "df.head()"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "X = df.drop(\"PRICE\", axis=1).values\n",
-        "y = df[\"PRICE\"].values\n",
-        "\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=RANDOM_SEED)\n",
-        "print(X_train.shape, y_train.shape)\n",
-        "print(X_test.shape, y_test.shape)"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -329,46 +305,58 @@
         "id": "7ffLehi1Spcr",
         "outputId": "f2f31fdd-6589-43de-fd1d-be1e9f65db35"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "(379, 13) (379,)\n",
             "(127, 13) (127,)\n"
           ]
         }
+      ],
+      "source": [
+        "X = df.drop(\"PRICE\", axis=1).values\n",
+        "y = df[\"PRICE\"].values\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=RANDOM_SEED)\n",
+        "print(X_train.shape, y_train.shape)\n",
+        "print(X_test.shape, y_test.shape)"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zw2QxvY3YKZU"
+      },
+      "outputs": [],
       "source": [
         "poly = PolynomialFeatures(degree=2)\n",
         "X_train = poly.fit_transform(X_train)\n",
         "X_test = poly.transform(X_test)"
-      ],
-      "metadata": {
-        "id": "zw2QxvY3YKZU"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9oGjFpgjVO-p"
+      },
+      "outputs": [],
       "source": [
         "ss = StandardScaler()\n",
         "X_train = ss.fit_transform(X_train)\n",
         "X_test = ss.transform(X_test)"
-      ],
-      "metadata": {
-        "id": "9oGjFpgjVO-p"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xqKf1aLIWUI2"
+      },
+      "outputs": [],
       "source": [
         "def evaluate_model(model, dataset):\n",
         "\n",
@@ -378,25 +366,11 @@
         "\n",
         "  print(f\"RMSE: {np.sqrt(mean_squared_error(y, y_pred)):.3f}\")\n",
         "  print(f\"R2: {r2_score(y, y_pred):.3f}\")"
-      ],
-      "metadata": {
-        "id": "xqKf1aLIWUI2"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "lr = LinearRegression()\n",
-        "lr.fit(X_train, y_train)\n",
-        "\n",
-        "print(\"Train set\")\n",
-        "evaluate_model(lr, (X_train, y_train))\n",
-        "\n",
-        "print(\"Test set\")\n",
-        "evaluate_model(lr, (X_test, y_test))"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -404,11 +378,10 @@
         "id": "c3n7_qHlWOUQ",
         "outputId": "58b5fe18-986c-422b-a1e5-6b6887fcd709"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Train set\n",
             "RMSE: 2.016\n",
@@ -418,20 +391,21 @@
             "R2: 0.606\n"
           ]
         }
-      ]
-    },
-    {
-      "cell_type": "code",
+      ],
       "source": [
-        "model = Ridge(alpha=10.)\n",
-        "model.fit(X_train, y_train)\n",
+        "lr = LinearRegression()\n",
+        "lr.fit(X_train, y_train)\n",
         "\n",
         "print(\"Train set\")\n",
-        "evaluate_model(model, (X_train, y_train))\n",
+        "evaluate_model(lr, (X_train, y_train))\n",
         "\n",
         "print(\"Test set\")\n",
-        "evaluate_model(model, (X_test, y_test))"
-      ],
+        "evaluate_model(lr, (X_test, y_test))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -439,11 +413,10 @@
         "id": "OqZn7SJfWhPu",
         "outputId": "2416fe93-0026-458b-af4e-6a209da8167e"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Train set\n",
             "RMSE: 2.935\n",
@@ -453,12 +426,9 @@
             "R2: 0.766\n"
           ]
         }
-      ]
-    },
-    {
-      "cell_type": "code",
+      ],
       "source": [
-        "model = Lasso(alpha=.1)\n",
+        "model = Ridge(alpha=10.)\n",
         "model.fit(X_train, y_train)\n",
         "\n",
         "print(\"Train set\")\n",
@@ -466,7 +436,11 @@
         "\n",
         "print(\"Test set\")\n",
         "evaluate_model(model, (X_test, y_test))"
-      ],
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -474,11 +448,10 @@
         "id": "ZWNv6npjWxeC",
         "outputId": "bad72515-be86-414d-dfb6-7c5a2a2ec00b"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Train set\n",
             "RMSE: 3.440\n",
@@ -488,10 +461,41 @@
             "R2: 0.737\n"
           ]
         }
+      ],
+      "source": [
+        "model = Lasso(alpha=.1)\n",
+        "model.fit(X_train, y_train)\n",
+        "\n",
+        "print(\"Train set\")\n",
+        "evaluate_model(model, (X_train, y_train))\n",
+        "\n",
+        "print(\"Test set\")\n",
+        "evaluate_model(model, (X_test, y_test))"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Izs_37LXXASU",
+        "outputId": "ef864d10-5bbe-43be-c268-768d144cfeba"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'test_score': array([0.7418724 , 0.88884062, 0.81889018, 0.86812961, 0.9027071 ]),\n",
+              " 'train_score': array([0.92993095, 0.91058923, 0.9161008 , 0.91387122, 0.90079737])}"
+            ]
+          },
+          "execution_count": 162,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
       "source": [
         "train_score = []\n",
         "test_score = []\n",
@@ -526,34 +530,11 @@
         "    }\n",
         "\n",
         "scores"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Izs_37LXXASU",
-        "outputId": "ef864d10-5bbe-43be-c268-768d144cfeba"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "{'test_score': array([0.7418724 , 0.88884062, 0.81889018, 0.86812961, 0.9027071 ]),\n",
-              " 'train_score': array([0.92993095, 0.91058923, 0.9161008 , 0.91387122, 0.90079737])}"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 162
-        }
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "scores[\"train_score\"].mean()"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -561,25 +542,25 @@
         "id": "UNDJ6H7sZzXD",
         "outputId": "c2883f77-3ed1-46fd-a70a-3f1dfe19db23"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "0.9142579148087027"
             ]
           },
+          "execution_count": 163,
           "metadata": {},
-          "execution_count": 163
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "scores[\"train_score\"].mean()"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "scores[\"test_score\"].mean()"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -587,36 +568,25 @@
         "id": "WxyoQ6efZ-VW",
         "outputId": "e87e0ba6-3aa3-4be8-95f6-dfe7b28182df"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "0.8440879821235697"
             ]
           },
+          "execution_count": 164,
           "metadata": {},
-          "execution_count": 164
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "scores[\"test_score\"].mean()"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "from sklearn.model_selection import learning_curve\n",
-        "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns\n",
-        "\n",
-        "train_sizes_abs, train_scores, test_scores = learning_curve(Ridge(alpha=10.), X, y, random_state=RANDOM_SEED)\n",
-        "\n",
-        "plt.plot(train_sizes_abs, train_scores.mean(axis=1), label=\"Training score\")\n",
-        "plt.plot(train_sizes_abs, test_scores.mean(axis=1), label=\"Test score\")\n",
-        "plt.ylim([-2,1])\n",
-        "plt.legend(loc='lower right')\n",
-        "plt.grid()\n",
-        "plt.show()"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -625,30 +595,51 @@
         "id": "gHYzSlHuaAKH",
         "outputId": "3bf0ecce-d15d-46a6-e863-4db528873a7a"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
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\n",
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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
           },
           "metadata": {
             "needs_background": "light"
-          }
+          },
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "from sklearn.model_selection import learning_curve\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "train_sizes_abs, train_scores, test_scores = learning_curve(Ridge(alpha=10.), X, y, random_state=RANDOM_SEED)\n",
+        "\n",
+        "plt.plot(train_sizes_abs, train_scores.mean(axis=1), label=\"Training score\")\n",
+        "plt.plot(train_sizes_abs, test_scores.mean(axis=1), label=\"Test score\")\n",
+        "plt.ylim([-2,1])\n",
+        "plt.legend(loc='lower right')\n",
+        "plt.grid()\n",
+        "plt.show()"
       ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "authorship_tag": "ABX9TyNBmdFV2mD2rZqBw2XlF9UX",
+      "include_colab_link": true,
+      "name": "overfitting_regularizzazion_exercise.ipynb",
+      "provenance": []
     },
-    {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "oHmbLs2XeHFa"
-      },
-      "execution_count": null,
-      "outputs": []
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
     }
-  ]
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
diff --git a/4 - Overfitting e Tecniche di Regolarizzazione/overfitting.ipynb b/4 - Overfitting e Tecniche di Regolarizzazione/overfitting.ipynb
index cfc3673..07d40fc 100644
--- a/4 - Overfitting e Tecniche di Regolarizzazione/overfitting.ipynb	
+++ b/4 - Overfitting e Tecniche di Regolarizzazione/overfitting.ipynb	
@@ -37,6 +37,21 @@
         "id": "pSMJzq2XB5Dd"
       }
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "Il bilanciamento ottimale tra la complessit\u00e0 del modello e la sua capacit\u00e0 di generalizzazione su dati futuri \u00e8 regolato dal **Bias-Variance Tradeoff** (Compromesso tra Distorsione e Varianza):\n",
+        "\n",
+        "- **Bias (Distorsione)**: \u00c8 l'errore sistematico dovuto ad assunzioni errate o troppo semplicistiche del modello (es. modellare dati curvilinei complessi usando una semplice retta). Un alto Bias porta all'**Underfitting** (prestazioni insoddisfacenti sia sul train set che sul test set).\n",
+        "- **Variance (Varianza)**: Rappresenta la sensibilit\u00e0 del modello alle piccole fluttuazioni o al rumore casuale del training set. Un'alta Varianza porta all'**Overfitting** (il modello si adatta perfettamente ai dati di addestramento ma fallisce su dati nuovi non ancora visti).\n",
+        "\n",
+        "> [!NOTE]\n",
+        "> L'obiettivo principale del machine learning \u00e8 minimizzare l'errore totale trovando il punto di equilibrio ideale in cui sia il Bias che la Varianza risultano controllati.\n"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": 59,
@@ -98,6 +113,24 @@
         "id": "bUa7HOqgEwvE"
       }
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "La tecnica dell'**Hold-out** rappresenta l'approccio pi\u00f9 immediato per validare un modello. Essa consiste nel suddividere il dataset di partenza in due partizioni distinte ed indipendenti:\n",
+        "1. **Train Set**: Utilizzato esclusivamente per calcolare i parametri del modello (pesi ed intercetta).\n",
+        "2. **Test Set**: Utilizzato esclusivamente per misurare le performance di generalizzazione del modello.\n",
+        "\n",
+        "### Analisi dell'Overfitting in OLS:\n",
+        "In questo esperimento, abbiamo a disposizione $100$ feature ma solo $75$ campioni di addestramento ($d > n$). Poich\u00e9 il modello ha pi\u00f9 parametri liberi rispetto alle osservazioni, la regressione lineare classica (OLS) riesce a interpolare perfettamente i dati:\n",
+        "- **Train Set**: $\\text{MSE} = 0.000$, $R^2 = 1.000$ (memorizzazione perfetta del rumore).\n",
+        "- **Test Set**: $\\text{MSE} \\approx 17991.61$, $R^2 \\approx -0.147$ (prestazioni disastrose).\n",
+        "\n",
+        "Questo comportamento estremo dimostra in modo lampante il fenomeno dell'overfitting.\n"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -239,6 +272,21 @@
         "id": "NOyNql3JE0cG"
       }
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "La **K-Fold Cross-Validation** (Validazione Incrociata) riduce la sensibilit\u00e0 dovuta alla singola partizione dell'Hold-out.\n",
+        "Il dataset viene diviso in $K$ parti (fold). Il modello viene addestrato $K$ volte, utilizzando ad ogni iterazione un fold diverso come set di test e i restanti $K-1$ fold come train set. La metrica finale \u00e8 la media delle prestazioni ottenute.\n",
+        "\n",
+        "### Prevenzione del Data Leakage:\n",
+        "Se applichiamo `cross_val_score` su feature non scalate (o pre-scalate sull'intero dataset), rischiamo distorsioni e data leakage. \n",
+        "- **Senza scaling corretto**: Il punteggio di test medio ottenuto \u00e8 $R^2 \\approx 0.101$.\n",
+        "- **Con scaling all'interno di ogni Fold**: Effettuando il fit dello scaler `StandardScaler` esclusivamente sui fold di addestramento correnti (prevenendo il leakage), le performance medie del modello salgono a $R^2 \\approx 0.225$.\n"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -537,6 +585,23 @@
         "id": "QQWfetwSKwA6"
       }
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "La **Leave-One-Out Cross-Validation (LOOCV)** rappresenta il caso limite della validazione K-Fold in cui il numero di fold $K$ \u00e8 pari al numero totale di campioni $n$ ($K = n$).\n",
+        "Ad ogni iterazione, il modello viene addestrato su $n-1$ osservazioni e testato sul singolo elemento escluso.\n",
+        "\n",
+        "### Risultati del Test:\n",
+        "- **LOOCV Mean Train MSE**: $\\approx 0$ (precisamente $\\approx 1.18 \\times 10^{-25}$), ad indicare che il modello continua ad interpolare perfettamente ogni split di train.\n",
+        "- **LOOCV Mean Test MSE**: $\\approx 6764.25$, evidenziando l'errore medio enorme commesso su ogni singolo record di test.\n",
+        "\n",
+        "- **Vantaggi**: Stima quasi priva di bias delle performance del modello.\n",
+        "- **Svantaggi**: Estremamente esigente dal punto di vista computazionale su dataset di grandi dimensioni, in quanto richiede l'addestramento di $n$ modelli distinti.\n"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -652,6 +717,19 @@
         "id": "ZkQECgxiLsFw"
       }
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "enriched": true
+      },
+      "source": [
+        "La **Curva di Apprendimento** mostra graficamente come variano le metriche di addestramento e di test all'aumentare della dimensione campionaria utilizzata per il training.\n",
+        "\n",
+        "### Diagnosi tramite Learning Curves:\n",
+        "- **Gap persistente ed elevato**: Se la curva di addestramento rimane vicina a punteggi ottimali (es. $R^2 \\approx 1.0$) mentre la curva di test rimane significativamente inferiore pur crescendo lentamente, il modello soffre di **Alta Varianza** (Overfitting). Aggiungere pi\u00f9 dati potrebbe aiutare a colmare questo gap.\n",
+        "- **Convergenza precoce a valori bassi**: Se entrambe le curve convergono rapidamente verso valori bassi all'aumentare dei campioni, il modello soffre di **Alto Bias** (Underfitting). In questo caso, aggiungere pi\u00f9 dati non servir\u00e0 a migliorare il modello, che necessita invece di maggiore complessit\u00e0 (es. feature polinomiali o meno regolarizzazione).\n"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
diff --git a/Progetto Finale - Cross Selling di Polizze/health_insurance_cross_sell_prediction.ipynb b/Progetto Finale - Cross Selling di Polizze/health_insurance_cross_sell_prediction.ipynb
index 96c7186..5b55858 100644
--- a/Progetto Finale - Cross Selling di Polizze/health_insurance_cross_sell_prediction.ipynb	
+++ b/Progetto Finale - Cross Selling di Polizze/health_insurance_cross_sell_prediction.ipynb	
@@ -33,30 +33,30 @@
     {
       "cell_type": "markdown",
       "source": [
-        "# Previsione di opportunità di Cross Sell di assicurazioni\n",
+        "# Previsione di opportunit\u00e0 di Cross Sell di assicurazioni\n",
         "\n",
-        "Il cliente è una compagnia di assicurazioni che ha fornito un'assicurazione sanitaria ai suoi clienti, adesso hanno bisogno del tuo aiuto per costruire un modello predittivo in grado di prevedere se gli assicurati dell'anno passato potrebbero essere interessati ad acquistare anche un'assicurazione per il proprio veicolo.\n",
+        "Il cliente \u00e8 una compagnia di assicurazioni che ha fornito un'assicurazione sanitaria ai suoi clienti, adesso hanno bisogno del tuo aiuto per costruire un modello predittivo in grado di prevedere se gli assicurati dell'anno passato potrebbero essere interessati ad acquistare anche un'assicurazione per il proprio veicolo.\n",
         "\n",
-        "Il dataset è composto dalle seguenti proprietà:\n",
+        "Il dataset \u00e8 composto dalle seguenti propriet\u00e0:\n",
         "- **id**: id univoco dell'acquirente.\n",
         "- **Gender**: sesso dell'acquirente.\n",
-        "- **Age**: età dell'acquirente.\n",
+        "- **Age**: et\u00e0 dell'acquirente.\n",
         "- **Driving_License**: 1 se l'utente ha la patente di guida, 0 altrimenti.\n",
         "- **Region_Code**: codice univoco della regione dell'acquirente.\n",
-        "- **Previously_Insured**: 1 se l'utente ha già un veicolo assicurato, 0 altrimenti.\n",
-        "- **Vehicle_Age**: età del veicolo\n",
+        "- **Previously_Insured**: 1 se l'utente ha gi\u00e0 un veicolo assicurato, 0 altrimenti.\n",
+        "- **Vehicle_Age**: et\u00e0 del veicolo\n",
         "- **Vehicle_Damage**: 1 se l'utente ha danneggiato il veicolo in passato, 0 altrimenti.\n",
         "- **Annual_Premium**: la cifra che l'utente deve pagare come premio durante l'anno.\n",
         "- **Policy_Sales_Channel**: codice anonimizzato del canale utilizzato per la proposta (es. per email, per telefono, di persona, ecc...)\n",
-        "- **Vintage**: numero di giorni dalla quale l'utente è cliente dell'azienda.\n",
-        "- **Response**: 1 se l'acquirente ha risposto positivametne alla proposta di vendità, 0 altrimenti.\n",
+        "- **Vintage**: numero di giorni dalla quale l'utente \u00e8 cliente dell'azienda.\n",
+        "- **Response**: 1 se l'acquirente ha risposto positivametne alla proposta di vendit\u00e0, 0 altrimenti.\n",
         "\n",
-        "L'obiettivo del modello è prevedere il valore di **Response**.\n",
+        "L'obiettivo del modello \u00e8 prevedere il valore di **Response**.\n",
         "\n",
         "**Tip**\n",
         "Fai attenzione alla distribuzione delle classi, dai uno sguardo a [questo approfondimento](https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/). In caso di classi sbilanciate puoi provare a:\n",
         "\n",
-        "- Penalizzare la classe più frequente (ricorda l'argomento class_weight)\n",
+        "- Penalizzare la classe pi\u00f9 frequente (ricorda l'argomento class_weight)\n",
         "- Utilizzare [l'oversampling o l'undersampling](https://machinelearningmastery.com/random-oversampling-and-undersampling-for-imbalanced-classification/).\n",
         "\n",
         "\n",
@@ -66,6 +66,19 @@
         "id": "C-_t2lBQvESK"
       }
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Il **Progetto Finale** rappresenta un caso d'uso end-to-end industriale: stimare la propensione dei clienti che possiedono gi\u00e0 una polizza sanitaria ad acquistare un'assicurazione per il proprio veicolo (cross-selling).\n",
+        "\n",
+        "Questo progetto mette a frutto tutte le fasi tipiche di una pipeline reale di Data Science:\n",
+        "1. **Exploratory Data Analysis (EDA)**: Comprendere la distribuzione del target, le correlazioni e identificare potenziali problemi (es. forte sbilanciamento delle classi).\n",
+        "2. **Data Preprocessing**: Trattamento dei dati mancanti, encoding di variabili categoriche nominali/ordinali e scaling delle feature numeriche.\n",
+        "3. **Model Selection & Validation**: Addestramento e ottimizzazione di diversi modelli di classificazione (es. regressione logistica, alberi decisionali, ensemble) tramite tecniche robuste come la Cross-Validation.\n",
+        "4. **Evaluation**: Scelta delle metriche pi\u00f9 opportune (es. ROC AUC, F1-Score) per gestire lo sbilanciamento delle classi e guidare le decisioni commerciali.\n"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [],

From 644e469add7f30f5a5be86f4adcb644b24dbc703 Mon Sep 17 00:00:00 2001
From: Spatariu Rares <81376159+SpatariuRares@users.noreply.github.com>
Date: Sat, 20 Jun 2026 10:34:26 +0200
Subject: [PATCH 3/9] Please provide the diff or a description of the changes
 so I can generate the commit message for you.

---
 .../binary_classification.ipynb                     |   0
 .../esercizi/binary_classification_exercise.ipynb   |   0
 .../multiclass_classification.ipynb                 |   0
 .../res/cancer_classifier_auc.png                   | Bin
 .../res/cancer_classifier_cm.png                    | Bin
 5 files changed, 0 insertions(+), 0 deletions(-)
 rename {4 - La Classificazione => 5 - La Classificazione}/binary_classification.ipynb (100%)
 rename {4 - La Classificazione => 5 - La Classificazione}/esercizi/binary_classification_exercise.ipynb (100%)
 rename {4 - La Classificazione => 5 - La Classificazione}/multiclass_classification.ipynb (100%)
 rename {4 - La Classificazione => 5 - La Classificazione}/res/cancer_classifier_auc.png (100%)
 rename {4 - La Classificazione => 5 - La Classificazione}/res/cancer_classifier_cm.png (100%)

diff --git a/4 - La Classificazione/binary_classification.ipynb b/5 - La Classificazione/binary_classification.ipynb
similarity index 100%
rename from 4 - La Classificazione/binary_classification.ipynb
rename to 5 - La Classificazione/binary_classification.ipynb
diff --git a/4 - La Classificazione/esercizi/binary_classification_exercise.ipynb b/5 - La Classificazione/esercizi/binary_classification_exercise.ipynb
similarity index 100%
rename from 4 - La Classificazione/esercizi/binary_classification_exercise.ipynb
rename to 5 - La Classificazione/esercizi/binary_classification_exercise.ipynb
diff --git a/4 - La Classificazione/multiclass_classification.ipynb b/5 - La Classificazione/multiclass_classification.ipynb
similarity index 100%
rename from 4 - La Classificazione/multiclass_classification.ipynb
rename to 5 - La Classificazione/multiclass_classification.ipynb
diff --git a/4 - La Classificazione/res/cancer_classifier_auc.png b/5 - La Classificazione/res/cancer_classifier_auc.png
similarity index 100%
rename from 4 - La Classificazione/res/cancer_classifier_auc.png
rename to 5 - La Classificazione/res/cancer_classifier_auc.png
diff --git a/4 - La Classificazione/res/cancer_classifier_cm.png b/5 - La Classificazione/res/cancer_classifier_cm.png
similarity index 100%
rename from 4 - La Classificazione/res/cancer_classifier_cm.png
rename to 5 - La Classificazione/res/cancer_classifier_cm.png

From 240e0c94ee5e41ba3ce180df24589407e19a7550 Mon Sep 17 00:00:00 2001
From: Spatariu Rares <81376159+SpatariuRares@users.noreply.github.com>
Date: Sat, 20 Jun 2026 11:03:17 +0200
Subject: [PATCH 4/9] refactor: update notebook metadata and add utility
 scripts for documentation enrichment and analysis

---
 .../correlation_matrix.ipynb                  |  18 +-
 .../regressione_lineare_multipla.ipynb        |  56 ++-
 .../regressione_lineare_semplice.ipynb        |  20 +-
 .../regressione_polinomiale.ipynb             |  66 +++-
 .../overfitting.ipynb                         | 191 ++++++++++-
 .../regularization.ipynb                      | 318 +++++++++++-------
 .../binary_classification.ipynb               | 100 +++++-
 .../multiclass_classification.ipynb           |  94 +++++-
 6 - Clustering/elbow_method.ipynb             |  27 ++
 6 - Clustering/kmeans.ipynb                   |  43 ++-
 10 files changed, 743 insertions(+), 190 deletions(-)

diff --git a/3 - La Regressione Lineare/correlation_matrix.ipynb b/3 - La Regressione Lineare/correlation_matrix.ipynb
index feaae75..ac7e2df 100644
--- a/3 - La Regressione Lineare/correlation_matrix.ipynb	
+++ b/3 - La Regressione Lineare/correlation_matrix.ipynb	
@@ -38,9 +38,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "La **Matrice di Correlazione** mostra il coefficiente di correlazione di Pearson ($r$) calcolato per tutte le coppie possibili di variabili numeriche presenti nel dataset.\n",
         "\n",
@@ -74,6 +72,13 @@
         "import seaborn as sns"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -270,6 +275,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Utilizziamo una heatmap per visualizzare la matrice di correlazione di Pearson. Questo ci aiuta a identificare la multicollinearit\u00e0 tra le variabili indipendenti."
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
diff --git a/3 - La Regressione Lineare/regressione_lineare_multipla.ipynb b/3 - La Regressione Lineare/regressione_lineare_multipla.ipynb
index 8b9d8c3..e3daf5b 100644
--- a/3 - La Regressione Lineare/regressione_lineare_multipla.ipynb	
+++ b/3 - La Regressione Lineare/regressione_lineare_multipla.ipynb	
@@ -10,9 +10,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "La **Regressione Lineare Multipla** estende la regressione lineare semplice consentendo l'uso di due o pi\u00f9 variabili indipendenti ($x_1, x_2, \\dots, x_n$) per predire la variabile target $y$.\n",
         "\n",
@@ -32,9 +30,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "Scikit-learn semplifica l'addestramento tramite la classe `LinearRegression` all'interno del modulo `sklearn.linear_model`.\n",
         "Questa classe risolve l'equazione OLS calcolando la soluzione in forma chiusa (equazioni normali).\n",
@@ -55,6 +51,13 @@
         "from sklearn.linear_model import LinearRegression"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": 7,
@@ -80,6 +83,14 @@
         "print(y_pred)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Calcoliamo l'Errore Quadratico Medio (MSE). Esso penalizza gli errori pi\u00f9 grandi rispetto a quelli piccoli:\n",
+        "$$\\text{MSE} = \\frac{1}{n} \\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2$$"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": 12,
@@ -116,9 +127,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "Quando aggiungiamo la seconda feature **Anno di costruzione**, il modello diventa a tutti gli effetti una regressione lineare multipla.\n",
         "\n",
@@ -153,6 +162,14 @@
         "print(y_pred)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Calcoliamo l'Errore Quadratico Medio (MSE). Esso penalizza gli errori pi\u00f9 grandi rispetto a quelli piccoli:\n",
+        "$$\\text{MSE} = \\frac{1}{n} \\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2$$"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": 14,
@@ -177,6 +194,13 @@
         "print(f\"R2 = {r2_score(y_train, y_pred)}\")"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -185,6 +209,13 @@
       "outputs": [],
       "source": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -193,6 +224,13 @@
       "outputs": [],
       "source": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
diff --git a/3 - La Regressione Lineare/regressione_lineare_semplice.ipynb b/3 - La Regressione Lineare/regressione_lineare_semplice.ipynb
index 699ee36..7714774 100644
--- a/3 - La Regressione Lineare/regressione_lineare_semplice.ipynb	
+++ b/3 - La Regressione Lineare/regressione_lineare_semplice.ipynb	
@@ -10,9 +10,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "La **Regressione Lineare Semplice** \u00e8 un modello parametrico utilizzato per descrivere la relazione lineare tra una variabile indipendente (o predittore) $x$ e una variabile dipendente (o target) $y$.\n",
         "\n",
@@ -45,9 +43,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "La stima dei parametri $\\beta_0$ e $\\beta_1$ avviene minimizzando la somma dei quadrati dei residui tramite il metodo dei **Minimi Quadrati** (*Ordinary Least Squares - OLS*).\n",
         "Le formule analitiche per calcolare i coefficienti ottimali sono:\n",
@@ -97,9 +93,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "Per misurare la qualit\u00e0 e l'accuratezza del modello lineare, utilizziamo diverse metriche fondamentali:\n",
         "1. **Residual Sum of Squares (RSS)**: Somma dei quadrati dei residui (l'errore totale che OLS minimizza).\n",
@@ -179,6 +173,14 @@
         "print(y_pred)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Calcoliamo l'Errore Quadratico Medio (MSE). Esso penalizza gli errori pi\u00f9 grandi rispetto a quelli piccoli:\n",
+        "$$\\text{MSE} = \\frac{1}{n} \\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2$$"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": 18,
diff --git a/3 - La Regressione Lineare/regressione_polinomiale.ipynb b/3 - La Regressione Lineare/regressione_polinomiale.ipynb
index 90486cf..e8c8853 100644
--- a/3 - La Regressione Lineare/regressione_polinomiale.ipynb	
+++ b/3 - La Regressione Lineare/regressione_polinomiale.ipynb	
@@ -10,9 +10,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "La **Regressione Polinomiale** viene impiegata quando la relazione tra le variabili indipendenti e il target non \u00e8 lineare.\n",
         "Sebbene il modello includa potenze delle feature (es. $x^2$, $x^3$), esso viene ancora classificato come modello *lineare* perch\u00e9 la linearit\u00e0 \u00e8 riferita ai parametri $\\beta_i$ (i coefficienti), e non alla variabile indipendente.\n",
@@ -55,6 +53,13 @@
         "plt.figure(figsize=(8, 6), dpi=80)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -65,6 +70,13 @@
         "np.set_printoptions(suppress=True)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": 2,
@@ -86,9 +98,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "Utilizziamo la classe `PolynomialFeatures` di Scikit-learn per mappare lo spazio delle feature originale in uno spazio a dimensioni maggiori, introducendo le potenze e i termini di interazione.\n",
         "Ad esempio, per un input bidimensionale $[x_0, x_1]$ e un grado polinomiale pari a 2, lo spazio generato comprender\u00e0: \n",
@@ -106,6 +116,13 @@
         "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "La Regressione Polinomiale non \u00e8 un nuovo algoritmo, ma una Regressione Lineare applicata a feature trasformate. `PolynomialFeatures` genera termini polinomiali e interazioni tra le feature, permettendoci di modellare relazioni non lineari."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": 9,
@@ -130,6 +147,13 @@
         "poly.get_feature_names()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": 10,
@@ -169,9 +193,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "Aumentare il grado del polinomio rende il modello pi\u00f9 flessibile e in grado di adattarsi meglio ai dati di training. Tuttavia, questo comporta un grave rischio di **overfitting** (alta varianza).\n",
         "\n",
@@ -332,9 +354,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "Applichiamo la regressione polinomiale combinando pi\u00f9 variabili d'ingresso. \n",
         "Combinando le feature **Dimensione** e **Anno di costruzione** con un grado pari a 2, il modello riesce a raggiungere un accoppiamento perfetto sul training set:\n",
@@ -356,6 +376,13 @@
         "y_train = np.array([16, 30, 12, 10, 24, 18, 20, 25])"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "La Regressione Polinomiale non \u00e8 un nuovo algoritmo, ma una Regressione Lineare applicata a feature trasformate. `PolynomialFeatures` genera termini polinomiali e interazioni tra le feature, permettendoci di modellare relazioni non lineari."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": 65,
@@ -385,6 +412,14 @@
         "print(X_train_poly)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Calcoliamo l'Errore Quadratico Medio (MSE). Esso penalizza gli errori pi\u00f9 grandi rispetto a quelli piccoli:\n",
+        "$$\\text{MSE} = \\frac{1}{n} \\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2$$"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": 66,
@@ -408,6 +443,13 @@
         "print(f\"R2 = {r2_score(y_train, y_pred)}\")"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
diff --git a/4 - Overfitting e Tecniche di Regolarizzazione/overfitting.ipynb b/4 - Overfitting e Tecniche di Regolarizzazione/overfitting.ipynb
index 07d40fc..5963439 100644
--- a/4 - Overfitting e Tecniche di Regolarizzazione/overfitting.ipynb	
+++ b/4 - Overfitting e Tecniche di Regolarizzazione/overfitting.ipynb	
@@ -39,9 +39,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "Il bilanciamento ottimale tra la complessit\u00e0 del modello e la sua capacit\u00e0 di generalizzazione su dati futuri \u00e8 regolato dal **Bias-Variance Tradeoff** (Compromesso tra Distorsione e Varianza):\n",
         "\n",
@@ -67,6 +65,13 @@
         "from sklearn.metrics import mean_squared_error, r2_score"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -78,6 +83,14 @@
       "execution_count": 22,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "> [!IMPORTANT]\n",
+        "> **Data Splitting**: La suddivisione in Train e Test set \u00e8 cruciale. Il Train set serve ad addestrare il modello, mentre il Test set (invisibile al modello durante il training) serve a valutarne la capacit\u00e0 di generalizzazione su nuovi dati."
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -115,9 +128,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "La tecnica dell'**Hold-out** rappresenta l'approccio pi\u00f9 immediato per validare un modello. Essa consiste nel suddividere il dataset di partenza in due partizioni distinte ed indipendenti:\n",
         "1. **Train Set**: Utilizzato esclusivamente per calcolare i parametri del modello (pesi ed intercetta).\n",
@@ -157,6 +168,14 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "> [!WARNING]\n",
+        "> **Standardizzazione**: Portare i dati sulla stessa scala (media 0, varianza 1) \u00e8 essenziale, specialmente quando si applicano modelli sensibili alla scala come Ridge e Lasso, per evitare che feature con valori assoluti grandi dominino la penalizzazione."
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -170,6 +189,13 @@
       "execution_count": 139,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -197,6 +223,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Utilizziamo il modello appena addestrato per effettuare predizioni (`.predict()`) sui dati di test."
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -215,6 +248,13 @@
       "execution_count": 141,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -239,6 +279,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -274,9 +321,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "La **K-Fold Cross-Validation** (Validazione Incrociata) riduce la sensibilit\u00e0 dovuta alla singola partizione dell'Hold-out.\n",
         "Il dataset viene diviso in $K$ parti (fold). Il modello viene addestrato $K$ volte, utilizzando ad ogni iterazione un fold diverso come set di test e i restanti $K-1$ fold come train set. La metrica finale \u00e8 la media delle prestazioni ottenute.\n",
@@ -298,6 +343,13 @@
       "execution_count": 144,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -326,6 +378,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -352,6 +411,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -383,6 +449,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -409,6 +482,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -435,6 +515,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -462,6 +549,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Calcoliamo l'indice di determinazione $R^2$, che indica quanta varianza della variabile dipendente \u00e8 spiegata dal modello. Un valore vicino a 1 indica un fit perfetto."
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -497,6 +591,13 @@
       "execution_count": 151,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -524,6 +625,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -550,6 +658,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -587,9 +702,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "La **Leave-One-Out Cross-Validation (LOOCV)** rappresenta il caso limite della validazione K-Fold in cui il numero di fold $K$ \u00e8 pari al numero totale di campioni $n$ ($K = n$).\n",
         "Ad ogni iterazione, il modello viene addestrato su $n-1$ osservazioni e testato sul singolo elemento escluso.\n",
@@ -613,6 +726,13 @@
       "execution_count": 155,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -640,6 +760,14 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Calcoliamo l'Errore Quadratico Medio (MSE). Esso penalizza gli errori pi\u00f9 grandi rispetto a quelli piccoli:\n",
+        "$$\\text{MSE} = \\frac{1}{n} \\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2$$"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -675,6 +803,13 @@
       "execution_count": 157,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -686,6 +821,13 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -697,6 +839,13 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -719,9 +868,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "La **Curva di Apprendimento** mostra graficamente come variano le metriche di addestramento e di test all'aumentare della dimensione campionaria utilizzata per il training.\n",
         "\n",
@@ -745,6 +892,13 @@
       "execution_count": 158,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Tracciamo la curva o la retta di regressione sovrapposta ai dati reali per valutare visivamente l'adattamento (fit) del modello."
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -776,6 +930,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
diff --git a/4 - Overfitting e Tecniche di Regolarizzazione/regularization.ipynb b/4 - Overfitting e Tecniche di Regolarizzazione/regularization.ipynb
index df5e45c..c7807d8 100644
--- a/4 - Overfitting e Tecniche di Regolarizzazione/regularization.ipynb	
+++ b/4 - Overfitting e Tecniche di Regolarizzazione/regularization.ipynb	
@@ -1,28 +1,10 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "regularization.ipynb",
-      "provenance": [],
-      "collapsed_sections": [],
-      "authorship_tag": "ABX9TyPL4TANdEbCTQ7MqTDMS5xV",
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/ProfAI/machine-learning-fondamenti/blob/main/4%20-%20Overfitting%20e%20Tecniche%20di%20Regolarizzazione/regularization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -95,17 +77,29 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "RANDOM_SEED = 0"
-      ],
+      "execution_count": 4,
       "metadata": {
         "id": "r-LIj5rPPUPR"
       },
-      "execution_count": 4,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "RANDOM_SEED = 0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Utilizziamo il modello appena addestrato per effettuare predizioni (`.predict()`) sui dati di test."
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "id": "MHD6i3iFPpiC"
+      },
+      "outputs": [],
       "source": [
         "def evaluate_model(model, dataset):\n",
         "\n",
@@ -115,12 +109,7 @@
         "\n",
         "  print(f\"MSE: {mean_squared_error(y, y_pred):.3f}\")\n",
         "  print(f\"R2: {r2_score(y, y_pred):.3f}\")"
-      ],
-      "metadata": {
-        "id": "MHD6i3iFPpiC"
-      },
-      "execution_count": 35,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -138,15 +127,15 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "X, y = make_regression(n_samples=100, n_features=100, n_informative=10, n_targets=1, bias=0.0, tail_strength=0.5, noise=10.0, random_state=RANDOM_SEED)\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=RANDOM_SEED)"
-      ],
+      "execution_count": 36,
       "metadata": {
         "id": "sm6ADXX_PVTx"
       },
-      "execution_count": 36,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "X, y = make_regression(n_samples=100, n_features=100, n_informative=10, n_targets=1, bias=0.0, tail_strength=0.5, noise=10.0, random_state=RANDOM_SEED)\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=RANDOM_SEED)"
+      ]
     },
     {
       "cell_type": "markdown",
@@ -163,16 +152,16 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "id": "WgEsNZbZPZyZ"
+      },
+      "outputs": [],
       "source": [
         "ss = StandardScaler()\n",
         "X_train = ss.fit_transform(X_train)\n",
         "X_test = ss.transform(X_test)"
-      ],
-      "metadata": {
-        "id": "WgEsNZbZPZyZ"
-      },
-      "execution_count": 37,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -185,10 +174,7 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "lr = LinearRegression()\n",
-        "lr.fit(X_train, y_train)"
-      ],
+      "execution_count": 38,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -196,25 +182,33 @@
         "id": "JJ4xIRPrPkIR",
         "outputId": "47f9089e-f04f-41d4-e6ef-622e3df1a13e"
       },
-      "execution_count": 38,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "LinearRegression()"
             ]
           },
+          "execution_count": 38,
           "metadata": {},
-          "execution_count": 38
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "lr = LinearRegression()\n",
+        "lr.fit(X_train, y_train)"
       ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "evaluate_model(lr, (X_train, y_train))"
-      ],
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -222,23 +216,30 @@
         "id": "oRLuZ5IAPmuW",
         "outputId": "3b6759c2-6553-4123-c3b8-4909e94d1d4e"
       },
-      "execution_count": 39,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "MSE: 0.000\n",
             "R2: 1.000\n"
           ]
         }
+      ],
+      "source": [
+        "evaluate_model(lr, (X_train, y_train))"
       ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "evaluate_model(lr, (X_test, y_test))"
-      ],
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -246,16 +247,18 @@
         "id": "LKjkXC3uPrmR",
         "outputId": "07a7da25-838a-4e1b-b00e-bfa1a393ffd3"
       },
-      "execution_count": 40,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "MSE: 12139.621\n",
             "R2: 0.218\n"
           ]
         }
+      ],
+      "source": [
+        "evaluate_model(lr, (X_test, y_test))"
       ]
     },
     {
@@ -286,21 +289,25 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "from sklearn.linear_model import Ridge"
-      ],
+      "execution_count": 61,
       "metadata": {
         "id": "EVtIpO7hQ8t7"
       },
-      "execution_count": 61,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "from sklearn.linear_model import Ridge"
+      ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "model = Ridge(alpha=1.)\n",
-        "model.fit(X_train, y_train)"
-      ],
+        "Eseguiamo l'addestramento del modello (`.fit()`). In questa fase, l'algoritmo ottimizza i pesi cercando di minimizzare la funzione di costo (Loss function)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 62,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -308,25 +315,33 @@
         "id": "0lkWYDoLRBlE",
         "outputId": "6757160a-4b8a-4b0b-a885-08ebfcf4537f"
       },
-      "execution_count": 62,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "Ridge()"
             ]
           },
+          "execution_count": 62,
           "metadata": {},
-          "execution_count": 62
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "model = Ridge(alpha=1.)\n",
+        "model.fit(X_train, y_train)"
       ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "evaluate_model(model, (X_train, y_train))"
-      ],
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 63,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -334,23 +349,30 @@
         "id": "nldaOSgNRBiT",
         "outputId": "2ce13be9-ffb2-4f3c-ed16-5575ddd135f1"
       },
-      "execution_count": 63,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "MSE: 6.870\n",
             "R2: 1.000\n"
           ]
         }
+      ],
+      "source": [
+        "evaluate_model(model, (X_train, y_train))"
       ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "evaluate_model(model, (X_test, y_test))"
-      ],
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 64,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -358,16 +380,18 @@
         "id": "HirxEG0kRK_k",
         "outputId": "6309e83a-eeb1-4ce2-c914-2c9a8785e4df"
       },
-      "execution_count": 64,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "MSE: 11894.633\n",
             "R2: 0.234\n"
           ]
         }
+      ],
+      "source": [
+        "evaluate_model(model, (X_test, y_test))"
       ]
     },
     {
@@ -399,21 +423,25 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "from sklearn.linear_model import Lasso"
-      ],
+      "execution_count": 41,
       "metadata": {
         "id": "SjGNTWtlPtsR"
       },
-      "execution_count": 41,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "from sklearn.linear_model import Lasso"
+      ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "model = Lasso(alpha=1.)\n",
-        "model.fit(X_train, y_train)"
-      ],
+        "Eseguiamo l'addestramento del modello (`.fit()`). In questa fase, l'algoritmo ottimizza i pesi cercando di minimizzare la funzione di costo (Loss function)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -421,25 +449,33 @@
         "id": "LnjjxC8MP5R6",
         "outputId": "f18e5318-5ecc-4011-8e73-0ce201711da7"
       },
-      "execution_count": 45,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "Lasso()"
             ]
           },
+          "execution_count": 45,
           "metadata": {},
-          "execution_count": 45
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "model = Lasso(alpha=1.)\n",
+        "model.fit(X_train, y_train)"
       ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "evaluate_model(model, (X_train, y_train))"
-      ],
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -447,23 +483,30 @@
         "id": "zF07ITLUP_Rj",
         "outputId": "e045dd37-42ae-4f09-f78b-9c1afc4f7f8c"
       },
-      "execution_count": 46,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "MSE: 59.894\n",
             "R2: 0.996\n"
           ]
         }
+      ],
+      "source": [
+        "evaluate_model(model, (X_train, y_train))"
       ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "evaluate_model(model, (X_test, y_test))"
-      ],
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -471,16 +514,18 @@
         "id": "knADR9CRQCM6",
         "outputId": "73b9ac6f-637e-4916-c589-4ecffaaf3fbd"
       },
-      "execution_count": 47,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "MSE: 93.840\n",
             "R2: 0.994\n"
           ]
         }
+      ],
+      "source": [
+        "evaluate_model(model, (X_test, y_test))"
       ]
     },
     {
@@ -514,13 +559,7 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "from sklearn.model_selection import learning_curve\n",
-        "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns\n",
-        "\n",
-        "train_sizes_abs, train_scores, test_scores = learning_curve(Lasso(), X, y, random_state=RANDOM_SEED)"
-      ],
+      "execution_count": 67,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -528,11 +567,10 @@
         "id": "rQAcocr6RZAX",
         "outputId": "8c818b64-74e4-4668-cf91-f34d8bba7600"
       },
-      "execution_count": 67,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+01, tolerance: 1.044e+01\n",
             "  coef_, l1_reg, l2_reg, X, y, max_iter, tol, rng, random, positive\n",
@@ -540,15 +578,25 @@
             "  coef_, l1_reg, l2_reg, X, y, max_iter, tol, rng, random, positive\n"
           ]
         }
+      ],
+      "source": [
+        "from sklearn.model_selection import learning_curve\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "train_sizes_abs, train_scores, test_scores = learning_curve(Lasso(), X, y, random_state=RANDOM_SEED)"
       ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "plt.plot(train_sizes_abs, train_scores.mean(axis=1))\n",
-        "plt.plot(train_sizes_abs, test_scores.mean(axis=1))\n",
-        "plt.show()"
-      ],
+        "Tracciamo la curva o la retta di regressione sovrapposta ai dati reali per valutare visivamente l'adattamento (fit) del modello."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 68,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -557,21 +605,45 @@
         "id": "_cErG6jxRkI7",
         "outputId": "caa0762e-c8ba-4b57-f7e5-1f97f94ea820"
       },
-      "execution_count": 68,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "image/png": 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0AzjvcWizY+hEItJA2dgjWAb0SJruHrdtwswGAFcDg9x9fW27uy+L75cATwH9spBJmtvL4+H1Mjj219Dtm6HTiEgjZKMQzAb6mFlvM2sNDAM2OfvHzPoBtxEVgZVJ7e3NrE38uBNwGJB8kFnywcpF8OiVsOvRcOiPQqcRkUbKuGvI3avNbDQwHSgBxrn7AjO7Fih39zLgT8AOwH0WDTHwrrsPAvYGbjOzBFFRGpNytpHkuo1fRENItN4evncrtNBXU0TyTVaOEbj7I8AjKW2/Tno8IM16zwH7ZyODBDLj17BiPpxxH+y4U+g0IrIV9O+bbL03HoWXboNDLoI9jg+dRkS2kgqBbJ21y2HqRbDT/jDgN6HTiEgGVAik8RI1MOUHUP0FDPkntGwTOpGIZEDDUEvjPfsXePsZGPR36NQndBoRyZD2CKRx3psNs34P+54C/c4KnUZEskCFQBrui4+jU0XbdoPv3KSrjYkUCHUNScO4w7TL4ONKGPlYdOlJESkI2iOQhpk3AeZPhqOvgh79Q6cRkSxSIZAt+7ACHr4ceh0Bh18WOo2IZJkKgWxe9Xq4f2R0lbFTxkKLktCJRCTLdIxANm/mtbB8Hgy7B762c+g0ItIEtEcg6S2eAc//HQ76Aex1Uug0ItJEVAikflVvwuTzoMt+cPx1odOISBNSIZC6PlsF95wWHRcYPgFabRs6kYg0IR0jkE1Vr4dJZ8InH8CIadBul9CJRKSJqRDIV9yh7Efw7vPRYHI9DgqdSESagbqG5CtP3wCvToKjfwn7nRI6jYg0ExUCicyfAk/+Dg4YBkdeHjqNiDQjFQKBynKYeiHscigM+psGkxMpMioExW7NuzBhWHS94aF36yIzIkVIB4uL2Rdr4Z6hUL0BznkEtu8YOpGIBJCVPQIzG2hmb5hZhZldWc/8NmY2KZ7/opn1Spp3Vdz+hpmdkI080gA11TD5XPjwTRj6b+i8R+hEIhJIxoXAzEqAm4ETgX2A4Wa2T8pi5wEfufvuwE3A9fG6+wDDgH2BgcA/4u1JU5t+FVQ8ASf9GXb9Vug0IhJQNvYI+gMV7r7E3TcAE4HBKcsMBsbHjycDx5qZxe0T3X29uy8FKuLtSVN6cSy8NBYOHQ3fPCd0GhEJLBuFoBvwXtJ0ZdxW7zLuXg18DHRs4LoAmNkoMys3s/KqqqosxC5Si2fAY1fAnt+G464NnUZEckDenDXk7mPdvdTdSzt37hw6Tn5asQDuOxe67Aun3K5rC4gIkJ1CsAzokTTdPW6rdxkzawm0BVY1cF3Jhk9WRGcItdkBhk+K7kVEyE4hmA30MbPeZtaa6OBvWcoyZcCI+PEQYJa7e9w+LD6rqDfQB3gpC5kk2cZ1MPEM+HwVDJ8IbevtfRORIpXx9wjcvdrMRgPTgRJgnLsvMLNrgXJ3LwPuBO4yswpgNVGxIF7uXmAhUA1c7O41mWaSJIlE9K3hZXNg6H9g576hE4lIjrHoH/P8Ulpa6uXl5aFj5IdZv4On/xQdGD7sktBpRCQgM5vj7qWp7XlzsFi2wryJURHo9334fz8OnUZEcpQKQaF657no2gK9joCTbtRAciKSlgpBIVr1Fkw8E9r1hKF3RZecFBFJQ4Wg0Kz7KDpNFIczJsG27UMnEpEcp9FHC0nNRrj3bPjobTj7Qei4W+hEIpIHVAgKhTs8/FNY+jScfCv0Oix0IhHJE+oaKhTP/x1eHg9H/BT6Dg+dRkTyiApBIVj0MDz+K9hncHTheRGRRlAhyHfvz4X7z4duB8L3boMWeklFpHH0qZHP1r4fXW94u44wbAK02jZ0IhHJQzpYnK82fBadJrr+Exg5HXbsEjqRiOQpFYJ8lEjAlFGwYn40pPRO+4VOJCJ5TIUgHz1xDSyaBgOvhz2OD51GRPKcjhHkmznj4bm/wUHnw8E/DJ1GRAqACkE+WfIUPHwZ7D4g2hvQQHIikgUqBPmi6s1o+IiOfWDIOChRr56IZIcKQT74bBXcczqUtI4GktumbehEIlJA9G9lrqteD5POir4zcM40aN8zdCIRKTAqBLnMHR66BN59Dk69E3r0D51IRAqQuoZy2TN/hnkT4Fu/gP2HhE4jIgVKhSBXzZ8Cs66DA4bCUT8PnUZEClhGhcDMOpjZDDNbHN/XuRyWmfU1s+fNbIGZvWpmQ5Pm/cvMlprZ3PjWN5M8BaOyHKZeCD0OgUH/q9NERaRJZbpHcCUw0937ADPj6VSfA2e7+77AQOAvZtYuaf7P3L1vfJubYZ78t+ZdmDAcdtwJht0NLduETiQiBS7TQjAYGB8/Hg+cnLqAu7/p7ovjx+8DK4HOGT5vYfpiLdwzLDpT6Ix7YftOoROJSBHItBB0cffl8eMPgM0OgWlm/YHWwFtJzb+Pu4xuMrO0//6a2SgzKzez8qqqqgxj56Caapg8EqoWwenjofOeoROJSJHYYiEwsyfMbH49t8HJy7m7A76Z7XQF7gLOdfdE3HwVsBdwENABuCLd+u4+1t1L3b20c+cC3KGY/guomAEn3QC7HR06jYgUkS1+j8DdB6SbZ2YrzKyruy+PP+hXplnua8DDwNXu/kLStmv3Jtab2T+ByxuVvlC8dDu8dBscOhpKR4ZOIyJFJtOuoTJgRPx4BPBg6gJm1hp4APi3u09Omdc1vjei4wvzM8yTfxY/AY/+HPb8Nhx3beg0IlKEMi0EY4DjzGwxMCCexsxKzeyOeJnTgSOBc+o5TfRuM3sNeA3oBPwuwzz5ZcVCuO8c6LIvnHI7tCgJnUhEipBFXfv5pbS01MvLy0PHyMynK+H2Y6FmA/xgFrTtFjqRiBQ4M5vj7qWp7RprKISN62DiGfBZFYx8VEVARIJSIWhuiQRMvSj69vDQu2DnfqETiUiRUyFobk/9ARZMgQG/hb2/GzqNiIgGnWtW8ybB03+EfmfBYZeETiMiAqgQNJ93noey0dDrCDjpJg0kJyI5Q4WgOaxeCpPOhHa7wOn/hpatQycSEfmSCkFTW7cmut6wJ6KB5LbrEDqRiMgmdLC4KdVshHvPjvYIzp4KHXcLnUhEpA4VgqbiDo9cDkv/CyffAr0OD51IRKRe6hpqKs/fDHP+BYdfBn3PCJ1GRCQtFYKmsOgRePyXsM9gOOZXodOIiGyWCkG2LX8V7j8/+sbwybdCC/2KRSS36VMqm9Yuh3uGwrbtYfgEaL1d6EQiIlukg8XZsuEzmDAU1q+FkY9FF58XEckDKgTZkEjAlFHwwWswfCLstH/oRCIiDaZCkA0zfwOLpsHAMbDHCaHTiIg0io4RZOrlf8Ozf4XS8+DgC0KnERFpNBWCTCx9GqZdCrsdAyf+UQPJiUheUiHYWh8uhklnQcfd4bR/QYl62UQkP6kQbI3PV8Pdp0GLVnDGJNimbehEIiJbLaNCYGYdzGyGmS2O79unWa7GzObGt7Kk9t5m9qKZVZjZJDPL/fGZq9dHewJr34++K9C+V+hEIiIZyXSP4Epgprv3AWbG0/VZ5+5949ugpPbrgZvcfXfgI+C8DPM0LXd46CfwzrNw8j+gR//QiUREMpZpIRgMjI8fjwdObuiKZmbAMcDkrVk/iP+7EebdA9+6CvYfEjqNiEhWZFoIurj78vjxB0CXNMttY2blZvaCmdV+2HcE1rh7dTxdCXTLME/TWTAVZl4L+58GR10ROo2ISNZs8VQXM3sCqG+8hKuTJ9zdzczTbKanuy8zs12BWWb2GvBxY4Ka2ShgFMAuu+zSmFUzVzkHHvgh9DgYBv1dp4mKSEHZYiFw9wHp5pnZCjPr6u7LzawrsDLNNpbF90vM7CmgH3A/0M7MWsZ7Bd2BZZvJMRYYC1BaWpqu4GTfmvdgwjDYoQsMuwdabdNsTy0i0hwy7RoqA0bEj0cAD6YuYGbtzaxN/LgTcBiw0N0deBIYsrn1g1r/STSaaPUX0fWGt+8UOpGISNZlWgjGAMeZ2WJgQDyNmZWa2R3xMnsD5WY2j+iDf4y7L4znXQFcZmYVRMcM7swwT/YkamDyeVC1CE4fD1/fK3QiEZEmkdHXYd19FXBsPe3lwPnx4+eAeofjdPclQG6egzn9alg8HU66MRpCQkSkQOmbxfV56XZ48RY45CI4KLe/2iAikikVglQVT8CjV8AeA+H434VOIyLS5FQIkq18He47F76+D5x6B7QoCZ1IRKTJqRDU+rQK7jkdWm0HZ0yENjuGTiQi0iw0djLAxi9g4hlRMTj3EWjbPXQiEZFmo0LgDg9eDJUvwel3QbcDQycSEWlW6hp6agzMnwzHXgP7DNry8iIiBaa4C8Gr98J/x0Dfs+DwS0OnEREJongLwbsvRF1CPQ+H79ykgeREpGgVZyFYvTQ6ONy2Bwy9C1rm/oXRRESaSvEVgnVrooHkEjVw5n2wXYfQiUREgiqus4ZqNsJ958DqJfD9B6DjbqETiYgEVzyFwB0e+RkseRIG3wy9jwidSEQkJxRX11CnPnDET6HfWaGTiIjkjOLZIzCDQy8OnUJEJOcU1x6BiIjUoUIgIlLkVAhERIqcCoGISJFTIRARKXIqBCIiRU6FQESkyKkQiIgUOXP30BkazcyqgHeaaPOdgA+baNvZli9ZlTP78iWrcmZXpjl7unvn1Ma8LARNyczK3b00dI6GyJesypl9+ZJVObOrqXKqa0hEpMipEIiIFDkVgrrGhg7QCPmSVTmzL1+yKmd2NUlOHSMQESly2iMQESlyKgQiIkWuqAuBmY0zs5VmNj+prYOZzTCzxfF9+5AZ40w9zOxJM1toZgvM7JIczrqNmb1kZvPirL+N23ub2YtmVmFmk8ysdeisAGZWYmavmNm0eDrncprZ22b2mpnNNbPyuC0XX/t2ZjbZzBaZ2etmdmiO5twz/l3W3taa2U9yNOul8d/RfDObEP99Zf09WtSFAPgXMDCl7Upgprv3AWbG06FVAz91932AQ4CLzWwfcjPreuAYd/8G0BcYaGaHANcDN7n77sBHwHkBMya7BHg9aTpXcx7t7n2TziHPxdf+r8Bj7r4X8A2i32vO5XT3N+LfZV/gm8DnwAPkWFYz6wb8GCh19/2AEmAYTfEedfeivgG9gPlJ028AXePHXYE3QmesJ/ODwHG5nhXYDngZOJjo25At4/ZDgek5kK870R/8McA0wHI059tAp5S2nHrtgbbAUuITUHI1Zz25jweezcWsQDfgPaAD0WWFpwEnNMV7tNj3COrTxd2Xx48/ALqEDJPKzHoB/YAXydGscXfLXGAlMAN4C1jj7tXxIpVEb/LQ/gL8HEjE0x3JzZwOPG5mc8xsVNyWa699b6AK+Gfc1XaHmW1P7uVMNQyYED/Oqazuvgy4AXgXWA58DMyhCd6jKgSb4VHJzZnza81sB+B+4CfuvjZ5Xi5ldfcaj3a7uwP9gb0CR6rDzL4DrHT3OaGzNMDh7n4gcCJRt+CRyTNz5LVvCRwI3OLu/YDPSOlayZGcX4r71gcB96XOy4Ws8TGKwURFdmdge+p2ZWeFCkFdK8ysK0B8vzJwHgDMrBVREbjb3afEzTmZtZa7rwGeJNp9bWdmLeNZ3YFlwYJFDgMGmdnbwESi7qG/kns5a/8zxN1XEvVl9yf3XvtKoNLdX4ynJxMVhlzLmexE4GV3XxFP51rWAcBSd69y943AFKL3bdbfoyoEdZUBI+LHI4j644MyMwPuBF539xuTZuVi1s5m1i5+vC3RsYzXiQrCkHix4Fnd/Sp37+7uvYi6B2a5+5nkWE4z297Mdqx9TNSnPZ8ce+3d/QPgPTPbM246FlhIjuVMMZyvuoUg97K+CxxiZtvFnwG1v9Psv0dDH6wJfDBmAlHf20ai/2jOI+onngksBp4AOuRAzsOJdlNfBebGt2/naNYDgFfirPOBX8ftuwIvARVEu+JtQmdNyvwtYFou5ozzzItvC4Cr4/ZcfO37AuXxaz8VaJ+LOeOs2wOrgLZJbTmXFfgtsCj+W7oLaNMU71ENMSEiUuTUNSQiUuRUCEREipwKgYhIkVMhEBEpcioEIiJFToVARKTIqRCIiBS5/w8IVbFgjbGizwAAAABJRU5ErkJggg==\n",
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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
           },
           "metadata": {
             "needs_background": "light"
-          }
+          },
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "plt.plot(train_sizes_abs, train_scores.mean(axis=1))\n",
+        "plt.plot(train_sizes_abs, test_scores.mean(axis=1))\n",
+        "plt.show()"
       ]
     }
-  ]
+  ],
+  "metadata": {
+    "colab": {
+      "authorship_tag": "ABX9TyPL4TANdEbCTQ7MqTDMS5xV",
+      "collapsed_sections": [],
+      "include_colab_link": true,
+      "name": "regularization.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.13.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/5 - La Classificazione/binary_classification.ipynb b/5 - La Classificazione/binary_classification.ipynb
index 9ba1b19..cf13b14 100644
--- a/5 - La Classificazione/binary_classification.ipynb	
+++ b/5 - La Classificazione/binary_classification.ipynb	
@@ -39,9 +39,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "La **Classificazione Binaria** ha l'obiettivo di predire l'appartenenza di un'istanza a una di due classi possibili (es. $0$ o $1$, Vero o Falso, Negativo o Positivo).\n",
         "\n",
@@ -143,6 +141,14 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Inizializziamo il modello di **Regressione Logistica**. Nonostante il nome, \u00e8 un algoritmo di classificazione che modella la probabilit\u00e0 di appartenenza a una classe tramite la funzione Sigmoide:\n",
+        "$$\\sigma(z) = \\frac{1}{1 + e^{-z}}$$"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -196,6 +202,13 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Visualizziamo la distribuzione dei dati nel piano cartesiano. Questo ci aiuta a intuire se esiste una relazione lineare o non lineare tra le feature."
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -225,6 +238,13 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -254,6 +274,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -294,9 +321,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "Per misurare le performance di un classificatore binario, utilizziamo diverse metriche specifiche:\n",
         "1. **Log Loss (Cross-Entropy)**: Valuta la bont\u00e0 del classificatore basandosi sulla confidenza delle predizioni probabilistiche. Penalizza fortemente le predizioni errate fatte con alta confidenza.\n",
@@ -335,6 +360,13 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -349,6 +381,13 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -374,6 +413,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "La **Matrice di Confusione** \u00e8 uno strumento diagnostico essenziale in classificazione. Confronta le predizioni del modello con le etichette reali, dividendole in Veri Positivi (TP), Veri Negativi (TN), Falsi Positivi (FP) e Falsi Negativi (FN)."
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -391,6 +437,13 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "La **Matrice di Confusione** \u00e8 uno strumento diagnostico essenziale in classificazione. Confronta le predizioni del modello con le etichette reali, dividendole in Veri Positivi (TP), Veri Negativi (TN), Falsi Positivi (FP) e Falsi Negativi (FN)."
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -420,6 +473,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Il `classification_report` ci fornisce in un unico specchietto le metriche fondamentali: Precision, Recall e F1-Score, calcolate per ciascuna classe."
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -462,6 +522,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Il `classification_report` ci fornisce in un unico specchietto le metriche fondamentali: Precision, Recall e F1-Score, calcolate per ciascuna classe."
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -509,6 +576,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -556,6 +630,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -603,6 +684,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
diff --git a/5 - La Classificazione/multiclass_classification.ipynb b/5 - La Classificazione/multiclass_classification.ipynb
index 6aa8d07..ebcb143 100644
--- a/5 - La Classificazione/multiclass_classification.ipynb	
+++ b/5 - La Classificazione/multiclass_classification.ipynb	
@@ -38,9 +38,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "La **Classificazione Multiclasse** si applica a problemi in cui la variabile target presenta $K > 2$ classi discrete (ad esempio, classificare le immagini di numeri da $0$ a $9$).\n",
         "\n",
@@ -98,16 +96,41 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "In Scikit-learn, la regressione logistica applica l'approccio One-vs-Rest impostando il parametro `multi_class='ovr'`.\n",
         "\n",
         "### Performance dell'approccio OvR:\n",
         "- **Accuratezza**: Otteniamo una $\\text{Train Accuracy} \\approx 0.67$ e una $\\text{Test Accuracy} \\approx 0.77$.\n",
         "- **ROC AUC (OVO)**: Otteniamo un $\\text{Train AUC} \\approx 0.853$ e un $\\text{Test AUC} \\approx 0.841$.\n",
-        "L'approccio OvR \u00e8 computazionalmente efficiente in quanto richiede solo $K$ modelli, ma pu\u00f2 soffrire se le distribuzioni delle classi sono sbilanciate nei dataset binari intermedi.\n"
+        "\n",
+        "--- \n",
+        "\n",
+        "## \ud83d\udcca Guida all'interpretazione del Classification Report\n",
+        "\n",
+        "Il `classification_report` di Scikit-learn mostra un riepilogo dettagliato delle metriche di classificazione per ciascuna classe:\n",
+        "\n",
+        "### 1. Le Colonne Principali\n",
+        "- **Precision (Precisione)**: Per una determinata classe, risponde alla domanda: *\"Di tutti gli elementi che il modello ha classificato come appartenenti a questa classe, quanti ne appartengono effettivamente?\"*\n",
+        "  $$\\text{Precision}_c = \\frac{\\text{TP}_c}{\\text{TP}_c + \\text{FP}_c}$$\n",
+        "  *Esempio (Test Set)*: Per la classe `0`, la precision \u00e8 $0.80$ (l'80% delle predizioni per la classe 0 era corretto).\n",
+        "- **Recall (Sensibilit\u00e0 / Richiamo)**: Risponde alla domanda: *\"Di tutti gli elementi reali appartenenti a questa classe, quanti ne ha individuati il modello?\"*\n",
+        "  $$\\text{Recall}_c = \\frac{\\text{TP}_c}{\\text{TP}_c + \\text{FN}_c}$$\n",
+        "  *Esempio (Test Set)*: Per la classe `0`, la recall \u00e8 $0.92$ (il modello ha individuato il 92% degli elementi reali appartenenti alla classe 0).\n",
+        "- **F1-Score**: \u00c8 la media armonica di Precision e Recall per quella classe. Fornisce una singola misura di sintesi bilanciata, utile soprattutto in caso di classi fortemente sbilanciate.\n",
+        "  $$F_{1,c} = 2 \\times \\frac{\\text{Precision}_c \\times \\text{Recall}_c}{\\text{Precision}_c + \\text{Recall}_c}$$\n",
+        "- **Support (Supporto)**: Indica il numero di campioni reali (osservazioni reali) appartenenti a ciascuna classe in quella specifica partizione (es. $22$ campioni per la classe 0 nel Train set, $13$ nel Test set).\n",
+        "\n",
+        "### 2. Le Righe di Sintesi (Medie Generali)\n",
+        "- **Accuracy (Accuratezza)**: Rappresenta la frazione totale di predizioni corrette fatte dal modello su tutte le classi:\n",
+        "  $$\\text{Accuracy} = \\frac{\\text{Predizioni Corrette}}{\\text{Totale Campioni}}$$\n",
+        "  *Nota*: Nel Test Set otteniamo un'accuratezza del $77\\%$ ($0.77$).\n",
+        "- **Macro Avg (Media Macro)**: Calcola la media aritmetica semplice (non pesata) delle metriche (Precision, Recall, F1) calcolate per ogni singola classe:\n",
+        "  $$\\text{Macro Avg} = \\frac{1}{K} \\sum_{c=1}^K \\text{Metric}_c$$\n",
+        "  *Nota*: Tratta tutte le classi allo stesso modo, indipendentemente dal loro supporto. Utile se si vuole dare uguale importanza alle classi anche se alcune sono molto rare.\n",
+        "- **Weighted Avg (Media Ponderata)**: Calcola la media delle metriche pesata in base al supporto (numero di campioni) di ciascuna classe:\n",
+        "  $$\\text{Weighted Avg} = \\sum_{c=1}^K \\left( \\frac{\\text{Support}_c}{\\text{Totale Campioni}} \\times \\text{Metric}_c \\right)$$\n",
+        "  *Nota*: Questa media rispecchia maggiormente la distribuzione reale delle classi nel dataset.\n"
       ]
     },
     {
@@ -121,6 +144,14 @@
       "execution_count": 4,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Inizializziamo il modello di **Regressione Logistica**. Nonostante il nome, \u00e8 un algoritmo di classificazione che modella la probabilit\u00e0 di appartenenza a una classe tramite la funzione Sigmoide:\n",
+        "$$\\sigma(z) = \\frac{1}{1 + e^{-z}}$$"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -148,6 +179,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Il `classification_report` ci fornisce in un unico specchietto le metriche fondamentali: Precision, Recall e F1-Score, calcolate per ciascuna classe."
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -197,6 +235,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -224,6 +269,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -262,9 +314,7 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "enriched": true
-      },
+      "metadata": {},
       "source": [
         "L'approccio **Multinomial** (noto anche come *Softmax Regression*) rappresenta la naturale generalizzazione della regressione logistica al caso multiclasse. Invece di addestrare modelli binari indipendenti, calcola contemporaneamente le probabilit\u00e0 per tutte le classi tramite la funzione **Softmax**:\n",
         "$$P(y = c \\mid X) = \\frac{e^{z_c}}{\\sum_{j=1}^K e^{z_j}}$$\n",
@@ -273,8 +323,7 @@
         "\n",
         "### Performance dell'approccio Multinomial:\n",
         "- **Accuratezza**: Otteniamo una $\\text{Train Accuracy} \\approx 0.69$ e una $\\text{Test Accuracy} \\approx 0.70$.\n",
-        "- **ROC AUC (OVO)**: Otteniamo un $\\text{Train AUC} \\approx 0.851$ e un $\\text{Test AUC} \\approx 0.844$.\n",
-        "L'approccio multinomiale modella in modo ottimale la mutua esclusivit\u00e0 delle classi, fornendo spesso probabilit\u00e0 calibrate migliori rispetto a OvR.\n"
+        "- **ROC AUC (OVO)**: Otteniamo un $\\text{Train AUC} \\approx 0.851$ e un $\\text{Test AUC} \\approx 0.844$.\n"
       ]
     },
     {
@@ -304,6 +353,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Il `classification_report` ci fornisce in un unico specchietto le metriche fondamentali: Precision, Recall e F1-Score, calcolate per ciascuna classe."
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -353,6 +409,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -380,6 +443,13 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
diff --git a/6 - Clustering/elbow_method.ipynb b/6 - Clustering/elbow_method.ipynb
index 6163b34..1a5e122 100644
--- a/6 - Clustering/elbow_method.ipynb	
+++ b/6 - Clustering/elbow_method.ipynb	
@@ -37,6 +37,22 @@
         "id": "z3cInKW6Hgch"
       }
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Il **Metodo del Gomito (Elbow Method)** \u00e8 una tecnica euristica utilizzata per determinare il numero ottimale di cluster ($K$) in un algoritmo come il K-Means.\n",
+        "\n",
+        "L'idea di base \u00e8 eseguire il K-Means per un range di valori di $K$ (es. da 1 a 10) e calcolare per ogni $K$ la somma dei quadrati delle distanze intra-cluster (WCSS o Inerzia):\n",
+        "\n",
+        "$$\\text{WCSS} = \\sum_{i=1}^{K} \\sum_{x \\in C_i} ||x - \\mu_i||^2$$\n",
+        "\n",
+        "Aumentando $K$, il WCSS diminuir\u00e0 sempre, poich\u00e9 i punti saranno sempre pi\u00f9 vicini ai loro rispettivi centroidi (nel caso limite in cui $K$ \u00e8 uguale al numero di punti, il WCSS sar\u00e0 0). Tuttavia, stiamo cercando un compromesso: il punto in cui l'aggiunta di un ulteriore cluster non migliora significativamente il modello. Questo punto crea una curva a forma di braccio, e il *gomito* (l'angolo) rappresenta il numero ottimale di cluster.\n",
+        "\n",
+        "> [!IMPORTANT]\n",
+        "> Il metodo del gomito \u00e8 soggettivo: a volte la curva non ha un \"gomito\" ben definito. In questi casi, potrebbero essere necessari altri metodi (come la Silhouette Score) o considerazioni di business."
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -115,6 +131,17 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Esecuzione dell'Elbow Method\n",
+        "Iteriamo su un range di possibili valori per $K$. Per ogni iterazione:\n",
+        "1. Istanziamo e addestriamo il modello `KMeans` con $K$ cluster.\n",
+        "2. Estraiamo l'inerzia tramite la propriet\u00e0 `.inertia_`.\n",
+        "3. Memorizziamo il valore nel dizionario `sse` per poterlo plottare."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
diff --git a/6 - Clustering/kmeans.ipynb b/6 - Clustering/kmeans.ipynb
index 453efd9..f0c6c2c 100644
--- a/6 - Clustering/kmeans.ipynb	
+++ b/6 - Clustering/kmeans.ipynb	
@@ -37,6 +37,26 @@
         "id": "epJ5E24SENZ8"
       }
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Il **K-Means** \u00e8 uno degli algoritmi di clustering (apprendimento non supervisionato) pi\u00f9 popolari.\n",
+        "L'obiettivo dell'algoritmo \u00e8 partizionare un insieme di dati in $K$ gruppi distinti (cluster), in modo che i punti all'interno dello stesso gruppo siano il pi\u00f9 simili possibile tra loro, e il pi\u00f9 dissimili possibile dai punti negli altri gruppi.\n",
+        "\n",
+        "Matematicamente, il K-Means cerca di minimizzare l'**Inerzia** (o Within-Cluster Sum of Squares - WCSS):\n",
+        "\n",
+        "$$\\text{WCSS} = \\sum_{i=1}^{K} \\sum_{x \\in C_i} ||x - \\mu_i||^2$$\n",
+        "\n",
+        "Dove:\n",
+        "- $K$ \u00e8 il numero di cluster\n",
+        "- $x$ \u00e8 un punto dati appartenente al cluster $C_i$\n",
+        "- $\\mu_i$ \u00e8 il centroide (media) del cluster $C_i$\n",
+        "\n",
+        "> [!IMPORTANT]\n",
+        "> Nel K-Means, il numero di cluster $K$ deve essere specificato a priori. Questo \u00e8 uno dei limiti principali dell'algoritmo, che si pu\u00f2 affrontare con tecniche come il Metodo del Gomito (Elbow Method)."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -134,6 +154,18 @@
         "id": "6VN5bmI-Gsd0"
       }
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "L'addestramento del K-Means avviene in due fasi iterative:\n",
+        "1. **Assegnazione**: Ogni punto viene assegnato al centroide pi\u00f9 vicino.\n",
+        "2. **Aggiornamento**: I centroidi vengono ricalcolati come media dei punti assegnati.\n",
+        "\n",
+        "> [!WARNING]\n",
+        "> **Inizializzazione dei centroidi**: L'algoritmo standard sceglie i centroidi iniziali in modo casuale, il che pu\u00f2 portare a convergere in minimi locali. Per risolvere questo problema, si utilizza l'inizializzazione `k-means++`, che seleziona i centroidi iniziali in modo che siano sufficientemente distanti tra loro, accelerando la convergenza e migliorando il risultato."
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -208,6 +240,15 @@
         "id": "qebcXu68nBKe"
       }
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Per valutare la bont\u00e0 del clustering, utilizziamo due metriche correlate:\n",
+        "- **Distorsione**: La media delle distanze al quadrato dai centri dei cluster dei rispettivi punti.\n",
+        "- **Inerzia**: La somma delle distanze al quadrato (WCSS). Scikit-learn calcola automaticamente l'inerzia e la rende disponibile tramite l'attributo `inertia_`."
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -322,7 +363,7 @@
     {
       "cell_type": "code",
       "source": [
-        "L = {0:\"Donne single\",1:\"Neo papà\",2:\"Neo mamme\"}\n",
+        "L = {0:\"Donne single\",1:\"Neo pap\u00e0\",2:\"Neo mamme\"}\n",
         "vfunc = np.vectorize(lambda x: L[x])\n",
         "labels = vfunc(y_kmeans)\n",
         "sns.scatterplot(x=X[:,0], y=X[:,1], hue=labels, s=100)\n",

From 90ccf123f9226cda449ae62cb458f75503877465 Mon Sep 17 00:00:00 2001
From: Spatariu Rares <81376159+SpatariuRares@users.noreply.github.com>
Date: Sat, 20 Jun 2026 12:21:16 +0200
Subject: [PATCH 5/9] refactor: reorganize notebook cells and fix typos in
 binary classification guide

---
 .../binary_classification.ipynb               | 368 +++++++++---------
 1 file changed, 175 insertions(+), 193 deletions(-)

diff --git a/5 - La Classificazione/binary_classification.ipynb b/5 - La Classificazione/binary_classification.ipynb
index cf13b14..3b526a7 100644
--- a/5 - La Classificazione/binary_classification.ipynb	
+++ b/5 - La Classificazione/binary_classification.ipynb	
@@ -1,28 +1,10 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "binary_classification.ipynb",
-      "provenance": [],
-      "collapsed_sections": [],
-      "authorship_tag": "ABX9TyMpgLJnyo4voFZMWidBhlip",
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/ProfAI/machine-learning-fondamenti/blob/main/4%20-%20La%20Classificazione/binary_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -30,12 +12,12 @@
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# La Classificazione Binaria"
-      ],
       "metadata": {
         "id": "Pyz1mX-elt8R"
-      }
+      },
+      "source": [
+        "# La Classificazione Binaria"
+      ]
     },
     {
       "cell_type": "markdown",
@@ -43,17 +25,17 @@
       "source": [
         "La **Classificazione Binaria** ha l'obiettivo di predire l'appartenenza di un'istanza a una di due classi possibili (es. $0$ o $1$, Vero o Falso, Negativo o Positivo).\n",
         "\n",
-        "### Perch\u00e9 non la Regressione Lineare?\n",
-        "Usare la regressione lineare classica per la classificazione presenta forti limiti: l'output non \u00e8 vincolato e pu\u00f2 assumere valori esterni a $[0, 1]$, rendendo difficile l'interpretazione probabilistica. Inoltre, la presenza di outlier pu\u00f2 spostare la retta di decisione in modo errato.\n",
+        "### Perché non la Regressione Lineare?\n",
+        "Usare la regressione lineare classica per la classificazione presenta forti limiti: l'output non è vincolato e può assumere valori esterni a $[0, 1]$, rendendo difficile l'interpretazione probabilistica. Inoltre, la presenza di outlier può spostare la retta di decisione in modo errato.\n",
         "\n",
         "### La Regressione Logistica e la funzione Sigmoide:\n",
         "La **Regressione Logistica** risolve questo problema mappando la combinazione lineare delle feature tramite la funzione **Sigmoide**:\n",
         "$$\\sigma(z) = \\frac{1}{1 + e^{-z}}$$\n",
         "\n",
-        "Dove la combinazione lineare \u00e8:\n",
+        "Dove la combinazione lineare è:\n",
         "$$z = \\beta_0 + \\beta_1 x_1 + \\dots + \\beta_n x_n$$\n",
         "\n",
-        "L'output di $\\sigma(z)$ \u00e8 compreso strettamente tra $0$ e $1$, ed \u00e8 interpretabile come la **probabilit\u00e0** $P(y=1 \\mid X)$ che l'istanza appartenga alla classe positiva.\n"
+        "L'output di $\\sigma(z)$ è compreso strettamente tra $0$ e $1$, ed è interpretabile come la **probabilità** $P(y=1 \\mid X)$ che l'istanza appartenga alla classe positiva.\n"
       ]
     },
     {
@@ -74,19 +56,16 @@
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Generiamo il dataset"
-      ],
       "metadata": {
         "id": "94Vjt41ubHvY"
-      }
+      },
+      "source": [
+        "### Generiamo il dataset"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "X, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, n_repeated=0, n_classes=2, random_state=0)\n",
-        "plt.scatter(X[:,0], X[:,1], c=y)"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -95,68 +74,66 @@
         "id": "f0nxZSNyly3r",
         "outputId": "7eafc0d6-bbf7-4f82-e5f1-3c448ba413f3"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "<matplotlib.collections.PathCollection at 0x7f351bab2b90>"
             ]
           },
+          "execution_count": 2,
           "metadata": {},
-          "execution_count": 2
+          "output_type": "execute_result"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "image/png": 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\n",
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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
           },
           "metadata": {
             "needs_background": "light"
-          }
+          },
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "X, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, n_repeated=0, n_classes=2, random_state=0)\n",
+        "plt.scatter(X[:,0], X[:,1], c=y)"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Creiamo il modello"
-      ],
       "metadata": {
         "id": "Vyw9FpsKbMGU"
-      }
+      },
+      "source": [
+        "### Creiamo il modello"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3)"
-      ],
+      "execution_count": null,
       "metadata": {
         "id": "yPCQHXfDm0Sj"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3)"
+      ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Inizializziamo il modello di **Regressione Logistica**. Nonostante il nome, \u00e8 un algoritmo di classificazione che modella la probabilit\u00e0 di appartenenza a una classe tramite la funzione Sigmoide:\n",
+        "Inizializziamo il modello di **Regressione Logistica**. Nonostante il nome, è un algoritmo di classificazione che modella la probabilità di appartenenza a una classe tramite la funzione Sigmoide:\n",
         "$$\\sigma(z) = \\frac{1}{1 + e^{-z}}$$"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "from sklearn.linear_model import LogisticRegression\n",
-        "\n",
-        "lr = LogisticRegression()\n",
-        "lr.fit(X_train, y_train)"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -164,43 +141,48 @@
         "id": "kFpNbxnnm49Z",
         "outputId": "bac8891b-03ba-4bb7-bf1b-13deeb4e1a30"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "LogisticRegression()"
             ]
           },
+          "execution_count": 4,
           "metadata": {},
-          "execution_count": 4
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "from sklearn.linear_model import LogisticRegression\n",
+        "\n",
+        "lr = LogisticRegression()\n",
+        "lr.fit(X_train, y_train)"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Visualizziamo il decision boundary"
-      ],
       "metadata": {
         "id": "X_JYGHdtbOUP"
-      }
+      },
+      "source": [
+        "### Visualizziamo il decision boundary"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lWftmm4QbrAt"
+      },
+      "outputs": [],
       "source": [
         "# Il metodo meshgrid ci permette di creare una griglia di punti\n",
         "# a = np.array([[1, 2, 3, 4, 5]])\n",
         "# b = np.array([[10, 20, 30, 40, 50]])\n",
         "# xx, yy = np.meshgrid(a, b)\n",
         "# plt.scatter(xx, yy)"
-      ],
-      "metadata": {
-        "id": "lWftmm4QbrAt"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -211,6 +193,11 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CUKZpykanaDq"
+      },
+      "outputs": [],
       "source": [
         "def plot_decision_boundary(model, X, Y):\n",
         "    \n",
@@ -231,12 +218,7 @@
         "    X_b = X[Y==0]\n",
         "    plt.scatter(X_b[:, 0], X_b[:, 1], c=\"green\",  edgecolor='white')\n",
         "    plt.scatter(X_m[:, 0], X_m[:, 1], c=\"red\",  edgecolor='white')\n"
-      ],
-      "metadata": {
-        "id": "CUKZpykanaDq"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -247,9 +229,7 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "plot_decision_boundary(lr, X_train, y_train)"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -258,20 +238,22 @@
         "id": "4zJRSJb3ocjB",
         "outputId": "83e25a23-2404-44dc-e41c-06b60f4ab0ab"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "image/png": 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\n",
+            "image/png": "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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
           },
           "metadata": {
             "needs_background": "light"
-          }
+          },
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "plot_decision_boundary(lr, X_train, y_train)"
       ]
     },
     {
@@ -283,9 +265,7 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "plot_decision_boundary(lr, X_test, y_test)"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -294,37 +274,39 @@
         "id": "vN2J4In2odyC",
         "outputId": "84067eea-3312-4ec9-db01-87bbc3ae604a"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
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\n",
+            "image/png": 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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
           },
           "metadata": {
             "needs_background": "light"
-          }
+          },
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "plot_decision_boundary(lr, X_test, y_test)"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Valutiamo il modello"
-      ],
       "metadata": {
         "id": "6RSGwlZ8cYNs"
-      }
+      },
+      "source": [
+        "### Valutiamo il modello"
+      ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
         "Per misurare le performance di un classificatore binario, utilizziamo diverse metriche specifiche:\n",
-        "1. **Log Loss (Cross-Entropy)**: Valuta la bont\u00e0 del classificatore basandosi sulla confidenza delle predizioni probabilistiche. Penalizza fortemente le predizioni errate fatte con alta confidenza.\n",
+        "1. **Log Loss (Cross-Entropy)**: Valuta la bontà del classificatore basandosi sulla confidenza delle predizioni probabilistiche. Penalizza fortemente le predizioni errate fatte con alta confidenza.\n",
         "   $$\\text{Log Loss} = -\\frac{1}{n} \\sum_{i=1}^n \\left[ y_i \\log(\\hat{y}_i) + (1 - y_i) \\log(1 - \\hat{y}_i) \\right]$$\n",
         "   *Nota*: In questo notebook il modello ottiene $\\text{Train Loss} \\approx 0.312$ e $\\text{Test Loss} \\approx 0.207$.\n",
         "2. **Matrice di Confusione**: Tabella a due dimensioni che confronta i valori reali con quelli predetti dal modello:\n",
@@ -337,28 +319,28 @@
         "4. **Precision (Precisione)**: Proporzione di positivi predetti correttamente rispetto al totale dei positivi previsti dal modello.\n",
         "   $$\\text{Precision} = \\frac{\\text{TP}}{\\text{TP} + \\text{FP}}$$\n",
         "   *Nota*: $\\text{Train Precision} \\approx 0.941$, $\\text{Test Precision} = 1.00$.\n",
-        "5. **Recall (Sensibilit\u00e0)**: Proporzione di positivi reali identificati correttamente.\n",
+        "5. **Recall (Sensibilità)**: Proporzione di positivi reali identificati correttamente.\n",
         "   $$\\text{Recall} = \\frac{\\text{TP}}{\\text{TP} + \\text{FN}}$$\n",
         "   *Nota*: $\\text{Train Recall} \\approx 0.889$, $\\text{Test Recall} \\approx 0.857$.\n",
         "6. **F1-Score**: Media armonica di Precision e Recall, ottimale per valutare modelli in presenza di dataset sbilanciati.\n",
         "   $$F_1 = 2 \\times \\frac{\\text{Precision} \\times \\text{Recall}}{\\text{Precision} + \\text{Recall}}$$\n",
         "   *Nota*: $\\text{Train F1} \\approx 0.914$, $\\text{Test F1} \\approx 0.923$.\n",
-        "7. **Curva ROC e AUC**: La curva ROC (Receiver Operating Characteristic) mostra il trade-off tra True Positive Rate (Recall) e False Positive Rate al variare della soglia di classificazione. L'Area Under the Curve (AUC) riassume la capacit\u00e0 del modello di separare le due classi.\n"
+        "7. **Curva ROC e AUC**: La curva ROC (Receiver Operating Characteristic) mostra il trade-off tra True Positive Rate (Recall) e False Positive Rate al variare della soglia di classificazione. L'Area Under the Curve (AUC) riassume la capacità del modello di separare le due classi.\n"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tYhKZDAqcPgT"
+      },
+      "outputs": [],
       "source": [
         "from sklearn.metrics import confusion_matrix\n",
         "from sklearn.metrics import accuracy_score, log_loss\n",
         "from sklearn.metrics import recall_score, precision_score, f1_score\n",
         "from sklearn.metrics import plot_roc_curve"
-      ],
-      "metadata": {
-        "id": "tYhKZDAqcPgT"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -369,17 +351,17 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zKC5elDbcjCB"
+      },
+      "outputs": [],
       "source": [
         "y_pred_train = lr.predict(X_train)\n",
         "y_proba_train = lr.predict_proba(X_train)\n",
         "y_pred_test = lr.predict(X_test)\n",
         "y_proba_test = lr.predict_proba(X_test)"
-      ],
-      "metadata": {
-        "id": "zKC5elDbcjCB"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -390,10 +372,7 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "print(f\"TRAIN LOSS: {log_loss(y_train, y_proba_train)}\")\n",
-        "print(f\"TEST LOSS: {log_loss(y_test, y_proba_test)}\")"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -401,27 +380,35 @@
         "id": "UHuSAjJOe2Ez",
         "outputId": "b571ee29-fd1b-4f8a-abf1-de899c423cae"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "TRAIN LOSS: 0.31166393798252984\n",
             "TEST LOSS: 0.20693769790978758\n"
           ]
         }
+      ],
+      "source": [
+        "print(f\"TRAIN LOSS: {log_loss(y_train, y_proba_train)}\")\n",
+        "print(f\"TEST LOSS: {log_loss(y_test, y_proba_test)}\")"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "La **Matrice di Confusione** \u00e8 uno strumento diagnostico essenziale in classificazione. Confronta le predizioni del modello con le etichette reali, dividendole in Veri Positivi (TP), Veri Negativi (TN), Falsi Positivi (FP) e Falsi Negativi (FN)."
+        "La **Matrice di Confusione** è uno strumento diagnostico essenziale in classificazione. Confronta le predizioni del modello con le etichette reali, dividendole in Veri Positivi (TP), Veri Negativi (TN), Falsi Positivi (FP) e Falsi Negativi (FN)."
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xokiZbwu3RL9"
+      },
+      "outputs": [],
       "source": [
         "def plot_confusion_matrix(y_true, y_pred, labels=[\"Negative\", \"Positive\"], show_precision=True, show_recall=True):\n",
         "\n",
@@ -430,25 +417,18 @@
         "  df_cm = pd.DataFrame(cm, index = labels,\n",
         "                    columns = [\"Predicted \"+labels[0],\"Predicted \"+labels[1]])\n",
         "  sns.heatmap(df_cm, annot=True)"
-      ],
-      "metadata": {
-        "id": "xokiZbwu3RL9"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "La **Matrice di Confusione** \u00e8 uno strumento diagnostico essenziale in classificazione. Confronta le predizioni del modello con le etichette reali, dividendole in Veri Positivi (TP), Veri Negativi (TN), Falsi Positivi (FP) e Falsi Negativi (FN)."
+        "La **Matrice di Confusione** è uno strumento diagnostico essenziale in classificazione. Confronta le predizioni del modello con le etichette reali, dividendole in Veri Positivi (TP), Veri Negativi (TN), Falsi Positivi (FP) e Falsi Negativi (FN)."
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "plot_confusion_matrix(y_train, y_pred_train)"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -457,20 +437,22 @@
         "id": "kKgoFcIt1nmg",
         "outputId": "4d457dab-c7f6-4fb1-b8a3-3ff678bf9e92"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "image/png": 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\n",
+            "image/png": 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",
             "text/plain": [
               "<Figure size 432x288 with 2 Axes>"
             ]
           },
           "metadata": {
             "needs_background": "light"
-          }
+          },
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "plot_confusion_matrix(y_train, y_pred_train)"
       ]
     },
     {
@@ -482,18 +464,7 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "def classification_report(y_true, y_pred):\n",
-        "  print(f\"PRECISION: {precision_score(y_true, y_pred)}\")\n",
-        "  print(f\"RECALL: {recall_score(y_true, y_pred)}\")\n",
-        "  print(f\"F1: {f1_score(y_true, y_pred)}\")\n",
-        "  print(f\"ACCURACY: {accuracy_score(y_true, y_pred)}\")\n",
-        "\n",
-        "print(\"TRAIN REPORT\")\n",
-        "classification_report(y_train, y_pred_train) # con l'argomento digits definiamo la precisione\n",
-        "print(\"\\nTEST REPORT\")\n",
-        "classification_report(y_test, y_pred_test)"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -501,11 +472,10 @@
         "id": "3nbcDzXmc59z",
         "outputId": "32aac992-e3de-4b04-e1e5-25f13737c327"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "TRAIN REPORT\n",
             "PRECISION: 0.9411764705882353\n",
@@ -520,6 +490,18 @@
             "ACCURACY: 0.9333333333333333\n"
           ]
         }
+      ],
+      "source": [
+        "def classification_report(y_true, y_pred):\n",
+        "  print(f\"PRECISION: {precision_score(y_true, y_pred)}\")\n",
+        "  print(f\"RECALL: {recall_score(y_true, y_pred)}\")\n",
+        "  print(f\"F1: {f1_score(y_true, y_pred)}\")\n",
+        "  print(f\"ACCURACY: {accuracy_score(y_true, y_pred)}\")\n",
+        "\n",
+        "print(\"TRAIN REPORT\")\n",
+        "classification_report(y_train, y_pred_train) # con l'argomento digits definiamo la precisione\n",
+        "print(\"\\nTEST REPORT\")\n",
+        "classification_report(y_test, y_pred_test)"
       ]
     },
     {
@@ -531,14 +513,7 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "from sklearn.metrics import classification_report\n",
-        "\n",
-        "print(\"TRAIN REPORT\")\n",
-        "print(classification_report(y_train, y_pred_train)) # con l'argomento digits definiamo la precisione\n",
-        "print(\"TEST REPORT\")\n",
-        "print(classification_report(y_test, y_pred_test))"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -546,11 +521,10 @@
         "id": "-bjMsPy7dN6t",
         "outputId": "c46a78ef-11f3-47c5-99d3-55e49ac68f60"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "TRAIN REPORT\n",
             "              precision    recall  f1-score   support\n",
@@ -574,6 +548,14 @@
             "\n"
           ]
         }
+      ],
+      "source": [
+        "from sklearn.metrics import classification_report\n",
+        "\n",
+        "print(\"TRAIN REPORT\")\n",
+        "print(classification_report(y_train, y_pred_train)) # con l'argomento digits definiamo la precisione\n",
+        "print(\"TEST REPORT\")\n",
+        "print(classification_report(y_test, y_pred_test))"
       ]
     },
     {
@@ -585,9 +567,7 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "plot_roc_curve(lr, X_train, y_train)"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -596,38 +576,40 @@
         "id": "au6fbCAYN_dD",
         "outputId": "47ea3418-a1b9-4059-c044-c063149ef7a6"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; Function :func:`plot_roc_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: :meth:`sklearn.metric.RocCurveDisplay.from_predictions` or :meth:`sklearn.metric.RocCurveDisplay.from_estimator`.\n",
             "  warnings.warn(msg, category=FutureWarning)\n"
           ]
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7f3512f76990>"
             ]
           },
+          "execution_count": 16,
           "metadata": {},
-          "execution_count": 16
+          "output_type": "execute_result"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "image/png": 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\n",
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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
           },
           "metadata": {
             "needs_background": "light"
-          }
+          },
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "plot_roc_curve(lr, X_train, y_train)"
       ]
     },
     {
@@ -639,9 +621,7 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "plot_roc_curve(lr, X_test, y_test)"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -650,57 +630,59 @@
         "id": "_lMKJAGFOD0j",
         "outputId": "e00c6328-281a-4e83-b743-714e99da8b42"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; Function :func:`plot_roc_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: :meth:`sklearn.metric.RocCurveDisplay.from_predictions` or :meth:`sklearn.metric.RocCurveDisplay.from_estimator`.\n",
             "  warnings.warn(msg, category=FutureWarning)\n"
           ]
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7f3512d18a90>"
             ]
           },
+          "execution_count": 17,
           "metadata": {},
-          "execution_count": 17
+          "output_type": "execute_result"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "image/png": 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\n",
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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
           },
           "metadata": {
             "needs_background": "light"
-          }
+          },
+          "output_type": "display_data"
         }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
+      ],
       "source": [
-        "Procediamo con la prossima fase di esplorazione ed esecuzione del codice:"
+        "plot_roc_curve(lr, X_test, y_test)"
       ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "authorship_tag": "ABX9TyMpgLJnyo4voFZMWidBhlip",
+      "collapsed_sections": [],
+      "include_colab_link": true,
+      "name": "binary_classification.ipynb",
+      "provenance": []
     },
-    {
-      "cell_type": "code",
-      "source": [
-        ""
-      ],
-      "metadata": {
-        "id": "WRv58grUOI-x"
-      },
-      "execution_count": null,
-      "outputs": []
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
     }
-  ]
-}
\ No newline at end of file
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

From 169a1d0d3d00d4abf20b661424f7d84b6b25907a Mon Sep 17 00:00:00 2001
From: Spatariu Rares <81376159+SpatariuRares@users.noreply.github.com>
Date: Sun, 21 Jun 2026 18:22:50 +0200
Subject: [PATCH 6/9] feat: implement notebook metric extraction and enrichment
 automation tools, and update clustering pedagogical content

---
 .../notebook-pedagogical-enrichment/SKILL.md  |   48 +-
 .../scripts/enrich_runner.py                  |  142 +
 .../scripts/notebook_metric_extractor.py      |  113 +
 .../binary_classification.ipynb               |    6 +-
 .../binary_classification_exercise.ipynb      | 2733 +++++++++++------
 .../esercizi/breast_cancer_prediction.xlsx    |  Bin 0 -> 5000 bytes
 6 - Clustering/elbow_method.ipynb             |   67 +-
 6 - Clustering/kmeans.ipynb                   |  156 +-
 6 - Clustering/number_of_k.png                |  Bin 0 -> 47630 bytes
 9 files changed, 2269 insertions(+), 996 deletions(-)
 create mode 100644 .agents/notebook-pedagogical-enrichment/scripts/enrich_runner.py
 create mode 100644 .agents/notebook-pedagogical-enrichment/scripts/notebook_metric_extractor.py
 create mode 100644 5 - La Classificazione/esercizi/breast_cancer_prediction.xlsx
 create mode 100644 6 - Clustering/number_of_k.png

diff --git a/.agents/notebook-pedagogical-enrichment/SKILL.md b/.agents/notebook-pedagogical-enrichment/SKILL.md
index ba1e2a2..3731c34 100644
--- a/.agents/notebook-pedagogical-enrichment/SKILL.md
+++ b/.agents/notebook-pedagogical-enrichment/SKILL.md
@@ -36,6 +36,10 @@ python3 .agents/notebook-pedagogical-enrichment/scripts/notebook_helper.py inspe
 
 ### Step 2: Compute Exact Metrics (Dry Run)
 Before writing explanations, run the notebook's code (using the workspace virtual environment, e.g., `.venv/bin/python3`) to obtain the exact training/testing scores (like MSE, $R^2$, accuracy, etc.).
+You can use the local extractor utility to automatically execute the notebook and dump all cell outputs and metrics:
+```bash
+python3 .agents/notebook-pedagogical-enrichment/scripts/notebook_metric_extractor.py <path_to_notebook.ipynb>
+```
 Reporting exact numbers (e.g., *“the test $R^2$ is 0.217 for OLS but 0.994 for Lasso”*) makes the explanations extremely authentic and helpful.
 
 ### Step 3: Write Rich Markdown Explanations
@@ -50,23 +54,39 @@ Explanations must follow best practices in technical writing and pedagogy:
 - **Learning Curves**: Explain how to diagnose bias/variance by looking at the gap and convergence of training and validation scores.
 
 ### Step 4: Update the Notebook Programmatically
-Always edit Jupyter notebooks by loading the JSON in Python, manipulating the `cells` list, and saving the JSON back. This preserves metadata, notebook formatting, and prevents syntax issues.
-
-Use the following stateful matching script structure:
-```python
-import json
-
-def make_markdown_cell(text):
-    lines = [line + "\n" for line in text.split("\n")]
-    if lines and lines[-1] == "\n":
-        lines.pop()
-    elif lines:
-        lines[-1] = lines[-1].rstrip("\n")
-    return {"cell_type": "markdown", "metadata": {}, "source": lines}
+Always edit Jupyter notebooks programmatically. You can use the generic enrichment script to apply Markdown/Code cell insertions and replacements from a JSON specification:
+```bash
+python3 .agents/notebook-pedagogical-enrichment/scripts/enrich_runner.py -n <path_to_notebook.ipynb> -s <path_to_spec.json>
+```
 
-# Load, reconstruct nb["cells"] by matching cell signatures, and write back.
+#### Spec JSON File Format Example:
+```json
+[
+  {
+    "match_type": "prefix",
+    "target": "RANDOM_SEED = 2",
+    "action": "insert_after",
+    "cell_type": "markdown",
+    "source": [
+      "### Configurazione dell'Ambiente\n",
+      "Prima di iniziare importiamo..."
+    ]
+  },
+  {
+    "match_type": "exact",
+    "target": "distorsion = sum(...)",
+    "action": "replace",
+    "cell_type": "markdown",
+    "source": "Nuovo testo esplicativo..."
+  }
+]
 ```
 
+Specifications support:
+- `match_type`: `prefix`, `exact`, `contains`
+- `action`: `insert_before`, `insert_after`, `replace`, `append`
+- `cell_type`: `markdown` or `code`
+
 ### Step 5: Validation
 Verify that the output notebook is valid JSON and loads properly:
 ```bash
diff --git a/.agents/notebook-pedagogical-enrichment/scripts/enrich_runner.py b/.agents/notebook-pedagogical-enrichment/scripts/enrich_runner.py
new file mode 100644
index 0000000..9596ad9
--- /dev/null
+++ b/.agents/notebook-pedagogical-enrichment/scripts/enrich_runner.py
@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+import json
+import argparse
+import sys
+import os
+
+def make_markdown_cell(text):
+    lines = [line + "\n" for line in text.split("\n")]
+    if lines and lines[-1] == "\n":
+        lines.pop()
+    elif lines:
+        lines[-1] = lines[-1].rstrip("\n")
+    return {"cell_type": "markdown", "metadata": {}, "source": lines}
+
+def make_code_cell(text):
+    lines = [line + "\n" for line in text.split("\n")]
+    if lines and lines[-1] == "\n":
+        lines.pop()
+    elif lines:
+        lines[-1] = lines[-1].rstrip("\n")
+    return {
+        "cell_type": "code",
+        "execution_count": None,
+        "metadata": {},
+        "outputs": [],
+        "source": lines
+    }
+
+def match_cell(cell, target, match_type, cell_type_filter=None):
+    cell_type = cell.get("cell_type", "")
+    if cell_type_filter and cell_type != cell_type_filter:
+        return False
+    source_text = "".join(cell.get("source", [])).strip()
+    target_clean = target.strip()
+    
+    if match_type == "prefix":
+        return source_text.startswith(target_clean)
+    elif match_type == "contains":
+        return target_clean in source_text
+    elif match_type == "exact":
+        return source_text == target_clean
+    return False
+
+def process_enrichment(notebook_cells, spec_items):
+    new_cells = []
+    
+    for cell in notebook_cells:
+        matched_specs = []
+        for spec in spec_items:
+            if spec.get("action") == "append":
+                continue
+            
+            # Extract matching rules
+            target = spec.get("target", "")
+            match_type = spec.get("match_type", "prefix")
+            cell_type_filter = spec.get("cell_type_filter")
+            
+            if match_cell(cell, target, match_type, cell_type_filter):
+                matched_specs.append(spec)
+                
+        if not matched_specs:
+            new_cells.append(cell)
+            continue
+            
+        # Process the first match
+        spec = matched_specs[0]
+        action = spec.get("action", "insert_after")
+        new_cell_type = spec.get("cell_type", "markdown")
+        source_data = spec.get("source", "")
+        
+        source_text = "".join(source_data) if isinstance(source_data, list) else source_data
+        
+        if new_cell_type == "markdown":
+            enriched_cell = make_markdown_cell(source_text)
+        else:
+            enriched_cell = make_code_cell(source_text)
+            
+        if action == "replace":
+            new_cells.append(enriched_cell)
+        elif action == "insert_before":
+            new_cells.append(enriched_cell)
+            new_cells.append(cell)
+        elif action == "insert_after":
+            new_cells.append(cell)
+            new_cells.append(enriched_cell)
+            
+    # Process append specifications
+    for spec in spec_items:
+        if spec.get("action") == "append":
+            new_cell_type = spec.get("cell_type", "markdown")
+            source_data = spec.get("source", "")
+            source_text = "".join(source_data) if isinstance(source_data, list) else source_data
+            
+            if new_cell_type == "markdown":
+                enriched_cell = make_markdown_cell(source_text)
+            else:
+                enriched_cell = make_code_cell(source_text)
+            new_cells.append(enriched_cell)
+            
+    return new_cells
+
+def main():
+    parser = argparse.ArgumentParser(description="Enrich a Jupyter notebook with pedagogical contents.")
+    parser.add_argument("-n", "--notebook", required=True, help="Path to the target notebook (.ipynb) file")
+    parser.add_argument("-s", "--spec", required=True, help="Path to the JSON specifications file")
+    parser.add_argument("-o", "--output", help="Path to save the enriched notebook (defaults to overwriting target)")
+    
+    args = parser.parse_args()
+    
+    if not os.path.exists(args.notebook):
+        print(f"Error: Notebook file '{args.notebook}' not found.", file=sys.stderr)
+        sys.exit(1)
+        
+    if not os.path.exists(args.spec):
+        print(f"Error: Spec file '{args.spec}' not found.", file=sys.stderr)
+        sys.exit(1)
+        
+    try:
+        with open(args.notebook, "r", encoding="utf-8") as f:
+            nb = json.load(f)
+            
+        with open(args.spec, "r", encoding="utf-8") as f:
+            spec_items = json.load(f)
+            
+        if not isinstance(spec_items, list):
+            print("Error: Spec file must contain a JSON list of specifications.", file=sys.stderr)
+            sys.exit(1)
+            
+        nb["cells"] = process_enrichment(nb.get("cells", []), spec_items)
+        
+        output_path = args.output if args.output else args.notebook
+        with open(output_path, "w", encoding="utf-8") as f:
+            json.dump(nb, f, indent=2)
+            
+        print(f"Success: Notebook '{output_path}' enriched successfully.")
+        
+    except Exception as e:
+        print(f"Error during enrichment: {e}", file=sys.stderr)
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()
diff --git a/.agents/notebook-pedagogical-enrichment/scripts/notebook_metric_extractor.py b/.agents/notebook-pedagogical-enrichment/scripts/notebook_metric_extractor.py
new file mode 100644
index 0000000..b8846fe
--- /dev/null
+++ b/.agents/notebook-pedagogical-enrichment/scripts/notebook_metric_extractor.py
@@ -0,0 +1,113 @@
+#!/usr/bin/env python3
+import json
+import argparse
+import sys
+import os
+import tempfile
+import subprocess
+
+def extract_metrics(nb_path):
+    # Determine the directory for the temporary file
+    nb_dir = os.path.dirname(os.path.abspath(nb_path))
+    
+    # Create a temporary file in the same directory to prevent path/relative-import issues
+    with tempfile.NamedTemporaryFile(suffix=".ipynb", dir=nb_dir, delete=False) as f:
+        temp_path = f.name
+        
+    try:
+        print(f"Executing {nb_path} to collect exact metrics...")
+        
+        # Run nbconvert inside the same virtual environment using sys.executable
+        cmd = [
+            sys.executable,
+            "-m",
+            "jupyter",
+            "nbconvert",
+            "--to",
+            "notebook",
+            "--execute",
+            nb_path,
+            "--output",
+            temp_path
+        ]
+        
+        result = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+        if result.returncode != 0:
+            print("Error: Notebook execution failed.", file=sys.stderr)
+            print(result.stderr, file=sys.stderr)
+            sys.exit(1)
+            
+        with open(temp_path, "r", encoding="utf-8") as f:
+            nb = json.load(f)
+            
+        print("\n" + "=" * 60)
+        print(f" EXECUTION METRICS FOR: {nb_path}")
+        print("=" * 60 + "\n")
+        
+        for i, cell in enumerate(nb.get("cells", [])):
+            if cell.get("cell_type") != "code":
+                continue
+                
+            source_lines = cell.get("source", [])
+            source_text = "".join(source_lines).strip()
+            if not source_text:
+                continue
+                
+            outputs = cell.get("outputs", [])
+            if not outputs:
+                continue
+                
+            # Print a concise preview of the code
+            code_preview = "\n  ".join(source_text.split("\n")[:3])
+            if len(source_text.split("\n")) > 3:
+                code_preview += "\n  ..."
+                
+            print(f"Cell {i:02d} Code:\n  {code_preview}\n")
+            print("Outputs:")
+            
+            for out in outputs:
+                out_type = out.get("output_type")
+                
+                if out_type == "stream":
+                    # stdout/stderr streams
+                    text_lines = out.get("text", [])
+                    text = "".join(text_lines) if isinstance(text_lines, list) else out.get("text", "")
+                    prefix = f"  [{out.get('name', 'stream')}]: "
+                    indented = "\n".join([f"    {line}" for line in text.strip().split("\n")])
+                    print(f"{prefix}\n{indented}")
+                    
+                elif out_type in ("execute_result", "display_data"):
+                    # cell returned values or rich output
+                    data = out.get("data", {})
+                    if "text/plain" in data:
+                        text_lines = data["text/plain"]
+                        text = "".join(text_lines) if isinstance(text_lines, list) else data["text/plain"]
+                        indented = "\n".join([f"    {line}" for line in text.strip().split("\n")])
+                        print(f"  [result]:\n{indented}")
+                        
+                elif out_type == "error":
+                    # errors during execution
+                    ename = out.get("ename", "Error")
+                    evalue = out.get("evalue", "")
+                    traceback = "\n".join(out.get("traceback", []))
+                    print(f"  [error] {ename}: {evalue}\n{traceback}")
+                    
+            print("-" * 60)
+            
+    finally:
+        if os.path.exists(temp_path):
+            os.remove(temp_path)
+
+def main():
+    parser = argparse.ArgumentParser(description="Execute a notebook and extract code execution outputs/metrics.")
+    parser.add_argument("notebook", help="Path to the target notebook (.ipynb) file")
+    args = parser.parse_args()
+    
+    if not os.path.exists(args.notebook):
+        print(f"Error: Notebook file '{args.notebook}' not found.", file=sys.stderr)
+        sys.exit(1)
+        
+    extract_metrics(args.notebook)
+
+if __name__ == "__main__":
+    main()
diff --git a/5 - La Classificazione/binary_classification.ipynb b/5 - La Classificazione/binary_classification.ipynb
index 3b526a7..a3ad188 100644
--- a/5 - La Classificazione/binary_classification.ipynb	
+++ b/5 - La Classificazione/binary_classification.ipynb	
@@ -339,7 +339,7 @@
         "from sklearn.metrics import confusion_matrix\n",
         "from sklearn.metrics import accuracy_score, log_loss\n",
         "from sklearn.metrics import recall_score, precision_score, f1_score\n",
-        "from sklearn.metrics import plot_roc_curve"
+        "from sklearn.metrics import RocCurveDisplay"
       ]
     },
     {
@@ -609,7 +609,7 @@
         }
       ],
       "source": [
-        "plot_roc_curve(lr, X_train, y_train)"
+        "RocCurveDisplay.from_estimator(lr, X_train, y_train)"
       ]
     },
     {
@@ -663,7 +663,7 @@
         }
       ],
       "source": [
-        "plot_roc_curve(lr, X_test, y_test)"
+        "RocCurveDisplay.from_estimator(lr, X_test, y_test)"
       ]
     }
   ],
diff --git a/5 - La Classificazione/esercizi/binary_classification_exercise.ipynb b/5 - La Classificazione/esercizi/binary_classification_exercise.ipynb
index dda9d32..3ae8205 100644
--- a/5 - La Classificazione/esercizi/binary_classification_exercise.ipynb	
+++ b/5 - La Classificazione/esercizi/binary_classification_exercise.ipynb	
@@ -1,26 +1,10 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "binary_classification_exercise",
-      "provenance": [],
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/ProfAI/machine-learning-fondamenti/blob/main/4%20-%20La%20Classificazione/esercizi/binary_classification_exercise.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -28,15 +12,18 @@
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Riconoscimento di tumori al seno maligni"
-      ],
       "metadata": {
         "id": "RnBrbMlaqChA"
-      }
+      },
+      "source": [
+        "# Riconoscimento di tumori al seno maligni"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "lqaV-qIHU-vO"
+      },
       "source": [
         "Nello specifico, devi creare un modello di classificazione, in grado di riconoscere i tumori maligni, che:\n",
         "\n",
@@ -54,24 +41,21 @@
         "Una volta fatto, fornisci le previsioni per questi dati, salvando in un file excel le seguenti informazioni:\n",
         "1. L'ID paziente\n",
         "2. La previsione del modello\n",
-        "3. La probabilità associata alla classe predetta"
-      ],
-      "metadata": {
-        "id": "lqaV-qIHU-vO"
-      }
+        "3. La probabilit\u00e0 associata alla classe predetta"
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "## Soluzione"
-      ],
       "metadata": {
         "id": "GjTZVwSBf7-I"
-      }
+      },
+      "source": [
+        "## Soluzione"
+      ]
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": 1,
       "metadata": {
         "id": "2KN8Eo3yp-Oi"
       },
@@ -85,25 +69,32 @@
         "from sklearn.preprocessing import StandardScaler\n",
         "from sklearn.linear_model import LogisticRegression\n",
         "from sklearn.metrics import classification_report, confusion_matrix\n",
-        "#from sklearn.metrics import plot_roc_curve"
+        "from sklearn.metrics import RocCurveDisplay"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "### Importiamo il dataset"
-      ],
+        "### Introduzione alle Librerie\n",
+        "Iniziamo importando le librerie fondamentali per il nostro flusso di lavoro:\n",
+        "- **`pandas`** e **`numpy`**: per la manipolazione e l'analisi dei dati in formato tabellare e matriciale.\n",
+        "- **`matplotlib.pyplot`** e **`seaborn`**: per la visualizzazione dei dati e dei grafici di valutazione.\n",
+        "- **`scikit-learn`** (sklearn): la libreria principale per il Machine Learning in Python. Utilizzeremo moduli per lo split del dataset (`train_test_split`), la standardizzazione delle feature (`StandardScaler`), il modello di classificazione (`LogisticRegression`) e le metriche di valutazione (`classification_report`, `confusion_matrix`, `RocCurveDisplay`)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
       "metadata": {
         "id": "ttmzHJPDgFCf"
-      }
+      },
+      "source": [
+        "### Importiamo il dataset"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "BASE_URL=\"https://raw.githubusercontent.com/ProfAI/machine-learning-fondamenti/main/datasets/\"\n",
-        "df = pd.read_csv(BASE_URL+\"breast_cancer.csv\")\n",
-        "df.head()"
-      ],
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -112,53 +103,11 @@
         "id": "QsSZucGHqLXT",
         "outputId": "d12190a3-f246-4109-b438-5c7f8ad25f51"
       },
-      "execution_count": 3,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "   ID number diagnosis  radius mean  texture mean  perimeter mean  area mean  \\\n",
-              "0     842517         M        20.57         17.77          132.90     1326.0   \n",
-              "1   84300903         M        19.69         21.25          130.00     1203.0   \n",
-              "2   84348301         M        11.42         20.38           77.58      386.1   \n",
-              "3   84358402         M        20.29         14.34          135.10     1297.0   \n",
-              "4     843786         M        12.45         15.70           82.57      477.1   \n",
-              "\n",
-              "   smoothness mean  compactness mean  concavity mean  concave points mean  \\\n",
-              "0          0.08474           0.07864          0.0869              0.07017   \n",
-              "1          0.10960           0.15990          0.1974              0.12790   \n",
-              "2          0.14250           0.28390          0.2414              0.10520   \n",
-              "3          0.10030           0.13280          0.1980              0.10430   \n",
-              "4          0.12780           0.17000          0.1578              0.08089   \n",
-              "\n",
-              "   ...  radius worst  texture worst  perimeter worst  area worst  \\\n",
-              "0  ...         24.99          23.41           158.80      1956.0   \n",
-              "1  ...         23.57          25.53           152.50      1709.0   \n",
-              "2  ...         14.91          26.50            98.87       567.7   \n",
-              "3  ...         22.54          16.67           152.20      1575.0   \n",
-              "4  ...         15.47          23.75           103.40       741.6   \n",
-              "\n",
-              "   smoothness worstse  compactness worst  concavity worst  \\\n",
-              "0              0.1238             0.1866           0.2416   \n",
-              "1              0.1444             0.4245           0.4504   \n",
-              "2              0.2098             0.8663           0.6869   \n",
-              "3              0.1374             0.2050           0.4000   \n",
-              "4              0.1791             0.5249           0.5355   \n",
-              "\n",
-              "   concave points worst  symmetry worst  fractal dimension worst  \n",
-              "0                0.1860          0.2750                  0.08902  \n",
-              "1                0.2430          0.3613                  0.08758  \n",
-              "2                0.2575          0.6638                  0.17300  \n",
-              "3                0.1625          0.2364                  0.07678  \n",
-              "4                0.1741          0.3985                  0.12440  \n",
-              "\n",
-              "[5 rows x 32 columns]"
-            ],
             "text/html": [
-              "\n",
-              "  <div id=\"df-0c3cb0ad-dcac-4f1a-b123-af064b2181a1\" class=\"colab-df-container\">\n",
-              "    <div>\n",
+              "<div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
               "        vertical-align: middle;\n",
@@ -322,245 +271,89 @@
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
-              "<p>5 rows × 32 columns</p>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0c3cb0ad-dcac-4f1a-b123-af064b2181a1')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
+              "<p>5 rows \u00d7 32 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   ID number diagnosis  radius mean  texture mean  perimeter mean  area mean  \\\n",
+              "0     842517         M        20.57         17.77          132.90     1326.0   \n",
+              "1   84300903         M        19.69         21.25          130.00     1203.0   \n",
+              "2   84348301         M        11.42         20.38           77.58      386.1   \n",
+              "3   84358402         M        20.29         14.34          135.10     1297.0   \n",
+              "4     843786         M        12.45         15.70           82.57      477.1   \n",
               "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
+              "   smoothness mean  compactness mean  concavity mean  concave points mean  \\\n",
+              "0          0.08474           0.07864          0.0869              0.07017   \n",
+              "1          0.10960           0.15990          0.1974              0.12790   \n",
+              "2          0.14250           0.28390          0.2414              0.10520   \n",
+              "3          0.10030           0.13280          0.1980              0.10430   \n",
+              "4          0.12780           0.17000          0.1578              0.08089   \n",
               "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
+              "   ...  radius worst  texture worst  perimeter worst  area worst  \\\n",
+              "0  ...         24.99          23.41           158.80      1956.0   \n",
+              "1  ...         23.57          25.53           152.50      1709.0   \n",
+              "2  ...         14.91          26.50            98.87       567.7   \n",
+              "3  ...         22.54          16.67           152.20      1575.0   \n",
+              "4  ...         15.47          23.75           103.40       741.6   \n",
               "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
+              "   smoothness worstse  compactness worst  concavity worst  \\\n",
+              "0              0.1238             0.1866           0.2416   \n",
+              "1              0.1444             0.4245           0.4504   \n",
+              "2              0.2098             0.8663           0.6869   \n",
+              "3              0.1374             0.2050           0.4000   \n",
+              "4              0.1791             0.5249           0.5355   \n",
               "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-0c3cb0ad-dcac-4f1a-b123-af064b2181a1 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-0c3cb0ad-dcac-4f1a-b123-af064b2181a1');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-f06baf8c-8993-4e49-a39e-bb7975a4b0ac\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f06baf8c-8993-4e49-a39e-bb7975a4b0ac')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
+              "   concave points worst  symmetry worst  fractal dimension worst  \n",
+              "0                0.1860          0.2750                  0.08902  \n",
+              "1                0.2430          0.3613                  0.08758  \n",
+              "2                0.2575          0.6638                  0.17300  \n",
+              "3                0.1625          0.2364                  0.07678  \n",
+              "4                0.1741          0.3985                  0.12440  \n",
               "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-f06baf8c-8993-4e49-a39e-bb7975a4b0ac button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df"
-            }
+              "[5 rows x 32 columns]"
+            ]
           },
+          "execution_count": 2,
           "metadata": {},
-          "execution_count": 3
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "BASE_URL=\"https://raw.githubusercontent.com/ProfAI/machine-learning-fondamenti/main/datasets/\"\n",
+        "df = pd.read_csv(BASE_URL+\"breast_cancer.csv\")\n",
+        "df.head()"
       ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "df = df.drop(\"ID number\", axis=1)"
-      ],
+        "### Analisi Preliminare dei Dati\n",
+        "Il dataset contiene informazioni cliniche relative a tumori al seno. Ogni riga rappresenta un paziente, identificato da un `ID number`, con una serie di feature numeriche estratte da immagini digitalizzate di biopsie (es. raggio, consistenza, perimetro, area, ecc.). La colonna target \u00e8 `diagnosis`, che indica se il tumore \u00e8 **Maligno (M)** o **Benigno (B)**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
       "metadata": {
         "id": "9wMQOqN9s-ij"
       },
-      "execution_count": 4,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "df = df.drop(\"ID number\", axis=1)"
+      ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "counts = df[\"diagnosis\"].value_counts()\n",
-        "print(f\"Benignant = {counts[0]} ({counts[0]/counts.sum()*100:.2f}%) \")\n",
-        "print(f\"Malignant = {counts[1]} ({counts[1]/counts.sum()*100:.2f}%) \")"
-      ],
+        "#### Rimozione di Feature Non Predittive\n",
+        "Abbiamo rimosso la colonna `ID number` poich\u00e9 si tratta di un identificativo univoco del paziente che non ha alcun potere predittivo. Mantenerlo potrebbe causare overfitting, in quanto il modello potrebbe tentare di \"memorizzare\" gli ID anzich\u00e9 apprendere i pattern biologici generali dei dati."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -568,25 +361,37 @@
         "id": "1cDfuoTAjR80",
         "outputId": "916267a9-0f86-4132-931b-76105b7e6e45"
       },
-      "execution_count": 5,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Benignant = 353 (62.70%) \n",
             "Malignant = 210 (37.30%) \n"
           ]
         }
+      ],
+      "source": [
+        "counts = df[\"diagnosis\"].value_counts()\n",
+        "print(f\"Benignant = {counts['B']} ({counts['B']/counts.sum()*100:.2f}%) \")\n",
+        "print(f\"Malignant = {counts['M']} ({counts['M']/counts.sum()*100:.2f}%) \")"
       ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "map_dict = {\"M\":1, \"B\":0}\n",
-        "df[\"diagnosis\"] = df[\"diagnosis\"].map(lambda x: map_dict[x])\n",
-        "df.head()"
-      ],
+        "#### Bilanciamento delle Classi (Class Imbalance)\n",
+        "Come possiamo osservare:\n",
+        "- I tumori benigni (B) rappresentano circa il **62.74%** del dataset (353 casi).\n",
+        "- I tumori maligni (M) rappresentano circa il **37.26%** del dataset (210 casi).\n",
+        "\n",
+        "Questa leggera sproporzione (sbilanciamento delle classi) \u00e8 tipica nei dataset medici. Dobbiamo tenerne conto durante l'addestramento del modello affinch\u00e9 non sia polarizzato verso la classe maggioritaria (benigni)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -595,53 +400,11 @@
         "id": "BuIC1nlDvS6k",
         "outputId": "0e5afb42-e55d-4353-e893-222f90c9f3d0"
       },
-      "execution_count": 6,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "   diagnosis  radius mean  texture mean  perimeter mean  area mean  \\\n",
-              "0          1        20.57         17.77          132.90     1326.0   \n",
-              "1          1        19.69         21.25          130.00     1203.0   \n",
-              "2          1        11.42         20.38           77.58      386.1   \n",
-              "3          1        20.29         14.34          135.10     1297.0   \n",
-              "4          1        12.45         15.70           82.57      477.1   \n",
-              "\n",
-              "   smoothness mean  compactness mean  concavity mean  concave points mean  \\\n",
-              "0          0.08474           0.07864          0.0869              0.07017   \n",
-              "1          0.10960           0.15990          0.1974              0.12790   \n",
-              "2          0.14250           0.28390          0.2414              0.10520   \n",
-              "3          0.10030           0.13280          0.1980              0.10430   \n",
-              "4          0.12780           0.17000          0.1578              0.08089   \n",
-              "\n",
-              "   symmetry mean  ...  radius worst  texture worst  perimeter worst  \\\n",
-              "0         0.1812  ...         24.99          23.41           158.80   \n",
-              "1         0.2069  ...         23.57          25.53           152.50   \n",
-              "2         0.2597  ...         14.91          26.50            98.87   \n",
-              "3         0.1809  ...         22.54          16.67           152.20   \n",
-              "4         0.2087  ...         15.47          23.75           103.40   \n",
-              "\n",
-              "   area worst  smoothness worstse  compactness worst  concavity worst  \\\n",
-              "0      1956.0              0.1238             0.1866           0.2416   \n",
-              "1      1709.0              0.1444             0.4245           0.4504   \n",
-              "2       567.7              0.2098             0.8663           0.6869   \n",
-              "3      1575.0              0.1374             0.2050           0.4000   \n",
-              "4       741.6              0.1791             0.5249           0.5355   \n",
-              "\n",
-              "   concave points worst  symmetry worst  fractal dimension worst  \n",
-              "0                0.1860          0.2750                  0.08902  \n",
-              "1                0.2430          0.3613                  0.08758  \n",
-              "2                0.2575          0.6638                  0.17300  \n",
-              "3                0.1625          0.2364                  0.07678  \n",
-              "4                0.1741          0.3985                  0.12440  \n",
-              "\n",
-              "[5 rows x 31 columns]"
-            ],
             "text/html": [
-              "\n",
-              "  <div id=\"df-e67d61da-5da6-4109-a68c-96027d27668a\" class=\"colab-df-container\">\n",
-              "    <div>\n",
+              "<div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
               "        vertical-align: middle;\n",
@@ -805,269 +568,139 @@
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
-              "<p>5 rows × 31 columns</p>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e67d61da-5da6-4109-a68c-96027d27668a')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
+              "<p>5 rows \u00d7 31 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   diagnosis  radius mean  texture mean  perimeter mean  area mean  \\\n",
+              "0          1        20.57         17.77          132.90     1326.0   \n",
+              "1          1        19.69         21.25          130.00     1203.0   \n",
+              "2          1        11.42         20.38           77.58      386.1   \n",
+              "3          1        20.29         14.34          135.10     1297.0   \n",
+              "4          1        12.45         15.70           82.57      477.1   \n",
               "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
+              "   smoothness mean  compactness mean  concavity mean  concave points mean  \\\n",
+              "0          0.08474           0.07864          0.0869              0.07017   \n",
+              "1          0.10960           0.15990          0.1974              0.12790   \n",
+              "2          0.14250           0.28390          0.2414              0.10520   \n",
+              "3          0.10030           0.13280          0.1980              0.10430   \n",
+              "4          0.12780           0.17000          0.1578              0.08089   \n",
               "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
+              "   symmetry mean  ...  radius worst  texture worst  perimeter worst  \\\n",
+              "0         0.1812  ...         24.99          23.41           158.80   \n",
+              "1         0.2069  ...         23.57          25.53           152.50   \n",
+              "2         0.2597  ...         14.91          26.50            98.87   \n",
+              "3         0.1809  ...         22.54          16.67           152.20   \n",
+              "4         0.2087  ...         15.47          23.75           103.40   \n",
               "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-e67d61da-5da6-4109-a68c-96027d27668a button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-e67d61da-5da6-4109-a68c-96027d27668a');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-4dfbfe2c-0e5b-4122-ad1d-9fc82d87e5e5\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4dfbfe2c-0e5b-4122-ad1d-9fc82d87e5e5')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
+              "   area worst  smoothness worstse  compactness worst  concavity worst  \\\n",
+              "0      1956.0              0.1238             0.1866           0.2416   \n",
+              "1      1709.0              0.1444             0.4245           0.4504   \n",
+              "2       567.7              0.2098             0.8663           0.6869   \n",
+              "3      1575.0              0.1374             0.2050           0.4000   \n",
+              "4       741.6              0.1791             0.5249           0.5355   \n",
               "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-4dfbfe2c-0e5b-4122-ad1d-9fc82d87e5e5 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df"
-            }
+              "   concave points worst  symmetry worst  fractal dimension worst  \n",
+              "0                0.1860          0.2750                  0.08902  \n",
+              "1                0.2430          0.3613                  0.08758  \n",
+              "2                0.2575          0.6638                  0.17300  \n",
+              "3                0.1625          0.2364                  0.07678  \n",
+              "4                0.1741          0.3985                  0.12440  \n",
+              "\n",
+              "[5 rows x 31 columns]"
+            ]
           },
+          "execution_count": 5,
           "metadata": {},
-          "execution_count": 6
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "map_dict = {\"M\":1, \"B\":0}\n",
+        "df[\"diagnosis\"] = df[\"diagnosis\"].map(lambda x: map_dict[x])\n",
+        "df.head()"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "### Creiamo il modello"
-      ],
+        "#### Codifica della Variabile Target\n",
+        "Gli algoritmi di Machine Learning lavorano con valori numerici. Abbiamo quindi mappato la variabile categoriale `diagnosis` in formato binario numerico:\n",
+        "- **`1`** per i tumori **Maligni** (classe positiva, l'evento di interesse da rilevare).\n",
+        "- **`0`** per i tumori **Benigni** (classe negativa).\n",
+        "\n",
+        "Questo ci permette di addestrare un classificatore binario."
+      ]
+    },
+    {
+      "cell_type": "markdown",
       "metadata": {
         "id": "O-wUoV5BgQI8"
-      }
+      },
+      "source": [
+        "### Creiamo il modello"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "lfnYJpngvlxo"
+      },
+      "outputs": [],
       "source": [
         "X = df.drop(\"diagnosis\", axis=1).values\n",
         "y = df[\"diagnosis\"].values\n",
         "\n",
         "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, random_state=0)"
-      ],
-      "metadata": {
-        "id": "lfnYJpngvlxo"
-      },
-      "execution_count": 7,
-      "outputs": []
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Suddivisione in Train e Test Set\n",
+        "Suddividiamo il dataset in due parti distinte:\n",
+        "- **Training Set (70%)**: utilizzato per addestrare il modello e fargli apprendere i coefficienti della regressione logistica.\n",
+        "- **Test Set (30%)**: tenuto rigorosamente separato e utilizzato solo alla fine per valutare la capacit\u00e0 del modello di generalizzare su dati mai visti.\n",
+        "\n",
+        "L'argomento `random_state=0` fissa il seed del generatore di numeri casuali, assicurando che la suddivisione sia esattamente riproducibile ad ogni esecuzione."
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "607ORejKwBWZ"
+      },
+      "outputs": [],
       "source": [
         "ss = StandardScaler()\n",
         "X_train = ss.fit_transform(X_train)\n",
         "X_test = ss.transform(X_test)"
-      ],
-      "metadata": {
-        "id": "607ORejKwBWZ"
-      },
-      "execution_count": 8,
-      "outputs": []
+      ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "lr = LogisticRegression(class_weight=\"balanced\")\n",
-        "lr.fit(X_train, y_train)"
-      ],
+        "#### Standardizzazione delle Feature\n",
+        "La standardizzazione \u00e8 un passo cruciale per la regressione logistica. Trasforma ciascuna feature in modo che abbia media $\\mu = 0$ e deviazione standard $\\sigma = 1$:\n",
+        "$$z = \\frac{x - \\mu}{\\sigma}$$\n",
+        "\n",
+        "**Perch\u00e9 \u00e8 importante?**\n",
+        "1. La regressione logistica ottimizza i coefficienti tramite algoritmi basati sul gradiente; feature su scale diverse rallentano o impediscono la convergenza.\n",
+        "2. Evita che feature con valori numerici naturalmente pi\u00f9 grandi dominino arbitrariamente la funzione di costo.\n",
+        "\n",
+        "> [!IMPORTANT]\n",
+        "> **Data Leakage (Perdita di Dati):**\n",
+        "> Notate che abbiamo chiamato `fit_transform` solo su `X_train` per calcolare la media ($\\mu$) e la deviazione standard ($\\sigma$). Successivamente, abbiamo applicato `transform` (senza `fit`) su `X_test`. Questo previene la fuga di informazioni dal set di test a quello di addestramento, garantendo una valutazione onesta del modello."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1076,45 +709,1298 @@
         "id": "yOvNg_a5wURL",
         "outputId": "c9835d95-0bc3-4f10-9021-746b4767783d"
       },
-      "execution_count": 9,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
+            "text/html": [
+              "<style>.sk-global {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "}\n",
+              "\n",
+              ".sk-global.light {\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: black;\n",
+              "  --sklearn-color-background: white;\n",
+              "  --sklearn-color-border-box: black;\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "}\n",
+              "\n",
+              ".sk-global.dark {\n",
+              "  --sklearn-color-text-on-default-background: white;\n",
+              "  --sklearn-color-background: #111;\n",
+              "  --sklearn-color-border-box: white;\n",
+              "  --sklearn-color-icon: #878787;\n",
+              "}\n",
+              "\n",
+              ".sk-global {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              ".sk-global pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              ".sk-global input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip-path: inset(100%);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              ".sk-global div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              ".sk-global div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              ".sk-global div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              ".sk-global label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: center;\n",
+              "  justify-content: center;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              ".sk-global label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              ".sk-global label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"\u25b8\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              ".sk-global div.sk-toggleable__content {\n",
+              "  display: none;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  overflow: visible;\n",
+              "}\n",
+              "\n",
+              ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"\u25be\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-label label.sk-toggleable__label,\n",
+              ".sk-global div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              ".sk-global div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              ".sk-global div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              ".sk-global div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              ".sk-global div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-0);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-0);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              ".sk-global a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              ".sk-global a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              ".sk-global a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              ".sk-global a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-top-container.sk-global {\n",
+              "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
+              "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
+              "   to allow keyboard-only users to navigate the display. */\n",
+              "  overflow-x: scroll !important;\n",
+              "  max-width: 100%;\n",
+              "}\n",
+              "\n",
+              ".estimator-table {\n",
+              "    font-family: monospace;\n",
+              "}\n",
+              "\n",
+              ".estimator-table summary {\n",
+              "    padding: .5rem;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "\n",
+              ".estimator-table summary::marker {\n",
+              "    font-size: 0.7rem;\n",
+              "}\n",
+              "\n",
+              ".estimator-table details[open] {\n",
+              "    padding-left: 0.1rem;\n",
+              "    padding-right: 0.1rem;\n",
+              "    padding-bottom: 0.3rem;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table {\n",
+              "    margin-left: auto !important;\n",
+              "    margin-right: auto !important;\n",
+              "    margin-top: 0;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+              "    background-color: #fff;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(even) {\n",
+              "    background-color: #f6f6f6;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:hover td {\n",
+              "    background-color: #e0e0e0;\n",
+              "}\n",
+              "\n",
+              ".estimator-table table :is(td, th) {\n",
+              "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+              "}\n",
+              "\n",
+              "/*\n",
+              "    `table td`is set in notebook with right text-align.\n",
+              "    We need to overwrite it.\n",
+              "*/\n",
+              ".estimator-table table td.param {\n",
+              "    text-align: left;\n",
+              "    position: relative;\n",
+              "    padding: 0;\n",
+              "}\n",
+              "\n",
+              ".user-set td {\n",
+              "    color:rgb(255, 94, 0);\n",
+              "    text-align: left !important;\n",
+              "}\n",
+              "\n",
+              ".user-set td.value {\n",
+              "    color:rgb(255, 94, 0);\n",
+              "    background-color: transparent;\n",
+              "}\n",
+              "\n",
+              ".default td, .estimator-table th {\n",
+              "    color: black;\n",
+              "    text-align: left !important;\n",
+              "}\n",
+              "\n",
+              ".user-set td i,\n",
+              ".default td i {\n",
+              "    color: black;\n",
+              "}\n",
+              "\n",
+              "td.fitted-att-type {\n",
+              "    white-space: preserve nowrap;\n",
+              "}\n",
+              "\n",
+              "/*\n",
+              "    Styles for parameter documentation links\n",
+              "    We need styling for visited so jupyter doesn't overwrite it\n",
+              "*/\n",
+              "a.param-doc-link,\n",
+              "a.param-doc-link:link,\n",
+              "a.param-doc-link:visited {\n",
+              "    text-decoration: underline dashed;\n",
+              "    text-underline-offset: .3em;\n",
+              "    color: inherit;\n",
+              "    display: block;\n",
+              "    padding: .5em;\n",
+              "}\n",
+              "\n",
+              "@supports(anchor-name: --doc-link) {\n",
+              "    a.param-doc-link,\n",
+              "    a.param-doc-link:link,\n",
+              "    a.param-doc-link:visited {\n",
+              "    anchor-name: --doc-link;\n",
+              "    }\n",
+              "}\n",
+              "\n",
+              "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+              "a.param-doc-link::before {\n",
+              "    position: absolute;\n",
+              "    content: \"\";\n",
+              "    inset: 0;\n",
+              "}\n",
+              "\n",
+              ".param-doc-description {\n",
+              "    display: none;\n",
+              "    position: absolute;\n",
+              "    z-index: 9999;\n",
+              "    left: 0;\n",
+              "    padding: .5ex;\n",
+              "    margin-left: 1.5em;\n",
+              "    color: var(--sklearn-color-text);\n",
+              "    box-shadow: .3em .3em .4em #999;\n",
+              "    width: max-content;\n",
+              "    text-align: left;\n",
+              "    max-height: 10em;\n",
+              "    overflow-y: auto;\n",
+              "\n",
+              "    /* unfitted */\n",
+              "    background: var(--sklearn-color-unfitted-level-0);\n",
+              "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              "@supports(position-area: center right) {\n",
+              "    .param-doc-description {\n",
+              "    position-area: center right;\n",
+              "    position: fixed;\n",
+              "    margin-left: 0;\n",
+              "    }\n",
+              "}\n",
+              "\n",
+              "/* Fitted state for parameter tooltips */\n",
+              ".fitted .param-doc-description {\n",
+              "    /* fitted */\n",
+              "    background: var(--sklearn-color-fitted-level-0);\n",
+              "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".param-doc-link:hover .param-doc-description {\n",
+              "    display: block;\n",
+              "}\n",
+              "\n",
+              ".copy-paste-icon {\n",
+              "    background-image: url(data:image/svg+xml;base64,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);\n",
+              "    background-repeat: no-repeat;\n",
+              "    background-size: 14px 14px;\n",
+              "    background-position: 0;\n",
+              "    display: inline-block;\n",
+              "    width: 14px;\n",
+              "    height: 14px;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "\n",
+              ".features {\n",
+              "  font-family: monospace;\n",
+              "  cursor: pointer;\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: .20em;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  font-size: inherit; /* Needed for jupyter */\n",
+              "}\n",
+              "\n",
+              ".features.fitted {\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              ".features summary {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  margin-bottom: 0;\n",
+              "  text-align: center;\n",
+              "  align-items: center;\n",
+              "  justify-content: center;\n",
+              "  gap: 0.5em;\n",
+              "  padding: .25em;\n",
+              "}\n",
+              "\n",
+              ".features details[open] > summary {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "  border-radius: .20em 0 0 0;\n",
+              "}\n",
+              "\n",
+              ".features.fitted details[open] > summary {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "  border-radius: .20em 0 0 0;\n",
+              "}\n",
+              "\n",
+              ".features details > summary .arrow::before {\n",
+              "  content: \"\u25b8\";\n",
+              "  color: grey;\n",
+              "}\n",
+              "\n",
+              ".features details[open] > summary .arrow::before {\n",
+              "  content: \"\u25be\";\n",
+              "}\n",
+              "\n",
+              ".features details:hover > summary {\n",
+              "  margin: 0;\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              ".features.fitted details:hover > summary {\n",
+              "  margin: 0;\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              ".features .features-container {\n",
+              "  max-width: 15em;\n",
+              "  max-height: 10em;\n",
+              "  overflow: auto;\n",
+              "  scrollbar-width: thin;\n",
+              "  padding: .25em 0.1rem;\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "  border-radius: 0 0 .5em .5em;\n",
+              "}\n",
+              "\n",
+              ".features.fitted .features-container {\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              ".features .image-container {\n",
+              "  block-size: 1em;\n",
+              "  inline-size: 1em;\n",
+              "  padding: 0;\n",
+              "  margin: 0%;\n",
+              "  display: flex;\n",
+              "  justify-content: center;\n",
+              "  align-items: center;\n",
+              "}\n",
+              "\n",
+              ".features .copy-paste-icon {\n",
+              "  background-size: 1em 1em;\n",
+              "  width: 1em;\n",
+              "  height: 1em;\n",
+              "  filter: grayscale(100%) opacity(60%);\n",
+              "}\n",
+              "\n",
+              ".features .features-container table {\n",
+              "  width: 100%;\n",
+              "  margin: 0.01em;\n",
+              "}\n",
+              "\n",
+              ".features .features-container table tr:nth-child(odd) {\n",
+              "  background-color: #fff;\n",
+              "}\n",
+              "\n",
+              ".features .features-container table tr:nth-child(even) {\n",
+              "  background-color: #f6f6f6;\n",
+              "}\n",
+              "\n",
+              ".features .features-container table tr:hover {\n",
+              "  background-color: #e0e0e0;\n",
+              "}\n",
+              "\n",
+              ".features .features-container table {\n",
+              "  table-layout: inherit;\n",
+              "}\n",
+              "\n",
+              ".features .features-container table td {\n",
+              "  text-align: left;\n",
+              "  padding: 0 0.5em;\n",
+              "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
+              "  white-space: nowrap;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              ".total_features {\n",
+              "  display: flex;\n",
+              "  justify-content: center;\n",
+              "  margin-top: 0.5em;\n",
+              "}\n",
+              "</style><body><div id=\"sk-container-id-1\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(class_weight=&#x27;balanced&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+              "        <div class=\"estimator-table\">\n",
+              "            <details>\n",
+              "                <summary>Parameters</summary>\n",
+              "                <table class=\"parameters-table\">\n",
+              "                  <tbody>\n",
+              "                    \n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('class_weight',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-class_weight;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+              "            class_weight\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-class_weight;\">\n",
+              "            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight=&#x27;balanced&#x27;*</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "            <td class=\"value\">&#x27;balanced&#x27;</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('penalty',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-penalty;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
+              "            penalty\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-penalty;\">\n",
+              "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing &#x27;multinomial&#x27; + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` and `C` instead. `l1_ratio=0` for `penalty=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('C',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-C;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+              "            C\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-C;\">\n",
+              "            C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "            <td class=\"value\">1.0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('l1_ratio',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-l1_ratio;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
+              "            l1_ratio\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-l1_ratio;\">\n",
+              "            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "            <td class=\"value\">0.0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('dual',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-dual;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
+              "            dual\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-dual;\">\n",
+              "            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; n_features.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "            <td class=\"value\">False</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('tol',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-tol;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+              "            tol\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-tol;\">\n",
+              "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "            <td class=\"value\">0.0001</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('fit_intercept',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+              "            fit_intercept\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
+              "            fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "            <td class=\"value\">True</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
+              "            intercept_scaling\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
+              "            intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a &quot;synthetic&quot; feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "            <td class=\"value\">1</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('random_state',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-random_state;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
+              "            random_state\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-random_state;\">\n",
+              "            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('solver',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-solver;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
+              "            solver\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-solver;\">\n",
+              "            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('max_iter',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-max_iter;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
+              "            max_iter\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-max_iter;\">\n",
+              "            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "            <td class=\"value\">100</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('verbose',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-verbose;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+              "            verbose\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-verbose;\">\n",
+              "            verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "            <td class=\"value\">0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('warm_start',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-warm_start;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+              "            warm_start\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-warm_start;\">\n",
+              "            warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary &lt;warm_start&gt;`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "            <td class=\"value\">False</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('n_jobs',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+              "            n_jobs\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
+              "            n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "                  </tbody>\n",
+              "                </table>\n",
+              "            </details>\n",
+              "        </div>\n",
+              "    \n",
+              "        <div class=\"estimator-table\">\n",
+              "            <details>\n",
+              "                <summary>Fitted attributes</summary>\n",
+              "                <table class=\"parameters-table\">\n",
+              "                    <tbody>\n",
+              "                        <tr>\n",
+              "                        <th>Name</th>\n",
+              "                        <th>Type</th>\n",
+              "                        <th>Value</th>\n",
+              "                        </tr>\n",
+              "                        \n",
+              "       <tr class=\"default\">\n",
+              "           <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-classes_;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%20%29\">\n",
+              "            classes_\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-classes_;\">\n",
+              "            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "           <td class=\"fitted-att-type\">ndarray[int64](2,)</td>\n",
+              "           <td>[0,1]</td>\n",
+              "\n",
+              "\n",
+              "       </tr>\n",
+              "    \n",
+              "\n",
+              "       <tr class=\"default\">\n",
+              "           <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-coef_;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=coef_,-ndarray%20or%20CSR%20matrix%20of%20shape%20%281%2C%20n_features%29%20or%20%28n_classes%2C%20n_features%29\">\n",
+              "            coef_\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-coef_;\">\n",
+              "            coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)<br><br>Coefficients of the features in the decision function.<br><br>`coef_` is of shape (1, n_features) when the given problem is binary.<br><br>By default, it will be created as a dense array, but can be turned to<br>sparse (CSR format) through :meth:`sparsify` (which can be beneficial<br>under L1 regularization when many coefficients are zero), and back to<br>dense through :meth:`densify`.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "           <td class=\"fitted-att-type\">ndarray[float64](1, 30)</td>\n",
+              "           <td>[[0.27,0.54,0.28,...,1.13,0.59,0.54]]</td>\n",
+              "\n",
+              "\n",
+              "       </tr>\n",
+              "    \n",
+              "\n",
+              "       <tr class=\"default\">\n",
+              "           <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-intercept_;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20or%20%28n_classes%2C%29\">\n",
+              "            intercept_\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-intercept_;\">\n",
+              "            intercept_: ndarray of shape (1,) or (n_classes,)<br><br>Intercept (a.k.a. bias) added to the decision function.<br><br>If `fit_intercept` is set to False, the intercept is set to zero.<br>`intercept_` is of shape (1,) when the given problem is binary.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
+              "           <td>[0.15]</td>\n",
+              "\n",
+              "\n",
+              "       </tr>\n",
+              "    \n",
+              "\n",
+              "       <tr class=\"default\">\n",
+              "           <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int\">\n",
+              "            n_features_in_\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
+              "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "           <td class=\"fitted-att-type\">int</td>\n",
+              "           <td>30</td>\n",
+              "\n",
+              "\n",
+              "       </tr>\n",
+              "    \n",
+              "\n",
+              "       <tr class=\"default\">\n",
+              "           <td class=\"param\">\n",
+              "        <a class=\"param-doc-link\"\n",
+              "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
+              "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_iter_,-ndarray%20of%20shape%20%281%2C%20%29\">\n",
+              "            n_iter_\n",
+              "            <span class=\"param-doc-description\"\n",
+              "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
+              "            n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br>    In SciPy &lt;= 1.0.0 the number of lbfgs iterations may exceed<br>    ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>\n",
+              "        </a>\n",
+              "    </td>\n",
+              "           <td class=\"fitted-att-type\">ndarray[int32](1,)</td>\n",
+              "           <td>[18]</td>\n",
+              "\n",
+              "\n",
+              "       </tr>\n",
+              "    \n",
+              "                    </tbody>\n",
+              "                </table>\n",
+              "            </details>\n",
+              "        </div>\n",
+              "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
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+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
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+              "    return false;\n",
+              "}\n",
+              "\n",
+              "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "\n",
+              "    const parent = element.parentElement;\n",
+              "    if (!parent || !parent.nextElementSibling) {\n",
+              "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
+              "        return;\n",
+              "    }\n",
+              "\n",
+              "    const paramName = element.parentElement.nextElementSibling\n",
+              "        .textContent.trim().split(' ')[0];\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+              "\n",
+              "    element.setAttribute('title', fullParamName);\n",
+              "});\n",
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+              " * Copy the list of feature names formatted as a Python list.\n",
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+              " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
+              " *   contain a `details` element and a table containing feature named.\n",
+              " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
+              " */\n",
+              "function copyFeatureNamesToClipboard(element) {\n",
+              "    var detailsElem = element.closest('.features').querySelector('details');\n",
+              "    var wasOpen = detailsElem.open;\n",
+              "    detailsElem.open = true;\n",
+              "    var content = element.closest('.features').querySelector('tbody')\n",
+              "                  .innerText.trim();\n",
+              "    if (!wasOpen) detailsElem.open = false;\n",
+              "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
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+              "        });\n",
+              "    return false;\n",
+              "}\n",
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+              " * Adapted from Skrub\n",
+              " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
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+              "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+              "\n",
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+              "\n",
+              "\n",
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+              "        estimatorElement.classList.add(theme);\n",
+              "    }\n",
+              "}\n",
+              "\n",
+              "forceTheme('sk-container-id-1');</script></body>"
+            ],
             "text/plain": [
               "LogisticRegression(class_weight='balanced')"
-            ],
-            "text/html": [
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             ]
           },
+          "execution_count": 8,
           "metadata": {},
-          "execution_count": 9
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "lr = LogisticRegression(class_weight=\"balanced\")\n",
+        "lr.fit(X_train, y_train)"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "### Valutiamo il modello"
-      ],
+        "#### Regressione Logistica con Classi Bilanciate\n",
+        "La Regressione Logistica modella la probabilit\u00e0 che un campione appartenga alla classe positiva ($y=1$) mediante la funzione logistica (o sigmoide):\n",
+        "$$P(y=1|x) = \\sigma(w^T x + b) = \\frac{1}{1 + e^{-(w^T x + b)}}$$\n",
+        "\n",
+        "Abbiamo istanziato il modello impostando `class_weight='balanced'`. Questo parametro assegna automaticamente un peso maggiore alla classe minoritaria (Maligno) e un peso minore alla classe maggioritaria (Benigno) nella funzione di costo, penalizzando maggiormente gli errori sulla classe positiva per compensare lo sbilanciamento dei dati."
+      ]
+    },
+    {
+      "cell_type": "markdown",
       "metadata": {
         "id": "1LwMkcUmgTpc"
-      }
+      },
+      "source": [
+        "### Valutiamo il modello"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "y_pred_train = lr.predict(X_train)\n",
-        "y_proba_train = lr.predict_proba(X_train)\n",
-        "y_pred_test = lr.predict(X_test)\n",
-        "y_proba_test =  lr.predict_proba(X_test)\n",
-        "\n",
-        "print(\"TRAIN REPORT\")\n",
-        "print(classification_report(y_train, y_pred_train))\n",
-        "print(\"TEST REPORT\")\n",
-        "print(classification_report(y_test, y_pred_test))"
-      ],
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1122,11 +2008,10 @@
         "id": "y-cZpVWZwYov",
         "outputId": "b50af2d6-0ae9-4c6c-ec84-11ee535510bd"
       },
-      "execution_count": 10,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "TRAIN REPORT\n",
             "              precision    recall  f1-score   support\n",
@@ -1150,10 +2035,56 @@
             "\n"
           ]
         }
+      ],
+      "source": [
+        "y_pred_train = lr.predict(X_train)\n",
+        "y_proba_train = lr.predict_proba(X_train)\n",
+        "y_pred_test = lr.predict(X_test)\n",
+        "y_proba_test =  lr.predict_proba(X_test)\n",
+        "\n",
+        "print(\"TRAIN REPORT\")\n",
+        "print(classification_report(y_train, y_pred_train))\n",
+        "print(\"TEST REPORT\")\n",
+        "print(classification_report(y_test, y_pred_test))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Valutazione con Soglia Standard (0.5)\n",
+        "Di default, la funzione `predict` converte le probabilit\u00e0 stimate in classi discrete usando una soglia (threshold) di **$0.5$**:\n",
+        "- Se $P(y=1|x) \\ge 0.5 \\implies$ Classe $1$ (Maligno)\n",
+        "- Se $P(y=1|x) < 0.5 \\implies$ Classe $0$ (Benigno)\n",
+        "\n",
+        "Con questa soglia standard otteniamo:\n",
+        "- Un'accuratezza globale sul Test Set del **$98\\%$**.\n",
+        "- Nel Test Set c'\u00e8 tuttavia **1 falso negativo** (un tumore maligno classificato erroneamente come benigno), il che si traduce in un Recall del **$98.4\\%$** (63/64). In ambito medico, non identificare un tumore maligno (falso negativo) \u00e8 un errore ad altissimo rischio."
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 459
+        },
+        "id": "s-cVHmuFww94",
+        "outputId": "32af79ec-5259-4024-a848-855828a0f397"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
       "source": [
         "def plot_confusion_matrix(y_true, y_pred, labels=[\"Negative\", \"Positive\"], show_precision_recall=True):\n",
         "\n",
@@ -1172,35 +2103,33 @@
         "\n",
         "y_pred_train = np.where(y_proba_train[:,1]>0.25,1,0)\n",
         "plot_confusion_matrix(y_train, y_pred_train, [\"Benignant\", \"Malignant\"])"
-      ],
-      "metadata": {
-        "id": "s-cVHmuFww94",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 459
-        },
-        "outputId": "32af79ec-5259-4024-a848-855828a0f397"
-      },
-      "execution_count": 11,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 2 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
       ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "y_pred_test = np.where(y_proba_test[:,1]>0.25,1,0)\n",
-        "plot_confusion_matrix(y_test, y_pred_test, [\"Benignant\", \"Malignant\"])"
-      ],
+        "#### Ottimizzazione della Soglia Decisionale (Soglia = 0.25)\n",
+        "Per ridurre i falsi negativi a zero (ottenendo un Recall del 100%), possiamo abbassare la soglia decisionale da $0.5$ a **$0.25$**. In questo modo, classifichiamo come \"Maligno\" qualsiasi paziente che abbia una probabilit\u00e0 stimata superiore al $25\\%$.\n",
+        "\n",
+        "**Le metriche della Matrice di Confusione:**\n",
+        "- **True Positives (TP)**: Tumori maligni correttamente classificati come maligni.\n",
+        "- **True Negatives (TN)**: Tumori benigni correttamente classificati come benigni.\n",
+        "- **False Positives (FP)**: Tumori benigni erroneamente classificati come maligni (errore di tipo I).\n",
+        "- **False Negatives (FN)**: Tumori maligni erroneamente classificati come benigni (errore di tipo II, critico!).\n",
+        "\n",
+        "Le formule delle metriche principali:\n",
+        "- **Precision (Precisione)**: La frazione di casi previsti come positivi che sono effettivamente positivi.\n",
+        "$$\\text{Precision} = \\frac{\\text{TP}}{\\text{TP} + \\text{FP}}$$\n",
+        "- **Recall (Sensibilit\u00e0 / Richiamo)**: La frazione di casi positivi reali che il modello \u00e8 riuscito a identificare.\n",
+        "$$\\text{Recall} = \\frac{\\text{TP}}{\\text{TP} + \\text{FN}}$$\n",
+        "- **Accuracy (Accuratezza)**: La percentuale complessiva di predizioni corrette.\n",
+        "$$\\text{Accuracy} = \\frac{\\text{TP} + \\text{TN}}{\\text{TP} + \\text{TN} + \\text{FP} + \\text{FN}}$$"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1209,25 +2138,43 @@
         "id": "kYfXI2BPV2fU",
         "outputId": "4a2f5812-9ca0-4070-d807-35582408099b"
       },
-      "execution_count": 12,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
             "text/plain": [
               "<Figure size 640x480 with 2 Axes>"
-            ],
-            "image/png": 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\n"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "y_pred_test = np.where(y_proba_test[:,1]>0.25,1,0)\n",
+        "plot_confusion_matrix(y_test, y_pred_test, [\"Benignant\", \"Malignant\"])"
       ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "plot_roc_curve(lr, X_train, y_train, name=\"Cancer Classifier\")"
-      ],
+        "#### Analisi del Trade-off Precision-Recall sul Test Set\n",
+        "Applicando la soglia di **$0.25$** sul set di test, osserviamo che:\n",
+        "1. Il numero di **Falsi Negativi scende a 0**, raggiungendo un **Recall pari a 1.0 (100%)**. Abbiamo soddisfatto il requisito clinico cruciale di non perdere nessun tumore maligno.\n",
+        "2. Il numero di **Falsi Positivi sale a 6**, causando una diminuzione della **Precision al 91.4%** (poich\u00e9 alcuni pazienti sani vengono allertati e sottoposti a ulteriori esami).\n",
+        "3. L'**Accuratezza scende al 96.45%**, che \u00e8 inferiore al target iniziale del $98\\%$.\n",
+        "\n",
+        "> [!WARNING]\n",
+        "> **Nota Pedagogica sul Requisito del Problema:**\n",
+        "> Esiste un trade-off intrinseco: non \u00e8 possibile massimizzare contemporaneamente l'Accuratezza (sopra il 98%) e la Recall (a 1.0) abbassando semplicemente la soglia in questo modello. La scelta clinica corretta privilegia la Recall pari a 1.0 (evitare assolutamente i falsi negativi), accettando una leggera perdita di accuratezza complessiva.\n",
+        ">\n",
+        "> *Nota linguistica sulla traccia dell'esercizio:* La traccia indicava \"recall di 1 (0 falsi positivi)\". Si tratta di un refuso comune: un Recall di 1 garantisce **0 falsi negativi** ($\\text{FN} = 0$), mentre sono i **falsi positivi** a degradare la Precision."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1236,26 +2183,35 @@
         "id": "QWmiITSAMYwb",
         "outputId": "a2db8ea3-ca44-42ca-cbdf-cfe53aee0636"
       },
-      "execution_count": 13,
       "outputs": [
         {
-          "output_type": "error",
-          "ename": "NameError",
-          "evalue": "name 'plot_roc_curve' is not defined",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-13-bd97c0b9d9bd>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot_roc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Cancer Classifier\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-            "\u001b[0;31mNameError\u001b[0m: name 'plot_roc_curve' is not defined"
-          ]
+          "data": {
+            "text/plain": [
+              "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x73746255fb60>"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "RocCurveDisplay.from_estimator(lr, X_train, y_train, name=\"Cancer Classifier\")"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "plot_roc_curve(lr, X_test, y_test, name=\"Cancer Classifier\")"
-      ],
+      "execution_count": 13,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1264,36 +2220,60 @@
         "id": "0ov0yMFCOS8Q",
         "outputId": "9ebb98b2-5eda-4f98-bb2e-e24a45dfd650"
       },
-      "execution_count": 14,
       "outputs": [
         {
-          "output_type": "error",
-          "ename": "NameError",
-          "evalue": "name 'plot_roc_curve' is not defined",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-14-f1159bd0bfe8>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot_roc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Cancer Classifier\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-            "\u001b[0;31mNameError\u001b[0m: name 'plot_roc_curve' is not defined"
-          ]
+          "data": {
+            "text/plain": [
+              "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x73745e292c10>"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "RocCurveDisplay.from_estimator(lr, X_test, y_test, name=\"Cancer Classifier\")"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "### Eseguiamo le previsioni"
-      ],
+        "#### Curva ROC e metriche AUC (Area Under the Curve)\n",
+        "La curva **ROC (Receiver Operating Characteristic)** mostra la capacit\u00e0 diagnostica del classificatore al variare della soglia decisionale, tracciando:\n",
+        "- **True Positive Rate (TPR / Recall)** sull'asse Y.\n",
+        "- **False Positive Rate (FPR / 1 - Specificit\u00e0)** sull'asse X, definito come $\\frac{\\text{FP}}{\\text{FP} + \\text{TN}}$.\n",
+        "\n",
+        "L'**AUC (Area Under the Curve)** misura l'abilit\u00e0 complessiva del modello di distinguere tra le due classi:\n",
+        "- Un classificatore casuale ha un $\\text{AUC} = 0.5$.\n",
+        "- Un classificatore perfetto ha un $\\text{AUC} = 1.0$.\n",
+        "\n",
+        "Il nostro classificatore sul Test Set ottiene un **AUC eccezionale di 0.99** (o superiore), dimostrando che il modello distingue in modo quasi perfetto le due classi, indipendentemente dalla specifica soglia operativa scelta."
+      ]
+    },
+    {
+      "cell_type": "markdown",
       "metadata": {
         "id": "audKuv_YgdDw"
-      }
+      },
+      "source": [
+        "### Eseguiamo le previsioni"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "df_pred = pd.read_csv(BASE_URL+\"breast_cancer_pred.csv\")\n",
-        "df_pred.head()"
-      ],
+      "execution_count": 14,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1302,53 +2282,11 @@
         "id": "WPC8MRWaOfu2",
         "outputId": "dac883a1-d0ac-4458-e1ea-31089276f33e"
       },
-      "execution_count": 15,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "   ID number  radius mean  texture mean  perimeter mean  area mean  \\\n",
-              "0     842302       17.990         10.38          122.80     1001.0   \n",
-              "1    8510426       13.540         14.36           87.46      566.3   \n",
-              "2    8510653       13.080         15.71           85.63      520.0   \n",
-              "3    8510824        9.504         12.44           60.34      273.9   \n",
-              "4     859487       12.780         16.49           81.37      502.5   \n",
-              "\n",
-              "   smoothness mean  compactness mean  concavity mean  concave points mean  \\\n",
-              "0          0.11840           0.27760         0.30010              0.14710   \n",
-              "1          0.09779           0.08129         0.06664              0.04781   \n",
-              "2          0.10750           0.12700         0.04568              0.03110   \n",
-              "3          0.10240           0.06492         0.02956              0.02076   \n",
-              "4          0.09831           0.05234         0.03653              0.02864   \n",
-              "\n",
-              "   symmetry mean  ...  radius worst  texture worst  perimeter worst  \\\n",
-              "0         0.2419  ...         25.38          17.33           184.60   \n",
-              "1         0.1885  ...         15.11          19.26            99.70   \n",
-              "2         0.1967  ...         14.50          20.49            96.09   \n",
-              "3         0.1815  ...         10.23          15.66            65.13   \n",
-              "4         0.1590  ...         13.46          19.76            85.67   \n",
-              "\n",
-              "   area worst  smoothness worstse  compactness worst  concavity worst  \\\n",
-              "0      2019.0              0.1622            0.66560          0.71190   \n",
-              "1       711.2              0.1440            0.17730          0.23900   \n",
-              "2       630.5              0.1312            0.27760          0.18900   \n",
-              "3       314.9              0.1324            0.11480          0.08867   \n",
-              "4       554.9              0.1296            0.07061          0.10390   \n",
-              "\n",
-              "   concave points worst  symmetry worst  fractal dimension worst  \n",
-              "0               0.26540          0.4601                  0.11890  \n",
-              "1               0.12880          0.2977                  0.07259  \n",
-              "2               0.07283          0.3184                  0.08183  \n",
-              "3               0.06227          0.2450                  0.07773  \n",
-              "4               0.05882          0.2383                  0.06410  \n",
-              "\n",
-              "[5 rows x 31 columns]"
-            ],
             "text/html": [
-              "\n",
-              "  <div id=\"df-71c231fe-a047-4ed1-8a38-95fb05515f1a\" class=\"colab-df-container\">\n",
-              "    <div>\n",
+              "<div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
               "        vertical-align: middle;\n",
@@ -1512,265 +2450,125 @@
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
-              "<p>5 rows × 31 columns</p>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-71c231fe-a047-4ed1-8a38-95fb05515f1a')\"\n",
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-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
+              "<p>5 rows \u00d7 31 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   ID number  radius mean  texture mean  perimeter mean  area mean  \\\n",
+              "0     842302       17.990         10.38          122.80     1001.0   \n",
+              "1    8510426       13.540         14.36           87.46      566.3   \n",
+              "2    8510653       13.080         15.71           85.63      520.0   \n",
+              "3    8510824        9.504         12.44           60.34      273.9   \n",
+              "4     859487       12.780         16.49           81.37      502.5   \n",
               "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
+              "   smoothness mean  compactness mean  concavity mean  concave points mean  \\\n",
+              "0          0.11840           0.27760         0.30010              0.14710   \n",
+              "1          0.09779           0.08129         0.06664              0.04781   \n",
+              "2          0.10750           0.12700         0.04568              0.03110   \n",
+              "3          0.10240           0.06492         0.02956              0.02076   \n",
+              "4          0.09831           0.05234         0.03653              0.02864   \n",
               "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
+              "   symmetry mean  ...  radius worst  texture worst  perimeter worst  \\\n",
+              "0         0.2419  ...         25.38          17.33           184.60   \n",
+              "1         0.1885  ...         15.11          19.26            99.70   \n",
+              "2         0.1967  ...         14.50          20.49            96.09   \n",
+              "3         0.1815  ...         10.23          15.66            65.13   \n",
+              "4         0.1590  ...         13.46          19.76            85.67   \n",
               "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
+              "   area worst  smoothness worstse  compactness worst  concavity worst  \\\n",
+              "0      2019.0              0.1622            0.66560          0.71190   \n",
+              "1       711.2              0.1440            0.17730          0.23900   \n",
+              "2       630.5              0.1312            0.27760          0.18900   \n",
+              "3       314.9              0.1324            0.11480          0.08867   \n",
+              "4       554.9              0.1296            0.07061          0.10390   \n",
               "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-71c231fe-a047-4ed1-8a38-95fb05515f1a button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-71c231fe-a047-4ed1-8a38-95fb05515f1a');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-175847ad-bf05-4a9e-89f2-cac523c4fa46\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-175847ad-bf05-4a9e-89f2-cac523c4fa46')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
+              "   concave points worst  symmetry worst  fractal dimension worst  \n",
+              "0               0.26540          0.4601                  0.11890  \n",
+              "1               0.12880          0.2977                  0.07259  \n",
+              "2               0.07283          0.3184                  0.08183  \n",
+              "3               0.06227          0.2450                  0.07773  \n",
+              "4               0.05882          0.2383                  0.06410  \n",
               "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-175847ad-bf05-4a9e-89f2-cac523c4fa46 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df_pred"
-            }
+              "[5 rows x 31 columns]"
+            ]
           },
+          "execution_count": 14,
           "metadata": {},
-          "execution_count": 15
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "df_pred = pd.read_csv(BASE_URL+\"breast_cancer_pred.csv\")\n",
+        "df_pred.head()"
       ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "X = df_pred.drop(\"ID number\", axis=1).values\n",
-        "X = ss.transform(X)"
-      ],
+        "### Applicazione del Modello su Nuovi Pazienti\n",
+        "Ora applichiamo il modello addestrato a un set di nuovi dati non etichettati (`breast_cancer_pred.csv`) per supportare il personale medico nella diagnosi precoce."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
       "metadata": {
         "id": "_mG0F3WIQmV9"
       },
-      "execution_count": 16,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "X = df_pred.drop(\"ID number\", axis=1).values\n",
+        "X = ss.transform(X)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Coerenza del Preprocessing\n",
+        "Prima di passare i nuovi dati al modello, dobbiamo standardizzarli. Utilizziamo lo stesso oggetto `StandardScaler` (`ss`) addestrato sul training set, richiamando esclusivamente il metodo `transform`. Questo garantisce che le feature dei nuovi pazienti siano scalate con le stesse identiche medie e varianze del set di addestramento."
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "OLLoycHkQuXQ"
+      },
+      "outputs": [],
       "source": [
         "y_proba = lr.predict_proba(X)\n",
         "condition = y_proba[:,1]>0.3\n",
         "y_pred = np.where(condition,1,0)"
-      ],
-      "metadata": {
-        "id": "OLLoycHkQuXQ"
-      },
-      "execution_count": 22,
-      "outputs": []
+      ]
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
-        "y_proba = np.where(condition, y_proba[:, 1], y_proba[:, 0])"
-      ],
+        "#### Scelta della Soglia di Produzione (0.3)\n",
+        "Per le previsioni finali, \u00e8 stata selezionata una soglia decisionale conservativa del **$0.3$**. Questo rappresenta una via di mezzo sciura per mantenere un'alta sensibilit\u00e0 clinica riducendo al contempo i falsi allarmi rispetto a una soglia di $0.25$."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
       "metadata": {
         "id": "obZX6dBl06j2"
       },
-      "execution_count": 24,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "y_proba = np.where(condition, y_proba[:, 1], y_proba[:, 0])"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "vYGfFKXmRDnP"
+      },
+      "outputs": [],
       "source": [
         "df_result = pd.DataFrame({\n",
         "    \"ID number\":df_pred[\"ID number\"],\n",
@@ -1779,21 +2577,50 @@
         "})\n",
         "\n",
         "df_result.to_excel(\"breast_cancer_prediction.xlsx\")"
-      ],
-      "metadata": {
-        "id": "vYGfFKXmRDnP"
-      },
-      "execution_count": 26,
-      "outputs": []
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Generazione del Report Finale\n",
+        "I risultati predittivi sono stati esportati in un file Excel (`breast_cancer_prediction.xlsx`). Per ogni paziente viene fornito l'ID, la classe predetta (Maligno/Benigno) e la probabilit\u00e0 associata alla predizione, offrendo uno strumento chiaro e interpretabile per i medici."
+      ]
     },
     {
       "cell_type": "code",
-      "source": [],
+      "execution_count": null,
       "metadata": {
         "id": "GEC7tcBM2Pow"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": []
     }
-  ]
-}
+  ],
+  "metadata": {
+    "colab": {
+      "include_colab_link": true,
+      "name": "binary_classification_exercise",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/5 - La Classificazione/esercizi/breast_cancer_prediction.xlsx b/5 - La Classificazione/esercizi/breast_cancer_prediction.xlsx
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diff --git a/6 - Clustering/elbow_method.ipynb b/6 - Clustering/elbow_method.ipynb
index 1a5e122..cd7059b 100644
--- a/6 - Clustering/elbow_method.ipynb	
+++ b/6 - Clustering/elbow_method.ipynb	
@@ -41,16 +41,27 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Il **Metodo del Gomito (Elbow Method)** \u00e8 una tecnica euristica utilizzata per determinare il numero ottimale di cluster ($K$) in un algoritmo come il K-Means.\n",
+        "Il **Metodo del Gomito (Elbow Method)** \u00e8 una tecnica euristica fondamentale utilizzata nell'apprendimento non supervisionato per determinare il numero ottimale di cluster ($K$) in algoritmi come il K-Means.\n",
         "\n",
-        "L'idea di base \u00e8 eseguire il K-Means per un range di valori di $K$ (es. da 1 a 10) e calcolare per ogni $K$ la somma dei quadrati delle distanze intra-cluster (WCSS o Inerzia):\n",
+        "#### Il Concetto e il Trade-off di Complessit\u00e0\n",
         "\n",
-        "$$\\text{WCSS} = \\sum_{i=1}^{K} \\sum_{x \\in C_i} ||x - \\mu_i||^2$$\n",
+        "La funzione obiettivo del K-Means, l'**Inerzia** o **WCSS (Within-Cluster Sum of Squares)**, misura la somma delle distanze al quadrato dei punti dai rispettivi centroidi:\n",
         "\n",
-        "Aumentando $K$, il WCSS diminuir\u00e0 sempre, poich\u00e9 i punti saranno sempre pi\u00f9 vicini ai loro rispettivi centroidi (nel caso limite in cui $K$ \u00e8 uguale al numero di punti, il WCSS sar\u00e0 0). Tuttavia, stiamo cercando un compromesso: il punto in cui l'aggiunta di un ulteriore cluster non migliora significativamente il modello. Questo punto crea una curva a forma di braccio, e il *gomito* (l'angolo) rappresenta il numero ottimale di cluster.\n",
+        "$$\\text{WCSS} = \\sum_{i=1}^{K} \\sum_{x \\in C_i} \\|x - \\mu_i\\|^2$$\n",
+        "\n",
+        "- Se aumentiamo il numero di cluster $K$, l'inerzia **diminuir\u00e0 sempre in modo monotono**. Nel caso limite in cui $K = N$ (dove $N$ \u00e8 il numero di campioni), ogni punto coincider\u00e0 con il proprio centroide, portando $\\text{WCSS} = 0$.\n",
+        "- Tuttavia, impostare $K = N$ non ha alcun valore pratico (overfitting estremo). Stiamo cercando un **compromesso** tra la compattezza dei cluster (basso WCSS) e la semplicit\u00e0 del modello (basso $K$), seguendo il principio del Rasoio di Ockham.\n",
+        "\n",
+        "#### Identificazione del \"Gomito\"\n",
+        "\n",
+        "Tracciando il grafico di WCSS in funzione di $K$:\n",
+        "1. All'inizio (per piccoli valori di $K$), l'inerzia decresce molto rapidamente.\n",
+        "2. Da un certo punto in poi, l'aggiunta di nuovi cluster produce solo piccoli miglioramenti (riduzioni marginali dell'inerzia).\n",
+        "3. Questo cambiamento repentino nella pendenza crea un angolo nella curva, simile a un **gomito (elbow)**. La coordinata $K$ corrispondente a questo angolo rappresenta la scelta ottimale, in cui si ottiene il miglior compromesso tra qualit\u00e0 del clustering e complessit\u00e0 computazionale.\n",
         "\n",
         "> [!IMPORTANT]\n",
-        "> Il metodo del gomito \u00e8 soggettivo: a volte la curva non ha un \"gomito\" ben definito. In questi casi, potrebbero essere necessari altri metodi (come la Silhouette Score) o considerazioni di business."
+        "> **Limiti dell'Elbow Method:**\n",
+        "> Il metodo del gomito \u00e8 di natura euristica e visiva, il che lo rende parzialmente soggettivo. Nei dataset reali e complessi, la curva potrebbe apparire liscia senza un gomito ben definito. In tali scenari si ricorre a metriche aggiuntive come il **Coefficiente di Silhouette (Silhouette Score)** o ad analisi basate su vincoli e logiche di business."
       ]
     },
     {
@@ -135,11 +146,25 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Esecuzione dell'Elbow Method\n",
-        "Iteriamo su un range di possibili valori per $K$. Per ogni iterazione:\n",
-        "1. Istanziamo e addestriamo il modello `KMeans` con $K$ cluster.\n",
-        "2. Estraiamo l'inerzia tramite la propriet\u00e0 `.inertia_`.\n",
-        "3. Memorizziamo il valore nel dizionario `sse` per poterlo plottare."
+        "#### Nota sul Preprocessing e Feature Scaling\n",
+        "\n",
+        "Anche in questo esperimento abbiamo generato dei dati fittizi tramite `make_blobs` e modificato la scala delle due feature (moltiplicando $X_0$ per 20 e $X_1$ per 6). \n",
+        "Come discusso per il K-Means di base, questa asimmetria nelle scale \u00e8 introdotta a fini didattici per ricordarci che:\n",
+        "- Gli algoritmi di clustering basati sulle distanze risentono pesantemente del range delle variabili.\n",
+        "- Prima di applicare l'Elbow Method a un dataset reale, \u00e8 **sempre obbligatorio standardizzare** le feature affinch\u00e9 ciascuna di esse contribuisca equamente alla metrica delle distanze."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Addestramento Iterativo e Calcolo dell'Inerzia\n",
+        "\n",
+        "Procediamo con l'implementazione pratica del metodo. Eseguiremo il K-Means impostando il numero di cluster $K$ da $1$ a $9$:\n",
+        "1. Inizializziamo il modello `KMeans` con `init=\"k-means++\"` per garantire la stabilit\u00e0 e la convergenza ottimale ad ogni ciclo.\n",
+        "2. Addestriamo il modello sul nostro dataset `X` tramite `.fit(X)`.\n",
+        "3. Estraiamo l'Inerzia tramite l'attributo nativo `kmeans.inertia_` e la inseriamo nel dizionario `sse` (Sum of Squared Errors).\n",
+        "4. Plottiamo l'andamento del WCSS per individuare visivamente il punto di svolta."
       ]
     },
     {
@@ -178,6 +203,28 @@
         "plt.savefig(\"number_of_k.png\")\n",
         "plt.show()\n"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Analisi Quantitativa dei Risultati dell'Elbow Method\n",
+        "\n",
+        "Esaminiamo i valori esatti dell'inerzia (WCSS) ottenuti per ciascun $K$ durante l'esecuzione del loop:\n",
+        "\n",
+        "- **$K=1$**: WCSS $\\approx 106.907,68$ (Tutti i punti sono raggruppati in un unico grande cluster).\n",
+        "- **$K=2$**: WCSS $\\approx 32.112,17$ (Un decremento netto di $\\approx 74.795,50$, pari a circa il **70%** di riduzione dell'inerzia rispetto a $K=1$).\n",
+        "- **$K=3$**: WCSS $\\approx 10.741,04$ (Un ulteriore decremento significativo di $\\approx 21.371,13$, circa il **20%** rispetto al valore iniziale).\n",
+        "- **$K=4$**: WCSS $\\approx 7.895,56$ (Un decremento di soli $\\approx 2.845,48$, circa il **2,6%** rispetto al valore iniziale).\n",
+        "- **$K=5$**: WCSS $\\approx 5.707,02$ (Un decremento marginale di $\\approx 2.188,54$, circa il **2%** rispetto al valore iniziale).\n",
+        "\n",
+        "#### Interpretazione del Gomito\n",
+        "Come si evince sia dal grafico che dall'analisi numerica:\n",
+        "- Il passaggio da **$K=1$ a $K=3$** apporta miglioramenti enormi nella spiegazione della varianza dei dati (riduzione drastica della distanza intra-cluster).\n",
+        "- Dal passaggio **$K=3$ a $K=4$** (e successivi), il guadagno in termini di riduzione dell'inerzia diventa estremamente ridotto (diminishing returns).\n",
+        "   \n",
+        "Il punto di flesso della curva (il **gomito**) si colloca esattamente a **$K=3$**. Questo risultato matematico \u00e8 coerente con la nostra generazione di dati, dove abbiamo esplicitamente impostato `centers=3` nella funzione `make_blobs`."
+      ]
     }
   ]
 }
\ No newline at end of file
diff --git a/6 - Clustering/kmeans.ipynb b/6 - Clustering/kmeans.ipynb
index f0c6c2c..44eb21a 100644
--- a/6 - Clustering/kmeans.ipynb	
+++ b/6 - Clustering/kmeans.ipynb	
@@ -41,20 +41,28 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Il **K-Means** \u00e8 uno degli algoritmi di clustering (apprendimento non supervisionato) pi\u00f9 popolari.\n",
-        "L'obiettivo dell'algoritmo \u00e8 partizionare un insieme di dati in $K$ gruppi distinti (cluster), in modo che i punti all'interno dello stesso gruppo siano il pi\u00f9 simili possibile tra loro, e il pi\u00f9 dissimili possibile dai punti negli altri gruppi.\n",
+        "Il **K-Means** \u00e8 uno degli algoritmi di clustering (apprendimento non supervisionato) pi\u00f9 popolari e semplici.\n",
+        "L'obiettivo principale dell'algoritmo \u00e8 partizionare un insieme di dati non etichettati in $K$ gruppi distinti (**cluster**), in modo che:\n",
+        "1. I punti all'interno dello stesso gruppo siano il pi\u00f9 simili possibile tra loro (**alta similarit\u00e0 intra-cluster**).\n",
+        "2. I punti in gruppi diversi siano il pi\u00f9 dissimili possibile (**bassa similarit\u00e0 inter-cluster**).\n",
         "\n",
-        "Matematicamente, il K-Means cerca di minimizzare l'**Inerzia** (o Within-Cluster Sum of Squares - WCSS):\n",
+        "#### La Formulazione Matematica dell'Inerzia\n",
         "\n",
-        "$$\\text{WCSS} = \\sum_{i=1}^{K} \\sum_{x \\in C_i} ||x - \\mu_i||^2$$\n",
+        "Per definire formalmente la similarit\u00e0, il K-Means cerca di minimizzare l'**Inerzia** (chiamata anche **Within-Cluster Sum of Squares - WCSS**). L'inerzia misura la somma delle distanze al quadrato tra ciascun punto e il centroide del suo cluster di appartenenza:\n",
+        "\n",
+        "$$\\text{WCSS} = \\sum_{i=1}^{K} \\sum_{x \\in C_i} \\|x - \\mu_i\\|^2$$\n",
         "\n",
         "Dove:\n",
-        "- $K$ \u00e8 il numero di cluster\n",
-        "- $x$ \u00e8 un punto dati appartenente al cluster $C_i$\n",
-        "- $\\mu_i$ \u00e8 il centroide (media) del cluster $C_i$\n",
+        "- $K$ \u00e8 il numero totale di cluster.\n",
+        "- $C_i$ rappresenta l'insieme dei punti assegnati al cluster $i$-esimo.\n",
+        "- $x$ \u00e8 un generico punto dati (un vettore in uno spazio a $d$ dimensioni).\n",
+        "- $\\mu_i$ \u00e8 il **centroide** (ovvero la media geometrica) di tutti i punti appartenenti al cluster $C_i$:\n",
+        "  $$\\mu_i = \\frac{1}{|C_i|} \\sum_{x \\in C_i} x$$\n",
+        "- $\\|x - \\mu_i\\|^2$ \u00e8 la distanza euclidea al quadrato tra il punto $x$ e il centroide $\\mu_i$.\n",
         "\n",
         "> [!IMPORTANT]\n",
-        "> Nel K-Means, il numero di cluster $K$ deve essere specificato a priori. Questo \u00e8 uno dei limiti principali dell'algoritmo, che si pu\u00f2 affrontare con tecniche come il Metodo del Gomito (Elbow Method)."
+        "> **Il Limite del Parametro K a Priori:**\n",
+        "> Uno dei principali limiti del K-Means \u00e8 che il numero di cluster $K$ deve essere specificato dall'utente prima dell'addestramento. Nella pratica, la scelta di $K$ non \u00e8 banale e si ricorre a metodi diagnostici come il **Metodo del Gomito (Elbow Method)** o il **Punteggio di Silhouette (Silhouette Score)**."
       ]
     },
     {
@@ -95,6 +103,19 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Configurazione dell'Ambiente e Import delle Librerie\n",
+        "\n",
+        "Prima di iniziare, importiamo i moduli necessari:\n",
+        "- **`numpy`** e **`pandas`**: essenziali per la manipolazione di array e dati tabellari.\n",
+        "- **`sklearn.datasets.make_blobs`**: una comoda utility per generare cluster artificiali isotropi con distribuzione gaussiana, ideale per testare algoritmi di clustering.\n",
+        "- **`matplotlib.pyplot`** e **`seaborn`**: per la creazione di grafici di qualit\u00e0.\n",
+        "- **`time`**: per misurare e confrontare i tempi di addestramento dei modelli."
+      ]
+    },
     {
       "cell_type": "markdown",
       "source": [
@@ -145,6 +166,26 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### L'importanza dello Scaling delle Feature nel Clustering\n",
+        "\n",
+        "Analizziamo attentamente le operazioni eseguite sui dati generati:\n",
+        "1. Generiamo cluster ideali con `make_blobs`.\n",
+        "2. Applichiamo una riscalatura asimmetrica: le feature della prima colonna ($X_0$) vengono moltiplicate per $20$, mentre quelle della seconda colonna ($X_1$) per $6$.\n",
+        "\n",
+        "**Perch\u00e9 questa riscalatura asimmetrica \u00e8 cruciale dal punto di vista didattico?**\n",
+        "K-Means calcola la distanza euclidea tra i punti dati e i centroidi:\n",
+        "$$d(p, q) = \\sqrt{(p_1 - q_1)^2 + (p_2 - q_2)^2}$$\n",
+        "\n",
+        "Se le feature hanno scale o intervalli di valori molto differenti (ad esempio, se $X_0$ spazia da 0 a 150 e $X_1$ spazia da 0 a 10), la feature con l'intervallo pi\u00f9 ampio dominer\u00e0 completamente il calcolo della distanza. Di conseguenza, il clustering avverr\u00e0 quasi esclusivamente lungo l'asse della feature dominante, ignorando l'altra.\n",
+        "\n",
+        "> [!TIP]\n",
+        "> Nella pratica reale con K-Means, \u00e8 **sempre fortemente raccomandato** applicare una standardizzazione (es. con `StandardScaler` di scikit-learn) o una normalizzazione MinMax prima dell'addestramento, in modo che ogni feature contribuisca equamente alla definizione dei cluster."
+      ]
+    },
     {
       "cell_type": "markdown",
       "source": [
@@ -158,12 +199,37 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "L'addestramento del K-Means avviene in due fasi iterative:\n",
-        "1. **Assegnazione**: Ogni punto viene assegnato al centroide pi\u00f9 vicino.\n",
-        "2. **Aggiornamento**: I centroidi vengono ricalcolati come media dei punti assegnati.\n",
+        "### Il Processo di Addestramento Iterativo (Algoritmo di Lloyd)\n",
+        "\n",
+        "L'addestramento del K-Means (noto anche come algoritmo di Lloyd) procede iterativamente alternando due fasi principali fino alla convergenza:\n",
+        "\n",
+        "1. **Fase di Assegnazione (Expectation)**:\n",
+        "   Ogni punto dati $x$ viene assegnato al cluster del centroide pi\u00f9 vicino, calcolando la distanza euclidea minima:\n",
+        "   $$S_i^{(t)} = \\left\\{ x : \\|x - \\mu_i^{(t)}\\|^2 \\le \\|x - \\mu_j^{(t)}\\|^2 \\quad \\forall j, 1 \\le j \\le K \\right\\}$$\n",
+        "   Dove $S_i^{(t)}$ rappresenta il gruppo di punti assegnati al centroide $\\mu_i$ all'iterazione $t$.\n",
+        "\n",
+        "2. **Fase di Aggiornamento (Maximization)**:\n",
+        "   I centroidi vengono ricalcolati determinando la media geometrica di tutti i punti assegnati a ciascun cluster nella fase precedente:\n",
+        "   $$\\mu_i^{(t+1)} = \\frac{1}{|S_i^{(t)}|} \\sum_{x \\in S_i^{(t)}} x$$\n",
+        "\n",
+        "Questo ciclo si ripete fino a quando la posizione dei centroidi non cambia pi\u00f9 in modo significativo o viene raggiunto il numero massimo di iterazioni preimpostato.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "#### Il Problema dei Minimi Locali e l'Inizializzazione dei Centroidi\n",
+        "\n",
+        "Poich\u00e9 la funzione obiettivo (Inerzia) non \u00e8 convessa, K-Means \u00e8 estremamente sensibile alla scelta delle posizioni iniziali dei centroidi. Un'inizializzazione sfortunata pu\u00f2 intrappolare l'algoritmo in **minimi locali subottimali**.\n",
+        "\n",
+        "Per mitigare questo problema, esistono due strategie principali impostabili tramite il parametro `init`:\n",
+        "\n",
+        "- **Inizializzazione Casuale (`init=\"random\"`)**:\n",
+        "  I centroidi iniziali vengono scelti in modo casuale estraendo $K$ campioni dal dataset. Questo approccio \u00e8 soggetto a forte variabilit\u00e0 del risultato finale.\n",
+        "  \n",
+        "- **Inizializzazione Intelligente (`init=\"k-means++\"`)**:\n",
+        "  Sceglie il primo centroide in modo casuale e i successivi con una probabilit\u00e0 proporzionale alla distanza al quadrato dal centroide pi\u00f9 vicino gi\u00e0 selezionato. Questo garantisce che i centroidi iniziali siano ben distanziati nello spazio, riducendo drasticamente il numero di iterazioni necessarie per convergere e migliorando la stabilit\u00e0 globale del clustering.\n",
         "\n",
-        "> [!WARNING]\n",
-        "> **Inizializzazione dei centroidi**: L'algoritmo standard sceglie i centroidi iniziali in modo casuale, il che pu\u00f2 portare a convergere in minimi locali. Per risolvere questo problema, si utilizza l'inizializzazione `k-means++`, che seleziona i centroidi iniziali in modo che siano sufficientemente distanti tra loro, accelerando la convergenza e migliorando il risultato."
+        "> [!NOTE]\n",
+        "> In Scikit-learn (dalla versione 0.24 in poi), l'inizializzazione predefinita \u00e8 impostata su `'k-means++'`. Nei blocchi di codice successivi confronteremo l'addestramento con inizializzazione implicita ed esplicita."
       ]
     },
     {
@@ -231,6 +297,15 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "> [!NOTE]\n",
+        "> **Analisi dei Tempi di Addestramento:**\n",
+        "> Se notate una differenza di tempo tra il primo addestramento ($\\approx 0.019$ s) e il secondo ($\\approx 0.001$ s), non \u00e8 dovuta all'efficienza intrinseca del parametro `init=\"k-means++\"` (che in realt\u00e0 \u00e8 attivo in entrambi i casi per default). In Python, la prima esecuzione di un metodo di una libreria complessa come `scikit-learn` comporta un overhead iniziale dovuto al caricamento in memoria dei moduli sottostanti e alla compilazione JIT o inizializzazione di thread-pool C. Le successive esecuzioni beneficiano della cache e dei moduli gi\u00e0 pronti in memoria."
+      ]
+    },
     {
       "cell_type": "markdown",
       "source": [
@@ -244,9 +319,37 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Per valutare la bont\u00e0 del clustering, utilizziamo due metriche correlate:\n",
-        "- **Distorsione**: La media delle distanze al quadrato dai centri dei cluster dei rispettivi punti.\n",
-        "- **Inerzia**: La somma delle distanze al quadrato (WCSS). Scikit-learn calcola automaticamente l'inerzia e la rende disponibile tramite l'attributo `inertia_`."
+        "### Metriche di Valutazione: Distorsione vs Inerzia\n",
+        "\n",
+        "Poich\u00e9 nel clustering non disponiamo di etichette reali (ground truth) per calcolare metriche classiche come accuratezza o precisione, dobbiamo valutare la qualit\u00e0 della segmentazione analizzando la compattezza dei cluster ottenuti.\n",
+        "\n",
+        "Utilizziamo due metriche fondamentali basate sulle distanze euclidee:\n",
+        "\n",
+        "1. **Distorsione (Distortion)**:\n",
+        "   Rappresenta la **media** delle distanze euclidee al quadrato tra ciascun punto e il rispettivo centroide assegnato.\n",
+        "   $$\\text{Distortion} = \\frac{1}{N} \\sum_{i=1}^{K} \\sum_{x \\in C_i} \\|x - \\mu_i\\|^2$$\n",
+        "   Dove $N$ \u00e8 il numero totale di campioni nel dataset.\n",
+        "\n",
+        "2. **Inerzia (Inertia o WCSS)**:\n",
+        "   Rappresenta la **somma** totale delle distanze euclidee al quadrato di tutti i punti dai propri centroidi.\n",
+        "   $$\\text{Inertia} = \\sum_{i=1}^{K} \\sum_{x \\in C_i} \\|x - \\mu_i\\|^2$$\n",
+        "\n",
+        "#### Relazione Matematica\n",
+        "\u00c8 evidente che l'Inerzia \u00e8 direttamente proporzionale alla Distorsione tramite la dimensione del dataset $N$:\n",
+        "$$\\text{Inertia} = N \\times \\text{Distortion}$$\n",
+        "\n",
+        "Scikit-learn calcola automaticamente l'inerzia durante il fitting del modello e la memorizza nell'attributo `inertia_` dell'oggetto `KMeans`.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "#### Analisi Quantitativa del Nostro Modello\n",
+        "\n",
+        "Nei passaggi successivi calcoleremo queste metriche sia manualmente (tramite la libreria `scipy.spatial.distance.cdist`) sia usando direttamente l'attributo nativo di scikit-learn. I risultati esatti ottenuti sul nostro dataset di $N = 100$ punti sono:\n",
+        "- **Distorsione Manuale**: $\\approx 107.4104$\n",
+        "- **Inerzia Manuale**: $\\approx 10741.0407$\n",
+        "- **Inerzia Nativa (`kmeans.inertia_`)**: $\\approx 10741.0407$\n",
+        "\n",
+        "Ci\u00f2 dimostra empiricamente la formula di conversione e la correttezza del calcolo nativo di Scikit-learn."
       ]
     },
     {
@@ -394,6 +497,27 @@
           "metadata": {}
         }
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Interpretazione di Business dei Cluster (Customer Segmentation)\n",
+        "\n",
+        "Il grafico soprastante mostra il risultato finale dell'algoritmo K-Means applicato ai dati di spesa di 100 clienti. Possiamo interpretare i tre cluster identificati come segue:\n",
+        "\n",
+        "1. **Neo mamme (Cluster con alta spesa in pannolini, bassa spesa in birra)**:\n",
+        "   Questo gruppo di clienti mostra un comportamento d'acquisto focalizzato sui beni di prima necessit\u00e0 per neonati. Le strategie di marketing potrebbero includere offerte per latte in polvere o omogeneizzati.\n",
+        "\n",
+        "2. **Neo pap\u00e0 (Cluster con alta spesa in pannolini, alta spesa in birra)**:\n",
+        "   Una celebre leggenda metropolitana del marketing narra che i padri inviati a comprare pannolini tendano ad associare l'acquisto a una gratificazione personale, come la birra. Questo cluster riflette esattamente tale pattern di co-acquisto. Le promozioni potrebbero posizionare questi prodotti in prossimit\u00e0 nel punto vendita.\n",
+        "\n",
+        "3. **Donne single (Cluster con bassa spesa in pannolini, spesa in birra moderata/alta)**:\n",
+        "   Clienti senza figli neonati, orientati a consumi differenti. La spesa in pannolini \u00e8 nulla o trascurabile, mentre quella per bevande/socializzazione \u00e8 rilevante.\n",
+        "\n",
+        "#### Il Ruolo dei Centroidi (Punti Rossi)\n",
+        "I centroidi rappresentano il \"profilo medio\" o tipico di ciascun segmento di clientela. Ad esempio, il centroide delle \"Neo mamme\" ci dice qual \u00e8 la spesa media in birra e pannolini di tutto quel gruppo, agendo da punto di riferimento per future campagne di targeting personalizzato."
+      ]
     }
   ]
 }
\ No newline at end of file
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HcmV?d00001


From aafa3824eae85ec74de4cf64714af5d5499bee78 Mon Sep 17 00:00:00 2001
From: Spatariu Rares <81376159+SpatariuRares@users.noreply.github.com>
Date: Sun, 21 Jun 2026 18:53:17 +0200
Subject: [PATCH 7/9] feat: add EDA pipeline and dataset loading logic to
 cross-sell project and update clustering notebooks

---
 .../esercizi/clustering_exercise.ipynb        | 539 ++++++---------
 .../esercizi/mall_customers_prediction.xlsx   | Bin 0 -> 4937 bytes
 6 - Clustering/kmeans.ipynb                   | 354 +++++-----
 ...alth_insurance_cross_sell_prediction.ipynb | 615 +++++++++++++++++-
 4 files changed, 974 insertions(+), 534 deletions(-)
 create mode 100644 6 - Clustering/esercizi/mall_customers_prediction.xlsx

diff --git a/6 - Clustering/esercizi/clustering_exercise.ipynb b/6 - Clustering/esercizi/clustering_exercise.ipynb
index c9f0c2d..a470cc9 100644
--- a/6 - Clustering/esercizi/clustering_exercise.ipynb	
+++ b/6 - Clustering/esercizi/clustering_exercise.ipynb	
@@ -1,28 +1,10 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "clustering_exercise.ipynb",
-      "provenance": [],
-      "collapsed_sections": [],
-      "authorship_tag": "ABX9TyPwzRcDDPKvpWr4DVLNVQur",
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/ProfAI/machine-learning-fondamenti/blob/main/6%20-%20Clustering/esercizi/clustering_exercise.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -30,6 +12,9 @@
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "LmM_rtTSmTLt"
+      },
       "source": [
         "### Clustering della clientela di un centro commerciale\n",
         "In questa esercitazione dovrai eseguire la segmentazione della clientela di un centro commerciale utilizzando il dataset che puoi trovare [qui](https://raw.githubusercontent.com/ProfAI/machine-learning-fondamenti/main/datasets/mall_customers.csv).\n",
@@ -51,14 +36,11 @@
         "Per ogni modello utilizza l'Elbow Method per determinare il numero di cluster e visualizza i cluster tramite uno scatterplot.<br/> Utilizza l'ultimo modello per associare questi clienti ad un cluster, esporta il risultato in un file EXCEL chiamato *mall_customers_prediction.xlsx* contentente due colonne:\n",
         " - **CustomerID**: il codice identificativo del cliente\n",
         " - **Customer Group**: il cluster di appartenenza"
-      ],
-      "metadata": {
-        "id": "LmM_rtTSmTLt"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 1,
       "metadata": {
         "id": "SKa0ygvdmOvS"
       },
@@ -73,34 +55,31 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "plt.rcParams[\"figure.figsize\"] = (16,10)\n",
-        "sns.set_theme(palette=\"dark\")"
-      ],
+      "execution_count": 2,
       "metadata": {
         "id": "coKvMWLUnc3a"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "plt.rcParams[\"figure.figsize\"] = (16,10)\n",
+        "sns.set_theme(palette=\"dark\")"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "BASE_URL = \"https://raw.githubusercontent.com/ProfAI/machine-learning-fondamenti/main/datasets/\"\n",
-        "RANDOM_SEED = 1"
-      ],
+      "execution_count": 3,
       "metadata": {
         "id": "1_RpcGjMmrYf"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "BASE_URL = \"https://raw.githubusercontent.com/ProfAI/machine-learning-fondamenti/main/datasets/\"\n",
+        "RANDOM_SEED = 1"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "df = pd.read_csv(BASE_URL+\"mall_customers.csv\")\n",
-        "df.head()"
-      ],
+      "execution_count": 4,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -109,16 +88,11 @@
         "id": "z__VEnk-mvUs",
         "outputId": "07c2caf8-0613-48fb-cac1-68cb8d6c37cd"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
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-              "    <div class=\"colab-df-container\">\n",
-              "      <div>\n",
+              "<div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
               "        vertical-align: middle;\n",
@@ -186,83 +160,7 @@
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
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+              "</div>"
             ],
             "text/plain": [
               "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
@@ -273,24 +171,34 @@
               "4           5  Female   31                  17                      40"
             ]
           },
+          "execution_count": 4,
           "metadata": {},
-          "execution_count": 4
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "df = pd.read_csv(BASE_URL+\"mall_customers.csv\")\n",
+        "df.head()"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "X = df[[\"Annual Income (k$)\", \"Spending Score (1-100)\"]].values"
-      ],
+      "execution_count": 5,
       "metadata": {
         "id": "PrwhSrcuy29d"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "X = df[[\"Annual Income (k$)\", \"Spending Score (1-100)\"]].values"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "tWpTA6A_BHXY"
+      },
+      "outputs": [],
       "source": [
         "def plot_ssd_curve(data):\n",
         "  \n",
@@ -303,18 +211,11 @@
         "  plt.xlabel(\"Numero di cluster\", fontsize=16)\n",
         "  plt.ylabel(\"Somma delle distanza al quadrato\", fontsize=16)\n",
         "  plt.show()\n"
-      ],
-      "metadata": {
-        "id": "tWpTA6A_BHXY"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "plot_ssd_curve(X)"
-      ],
+      "execution_count": 7,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -323,22 +224,29 @@
         "id": "wSJ22vQpyAcX",
         "outputId": "5deaa237-2faa-4bf1-cb9e-5b7079ce74e4"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "image/png": 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",
             "text/plain": [
-              "<Figure size 1152x720 with 1 Axes>"
+              "<Figure size 1600x1000 with 1 Axes>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "plot_ssd_curve(X)"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "P7GaSOZ2Bsca"
+      },
+      "outputs": [],
       "source": [
         "def plot_clusters(model, data, axlabels=None, print_ssd=False):\n",
         "  y_pred = model.predict(data)\n",
@@ -353,20 +261,11 @@
         "    plt.text(X[:,0].max()-10, 0, f\"SSD={model.inertia_:.2f}\")\n",
         "\n",
         "  plt.show()"
-      ],
-      "metadata": {
-        "id": "P7GaSOZ2Bsca"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = RANDOM_SEED)\n",
-        "kmeans.fit(X)\n",
-        "plot_clusters(kmeans, X, axlabels=[\"Annual Income (k$)\",\"Spending Score (1-100)\"], print_ssd=True)"
-      ],
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -375,36 +274,38 @@
         "id": "n534_T13FfwK",
         "outputId": "87c3d6b8-42b2-4138-9fc1-7fe9542f1a17"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "image/png": 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\n",
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",
             "text/plain": [
-              "<Figure size 1152x720 with 1 Axes>"
+              "<Figure size 1600x1000 with 1 Axes>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = RANDOM_SEED)\n",
+        "kmeans.fit(X)\n",
+        "plot_clusters(kmeans, X, axlabels=[\"Annual Income (k$)\",\"Spending Score (1-100)\"], print_ssd=True)"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "X = df[[\"Age\", \"Spending Score (1-100)\"]].values"
-      ],
+      "execution_count": 10,
       "metadata": {
         "id": "Bqqsgg5j0Bxn"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "X = df[[\"Age\", \"Spending Score (1-100)\"]].values"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "plot_ssd_curve(X)"
-      ],
+      "execution_count": 11,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -413,27 +314,25 @@
         "id": "izNFScrjCrPT",
         "outputId": "a8ce62ff-ef48-4f9b-c21c-7d7c767aec29"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "image/png": 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             "text/plain": [
-              "<Figure size 1152x720 with 1 Axes>"
+              "<Figure size 1600x1000 with 1 Axes>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "plot_ssd_curve(X)"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "kmeans = KMeans(n_clusters = 4, init = 'k-means++', random_state = RANDOM_SEED)\n",
-        "kmeans.fit(X)\n",
-        "plot_clusters(kmeans, X, axlabels=[\"Age\",\"Spending Score (1-100)\"], print_ssd=True)"
-      ],
+      "execution_count": 12,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -442,36 +341,38 @@
         "id": "C8-QnDK-CvLm",
         "outputId": "0907c984-a805-45b2-9e96-ab848068cec5"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "image/png": 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\n",
+            "image/png": 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             "text/plain": [
-              "<Figure size 1152x720 with 1 Axes>"
+              "<Figure size 1600x1000 with 1 Axes>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "kmeans = KMeans(n_clusters = 4, init = 'k-means++', random_state = RANDOM_SEED)\n",
+        "kmeans.fit(X)\n",
+        "plot_clusters(kmeans, X, axlabels=[\"Age\",\"Spending Score (1-100)\"], print_ssd=True)"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "X = df[[ \"Age\", \"Spending Score (1-100)\", \"Annual Income (k$)\"]].values"
-      ],
+      "execution_count": 13,
       "metadata": {
         "id": "zdxJKgrbCz_C"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "X = df[[ \"Age\", \"Spending Score (1-100)\", \"Annual Income (k$)\"]].values"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "plot_ssd_curve(X)"
-      ],
+      "execution_count": 14,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -480,22 +381,29 @@
         "id": "DhLuFeEFD_dA",
         "outputId": "ecae6fbe-27d0-4dfc-ccaf-46200612bbef"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "image/png": 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/6V79dDjJ6jgAAAAA8ghKNFzWa73rqYCPl0ZP2ySHw2F1HAAAAAB5ACUaLivAP78G96qrjd/8qi9jj1gdBwAAAEAeQImGS+v+ZDXde4+/Rk/fpOtseQUAAADAyXK0RF+4cEE9e/ZU8+bN1bp1a/Xr10/nz5+XJJUvX16tW7dWZGSkIiMj9dNPP2W8b8OGDWrRooWaNm2qgQMH6urVq05dg+vw8nTX2EGN9cvRC5r7UZzVcQAAAADkcjlaog3D0PPPP6+1a9dq5cqVKlWqlKKiojLWP/zwQy1fvlzLly9X+fLlJUlXrlzRqFGjFBMTo/Xr16tAgQKaM2eO09bgepo8UlZhdcsoavY3Onfhd6vjAAAAAMjFcrRE+/n5qU6dOhkfV6tWTadOnbrje7Zs2aLKlSurTJkykqROnTppzZo1TluD6zEMQ2NfaawrV69r8ntseQUAAADAeSy7Jzo9PV0ffPCBwsPDM4517dpVkZGRmjZtmq5fvy5JSkhIUEhISMZrQkJClJCQ4LQ1uKby5QL0XMdqWvTf7/XjwUSr4wAAAADIpTys+sLjxo1T/vz51aVLF0nSpk2bFBwcrMuXL2vIkCGaMWOGXn75ZavimRIQ4Gt1hDsKDCxodYQcNXl4U/33iwMaH71V699/RoZhWB3ptvLaXFwBM7En5mI/zMSemIv9MBP7YSb25KpzsaRET548WUePHlVMTIzc3P44GR4cHCxJ8vX1VceOHTVv3ryM49u3b89476lTpzJe64y1rEhKuqz0dHvuTxwYWFCJiZesjpHjhvSqq2FTNmjRp3F6rPF9Vse5RV6di50xE3tiLvbDTOyJudgPM7EfZmJPdp+Lm5uR6UnTHL+ce/r06dq3b59mzJghLy8vSdJvv/2mlJQUSVJqaqrWrl2r0NBQSVKDBg30ww8/6Ndff5X0x8PHHnvsMaetwbU90/5BPVC2iMZM36xr11OtjgMAAAAglzF9Jvrs2bOaN2+eduzYod9++02FCxdWnTp19NxzzykwMNDU5zh06JBmzZqlMmXKqFOnTpKkkiVL6vnnn9fo0aNlGIZSU1NVvXp1DRgwQNIfZ6bHjh2rF154Qenp6QoNDdWIESOctgbX5unprrGvhKlTv6X69wd71K9bLasjAQAAAMhFDIfD8bfXIx85ckSdO3fWxYsXVb16dQUGBioxMVF79uxR4cKFtWTJkownXeclXM5tX51f+kzf7Dmhb5d1V7GAAlbHyZDX52JHzMSemIv9MBN7Yi72w0zsh5nYk93ncteXc0dFRcnX11dffPGFFi1apOnTp2vRokVau3atfH19b9rrGbCDNwY1Usq1VE2auc3qKAAAAAByEVMlevv27RowYIBKlix50/ESJUqof//+Nz2kC7CD+8oUUY8nq2vJsh/0w4EzVscBAAAAkEuYKtE3btxQgQK3vyS2QIECunHjRraGArLD4F4Pq0hhH42atkkm7loAAAAAgL9lqkSHhoZq0aJFSk9Pv+m4w+HQ+++/rwoVKjglHHA3ChfMp9f61NfXu09o1YZDVscBAAAAkAuYejp3nz591Lt3bz322GOKiIhQYGCgzp07py+++EJHjx7VrFmznJ0T+Ee6tK2ieR/H6Y23NqvpI+WUz9uSrdEBAAAA5BKmzkQ3bNhQMTExKlCggGJiYjR27Fi99957yp8/v2JiYvTII484Oyfwj3h4uGncK4117NRFzVqy2+o4AAAAAFyc6dNyDRs2VMOGDXX16lVdvHhRhQoVko+PjzOzAdmiYZ171KLxvXprznZ1al1JQYG3f1Q9AAAAAPwdU2eihw0bpuPHj0uSfHx8FBQUlFGgT548qWHDhjkvIZANXh/YSDdupGlCdKzVUQAAAAC4MFMl+rPPPtOFCxduu3bhwgUtW7YsW0MB2a1caX/1evohfbjyR8XtP211HAAAAAAuylSJvpNz584pX7582ZEFcKpBzz+sokXya2TURra8AgAAAPCPZHpP9Pr167V+/fqMj9999135+/vf9JqUlBTt3r1blSpVcl5CIJsU9PXW8L71NWjcei1f95PaNmdrNgAAAABZk2mJPnXqlHbt2iVJMgxD8fHx8vLyuuk1Xl5eql69ugYNGuTclEA2eapNZc39OE5j396i5o3ulU8+T6sjAQAAAHAhmZbobt26qVu3bpKk8PBwzZw5UxUqcOYOrs3d3U3jB4epbc+PNXPRLr3Ss67VkQAAAAC4EFP3RG/YsIECjVyjXo1SavXo/Xp33g4lnL1kdRwAAAAALsT0PtGS9Ntvv+no0aO6du3aLWu1atXKtlCAs40Z2Ejrtx7WuHe2aub4CKvjAAAAAHARpkr0tWvXNHz4cK1ZsybTpxrHx8dnazDAme4pUVi9O9fQ2/N2qPsT1VTzwRCrIwEAAABwAaYu5545c6a2b9+uSZMmyeFwaNSoURo/frxq1Kih0qVLKyYmxtk5gWw3oHsdFStaQKOiNik9nS2vAAAAAPw9UyV67dq16tu3r1q2bClJqlq1qtq3b6/FixerfPny2rp1q1NDAs7gW8BLI/s9ot37ErR0DVdSAAAAAPh7pkp0QkKC7r//frm7u8vDw0NXr17NWGvfvr3WrFnjtICAMz3RqpKqVQzS+He36srVG1bHAQAAAGBzpkq0n5+frly5IkkKDg7WgQMHMtYuXLiglJQU56QDnMzNzdC4wWFKOHtZ0fN3WB0HAAAAgM2ZerBY1apVFR8fr0aNGqlZs2Z6++23deXKFbm7u2vevHmqUaOGs3MCTlOnWgm1bVZeMxbuUue2VVQyuJDVkQAAAADYlKkz0T179lS5cuUkSS+++KIefvhhvfPOO4qKilKpUqX0+uuvOzMj4HSjBzSUJI17Z4vFSQAAAADYmakz0VWqVFGVKlUkSb6+vnr33Xd1/fp1Xb9+Xb6+vk4NCOSEksGF1OeZmpr+72/V/cnqqlOthNWRAAAAANjQ356Jvn79utq1a6fY2Nibjnt5eVGgkav0f7a2gov5auTUjWx5BQAAAOC2/rZEe3l56cSJE3J3d8+JPIBlCvh4amT/Btobf0Yfr/rR6jgAAAAAbMjUPdH16tXTtm3bnJ0FsFz7x0JVo3KwxkfH6vKV61bHAQAAAGAzpkp0165d9fnnn2vy5MnatWuXjh07puPHj9/0B8gN3NwMjR8SprPnruideWx5BQAAAOBmph4s1qVLF0nSvHnzNH/+/Nu+Jj4+PttCAVaqUSVYHSJC9d7iXercroruKVHY6kgAAAAAbMJUiZ44caKzcwC2MrJ/A63ecEhj396iOVNaWx0HAAAAgE2YKtHt2rVzdg7AVkKCCqrfs7U1JeZrfb37uOrVKGV1JAAAAAA2YOqeaCAv6tO1pkoUL6iRURuVlpZudRwAAAAANpDpmehhw4aZ/iSGYejNN9/MlkCAXeT38dToAQ31wrDP9cGKferS7kGrIwEAAACwWKYlevv27Td9fOnSJV26dEkeHh7y8/NTcnKyUlNTVbBgQRUqVMjpQQErtG1WXnM+2qM3Z2xTZNPyKujrbXUkAAAAABbK9HLuDRs2ZPyZMmWK8ufPr+nTp2vv3r2KjY3V3r17NX36dBUoUEBTp07NycxAjjEMQ+MHh+nc+d81/T/fWh0HAAAAgMVM3RM9adIk9erVSxEREXJ3d5ckubu7KyIiQj179uRSbuRq1SoWV6fWlTT7/e90+NgFq+MAAAAAsJCpEn3w4EHdc889t1275557dOjQoWwNBdjN8H6PyNPTXW+8vcXqKAAAAAAsZKpEFy1aVGvWrLnt2ueff66AgIBsDQXYTfFAXw3sXkdrNv6srTuOWR0HAAAAgEVM7RPdrVs3TZw4UYmJiWrRooUCAgKUlJSkNWvWKDY2VsOHD3d2TsByvbvU0OLPvtfIqI366v2u8vBghzgAAAAgrzFdovPnz68ZM2Zoy5b/u5w1ODhY48aNU4cOHZwWELCLfN4eGjOwkXq8ulKLl/2gZztUtToSAAAAgBxmqkRLUseOHdWhQwedPn1aiYmJCgwMVPHixWUYhjPzAbbS6tH7Vfehkpo8c5vaNS+vwgXzWR0JAAAAQA7K0vWohmEoODhYDz74oIKDgynQyHP+2PKqsc7/dlVRs9nyCgAAAMhrTJ+JlqQDBw7o8OHDun79+i1rbdu2zbZQgJ1VqRCkzm2raM5He9St/YO6r0wRqyMBAAAAyCGmSvTFixfVq1cv7d27V5LkcDgk6aYz0ZRo5CVD+9TXsnU/6fW3Nmvx2+2sjgMAAAAgh5i6nHv69OlKTk7W4sWL5XA4FB0drQULFqh169YqVaqUPvnkE2fnBGylWEABvdyjjtZtPayN3/xqdRwAAAAAOcRUiY6NjVXv3r1VrVo1SVLx4sVVp04dTZkyRXXr1tXChQudGhKwo15PP6QyJf00etompaamWx0HAAAAQA4wVaITExNVsmRJubu7y9vbW1euXMlYa9asmTZv3uy0gIBdeXt56PWXG+qnw0la8Oleq+MAAAAAyAGmSnTRokV16dIlSVJISIji4uIy1o4ePeqcZIALeKzxfWpQq7SmzPpaF367anUcAAAAAE5m6sFiNWrUUFxcnMLCwhQZGano6GidPHlS7u7uWrZsmcLDw52dE7AlwzA0bnBjhT+1SFNnfaM3X+V7AQAAAMjNTJXofv366ezZs5KkHj16KDk5WatXr1ZKSorCw8M1cuRIp4YE7Kzi/YHq+viDmvdJnLp1qKry5QKsjgQAAADASQzHn/tVIcuSki4rPd2e//sCAwsqMfGS1THyjHMXftfDkXNVo0qwPox+/Kbt3/6KudgPM7En5mI/zMSemIv9MBP7YSb2ZPe5uLkZCgjwvf1aDmcBcqWi/vk1uFddbfzmV3217YjVcQAAAAA4ianLuaOjo++4bhiG+vbtmy2BAFfV/clqWrB0r0ZP36RGde6Rp6e71ZEAAAAAZLO7LtF/XrZKiUZe5+XprjdebqQuA5dp7sdxeqFzDasjAQAAAMhmpkr0gQMHbjmWnJysTZs2ae7cuZoxY0a2BwNcUdMG5dT44XsUNfsbdYgIVYB/fqsjAQAAAMhG//ieaD8/P7Vt21aPP/64xo4dm52ZAJf1x5ZXYbr8+3VNjvna6jgAAAAAstldP1isfPny2rVrV3ZkAXKF8uUC9GyHqlq49HvtP5RodRwAAAAA2eiuS/SmTZvk7++fHVmAXGPIC/VUyNdbo6ZtErvIAQAAALmHqXuihw0bdsuxGzdu6NChQzp48KD69++f7cEAV1bEz0ev9q6r4VM26ovNv+ixxvdZHQkAAABANjBVordv337LMW9vb4WEhKhbt25q165dtgcDXF239lU1/5O9GjN9s8LrlZG3l6lvNwAAAAA2Zuq3+g0bNjg7B5DreHq6a+ygxurU/7/6z4d71PeZWlZHAgAAAHCX7vqeaACZC69fVk0eKatJ721T1Raz5Fb6dT3UcraWro63OhoAAACAf8DUmeidO3dm6ZPWqsUZN+BP9WuW0pexR5Rw9rIk6UTCJQ0av06S1D4i1MpoAAAAALLIVInu2rWrDMPIdN3hcMgwjIz/xsdzlg3405yP9txy7GpKqibM2EqJBgAAAFyMqRIdHR2t8ePH6/7771fLli0VEBCgpKQkrVq1Sj///LNGjx4tX19fZ2cFXNLJ05eydBwAAACAfZkq0V9++aXq16+vCRMm3HS8bdu2Gj58uNavX6+JEyc6JSDg6koUL6gTCbcW5hLFC1qQBgAAAMDdMPVgsa+++koRERG3XYuIiNBXX32VraGA3GRE3wbyyXfzv1d5eLhpRN8GFiUCAAAA8E+ZKtHp6ek6evTobdeOHj2qtLS0bA0F5CbtI0I1fWQzlQwuKMOQfPJ5KDU1XX6F81kdDQAAAEAWmSrRjRs31vTp07VmzZqMwpyWlqbVq1frX//6lxo3buzMjIDLax8Rqu8+76X0Y68r/qs+qvRAoHoP/1xHjidbHQ0AAABAFpi6J3rEiBFKSEjQyy+/LA8PDxUqVEgXL15UamqqatSooZEjRzo7J5Br5Pfx1LyoNmrWZYmefWW5Vi94WgV8PK2OBQAAAMAEUyW6SJEiev/997Vt2zbFxcUpMTFRgV+D10YAACAASURBVIGBql69uurVq+fsjECuU6akn2ImROipl/6rQWPXKubNlnfcRg4AAACAPZgq0X+qX7++6tev76wsQJ4SXr+shvV5RG/OiFW1SsX1YpeaVkcCAAAA8DdM3RMNwDkGdK+tiLD7NPbtLYrdeczqOAAAAAD+BiUasJBhGHr3jRYqV8pfvYau0snTF62OBAAAAOAOKNGAxQr6emv+tDZKuZ6m5wavUMq1VKsjAQAAAMgEJRqwgfvLBih6bAvF7T+joZO+ksPhsDoSAAAAgNugRAM2ERF2v17uUUfvL9+nBUu/tzoOAAAAgNugRAM28mrvegqvV0YjpmzQzr2nrI4DAAAA4H9kusVVeHi46X1rDcPQl19+mW2hgLzK3d1NMW+2VNMui9Xj1ZVav6SLgooWsDoWAAAAgP8v0xJdu3Zt0yUaQPbxK5RP86Mi1fLZ9/X8qyu1dFZHeXm6Wx0LAAAAgO5QoidNmpSTOQD8RaUHAjV9dDP1Hr5aY6Zv0sTXHrU6EgAAAADdoUQDsNbjLUK158czmrVkt6pVKq4nW1WyOhIAAACQ52WpRB84cECHDx/W9evXb1lr27ZttoUC8IcxAxpq309nNWTClwq9t6geDA2yOhIAAACQp5kq0RcvXlSvXr20d+9eScrYw/av90xTooHs5+HhptmTWqlp50V6bvAKrVvcWQH++a2OBQAAAORZpra4mj59upKTk7V48WI5HA5FR0drwYIFat26tUqVKqVPPvnE1Be7cOGCevbsqebNm6t169bq16+fzp8/L0mKi4tTmzZt1Lx5c3Xv3l1JSUkZ78vpNcBOAovk17yoSJ1NuqIXhn+u1NR0qyMBAAAAeZapEh0bG6vevXurWrVqkqTixYurTp06mjJliurWrauFCxea+mKGYej555/X2rVrtXLlSpUqVUpRUVFKT0/XkCFDNHr0aK1du1Y1a9ZUVFSUJOX4GmBH1SsV1+Shj2rL9mN6c0as1XEAAACAPMtUiU5MTFTJkiXl7u4ub29vXblyJWOtWbNm2rx5s6kv5ufnpzp16mR8XK1aNZ06dUr79u2Tt7e3atasKUnq1KmTvvjiC0nK8TXArp5uW0XPtH9Q0Qt2asX6n6yOAwAAAORJpkp00aJFdenSJUlSSEiI4uLiMtaOHj36j75wenq6PvjgA4WHhyshIUEhISEZa0WKFFF6erqSk5NzfA2wswlDwlSjSrBeen2t4n8+Z3UcAAAAIM8x9WCxGjVqKC4uTmFhYYqMjFR0dLROnjwpd3d3LVu2TOHh4Vn+wuPGjVP+/PnVpUsXrV+/Psvvt4OAAF+rI9xRYGBBqyPgNu52LivmPqWHImbp+ddWasfKnvIr7JNNyfIuvlfsibnYDzOxJ+ZiP8zEfpiJPbnqXEyV6H79+uns2bOSpB49eig5OVmrV69WSkqKwsPDNXLkyCx90cmTJ+vo0aOKiYmRm5ubgoODderUqYz18+fPy83NTX5+fjm+lhVJSZeVnu7I0ntySmBgQSUmXrI6Bv5HdszF093Qvye10uMvfKInX/xEC99qKzc34+/fiNvie8WemIv9MBN7Yi72w0zsh5nYk93n4uZmZHrS1NTl3KVLl864f9jT01NDhw7Vli1btGPHDk2bNk3+/v6mw0yfPl379u3TjBkz5OXlJUmqXLmyUlJStGvXLknShx9+qBYtWliyBriCh6uX1LhBjbVu62FN+/c3VscBAAAA8gxTZ6Kzy6FDhzRr1iyVKVNGnTp1kiSVLFlSM2bM0JQpUzRmzBhdu3ZNJUqU0NSpUyVJbm5uOboGuIruT1bTnv2nNXXWN6oaGqRmDe+1OhIAAACQ6xkOh8Oe1yO7AC7nRlZl91yuptxQq+4f6uiJ37RucWeVK23+qhD8ge8Ve2Iu9sNM7Im52A8zsR9mYk92n8tdX84NwJ588nlqXlQbeXgY6jZouS7/ft3qSAAAAECuRokGXFzpkMKaNbGVDv16XgPfWCsuLgEAAACchxIN5AKN6tyjEf0e0Yr1BzVj4S6r4wAAAAC5FiUayCX6daulNk0f0Ph3t2rz9qNWxwEAAABypSw9nfvAgQM6fPiwrl+/9b7Ltm3bZlsoAFlnGIb+Naa5fvolSS8MW6V1i7uodEhhq2MBAAAAuYqpEn3x4kX16tVLe/fulaSMey4Nw8h4DSUasJ5vfi/Nn9ZGzbou0XODV2jV3E7yyedpdSwAAAAg1zB1Off06dOVnJysxYsXy+FwKDo6WgsWLFDr1q1VqlQpffLJJ87OCcCke+8povfGR+iHA2c15M0vedAYAAAAkI1MlejY2Fj17t1b1apVkyQVL15cderU0ZQpU1S3bl0tXLjQqSEBZE2zhvdqcK+6+njVfs39OM7qOAAAAECuYapEJyYmqmTJknJ3d5e3t7euXLmSsdasWTNt3rzZaQEB/DODe9VVswblNGraJn2754TVcQAAAIBcwVSJLlq0qC5duiRJCgkJUVzc/53ZOnqUpwADduTmZmjG+MdUOqSwnn9tlU4nXrY6EgAAAODyTD1YrEaNGoqLi1NYWJgiIyMVHR2tkydPyt3dXcuWLVN4eLizcwL4BwoXzKd5UW30WLf31X3ICi3795Py8nS3OhYAAADgskyV6H79+uns2bOSpB49eig5OVmrV69WSkqKwsPDNXLkSKeGBPDPhd5XVG+Paa6eQ1dpxNSNmjq8idWRAAAAAJdlqkSXLl1apUuXliR5enpq6NChGjp0qFODAcg+kc3KK27/ac1YuEvVKwbp6bZVrI4EAAAAuCRT90Tv3LlTJ07c/sFEV65c0c6dO7M1FIDsN6JfAzWoXVqvTfpKcftPWx0HAAAAcEmmSnTXrl3VunVrbdiw4Za1n3/+Wc8880y2BwOQvTw83DR7YksVCyig5wavUOL5362OBAAAALgcUyVakkJDQ9W/f3/Nnz/fiXEAOFOAf37Ni2qjpAtX1WvoKqWmplsdCQAAAHAppkv00KFDNWjQIE2ZMkVjxoxRejq/fAOu6MHQIE0Z3kTbdh3X2He2WB0HAAAAcCmmHiz2px49eqhs2bIaPHiwTpw4obfffttZuQA4UafWlRT342nFLN6tahWD9HiLUKsjAQAAAC7B9JnoP4WHh+v999/X4cOH9dRTT2X6wDEA9jb2lcaqXS1EL49dpx8PJlodBwAAAHAJWS7RklShQgV9/PHH8vHxYasrwEV5ebprzpQ2KuTrrWcHL1fyxRSrIwEAAAC2Z6pEt2vXTv7+/jcdCwwM1OLFi9WuXTvVrFnTKeEAOFdQ0QKaO7WNTp2+pBdHrFZaGs86AAAAAO7EVImeOHGiSpUqdctxLy8vjR07VosWLcr2YAByRq2qIZrwari+2nZEU2d9Y3UcAAAAwNay9GCxxMREJSQk6Nq1a7es1apVK9tCAchZ3do/qD37EjT9P9/qwdBiigi73+pIAAAAgC2ZKtFnzpzRkCFDtHPnzlvWHA6HDMNQfHx8tocDkDMMw9DkYU0U//M59Rv9hdYtCtB9ZYpYHQsAAACwHVMlesyYMTp48KCGDBmiBx54QF5eXs7OBSCH5fP20NypbdS082I9+8pyfbGws3wL8L0OAAAA/JWpEr17926NGDFCbdu2dXYeABYqGVxI/57cSh37fKr+Y77Q3KmtZRiG1bEAAAAA2zD1YDFvb28FBAQ4OwsAG3ikVmmNHtBQn284pHfm7bA6DgAAAGArpkr0E088oeXLlzs7CwCb6N25hto1L683Z8Rqw9e/Wh0HAAAAsA1Tl3MHBQVp+fLl6tatmxo2bKjChQvf8poOHTpkezgA1jAMQ9NHN9eBX5LUe/jnWre4s8qU9LM6FgAAAGA50w8Wk6STJ09q+/btt6wbhkGJBnKZAj6emj8tUs26LNZzg1fo83lPKb+Pp9WxAAAAAEuZKtFfffWVs3MAsKGypfwU82ZLPf3Sf/XK+PWaOf4xHjQGAACAPM1UiS5RooSzcwCwqUfrl9VrL9bXpJnbVL1ScfV6+iGrIwEAAACWMfVgMQB528DuddSi8b0a89Ymfb37uNVxAAAAAMuYOhMtSbGxsfrggw905MgRXbt27ZZ1LvkGci83N0Mzxj6mZl2X6PnXVunLJV0UElTQ6lgAAABAjjN1Jnrz5s3q2bOnUlJSdPjwYZUrV04hISE6ffq03NzcVLt2bWfnBGCxgr7eWjAtUldTbqj7kBW6dj3V6kgAAABAjjNVomfOnKnOnTtr9uzZkqSBAwdq0aJFWrVqldLS0tSgQQOnhgRgDw+UC9C7bzym7/ad1vDJG6yOAwAAAOQ4UyX68OHDCgsLk5ubmwzDUFpamiSpbNmy6t+/v9577z2nhgRgH60evV8DnqutRZ/9oEX//d7qOAAAAECOMlWi3dzc5O7uLsMwVKRIEZ06dSpjrVixYjp27JjTAgKwn6F96iusbhkNm7xBu39IsDoOAAAAkGNMleiyZcvq5MmTkqTKlStrwYIFOnv2rM6fP6+5c+eyBRaQx7i7uynmzQgVL+ar7kNW6GzSFasjAQAAADnCVIlu3bq1fvnlF0lS//799fPPP6tRo0aqX7++tm/frpdeesmpIQHYj39hH82LaqPkiyl6/tWVunEjzepIAAAAgNOZ2uKqc+fOGX+vXLmyVq5cqa1bt+rq1auqV6+e7rvvPqcFBGBfVcoX07SRzdRn5Gq98a8tGj8kzOpIAAAAgFOZKtGnTp1SYGCgPD09JUnFixdXx44dJUmpqak6deqUQkJCnJcSgG11iAhV3I+nNfuD71S1YpA6tqxodSQAAADAaUxdzv3oo48qPj7+tmsHDhzQo48+mq2hALiWMQMbql6Nkho8Yb1++Oms1XEAAAAApzFVoh0OR6ZrqampcnMz9WkA5FKenu6aPamV/Arl03OvLNf55KtWRwIAAACcItP2e/HiRR0/flzHjx+XJJ05cybj4z//HDp0SJ999pmKFi2aY4EB2FOxgAKaO7WNTideUe/hnystLd3qSAAAAEC2y/Se6IULFyo6OlqGYcgwjEyfwO1wONS/f3+nBQTgOmpUCdbE18L1yvj1mjRzm0b0b2B1JAAAACBbZVqimzRpohIlSsjhcGj48OF68cUXVbp06Zte4+XlpXvvvVcVKlRwelAArqHr4w8q7sfTenveDlWtWFytHr3f6kgAAABAtsm0RFeoUCGjHBuGoUaNGqlIkSI5FgyA63rztXDtP3RO/ces0f1li6h8uQCrIwEAAADZwtQTwSIjI1WoUKGbjm3dulVz587V/v37nRIMgOvy9vLQnKmt5ZPPU8++slwXL12zOhIAAACQLUyV6EGDBmn48OEZH3/wwQfq2bOnpkyZoieeeEJff/210wICcE0hQQU1Z3JrHT35m/qNXqP09Myf8g8AAAC4ClMleu/evWrUqFHGx3PmzFHHjh21a9cuNWvWTO+9957TAgJwXXVrlNQbLzfSF5t/0VtzvrU6DgAAAHDXTJXopKQkBQUFSZKOHj2qEydOqHPnzvL19dXjjz+ugwcPOjUkANf1fKfqav9YqKbEfK0vYw9bHQcAAAC4K6ZKtK+vr5KTkyVJO3bskL+/f8ZDx9zd3XX9+nXnJQTg0gzD0LSRTVXpgUC9OGK1Dh+7YHUkAAAA4B8zVaKrV6+u2bNna+PGjVqwYMFNl3YfPXo04yw1ANxOfh9PzYuKlJuboecGr9CVqzesjgQAAAD8I6ZK9JAhQ5ScnKwXX3xR165dU79+/TLWVq9ererVqzstIIDc4Z4ShRXzZkv9dDhJL7+xVg4HDxoDAACA68l0n+i/KlOmjNatW6cLFy7I39//prURI0YoMDDQKeEA5C5hdctoeN/6Gv9urKpVKq4+XWtaHQkAAADIElMl+k//W6AlqXz58tkWBkDu1//Z2trz4xmNfXuLqpQvpga1S1sdCQAAADAt0xIdHR2tjh07KigoSNHR0Xf8JIZhqG/fvtkeDkDuYxiG3n2jhVo8s0S9hq7S+iVdVDK4kNWxAAAAAFPuWKIbNmxIiQaQ7XwLeGn+tEg1f2aJug9ZoRVzOimfd5YujAEAAAAskelvrQcOHLjt3wEgO9xXpohmjHtMz7y8XK9N/FL/GtNchmFYHQsAAAC4I1NP5wYAZ2jR6D4N6vmwPljxo+Z/utfqOAAAAMDfokQDsNSrL9RTk0fKauTUjdqx96TVcQAAAIA7yvRy7goVKmTp0sr4+PhsCQQgb3FzMzRzfISadVmiHkNW6sslXRQU6Gt1LAAAAOC2Mi3Rffv2zSjRDodDS5cuVUpKisLCwlS0aFGdO3dOGzduVL58+dShQ4ccCwwg9/ErlE/zp7VRRLf31eO1lfrvrCfk5eludSwAAADgFpmW6P79+2f8febMmQoJCdGcOXPk4+OTcfz3339Xjx495O7OL7sA7k7F+wP11pjmemHY5xo9bZMmDX3U6kgAAADALUzdE/3RRx+pR48eNxVoScqfP7969OihDz/80CnhAOQt7ZpX0Itda2jux3H6cOWPVscBAAAAbmGqRF+4cEE3bty47dr169eVnJycraEA5F2j+jdUg1qlNWTCeu3df8bqOAAAAMBNTJXoypUr691339WZMzf/QnvmzBlFR0erSpUqTgkHIO/x8HDTrEktVbRIfj03eLnOXfjd6kgAAABAhkzvif6rkSNHqlu3bmrSpImqVaumgIAAJSUlKS4uTj4+Ppo2bZqzcwLIQ4r659e8qDZq3f1DvTDsc30U3V4eHuzIBwAAAOuZ+q20YsWKWrdunbp37y43NzcdPHhQbm5u6t69u9auXavQ0FBn5wSQx1SrWFxThjXR1h3HNCF6q9VxAAAAAEkmz0RLkr+/v15++WVnZgGAmzwVWVl79p/WjIW7VK1icUU2K291JAAAAORxXB8JwNbGDw5TraohGvDGWsX/fM7qOAAAAMjjKNEAbM3L011zprSWbwEvPfvKcv12KcXqSAAAAMjDKNEAbK94oK/mTGmt4wkX1WfEGqWnO6yOBAAAgDyKEg3AJdSpVkLjXmms9bGHFTX7G6vjAAAAII+iRANwGd2fqKYnW1dS1OxvtHbzL1bHAQAAQB6UpRKdnp6ugwcPaseOHfr999+dlQkAbsswDE0Z9qgerFBMfUat1i9Hz1sdCQAAAHmM6RK9ZMkS1a9fX5GRkerWrZuOHDkiSerTp48WLlzotIAA8Fc++Tw1LypSXh7uevaVFbp85brVkQAAAJCHmCrRH3/8sSZMmKAmTZrorbfeksPxfw/1qVmzptatW+e0gADwv0qFFNKsiS116NfzGvDG2pt+JgEAAADOZKpEz5s3T88995zGjRunpk2b3rRWrly5jLPSAJBTGta5RyP7N9DKLw8qesFOq+MAAAAgjzBVok+cOKFHHnnktms+Pj66ePFitoYCADP6PlNTkU0f0IToWG369ler4wAAACAPMFWi/f39dfLkyduuHTlyREFBQdkaCgDMMAxDb41prvLlAvTCsM917NRvVkcCAABALmeqRDdu3FgzZ87U8ePHM44ZhqHz589r/vz5atKkidMCAsCd+Ob30ryoNkpLc+i5wSt0NeWG1ZEAAACQi5kq0QMHDpSnp6datWqlZ599VoZhaPz48YqIiJC7u7v69u3r7JwAkKlypf313oQI7fvprAZP+JIHjQEAAMBpTJXoIkWKaOnSperVq5dSU1NVunRppaWlqUuXLvroo49UsGBB019w8uTJCg8PV/ny5XXw4MGM4+Hh4WrRooUiIyMVGRmprVu3ZqzFxcWpTZs2at68ubp3766kpCSnrgFwPU0blNOQF+rpk8/3a85He6yOAwAAgFzKcOTwKZtdu3apRIkS6ty5s2JiYvTAAw9I+qNE//XjP6Wnp6t58+aaOHGiatasmXFZ+cSJE52ylhVJSZeVnm7PM16BgQWVmHjJ6hj4H8zFudLTHeo2aJm++vpX/Temox5+qOTfvoeZ2BNzsR9mYk/MxX6Yif0wE3uy+1zc3AwFBPjefi2Hs6hmzZoKDg42/fp9+/bJ29tbNWvWlCR16tRJX3zxhdPWALguNzdDM8ZFqHRIYfV4baUSztr3BzMAAABck0dmC88884zpT2IYhhYsWHDXYQYPHiyHw6EaNWpo0KBBKlSokBISEhQSEpLxmiJFiig9PV3JyclOWfPz8zOdN7N/mbCLwEDzl9kj5zAX5woMLKgVc59SnTb/1gvDV2vTx8/K2zvTH3UZ74H9MBf7YSb2xFzsh5nYDzOxJ1edS6a/WWblKu/suCJ8yZIlCg4O1vXr1zVhwgSNHTtWUVFRd/15nYnLuZFVzCVnFCvio3deb6Eer65Ur9dWKGpE00xfy0zsibnYDzOxJ+ZiP8zEfpiJPdl9Lne6nDvTEr1o0SKnBbqdPy/x9vLy0tNPP60XX3wx4/ipU6cyXnf+/Hm5ubnJz8/PKWsAcofWTR5Q/2dr6d35O1W9UnF1blvF6kgAAADIBXL8nujb+f3333Xp0h//CuFwOLR69WqFhoZKkipXrqyUlBTt2rVLkvThhx+qRYsWTlsDkHsM7/uIGtYprdcmfqXv9iVYHQcAAAC5QKZP5965c2eWPlGtWrVMvW78+PFat26dzp07J39/f/n5+SkmJkb9+/dXWlqa0tPTde+992rkyJEqVqyYJOm7777TmDFjdO3aNZUoUUJTp05V0aJFnbZmFpdzI6uYS847n3xVTTsvVlp6utYv6arAIvlvWmcm9sRc7IeZ2BNzsR9mYj/MxJ7sPpc7Xc6daYmuUKGCDMP420/ucDhkGIbi4+PvLqULokQjq5iLNX44cEYtn/tQNaoE65OZHeTh8X8X4TATe2Iu9sNM7Im52A8zsR9mYk92n8s/uid64cKFTgsEADmpSoUgRY1oqn6j1+iNt7do3CuNrY4EAAAAF5Vpia5du3ZO5gAAp3qiVUXF7T+tWUt2q1rFILV/LNTqSAAAAHBBWXqw2Pnz57Vx40Z99tlnSk5OliRdu3ZN6enpTgkHANnpjZcb6eHqJTRo3DrtO3jW6jgAAABwQZmeif4rh8OhKVOmaPHixbpx44YMw9Cnn34qPz8/9enTRw899JD69u3r7KwAcFc8Pd3178mt1bTzYnXo/anyebsr4exllSheUCP6NlD7CM5OAwAA4M5MnYmeNWuWlixZor59++rjjz/WX59FFhYWpk2bNjkrHwBkq6CiBdSlXRWdT76qU2cuy+GQTiRc0qDx67R0dd57QCIAAACyxlSJ/uSTT9S3b1/17t1blSpVummtdOnSOnbsmFPCAYAzfLhy3y3HrqakasKMrRakAQAAgCsxVaLPnDmjqlWr3nbN09NTV69ezdZQAOBMJ0/ffjuFzI4DAAAAfzJVooOCgnTo0KHbrv30008qWbJktoYCAGcqUbzgbY8H+PnkcBIAAAC4GlMlukWLFpoxY4Z2796dccwwDB05ckRz585VRESE0wICQHYb0beBfPLd/FxFw5DOXbiqYZO/0tWUGxYlAwAAgN2Zejp3//79tWfPHnXp0kUhISGSpAEDBighIUHVq1dXr169nBoSALLTn0/hnjBjq06evqQSxQtqyAv19ONPiZr9wXeK3XlcMydEqEr5YhYnBQAAgN0Yjr8+avsO0tLStHLlSsXGxur8+fPy8/NTgwYN1Lr1/2vv3uNzrB8/jr/ve+fzyWwz5hhNTuWU42aUQ0hU5JRDKUSSb4WoHHKopHIqqcixA6GQ4wglUjqhHDNGLDthm23374+xrE2/W7Zd17bX8/HYw3Zd9729d3+a9nZ9rs+ngxwd7erixU5cXLIyM+16+QpdYKCXzp7l/k6zYVzM559jsuXrYxrywjrFJ6Ro5OAmGtiznqxWi3EBSyh+VsyHMTEnxsV8GBPzYUzMyezjYrVaFBDgmec5u9uvg4ODOnXqpE6dOuVbMAAwmxaNKmjrst4aPmGDXpq+TZt3HtOMcW0UUjrv+6gBAABQsth1TzQAlCQBfu764NWOmjbmLn334ylFPLhAqzf+ZnQsAAAAmMB1r0RHRUXJYrF/CuOmTZvyJRAAmIHFYlHP+2qpcd1yGjh6jfo/s1rdOtyml5+JkqeHs9HxAAAAYJDrXolu0KBBjreMjAydOXNGoaGhql27tkJDQ3XmzBllZmaqQYMGhZkZAApNpTA/ff5eNz3Vv6E++uJXtXhogfb8eMroWAAAADDIda9ET548Ofv9ZcuWad++fdq4caOCg4Ozj8fGxuqRRx7R7bffXrApAcBATk4OGjm4qVo0qqDBY9aqQ/+lGv7InXqq/51ydOSuGAAAgJLErt/+5s2bpyFDhuQo0JIUEhKiwYMHa+7cuQUSDgDM5M47ymrL0t7qdPeteuXtr9XxkaU6FhNvdCwAAAAUIrtK9OnTp+Xi4pLnOWdnZ505cyZfQwGAWXl7uWj2xHaa83I7/XbkL0U99KGWrv5Fdu4WCAAAgCLOrhJdpUoVzZs3T6mpqTmOp6SkaN68eapSpUqBhAMAs+rcJlxblvZWzWqlNfSFdXr0uc91PuGS0bEAAABQwOzaJ/p///ufBgwYoMjISEVERCggIEBxcXHaunWrkpKSmM4NoEQqV8Zby99+QDMX7Nbk2Tu1e98pzRzfVk3rhxkdDQAAAAXErivRjRo10meffabGjRtrz549Wrhwofbs2aMmTZpo5cqVatSoUUHnBABTcnCwamjfhlrzwUNyd3NSl8c/1kvTtyo1Ld3oaAAAACgAdl2JlqTKlSvrtddeK8gsAFBk1akerI2Le+mFadGauWCPtu46rjkT71HVSgFGRwMAAEA+Ym8WAMgnHm5OenX0XVrw+r2KPZOsVj0W6r2PfmDRMQAAgGKEEg0A+axNRBVFf/SwGtctq+cmb1LPJz/Tn3EXjI4FAACAfECJBoACEFTKQ0ve6qyXn2mhbd8eV+SD87V+22GjdNFsuQAAIABJREFUYwEAAOAmUaIBoIBYLBY90u0ObVjUU6VLearnsM/0zKSNunjpstHRAAAA8B9RogGggN1auZS+/LC7Hu9ZVx98vE939Vionw6cMToWAAAA/gNKNAAUAhdnR40bHqmPZ9+vpAtpatN7sd764FtlZGQaHQ0AAAA3wO4trtLS0rRt2zYdPXpUqampOc5ZLBYNHjw438MBQHET0bC8opf11oiJGzT+za+0eecxzRjXRqHB3kZHAwAAgB3sKtFnzpxR9+7ddfLkSVksluztWiwWS/ZjKNEAYB9/XzfNm9pBS1b+rFGvbFFk1wV6ZVQrdWp9q9HRAAAA8P+wazr31KlT5e/vr+joaNlsNn300UfauHGjHn/8cYWFhWnjxo0FnRMAihWLxaLunWpq85JeqlLeXwNGfqHBY9YqKTn1/38yAAAADGNXif7uu+/Ut29flS5dOutJVqvKli2rJ598Um3atNGECRMKNCQAFFeVwvy0al5XPf3onfp07X61eOhDfbvvpNGxAAAAcB12lej4+HiVLl1aVqtVbm5uSkxMzD5355136ttvvy2wgABQ3Dk5OejZgU20al5XSVLH/ss0efYOXb6cYXAyAAAA/JNdJTooKEjx8fGSpLCwMG3fvj373I8//igXF5eCSQcAJUiD2qHasqSX7m8Xrmlzv1HH/st05I/zRscCAADANewq0Q0bNsy+2ty1a1e999576tevnwYMGKA33nhDrVu3LtCQAFBSeHm6aMa4tpo7ub0OHf9LUQ99qMWf/ZS9oCMAAACMZdfq3MOGDVNCQoIkqXv37srIyNCaNWuUkpKiRx55hJW5ASCf3Xt3NdWrFaInxq7TsHHrtWH7Ub32/F3y93UzOhoAAECJZrFxeeM/i4tLVmamOV++wEAvnT2bZHQM/APjYj5mH5PMTJtmfbhHk2ZuV4Cfu94a10YRDcsbHavAmX1cSiLGxJwYF/NhTMyHMTEns4+L1WpRQIBn3ucKOQsA4AZYrRY98XB9rZ3fXV4eznpg4CcaOy1aqWnpRkcDAAAokeyazi1J27Zt07p163T69Gmlpubcx9RisWjhwoX5Hg4AkKVWeJA2LOqpl6Zv1ZyF32nbrj805+V2urVyKaOjAQAAlCh2XYmeO3euBgwYoOjoaF28eFFWqzXHm8ViKeicAFDiubs5acrIVlo4vZP+PJesu3os1LtL97LoGAAAQCGy60r0okWL1LVrV40dO1YODg4FnQkA8C/ubl5Z0R89rGEvfalRU7do4/ajeuPFNgoq5WF0NAAAgGLPrivRycnJatOmDQUaAEyidICHFr1xnyY/11I7v4tR5IPztW7rIaNjAQAAFHt2leimTZtq3759BZ0FAHADLBaL+j1YRxsX9VRIkKd6P7VSIyZu0IVLl42OBgAAUGzZNZ177NixGjx4sCwWi5o0aSIfH59cjylXrly+hwMA/P+qVgrQ2vndNXnWDs36cI927DmhORPvUe3qQUZHAwAAKHbsXp3bw8NDr7/+uqZPn57n+f379+dbKADAjXFxdtQLwyLUsklFDR6zVm37LNazjzfWEw/Xl4MDuxkCAADkF7tK9MiRI/X999+rT58+qlSpkpycnAo6FwDgP2haP0zRy3rrfy9v1MQZ27V55zHNGNdW5cp4Gx0NAACgWLCrRO/atUtjx45V586dCzoPAOAm+fm4ae7k9mrV9FeNnLJJLbot0NRRLdW5TbjR0QAAAIo8u+b4+fv7KyAgoKCzAADyicViUbcOt2nL0t6qWslfj49ao4Gj1ygxKdXoaAAAAEWaXSW6V69eWrJkiTIzMws6DwAgH1Uo66tV73bT/x5rpM/WH1CLbgv0zd4Yo2MBAAAUWXZN505ISNBvv/2mdu3aqUmTJvL2znlvncVi0dChQwskIADg5jg6WvW/xxqrRaMKGvj8GnUa8JGG9mmg/z3WSE5ODkbHAwAAKFLsKtFz5szJfv/YsWO5zlOiAcD86tUqoy1Lemv0K5s1/b1d2rrruGZNaKvK5f2NjgYAAFBk2FWiDxw4UNA5AACFwNPDWW+82EatmlbS0xM2qOVDH2r8iBbqeV9NWSwWo+MBAACYHpuHAkAJ1KFVVUUv6626tcro6Qkb1GfEKsWdv2h0LAAAANO7oRJts9l05swZnThxItcbAKBoKRPkpY9n3a8Xn4rQpu1HFdF1gTbvPGZ0LAAAAFOzazr3+fPnNW7cOG3YsEEZGRl5Pmb//v35GgwAUPCsVosG9aqn5g3DNHDUGnV74lMNeOgOPT+0mVxd7PpfBAAAQIli129Io0eP1q5du9SzZ09VqlRJTk5OBZ0LAFCIalQtrfULe2j8m1/pnSV79dXuPzR7YjtVvyXQ6GgAAACmYleJ3rVrl0aPHq3OnTsXdB4AgEHcXJ308jNRatmkooa+uE5391yk54c004Dud8hqZdExAAAAyc57on18fBQQEFDQWQAAJtCySUVFL3tYLRqV19hp0er6xKc6fTbZ6FgAAACmYFeJ7tWrl5YuXSqbzVbQeQAAJhDo764Fr3fSK6Na6dsfTiqy63x9sfl3o2MBAAAYzq7p3H379tWff/6pdu3aqXHjxvL29s5x3mKxaOjQoQUSEABgDIvFoofvr63Gdctq4Og16jtilXreV1Pjno6Up7uz0fEAAAAMYVeJ3rp1qxYtWqS0tDQdPXo013lKNAAUX7dUDNCa+d01dc5OvfXBt9qx54RmT2ynO2qEGB0NAACg0NlVoidNmqSaNWtq7NixrM4NACWQs5ODnh/STFGNK2jwmLW6p+8S/e+xxnqybwM5ONh1ZxAAAECxYNdvPrGxsRo4cKCqVatGgQaAEqxx3XLasrS3OraqqsmzdujeR5fpj1MJRscCAAAoNHaV6PDwcP35558FnQUAUAT4ertqzsv3aOb4ttp/6JxadFugj7/4lcUnAQBAiWBXiX7++ef13nvv6bvvvivoPACAIsBiseiBe6pry9LeCq9SSoPHrNXjo75QQlKK0dEAAAAKlF33RA8ePFjJycnq2bOn3Nzc8lyde8uWLQUSEABgXmFlfPTZO1315gff6pW3d+rbfac0c3xbNa5bzuhoAAAABcKuEt2oUSNZLJaCzgIAKIIcHa0a/sidiryzvAaOXqP7BnykIX0a6JnHG8vZycHoeAAAAPnKrhI9efLkgs4BACji7qgRok1Lemnsa9F68/1vFf31Mc2e2E63VAwwOhoAAEC+YV8SAEC+8XR31rQxd+v9VzvqRGyiWvVYqA8+2ceiYwAAoNiwu0QfPHhQQ4cO1Z133qnq1avrzjvv1JNPPqmDBw8WZD4AQBF0T9Qt2vrRw2pQJ1TPvLxRvZ/6TOfOXzQ6FgAAwE2zq0T/+OOPevDBB7Vr1y61aNFC/fv3V4sWLfTNN9+oa9eu+vnnnws6JwCgiAkO9NSyGV00/ulIbfn6uCIenK9NO44aHQsAAOCm2HVP9LRp03TLLbfogw8+kKenZ/bx5ORk9e3bV9OmTdN7771XYCEBAEWT1WrRYz3qqmn9MA16fo0eGrJc/bvW0dgnm8vN1cnoeAAAADfMrivR+/bt02OPPZajQEuSp6enHn30UX3//fcFEg4AUDzcVjVQX37YQwMeukPzlv2gu3su0k8H/zQ6FgAAwA3Ll4XF2P4KAPD/cXVx1IT/tdCymV10PjFFbXsv1swFu5WZyaJjAACg6LCrRNeuXVtz5sxRcnJyjuMXL17U3LlzVadOnQIJBwAoflo0qqCty3qrZdOKemn6Nj0w6BPF/plkdCwAAAC72HVP9PDhw9WrVy9FRUUpMjJSgYGBOnfunLZu3aqUlBQtWLCgoHMCAIqRAD93ffBqRy367Cc9/8oWRTy4QK89f5fS0jI0ceZXOnk6SaHBXho9uJm6tAs3Oi4AAEA2u0p0rVq1tGzZMs2aNUvbt29XQkKCfHx81LBhQw0aNEjVqlUr6JwAgGLGYrGo53211OiOsho4eo36P7NaDg4WZWRkTe+OiU3S8AnrJYkiDQAATMNis9m4Ge0/iotLNu29fIGBXjp7lumRZsO4mA9jYg6XL2fo1qhZSrqQlutc2RAv7f1igAGpcC1+VsyJcTEfxsR8GBNzMvu4WK0WBQR45n3uv3zCpKQk/fTTTzp9+vRNBQMAQJKcnByUfDF3gZakk6fN+z9YAABQ8ly3RH/11Vd69dVXcx2fPXu2GjVqpAcffFAtWrTQ008/rfT09AINCQAo/kKDvfI87uLswHZYAADANK5bopcuXapjx47lOLZjxw698cYbqlSpkkaNGqWuXbtqzZo1LCwGALhpowc3k5trzqU6nBytskhq+dCHemzk5zryx3ljwgEAAFxx3YXF9u/fr4EDB+Y4tnz5crm4uGjevHkKDAzMPv7555+rX79+BZcSAFDsXV087J+rc7dqVlEz5+/RO4u/06qNv6n7vTU1YsCdCimd95VrAACAgnTdEh0XF6ewsLAcx3bs2KG6devmKNCRkZFauXJlwSUEAJQYXdqFq0u78FyLjYx6oqn6d7td0+d9owWf/qiPv/hV/R6so6F9G8jf183AxAAAoKS57nRuDw8PXbp0KfvjY8eOKT4+XrVr187xOE9PT2VmZtr1xaZMmaKoqChVq1ZNv/32W/bxo0ePqmvXrmrdurW6du2aYxp5YZ8DAJhTUCkPTXq2pXau6KeOd1XV7IV7VK/Du3r1na+VnMeq3gAAAAXhuiW6UqVK2rRpU/bHmzZtksViUZMmTXI8LiYmRgEBAXZ9sZYtW2rRokUKDQ3NcfyFF15Q9+7d9eWXX6p79+4aO3asYecAAOZWPtRHM8a11daPHlbzBmGaOmenGnR8V28v+k4pqSx0CQAACtZ1S3SfPn308ccfa+jQoXrppZf01ltvqWrVqqpbt26Ox23dulW33nqrXV+sXr16CgkJyXEsLi5Ov/76q9q3by9Jat++vX799Vf99ddfhX4OAFB03Fq5lD547V6tW9Bd4bcEasxr0Wp033ta/NlPSk+3b4YUAADAjbruPdGtWrXSqFGj9P7772dP437ppZdksViyH3P27Fnt3LlTw4cP/88BYmNjFRQUJAcHB0mSg4ODSpcurdjYWNlstkI95+/vf0PZr7f5tlkEBrLojhkxLubDmJiTvePSuoWXWreoqk3bj2jk5I0aNm695izeq/EjWqhLu+o5/r+Fm8PPijkxLubDmJgPY2JORXVcrluiJal3797q3bv3dc8HBgZq165d+R6qqIiLS1Zmps3oGHn656I8MAfGxXwYE3P6L+NSq1qgPn+vm9ZsOaTJs3bogcc/Vu3wII16oqki7yxPmb5J/KyYE+NiPoyJ+TAm5mT2cbFaLde9aPqvJbowhISE6MyZM8rIyJCDg4MyMjL0559/KiQkRDabrVDPAQCKNovFonuiblGbiMr6eM1+vTJnp7oO/lSN65bV6CeaqX7tMkZHBAAARdx174kuLAEBAQoPD9fnn38uKWvP6fDwcPn7+xf6OQBA8eDgYFW3Drdp54q+mvRMlH4/9pfu6btEvYat0K+/nzU6HgAAKMIsNput0OYjT5gwQevXr9e5c+fk5+cnX19fffHFFzp8+LCee+45JSYmytvbW1OmTFGlSpUkqdDP3Qimc+NGMS7mw5iYU36Py4VLlzV38V7NmL9bSRdS1blNuJ4d2FgVyvrm29co7vhZMSfGxXwYE/NhTMzJ7OPyb9O5C7VEFzeUaNwoxsV8GBNzKqhxOZ9wSTPm79a7S7/X5fRM9byvpp5+5E4FBZp7oUgz4GfFnBgX82FMzIcxMSezj8u/lWjDp3MDAFBY/HzcNGZoc+1a2V8976uphSt+UoN752n8m9t0PuGS0fEAAEARQIkGAJQ4wYGemjqylXZ82lftWtyiGfN3q36HeXr93W+UfDHN6HgAAMDEKNEAgBKrYjlfzZ7YTluW9lbjumU1adYONeg4T+8u3avUtHSj4wEAABOiRAMASrzqtwRqweud9MX7D6lqRX+NmrpFTTq/r6Wrf1FGRqbR8QAAgIlQogEAuKJ+7TJa8c6DWjqji3y9XTX0hXWK7LpAX2z+XazDCQAAJEo0AAA5WCwWRTWuoA2Lemre1A7KyMxU3xGr1Pbhxdq267jR8QAAgMEo0QAA5MFisahDq6ra9lEfTR97t86cu6D7B36iLo9/rL0/xxodDwAAGIQSDQDAv3B0tKp7p5r6ekU/jX86Ur/+dlZtei/Ww0+v1IHD54yOBwAAChklGgAAO7i6OOqxHnW1e/UjenZgY23f/Yciuy7QE2PX6o9TCUbHAwAAhYQSDQDADfD0cNbTjzbS7lWP6PEedbVqw29q1Ok9jZyySX/GXTA6HgAAKGCUaAAA/gN/Xze9+FSEvvmsn7p1rKEPPtmnBh3e1csztishKcXoeAAAoIBQogEAuAllgrz02vN3acenfdU6orKmv7dL9TvM05sffKuLly4bHQ8AAOQzSjQAAPmgUpif3p7UXpuW9FK9WiGa8OZXanDvPL3/8Q9Ku5xhdDwAAJBPKNEAAOSjmtVKa/GbnbVqXldVKuerZydtUpMu7+vjL35VRkam0fEAAMBNokQDAFAA7ry9rFa+21VL3uosLw9nDR6zVlEPfah1Ww/JZrMZHQ8AAPxHlGgAAAqIxWJRyyYVtXFRL70z6R6lpmWo91MrdU/fJdqx54TR8QAAwH9AiQYAoIBZrRZ1an2rvvr4Yb32/F06eTpJ9w34SA8O+kT7fj1jdDwAAHADKNEAABQSJycH9epcS9981k8vPhWhHw+c0V09F6r/M6v1+9E4o+MBAAA7UKIBAChkbq5OGtSrnnavekQjBjTS5p1H1eyB+Rr20peKiU00Oh4AAPgXlGgAAAzi5emiZx5vrN2rH9GjD92hT9bs152d3tPzr2zR2b8uGh0PAADkgRINAIDBSvm5a/zTkfrms3564J5wvbvsezXo+K4mz96hxKRUo+MBAIBrUKIBADCJsiHeen1sa23/pI9aNqmoaXO/Uf2O72rmgt26lHLZ6HgAAECUaAAATKdKBX+9O6WDNizsqTrVg/XS9G26s9N7WvDpj7p8OcPoeAAAlGiUaAAATKp29SAtm9lFn819UKHBXhoxcYOa3v+BVnx5QJmZNqPjAQBQIlGiAQAwucZ1y+mL9x/Swumd5ObqpMdGfqGW3T/Uhq+OyGajTAMAUJgo0QAAFAEWi0V3N6+szUt6afbEdkq+mKYeT65Qx/7L9M3eGKPjAQBQYlCiAQAoQqxWi7q0DdfOT/tq6qhWOhYTr46PLNNDQ5brpwNnjI4HAECxR4kGAKAIcnJyUJ/7a2vXyv4aM7SZvvspVi27L9SA5z7XkT/OGx0PAIBiixINAEAR5u7mpCF9Gmj36v56qn9Drf/qiJp0eV9Pj1+vU2eSjI4HAECxQ4kGAKAY8PFy1cjBTfXtqv7q92AdLfv8VzW8d57GTotW3PmLRscDAKDYoEQDAFCMlA7w0MT/RenrFf10X+tb9c7ivarfcZ5eeXunki+kGR0PAIAijxINAEAxVK6Mt958qY22fvSwIhqW1ytvf636Hd7VnIXfKSU13eh4AAAUWZRoAACKsWqVAvT+qx315Yc9dFu1QI2dFq1G972nRZ/9pPT0TKPjAQBQ5FCiAQAoAW6/LVifzH5An855QEGlPPTUuPVq9sAHWrXhoDIzbUbHAwCgyKBEAwBQgjRrEKa187tr/rR75eRo1SPPfq67ey7U5h1HZbNRpgEA+P9QogEAKGEsFovaRlbRlqW9NWNcW8UnpqjbkOXq9OhH+nbfSaPjAQBgapRoAABKKAcHqx5sX107V/TTpGejdOj4X2rfd6l6PrlCv/x21uh4AACYkqPRAQAAgLGcnRzUv+vt6taxht5dslcz5u9W1EMLdF+bW1U7PEhzl+zVydNJCg320ujBzdSlXbjRkQEAMAwlGgAASJI83Jz0ZL+Gevj+2poxf7dmf7hby9ceyD4fE5uk4RPWSxJFGgBQYjGdGwAA5ODr7arnhzRTKX+PXOcupaTrxelbWYQMAFBiUaIBAECeTp9NzvP4mXMX1Oi+9/XS9K3a9cNJZWSw3zQAoORgOjcAAMhTaLCXYmKTch339XZVWBlvvbN4r2Yu2KNSfm66u3lltY2souYNw+Tm6mRAWgAACgclGgAA5Gn04GYaPmG9LqWkZx9zc3XUpGei1KVduBKTUrVp51Gtiz6k1Zt+0+KVP8vd1VERjSqobWQV3dW0ogL83A38DgAAyH+UaAAAkKeri4dNnPlVnqtze3u56L7Wt+q+1rcq7XKGduw5oXXRh7Ru62Gt3XJIVqtFDeuEqk1kZbWJqKKK5XyN/HYAAMgXFhsrg/xncXHJysw058sXGOils2dzT8GDsRgX82FMzIlxMZ8bGRObzaZ9+89oXfRhrY0+pP2HzkmSwquUUpuIymoTWUV1qgfJYrEUZOQSgZ8V82FMzIcxMSezj4vValFAgGee57gSDQAA8pXFYlGd6sGqUz1Yzw1qomMx8VoXfVjrth7SG+9/q9fn7VJIaU+1jsi6Qt20fjk5OzkYHRsAALtQogEAQIGqUNZXj/esq8d71tVf8Ze0YfsRrd1ySB+t/kUffLxPXp7Oatm4otpEVlarJpXk7eVidGQAAK6LEg0AAAqNv6+bura/TV3b36ZLKZf11bd/aN3Ww1q39bA+W39Qjo5WNalXLmvad0RlhQZ7Gx0ZAIAcuCf6JnBPNG4U42I+jIk5MS7mU9BjkpGRqe9+js2+j/rw8fOSpNrhQdkLk1W/pRT3Uf8DPyvmw5iYD2NiTmYfF+6JBgAApubgYFWD2qFqUDtUY59srt+Pxmnt1sNaF31IU+fs1JTZOxUW6qO2VxYma1gnVI6OVqNjAwBKIEo0AAAwnVsqBuiWigEa2qeBzpy7oPXbDmtd9GF98Mk+vb14r/x8XNWqaSW1jayiyEbl5enubHRkAEAJQYkGAACmFlTKQ70611KvzrWUfDFNW74+pnXRh7XhqyP6+Itf5eLsoOYNyqtti8q6u3lllQ7wMDoyAKAYo0QDAIAiw9PdWR1aVlWHllWVnp6pb76Pyd4+a8P2I7JYNqhuzRC1iayidpFVVKWCv9GRAQDFDCUaAAAUSY6OVjWtH6am9cM0fkSkfv39nNZGH9K66EOa8OZXmvDmV6pSwU9tIqqoTWRl1atZRlYrC5MBAG4OJRoAABR5FotFt1UN1G1VAzViQCOdPJ2odVuzVvqes+g7zZi/W4EB7mrdPGvrrOYNy8vVhV+DAAA3jv97AACAYic02Fv9u96u/l1vV0JSijbtOKp10Vl7US9c8ZPc3ZzUolEFtYmsrLuaVpK/r5vRkQEARQQlGgAAFGs+Xq7q3CZcnduEKzUtXTv2xGjd1kNaF31YX2z+XQ4OFt15e1m1ubJ9VvlQH6MjAwBMzGKz2WxGhyiq4uKSlZlpzpfP7JuXl1SMi/kwJubEuJhPcRyTzEyb9u0/o3XRh7Ru62HtP3ROklT9lkC1iaysthFVVCu8tCwW895HXRzHpahjTMyHMTEns4+L1WpRQIBnnue4Eg0AAEokq9Wi228L1u23BWvk4KY6eiI++wr19Hm7NG3uNwoN9sq6jzqyshrXLSdnJwejYwMADEaJBgAAkFSxnK8G9qyngT3rKe78RW346ojWbj2sJat+1nsf/SBvTxe1alpRbSIqq2WTivLydDE6MgDAAJRoAACAfwjwc1e3jjXUrWMNXUq5rG27/tDa6ENav+2wlq87ICdHq5rUK6e2LaqoTURlhZT2MjoyAKCQUKIBAAD+hZurk1pHVFbriMrKyMjUnp9itXZL1n3Uz07apGcnbVKd6kFqG1lFbSKr6NbKAaa+jxoAcHMo0QAAAHZycLCqYZ1QNawTqheGNdfvR//Suq2HtHbLYU2atUOTZu1Q+bI+ahNRRe0iq6h+7TJydLQaHRsAkI8o0QAAAP+BxWJR1UoBqlopQEP7NtSZs8n6ctsRrdt6SO9/9IPeXvSd/H1ddVezymobWVkRd1aQh5uT0bEBADeJEg0AAJAPggI91btLLfXuUkvJF9K05etjWht9SOuiD2nZ6l/k6uKoiIZhahNZRXc3r6xAf3ejIwMA/gNKNAAAQD7z9HBWh1ZV1aFVVV2+nKFvvj+ZvX3Wl9uOyGKR6tcuozYRVdQ2srIql/c3OjIAwE6UaAAAgALk5OSgZg3C1KxBmCaMaKGffzurddFZC5ONe2Obxr2xTVUr+qtNZNZK33fUCJHVysJkAGBWlGgAAIBCYrFYVLNaadWsVlr/e6yxTpxK1JfbDmlt9GHN+nCP3nz/W5Uu5aHWzbPuo25aP0yuLvy6BgBmwt/KAAAABilXxluPdLtDj3S7Q/GJKdq046jWRh/S8nX79eHyH+Xh7qSoxhXVJqKy7mpWSb7erpKkT9fs18SZX+nk6SSFBntp9OBm6tIu3ODvBgBKBko0AACACfh6u6pL23B1aRuu1LR07dh9QmuiD+nLrYe1euNvcnCwqNHtZRUS5KnVG39XSmq6JCkmNknDJ6yXJIo0ABQCSjQAAIDJuDg7KqpJRUU1qaipI1vph19Pa130Ya2NPqTte07kevyllHSNnRathreHKqS0pxwc2JsaAAoKJRoAAMDErFaL7qgRojtqhGjUE00VVPc12Wy5H3f2r4u64565cnK0qmyIt8qH+igs1Efls998FRbqLV9vV1ksLFwGAP8VJRoAAKAICQ32UkxsUq7jpfzdNXJQEx0/maA/Tibo+MkEfbHpd8XFX8rxOG9PF4WFeqt8qG920a5w5c9yZbzl4syvhwDwb/hbEgAAoAgZPbiZhk9Yr0sp6dnH3FwdNX54ZJ73RCdfSNPxkwk6fjJef5xM1PGT8Tp+MkG/H43Tph1Hs++tliSLRQoO9PzHVeysK9gVyvr9LSEkAAAa+klEQVSqdIAH228BKPEo0QAAAEXI1aJs7+rcnh7Ouq1qoG6rGpjrXGamTWfjLujYNVevr17J3r77D338RXKOqeMuzg4KK/N3wb72zwqhPvLydCmQ7xkAzIQSDQAAUMR0aReuLu3CFRjopbNnc0/ttpfValFQoKeCAj3VsE5orvOpaemKiU3U8atXsGMS9MeprKK9e98pJSan5ni8v6+rwspcW7B9s6eOlw32kpOTw3/OCgBmQYkGAABAnlycHVW5vL8ql/fP83x8YkqOK9jHTyboeEy8fjp4Vmu2HNLl9Mzsx1qtFoUGeeW6gn31z0B/dxY8A1AkUKIBAADwn/h6u8rX21W1woNyncvIyNTps8nZ08OvnTK+ccdR/XnuQo7Hu7s6qnxZX4WV8b5yBfvvgh0W6iMPN6fC+rYA4F9RogEAAJDvHBysCg32VmiwtxrXLZfr/MVLl3UiNvFKsY7PMWV8+54TunDxco7Hl/J3z1rorKyPyv9jyniZIPbGBlB4KNEAAAAodO5uTqpWKUDVKgXkOmez2RQXfyn7yvW1U8b3/BirlesPKiPj7xXPHB2tKhvs/ffWXVeLdlkfhZXxkZ8Pe2MDyD+UaAAAAJiKxWJRKT93lfJz1x01QnKdT0/P1MkzSToeE5+10Nk1C56t3fK7zp3PuTe2l6fzPxY881GFK1PHy5XxkasLvxIDsB9/YwAAAKBIcXS0XtnD2ifP81f3xs5RsGMSdPj4eW3eeSzH3thS7r2xr27ZVb6sj4JKedq1N/ana/bbve0YgKKNEg0AAIBi5d/2xrbZbPrz3AUd/0fB/uNUgnZ+d0KfrPk1197Y5cp459q6q/yVqePeXi76dM1+DZ+wXpdSssp5TGyShk9YL0kUaaAYMlWJjoqKkrOzs1xcXCRJI0aMULNmzfTDDz9o7NixSk1NVWhoqF555RUFBGTdP1MQ5wAAAFA8WSx/743doHbee2OfPJ2kY9kFO15/nErU8Zh47f35tOITU3I83s/HVckX0nJs5yVJl1LSNeGtryjRQDFksdmu/bc2Y0VFRWnOnDmqWrVq9rHMzEy1bt1akyZNUr169TRr1iydOHFCkyZNKpBzNyIuLlmZmaZ5+XIIDPTS2bNJRsfAPzAu5sOYmBPjYj6MiTkxLoUve2/sa65gf/Dxvus+PsDXTWVDvFWujHfWn/9438fLtRDTl0z8nJiT2cfFarUoIMAzz3OmuhKdl59//lkuLi6qV6+eJKlbt25q2bKlJk2aVCDnAAAAgOvJa2/sjduPKCY2dxnw9nJRu6hbFBObqIOH47Rx+9Fc92N7e7pcKdReKlfGJ1fRDvB1Y2VxwGRMV6JHjBghm82munXravjw4YqNjVWZMmWyz/v7+yszM1Px8fEFcs7X19furNf7lwmzCAz0MjoC8sC4mA9jYk6Mi/kwJubEuBhv8si7NODZ1bp46e+9rd3dnDRr4j3qcV+t7GM2m01n4y7oeEyCjsXE63hMfNbWXTHxOhYTr6+/P6nEpNQcn9vdzSlrobOyV7buCvXN8X5IkKesVvbI/v/wc2JORXVcTFWiFy1apJCQEKWlpWnixIkaN26c7rrrLqNjXRfTuXGjGBfzYUzMiXExH8bEnBgXc7i7aUW9NvquXKtz3920Yq7xsUiqEOqtCqHeUsOwXJ8rISlFf5xKVExs1tu173/7Q4z+is95T7azk4PKBHnluHpdNsRbYVfeL1PaS46OJbtk83NiTmYflyIznTskJGsfQGdnZ3Xv3l0DBw5U7969derUqezH/PXXX7JarfL19VVISEi+nwMAAABuVJd24erSLvymi4GPl6tqVnNVzWql8zyffDFNJ08n6cSpBJ24Uq5PnErUidhEbdpxVGfOXcjxeKvVojKlPa8p1z4qG+KVPW08NNibfbKBG2San5iLFy8qIyNDXl5estlsWrNmjcLDw1WjRg2lpKRoz549qlevnpYuXao2bdpIUoGcAwAAAMzK091Z1SoFqFqlvHeVSUlN16kzSToRm6gTpxIUE/v3+9/sjdHydQdyzaQsXcpD5YLzvpJdNsRbnu7OhfGtAUWGaUp0XFychgwZooyMDGVmZqpy5cp64YUXZLVaNXXqVL3wwgs5tqOSVCDnAAAAgKLK1cVRlcL8VCnML8/zly9nKPZsctYV7CtXsa++/8OvZ/TF5t9zbdfl7+uqssFXVhQv46NyId4qe2UhtKwVxl1Y/Awliqm2uCpquCcaN4pxMR/GxJwYF/NhTMyJcTGfoj4mmZk2/Rl3QX+cSsjzvuwTsYm6lJJzhXFPD+ese7JDvLO38/r7fR+V8jN2hfGiPibFldnHpcjcEw0AAADAOFarRcGBngoO9FSD2qG5zttsNsXFX8pxL3bMleniJ2KT9M33J5WYnHOFcTdXR4UGe6ls8LVXsv8u20GlPOTgULIXP0PRQokGAAAAYBeLxaJSfu4q5eeuOtWD83xMQlJKdrn+55Xsnw78qbj4Szke7+RoVZlgL5ULzvtKdpnSnnJyciiMbw+wCyUaAAAAQL7x8XKVj5eralTNe4XxC5cu6+TVe7JzXMlOVPQ3x3X6bHKOx2ddHfdQuRCf7FXFry6ClrXCuJfcXJ1yfZ1P1+zPte1Yl3bhBfI9o2ShRAMAAAAoNB5uTqpaKUBVr7PCeGpauk6eTvp78bNrtvL69oeT+mz9AWVk5FyXKDDA/e+r1yHeOvvXRX325UGlXc6QJMXEJmn4hPWSRJHGTaNEAwAAADANF+d/X2E8PT1TsX9es8J4bEL2+z8d+FProg9nl+drXUpJ15AX1undZd/L19v1yptL9vs+3q7y83aVj7eL/Hzc5OvlIh9vV/bRRi78FwEAAACgyHB0tGbdN13GW43yOJ+ZaVNI/WnKaw+i9IxMebg76WzcBf1+7C8lJKYoISk19wOv4ebqKB+vnIX7aum+9pjfP475eLnK0ZEF04ojSjQAAACAYsNqtSg02Esxsbm3Tyob4qVPZj+Q41hGRqYSk1MVn5iq+MSUHG8Jiak6n3hJCdec++NUon48cEbxiam6eOnyv2bx9HD+++q2t5t8chRxF/l4ucrP59qr4FnHvTxcZLWy97ZZUaIBAAAAFCujBzfT8Anrc+xp7ebqqNGDm+V6rIODVX4+bvLzcbvhr5N2OeNK2U75lxJ+5XxSin47kqz4pFTFJ6TkOeX8KqvVIh+va6aae7lkl21fL1f5+rjK18sl688rV72vnnd3dTR0X+6SgBINAAAAoFi5unhYQa/O7ezkoNIBHiod4HFDz7PZbLqUkq6EpBSdT8gq2/FJKYpPSPn7zytTzc8npCghKesKeHziJcUnpiozM4+56lc4OVqvlGzX7D+z7vO+pmxfnZ6efT7rYxdn6qE9eJUAAAAAFDtd2oWrS7twBQZ66ezZ3FO7jWSxWOTu5iR3NyeFlPa6oedmZtqUfCFN8UlZV7izSnbWVfDzeVwVP30uWQePnFN8YqoSk//9/m93V0f5Zpfsv694XzvdPEcJv+ZKuYODffd/F4etxyjRAAAAAFBEWK0WeXu5yNvLRSrjc0PPTU+/ev/31SnnqdlXtxOuLeFXppwfOxGfPV394jVT4/Pi7eny96Jq1yzAdm3Z3n/onD5c/qNS04r21mOUaAAAAAAoARwdrfL3dZO/743f/52alp5dtv+ecv53If/n1PQDh85lP+5yeuZ1P++llHRNnPkVJRoAAAAAUHy4ODsqqJSjgkrd+P3fF1PSlZCYotvbvZPn1mMnT5truv3/h43LAAAAAAAFwmKxyMPNSWWCvBQanPf939c7blaUaAAAAABAgRs9uJncXHNOhr7e1mNmxnRuAAAAAECBK6ytxwoaJRoAAAAAUCjMvPWYvZjODQAAAACAnSjRAAAAAADYiRINAAAAAICdKNEAAAAAANiJEg0AAAAAgJ0o0QAAAAAA2IkSDQAAAACAnSjRAAAAAADYiRINAAAAAICdKNEAAAAAANiJEg0AAAAAgJ0o0QAAAAAA2IkSDQAAAACAnSjRAAAAAADYiRINAAAAAICdKNEAAAAAANiJEg0AAAAAgJ0o0QAAAAAA2MnR6ABFmdVqMTrCvzJ7vpKKcTEfxsScGBfzYUzMiXExH8bEfBgTczLzuPxbNovNZrMVYhYAAAAAAIospnMDAAAAAGAnSjQAAAAAAHaiRAMAAAAAYCdKNAAAAAAAdqJEAwAAAABgJ0o0AAAAAAB2okQDAAAAAGAnSjQAAAAAAHaiRAMAAAAAYCdHowMgf02ZMkVffvmlTp48qdWrV6tq1apGRyrxzp8/r2eeeUZ//PGHnJ2dVb58eY0bN07+/v5GRyvxBg0apJiYGFmtVrm7u2vMmDEKDw83OhYkzZgxQ2+99RZ/j5lAVFSUnJ2d5eLiIkkaMWKEmjVrZnAqpKam6uWXX9bXX38tFxcX1alTR+PHjzc6VokVExOjwYMHZ3+clJSk5ORkffvttwamgiRt2bJFb7zxhmw2m2w2m5544gndfffdRscq0aKjo/XGG28oPT1dPj4+mjRpksqVK2d0rBtisdlsNqNDIP/s2bNHoaGh6tGjh+bMmcMvnyYQHx+vgwcPqmHDhpKy/qEjISFBL7/8ssHJkJSUJC8vL0nSxo0bNXPmTK1YscLgVPjll1/0+uuv68iRI/w9ZgJRUVGMgwlNmDBBVqtVI0eOlMVi0blz51SqVCmjY+GKiRMnKiMjQ2PHjjU6Solms9nUoEEDLVq0SFWrVtWBAwf00EMP6bvvvpPVyoRcIyQkJOjuu+/W0qVLVbFiRa1cuVKrVq3SvHnzjI52Q/ivp5ipV6+eQkJCjI6Ba/j6+mYXaEmqU6eOTp06ZWAiXHW1QEtScnKyLBaLgWkgSWlpaRo3bpxefPFFo6MApnXhwgV99tlnevLJJ7P/3qJAm0daWppWr16tLl26GB0FkqxWq5KSkiRl/eN56dKlKdAGOn78uEqVKqWKFStKkiIiIrR9+3b99ddfBie7MUznBgpRZmamlixZoqioKKOj4IrRo0drx44dstlsevfdd42OU+K98cYb6tixo8qWLWt0FFxjxIgRstlsqlu3roYPHy5vb2+jI5VoJ06ckK+vr2bMmKFdu3bJw8NDTz75pOrVq2d0NEjavHmzgoKCdNtttxkdpcSzWCyaPn26Bg0aJHd3d124cEHvvPOO0bFKtIoVK+rcuXP68ccfVatWLa1evVqSFBsbW6RudeSfYYBCNH78eLm7u6tnz55GR8EVEydOVHR0tJ566ilNnTrV6Dgl2vfff6+ff/5Z3bt3NzoKrrFo0SKtWrVKn376qWw2m8aNG2d0pBIvIyNDJ06cUPXq1bV8+XKNGDFCQ4YMUXJystHRIOnTTz/lKrRJpKen6+2339asWbO0ZcsWzZ49W8OGDdOFCxeMjlZieXl56fXXX9ekSZPUuXNnxcXFydvbWw4ODkZHuyGUaKCQTJkyRcePH9f06dOZRmRCnTp10q5du3T+/Hmjo5RYu3fv1uHDh9WyZUtFRUXp9OnT6t+/v7Zv3250tBLt6i1Czs7O6t69u/bu3WtwIoSEhMjR0VHt27eXJNWuXVt+fn46evSowclw5swZ7d69Wx06dDA6CiTt379ff/75p+rWrStJqlu3rtzc3HT48GGDk5VsjRs31pIlS7R8+XL17NlTKSkpCgsLMzrWDeE3eaAQTJs2TT///LNmzpwpZ2dno+NAWfcUxsbGZn+8efNm+fj4yNfX18BUJduAAQO0fft2bd68WZs3b1ZwcLDmzZunpk2bGh2txLp48WL2vYQ2m01r1qxhBXsT8Pf3V8OGDbVjxw5J0tGjRxUXF6fy5csbnAwrVqxQRESE/Pz8jI4CScHBwTp9+rSOHDkiSTp8+LDi4uKKXGErbs6ePSsp6zbHadOmqVu3bnJ3dzc41Y1hde5iZsKECVq/fr3OnTsnPz8/+fr66osvvjA6Von2+++/q3379qpQoYJcXV0lSWXLltXMmTMNTlaynTt3ToMGDdKlS5dktVrl4+OjZ599lnvYTIRVoY134sQJDRkyRBkZGcrMzFTlypX1/PPPq3Tp0kZHK/FOnDihUaNGKT4+Xo6Ojho2bJgiIiKMjlXitW7dWqNHj1bz5s2NjoIrVq1apblz52Yvwjd06FC1atXK4FQl2+jRo7V3715dvnxZTZo00ahRo7K3USwqKNEAAAAAANiJ6dwAAAAAANiJEg0AAAAAgJ0o0QAAAAAA2IkSDQAAAACAnSjRAAAAAADYiRINAMAVy5cvV7Vq1VSvXj0lJCTkOJeenq5q1arprbfeMiiduTz33HOKiorK/jgmJkbVqlXT8uXLb/pz9+rVS7169brpz5OXDz74QOvXry+Qzw0AKBko0QAA/ENSUpLmzp1rdIwipXTp0lq2bJkiIyONjvKvFixYQIkGANwUSjQAAP/QtGlTLVy4UOfOnTM6yk1LS0srlK/j7OysOnXqyN/fv1C+npkU1msMADAHSjQAAP8wcOBASdLs2bP/9XFvvfWWqlWrluv49aY6L1myRK+99pqaNGmi22+/XSNGjNClS5d0/Phx9e/fX7fffrvuuusurVixItfnPHDggB5//HHVr19ftWrVUrdu3bRnz55cX7d58+b6/vvv1a1bN9WqVUtTp06VJB05ckSDBw9WvXr1VKtWLT344IPatm2bXa/H119/rfvuu081a9ZUq1attHTp0lyPuZHp3AcOHNDgwYPVsGFD1apVS61bt9bbb7993cdfnWYfExOT43her//8+fPVtm1b1apVS/Xr11fnzp21YcMGSVJUVJROnjyp1atXq1q1aqpWrZqee+65HLlu5jUGAJQMjkYHAADAbAIDA9WjRw/Nnz9f/fr1U2hoaL583nfeeUcNGjTQ5MmTdfjwYb3yyiuyWq3av3+/HnjgAfXr109LlizRyJEjVaNGDd1yyy2SpF9++UU9evRQeHi4xo8fLzc3Ny1ZskR9+vTR0qVLVaN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             "text/plain": [
-              "<Figure size 1152x720 with 1 Axes>"
+              "<Figure size 1600x1000 with 1 Axes>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "plot_ssd_curve(X)"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "pgVq8Lt-E9vs"
+      },
+      "outputs": [],
       "source": [
         "from mpl_toolkits import mplot3d\n",
         "\n",
@@ -505,21 +413,11 @@
         "  ax = plt.axes(projection =\"3d\")\n",
         "  ax.scatter3D(data[:,0], data[:,1], data[:,2], edgecolors= \"black\", c=y_pred)\n",
         "  ax.scatter3D(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], kmeans.cluster_centers_[:,2], color=\"red\", s=100)"
-      ],
-      "metadata": {
-        "id": "pgVq8Lt-E9vs"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "kmeans = KMeans(n_clusters=5, init = 'k-means++', random_state = RANDOM_SEED)\n",
-        "kmeans.fit(X)\n",
-        "print(kmeans.inertia_)\n",
-        "plot_clusters3d(kmeans, X, axlabels=[\"Age\",\"Spending Score (1-100)\"])"
-      ],
+      "execution_count": 16,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -528,57 +426,82 @@
         "id": "1a8lu5s4EvR3",
         "outputId": "eb5643f0-5ac4-40c6-bfb9-cfc0f67b0415"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "75350.77917248776\n"
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
-            "image/png": 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\n",
+            "image/png": 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",
             "text/plain": [
-              "<Figure size 1152x720 with 1 Axes>"
+              "<Figure size 1600x1000 with 1 Axes>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "kmeans = KMeans(n_clusters=5, init = 'k-means++', random_state = RANDOM_SEED)\n",
+        "kmeans.fit(X)\n",
+        "print(kmeans.inertia_)\n",
+        "plot_clusters3d(kmeans, X, axlabels=[\"Age\",\"Spending Score (1-100)\"])"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "%matplotlib notebook"
-      ],
+      "execution_count": 17,
       "metadata": {
         "id": "e27qWuWvBAV0"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "%matplotlib notebook"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "kcWYM7rDBTIr"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n    if (typeof WebSocket !== 'undefined') {\n        return WebSocket;\n    } else if (typeof MozWebSocket !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert(\n            'Your browser does not have WebSocket support. ' +\n                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n                'Firefox 4 and 5 are also supported but you ' +\n                'have to enable WebSockets in about:config.'\n        );\n    }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = this.ws.binaryType !== undefined;\n\n    this.capture_scroll = false;\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById('mpl-warnings');\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent =\n                'This browser does not support binary websocket messages. ' +\n                'Performance may be slow.';\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = document.createElement('div');\n    this.root.setAttribute('style', 'display: inline-block');\n    this._root_extra_style(this.root);\n\n    parent_element.appendChild(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen = function () {\n        fig.send_message('supports_binary', { value: fig.supports_binary });\n        fig.send_message('send_image_mode', {});\n        if (fig.ratio !== 1) {\n            fig.send_message('set_device_pixel_ratio', {\n                device_pixel_ratio: fig.ratio,\n            });\n        }\n        fig.send_message('refresh', {});\n    };\n\n    this.imageObj.onload = function () {\n        if (fig.image_mode === 'full') {\n            // Full images could contain transparency (where diff images\n            // almost always do), so we need to clear the canvas so that\n            // there is no ghosting.\n            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n        }\n        fig.context.drawImage(fig.imageObj, 0, 0);\n    };\n\n    this.imageObj.onunload = function () {\n        fig.ws.close();\n    };\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n    var titlebar = document.createElement('div');\n    titlebar.classList =\n        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n    var titletext = document.createElement('div');\n    titletext.classList = 'ui-dialog-title';\n    titletext.setAttribute(\n        'style',\n        'width: 100%; text-align: center; padding: 3px;'\n    );\n    titlebar.appendChild(titletext);\n    this.root.appendChild(titlebar);\n    this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n    var fig = this;\n\n    var canvas_div = (this.canvas_div = document.createElement('div'));\n    canvas_div.setAttribute('tabindex', '0');\n    canvas_div.setAttribute(\n        'style',\n        'border: 1px solid #ddd;' +\n            'box-sizing: content-box;' +\n            'clear: both;' +\n            'min-height: 1px;' +\n            'min-width: 1px;' +\n            'outline: 0;' +\n            'overflow: hidden;' +\n            'position: relative;' +\n            'resize: both;' +\n            'z-index: 2;'\n    );\n\n    function on_keyboard_event_closure(name) {\n        return function (event) {\n            return fig.key_event(event, name);\n        };\n    }\n\n    canvas_div.addEventListener(\n        'keydown',\n        on_keyboard_event_closure('key_press')\n    );\n    canvas_div.addEventListener(\n        'keyup',\n        on_keyboard_event_closure('key_release')\n    );\n\n    this._canvas_extra_style(canvas_div);\n    this.root.appendChild(canvas_div);\n\n    var canvas = (this.canvas = document.createElement('canvas'));\n    canvas.classList.add('mpl-canvas');\n    canvas.setAttribute(\n        'style',\n        'box-sizing: content-box;' +\n            'pointer-events: none;' +\n            'position: relative;' +\n            'z-index: 0;'\n    );\n\n    this.context = canvas.getContext('2d');\n\n    var backingStore =\n        this.context.backingStorePixelRatio ||\n        this.context.webkitBackingStorePixelRatio ||\n        this.context.mozBackingStorePixelRatio ||\n        this.context.msBackingStorePixelRatio ||\n        this.context.oBackingStorePixelRatio ||\n        this.context.backingStorePixelRatio ||\n        1;\n\n    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n        'canvas'\n    ));\n    rubberband_canvas.setAttribute(\n        'style',\n        'box-sizing: content-box;' +\n            'left: 0;' +\n            'pointer-events: none;' +\n            'position: absolute;' +\n            'top: 0;' +\n            'z-index: 1;'\n    );\n\n    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n    if (this.ResizeObserver === undefined) {\n        if (window.ResizeObserver !== undefined) {\n            this.ResizeObserver = window.ResizeObserver;\n        } else {\n            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n            this.ResizeObserver = obs.ResizeObserver;\n        }\n    }\n\n    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n        // There's no need to resize if the WebSocket is not connected:\n        // - If it is still connecting, then we will get an initial resize from\n        //   Python once it connects.\n        // - If it has disconnected, then resizing will clear the canvas and\n        //   never get anything back to refill it, so better to not resize and\n        //   keep something visible.\n        if (fig.ws.readyState != 1) {\n            return;\n        }\n        var nentries = entries.length;\n        for (var i = 0; i < nentries; i++) {\n            var entry = entries[i];\n            var width, height;\n            if (entry.contentBoxSize) {\n                if (entry.contentBoxSize instanceof Array) {\n                    // Chrome 84 implements new version of spec.\n                    width = entry.contentBoxSize[0].inlineSize;\n                    height = entry.contentBoxSize[0].blockSize;\n                } else {\n                    // Firefox implements old version of spec.\n                    width = entry.contentBoxSize.inlineSize;\n                    height = entry.contentBoxSize.blockSize;\n                }\n            } else {\n                // Chrome <84 implements even older version of spec.\n                width = entry.contentRect.width;\n                height = entry.contentRect.height;\n            }\n\n            // Keep the size of the canvas and rubber band canvas in sync with\n            // the canvas container.\n            if (entry.devicePixelContentBoxSize) {\n                // Chrome 84 implements new version of spec.\n                canvas.setAttribute(\n                    'width',\n                    entry.devicePixelContentBoxSize[0].inlineSize\n                );\n                canvas.setAttribute(\n                    'height',\n                    entry.devicePixelContentBoxSize[0].blockSize\n                );\n            } else {\n                canvas.setAttribute('width', width * fig.ratio);\n                canvas.setAttribute('height', height * fig.ratio);\n            }\n            /* This rescales the canvas back to display pixels, so that it\n             * appears correct on HiDPI screens. */\n            canvas.style.width = width + 'px';\n            canvas.style.height = height + 'px';\n\n            rubberband_canvas.setAttribute('width', width);\n            rubberband_canvas.setAttribute('height', height);\n\n            // And update the size in Python. We ignore the initial 0/0 size\n            // that occurs as the element is placed into the DOM, which should\n            // otherwise not happen due to the minimum size styling.\n            if (width != 0 && height != 0) {\n                fig.request_resize(width, height);\n            }\n        }\n    });\n    this.resizeObserverInstance.observe(canvas_div);\n\n    function on_mouse_event_closure(name) {\n        /* User Agent sniffing is bad, but WebKit is busted:\n         * https://bugs.webkit.org/show_bug.cgi?id=144526\n         * https://bugs.webkit.org/show_bug.cgi?id=181818\n         * The worst that happens here is that they get an extra browser\n         * selection when dragging, if this check fails to catch them.\n         */\n        var UA = navigator.userAgent;\n        var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n        if(isWebKit) {\n            return function (event) {\n                /* This prevents the web browser from automatically changing to\n                 * the text insertion cursor when the button is pressed. We\n                 * want to control all of the cursor setting manually through\n                 * the 'cursor' event from matplotlib */\n                event.preventDefault()\n                return fig.mouse_event(event, name);\n            };\n        } else {\n            return function (event) {\n                return fig.mouse_event(event, name);\n            };\n        }\n    }\n\n    canvas_div.addEventListener(\n        'mousedown',\n        on_mouse_event_closure('button_press')\n    );\n    canvas_div.addEventListener(\n        'mouseup',\n        on_mouse_event_closure('button_release')\n    );\n    canvas_div.addEventListener(\n        'dblclick',\n        on_mouse_event_closure('dblclick')\n    );\n    // Throttle sequential mouse events to 1 every 20ms.\n    canvas_div.addEventListener(\n        'mousemove',\n        on_mouse_event_closure('motion_notify')\n    );\n\n    canvas_div.addEventListener(\n        'mouseenter',\n        on_mouse_event_closure('figure_enter')\n    );\n    canvas_div.addEventListener(\n        'mouseleave',\n        on_mouse_event_closure('figure_leave')\n    );\n\n    canvas_div.addEventListener('wheel', function (event) {\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        if (fig.capture_scroll) {\n            event.preventDefault();\n        }\n        on_mouse_event_closure('scroll')(event);\n    });\n\n    canvas_div.appendChild(canvas);\n    canvas_div.appendChild(rubberband_canvas);\n\n    this.rubberband_context = rubberband_canvas.getContext('2d');\n\n    this._resize_canvas = function (width, height, forward) {\n        if (forward) {\n            canvas_div.style.width = width + 'px';\n            canvas_div.style.height = height + 'px';\n        }\n    };\n\n    // Disable right mouse context menu.\n    canvas_div.addEventListener('contextmenu', function (_e) {\n        event.preventDefault();\n        return false;\n    });\n\n    function set_focus() {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'mpl-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'mpl-button-group';\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'mpl-button-group';\n            continue;\n        }\n\n        var button = (fig.buttons[name] = document.createElement('button'));\n        button.classList = 'mpl-widget';\n        button.setAttribute('role', 'button');\n        button.setAttribute('aria-disabled', 'false');\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n        var icon_img = document.createElement('img');\n        icon_img.src = '_images/' + image + '.png';\n        icon_img.srcset = '_images/' + image + '_large.png 2x';\n        icon_img.alt = tooltip;\n        button.appendChild(icon_img);\n\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    var fmt_picker = document.createElement('select');\n    fmt_picker.classList = 'mpl-widget';\n    toolbar.appendChild(fmt_picker);\n    this.format_dropdown = fmt_picker;\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = document.createElement('option');\n        option.selected = fmt === mpl.default_extension;\n        option.innerHTML = fmt;\n        fmt_picker.appendChild(option);\n    }\n\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n    var size = msg['size'];\n    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1], msg['forward']);\n        fig.send_message('refresh', {});\n    }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n    var x0 = msg['x0'] / fig.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n    var x1 = msg['x1'] / fig.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n\n    var ctx = fig.rubberband_context;\n    ctx.clearRect(\n        0,\n        0,\n        fig.canvas.width / fig.ratio,\n        fig.canvas.height / fig.ratio\n    );\n\n    var drawRubberband = function () {\n        // Draw the lines from x0, y0 towards x1, y1 so that the\n        // dashes don't \"jump\" when moving the zoom box.\n        ctx.beginPath();\n        ctx.moveTo(x0, y0);\n        ctx.lineTo(x0, y1);\n        ctx.moveTo(x0, y0);\n        ctx.lineTo(x1, y0);\n        ctx.moveTo(x0, y1);\n        ctx.lineTo(x1, y1);\n        ctx.moveTo(x1, y0);\n        ctx.lineTo(x1, y1);\n        ctx.stroke();\n    };\n\n    fig.rubberband_context.lineWidth = 1;\n    fig.rubberband_context.setLineDash([3]);\n    fig.rubberband_context.lineDashOffset = 0;\n    fig.rubberband_context.strokeStyle = '#000000';\n    drawRubberband();\n    fig.rubberband_context.strokeStyle = '#ffffff';\n    fig.rubberband_context.lineDashOffset = 3;\n    drawRubberband();\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n    fig.canvas_div.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n    for (var key in msg) {\n        if (!(key in fig.buttons)) {\n            continue;\n        }\n        fig.buttons[key].disabled = !msg[key];\n        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n    }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n    if (msg['mode'] === 'PAN') {\n        fig.buttons['Pan'].classList.add('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    } else if (msg['mode'] === 'ZOOM') {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.add('active');\n    } else {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    }\n};\n\nmpl.figure.prototype.handle_capture_scroll = function (fig, msg) {\n    fig.capture_scroll = msg['capture_scroll'];\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Called whenever the canvas gets updated.\n    this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            var img = evt.data;\n            if (img.type !== 'image/png') {\n                /* FIXME: We get \"Resource interpreted as Image but\n                 * transferred with MIME type text/plain:\" errors on\n                 * Chrome.  But how to set the MIME type?  It doesn't seem\n                 * to be part of the websocket stream */\n                img.type = 'image/png';\n            }\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src\n                );\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                img\n            );\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        } else if (\n            typeof evt.data === 'string' &&\n            evt.data.slice(0, 21) === 'data:image/png;base64'\n        ) {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig['handle_' + msg_type];\n        } catch (e) {\n            console.log(\n                \"No handler for the '%s' message type: \",\n                msg_type,\n                msg\n            );\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '%s' message: \", msg_type, msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\n                    \"Exception inside the 'handler_%s' callback:\",\n                    msg_type,\n                    e,\n                    e.stack,\n                    msg\n                );\n            }\n        }\n    };\n};\n\nfunction getModifiers(event) {\n    var mods = [];\n    if (event.ctrlKey) {\n        mods.push('ctrl');\n    }\n    if (event.altKey) {\n        mods.push('alt');\n    }\n    if (event.shiftKey) {\n        mods.push('shift');\n    }\n    if (event.metaKey) {\n        mods.push('meta');\n    }\n    return mods;\n}\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n    return Object.keys(original).reduce(function (obj, key) {\n        if (typeof original[key] !== 'object') {\n            obj[key] = original[key];\n        }\n        return obj;\n    }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n    if (name === 'button_press') {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    // from https://stackoverflow.com/q/1114465\n    var boundingRect = this.canvas.getBoundingClientRect();\n    var x = (event.clientX - boundingRect.left) * this.ratio;\n    var y = (event.clientY - boundingRect.top) * this.ratio;\n\n    this.send_message(name, {\n        x: x,\n        y: y,\n        button: event.button,\n        step: event.step,\n        buttons: event.buttons,\n        modifiers: getModifiers(event),\n        guiEvent: simpleKeys(event),\n    });\n\n    return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n    // Prevent repeat events\n    if (name === 'key_press') {\n        if (event.key === this._key) {\n            return;\n        } else {\n            this._key = event.key;\n        }\n    }\n    if (name === 'key_release') {\n        this._key = null;\n    }\n\n    var value = '';\n    if (event.ctrlKey && event.key !== 'Control') {\n        value += 'ctrl+';\n    }\n    else if (event.altKey && event.key !== 'Alt') {\n        value += 'alt+';\n    }\n    else if (event.shiftKey && event.key !== 'Shift') {\n        value += 'shift+';\n    }\n\n    value += 'k' + event.key;\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n    return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n    if (name === 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message('toolbar_button', { name: name });\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n    this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n\nmpl.extensions = [\"avif\", \"eps\", \"gif\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.binaryType = comm.kernel.ws.binaryType;\n    ws.readyState = comm.kernel.ws.readyState;\n    function updateReadyState(_event) {\n        if (comm.kernel.ws) {\n            ws.readyState = comm.kernel.ws.readyState;\n        } else {\n            ws.readyState = 3; // Closed state.\n        }\n    }\n    comm.kernel.ws.addEventListener('open', updateReadyState);\n    comm.kernel.ws.addEventListener('close', updateReadyState);\n    comm.kernel.ws.addEventListener('error', updateReadyState);\n\n    ws.close = function () {\n        comm.close();\n    };\n    ws.send = function (m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function (msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        var data = msg['content']['data'];\n        if (data['blob'] !== undefined) {\n            data = {\n                data: new Blob(msg['buffers'], { type: data['blob'] }),\n            };\n        }\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(data);\n    });\n    return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = document.getElementById(id);\n    var ws_proxy = comm_websocket_adapter(comm);\n\n    function ondownload(figure, _format) {\n        window.open(figure.canvas.toDataURL());\n    }\n\n    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element;\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error('Failed to find cell for figure', id, fig);\n        return;\n    }\n    fig.cell_info[0].output_area.element.on(\n        'cleared',\n        { fig: fig },\n        fig._remove_fig_handler\n    );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n    var width = fig.canvas.width / fig.ratio;\n    fig.cell_info[0].output_area.element.off(\n        'cleared',\n        fig._remove_fig_handler\n    );\n    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable();\n    fig.parent_element.innerHTML =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n    fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width / this.ratio;\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message('ack', {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () {\n        fig.push_to_output();\n    }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'btn-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'btn-group';\n    var button;\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'btn-group';\n            continue;\n        }\n\n        button = fig.buttons[name] = document.createElement('button');\n        button.classList = 'btn btn-default';\n        button.href = '#';\n        button.title = name;\n        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    // Add the status bar.\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message pull-right';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n\n    // Add the close button to the window.\n    var buttongrp = document.createElement('div');\n    buttongrp.classList = 'btn-group inline pull-right';\n    button = document.createElement('button');\n    button.classList = 'btn btn-mini btn-primary';\n    button.href = '#';\n    button.title = 'Stop Interaction';\n    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n    button.addEventListener('click', function (_evt) {\n        fig.handle_close(fig, {});\n    });\n    button.addEventListener(\n        'mouseover',\n        on_mouseover_closure('Stop Interaction')\n    );\n    buttongrp.appendChild(button);\n    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n    var fig = event.data.fig;\n    if (event.target !== this) {\n        // Ignore bubbled events from children.\n        return;\n    }\n    fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n    // this is important to make the div 'focusable\n    el.setAttribute('tabindex', 0);\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    } else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n    // Check for shift+enter\n    if (event.shiftKey && event.which === 13) {\n        this.canvas_div.blur();\n        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i = 0; i < ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code') {\n            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] === html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n",
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div id='1526817d-92f8-4cb4-b3e8-21c12c264ff7'></div>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
       "source": [
         "kmeans = KMeans(n_clusters=5, init = 'k-means++', random_state = RANDOM_SEED)\n",
         "kmeans.fit(X)\n",
         "plot_clusters3d(kmeans, X, axlabels=[\"Age\",\"Spending Score (1-100)\"]) # non funzionante su Google Colaboratory"
-      ],
-      "metadata": {
-        "id": "kcWYM7rDBTIr"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "df_pred = pd.read_csv(BASE_URL+\"mall_customers_predict.csv\")\n",
-        "df_pred.head()"
-      ],
+      "execution_count": 19,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -587,16 +510,11 @@
         "id": "2LyZnmQzZbRB",
         "outputId": "49ecf4e4-4e14-43b6-e6e7-eac6d10f5834"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/html": [
-              "\n",
-              "  <div id=\"df-3e06b10c-fa7d-4e89-a0cc-f74dbd586077\">\n",
-              "    <div class=\"colab-df-container\">\n",
-              "      <div>\n",
+              "<div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
               "        vertical-align: middle;\n",
@@ -664,83 +582,7 @@
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
-              "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3e06b10c-fa7d-4e89-a0cc-f74dbd586077')\"\n",
-              "              title=\"Convert this dataframe to an interactive table.\"\n",
-              "              style=\"display:none;\">\n",
-              "        \n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
-              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
-              "  </svg>\n",
-              "      </button>\n",
-              "      \n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      flex-wrap:wrap;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-3e06b10c-fa7d-4e89-a0cc-f74dbd586077 button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-3e06b10c-fa7d-4e89-a0cc-f74dbd586077');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
+              "</div>"
             ],
             "text/plain": [
               "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
@@ -751,28 +593,30 @@
               "4           5  Female   29                  98                      88"
             ]
           },
+          "execution_count": 19,
           "metadata": {},
-          "execution_count": 25
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "df_pred = pd.read_csv(BASE_URL+\"mall_customers_predict.csv\")\n",
+        "df_pred.head()"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "X = df_pred[[ \"Age\", \"Spending Score (1-100)\", \"Annual Income (k$)\"]].values"
-      ],
+      "execution_count": 20,
       "metadata": {
         "id": "STPkXbcga08E"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "X = df_pred[[ \"Age\", \"Spending Score (1-100)\", \"Annual Income (k$)\"]].values"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "y_pred = kmeans.predict(X)\n",
-        "y_pred"
-      ],
+      "execution_count": 21,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -780,22 +624,30 @@
         "id": "YDF_gbLsa4t8",
         "outputId": "a2e5f821-b39e-4199-cdea-bbcd4224cc26"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "array([2, 0, 4, 0, 2], dtype=int32)"
+              "array([3, 1, 0, 1, 3], dtype=int32)"
             ]
           },
+          "execution_count": 21,
           "metadata": {},
-          "execution_count": 29
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "y_pred = kmeans.predict(X)\n",
+        "y_pred"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "S52Ri0T5a9vk"
+      },
+      "outputs": [],
       "source": [
         "df_result = pd.DataFrame({\n",
         "    \"CustomerID\":df_pred[\"CustomerID\"],\n",
@@ -803,12 +655,35 @@
         "})\n",
         "\n",
         "df_result.to_excel(\"mall_customers_prediction.xlsx\")"
-      ],
-      "metadata": {
-        "id": "S52Ri0T5a9vk"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "authorship_tag": "ABX9TyPwzRcDDPKvpWr4DVLNVQur",
+      "collapsed_sections": [],
+      "include_colab_link": true,
+      "name": "clustering_exercise.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
       },
-      "execution_count": null,
-      "outputs": []
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.12"
     }
-  ]
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
diff --git a/6 - Clustering/esercizi/mall_customers_prediction.xlsx b/6 - Clustering/esercizi/mall_customers_prediction.xlsx
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diff --git a/6 - Clustering/kmeans.ipynb b/6 - Clustering/kmeans.ipynb
index 44eb21a..d140d2a 100644
--- a/6 - Clustering/kmeans.ipynb	
+++ b/6 - Clustering/kmeans.ipynb	
@@ -1,28 +1,10 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "kmeans.ipynb",
-      "provenance": [],
-      "collapsed_sections": [],
-      "authorship_tag": "ABX9TyNh7IFD30OZ7Qg+83hbXdZ5",
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/ProfAI/machine-learning-fondamenti/blob/main/6%20-%20Clustering/kmeans.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -30,44 +12,44 @@
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# L'algoritmo K-means"
-      ],
       "metadata": {
         "id": "epJ5E24SENZ8"
-      }
+      },
+      "source": [
+        "# L'algoritmo K-means"
+      ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Il **K-Means** \u00e8 uno degli algoritmi di clustering (apprendimento non supervisionato) pi\u00f9 popolari e semplici.\n",
-        "L'obiettivo principale dell'algoritmo \u00e8 partizionare un insieme di dati non etichettati in $K$ gruppi distinti (**cluster**), in modo che:\n",
-        "1. I punti all'interno dello stesso gruppo siano il pi\u00f9 simili possibile tra loro (**alta similarit\u00e0 intra-cluster**).\n",
-        "2. I punti in gruppi diversi siano il pi\u00f9 dissimili possibile (**bassa similarit\u00e0 inter-cluster**).\n",
+        "Il **K-Means** è uno degli algoritmi di clustering (apprendimento non supervisionato) più popolari e semplici.\n",
+        "L'obiettivo principale dell'algoritmo è partizionare un insieme di dati non etichettati in $K$ gruppi distinti (**cluster**), in modo che:\n",
+        "1. I punti all'interno dello stesso gruppo siano il più simili possibile tra loro (**alta similarità intra-cluster**).\n",
+        "2. I punti in gruppi diversi siano il più dissimili possibile (**bassa similarità inter-cluster**).\n",
         "\n",
         "#### La Formulazione Matematica dell'Inerzia\n",
         "\n",
-        "Per definire formalmente la similarit\u00e0, il K-Means cerca di minimizzare l'**Inerzia** (chiamata anche **Within-Cluster Sum of Squares - WCSS**). L'inerzia misura la somma delle distanze al quadrato tra ciascun punto e il centroide del suo cluster di appartenenza:\n",
+        "Per definire formalmente la similarità, il K-Means cerca di minimizzare l'**Inerzia** (chiamata anche **Within-Cluster Sum of Squares - WCSS**). L'inerzia misura la somma delle distanze al quadrato tra ciascun punto e il centroide del suo cluster di appartenenza:\n",
         "\n",
         "$$\\text{WCSS} = \\sum_{i=1}^{K} \\sum_{x \\in C_i} \\|x - \\mu_i\\|^2$$\n",
         "\n",
         "Dove:\n",
-        "- $K$ \u00e8 il numero totale di cluster.\n",
+        "- $K$ è il numero totale di cluster.\n",
         "- $C_i$ rappresenta l'insieme dei punti assegnati al cluster $i$-esimo.\n",
-        "- $x$ \u00e8 un generico punto dati (un vettore in uno spazio a $d$ dimensioni).\n",
-        "- $\\mu_i$ \u00e8 il **centroide** (ovvero la media geometrica) di tutti i punti appartenenti al cluster $C_i$:\n",
+        "- $x$ è un generico punto dati (un vettore in uno spazio a $d$ dimensioni).\n",
+        "- $\\mu_i$ è il **centroide** (ovvero la media geometrica) di tutti i punti appartenenti al cluster $C_i$:\n",
         "  $$\\mu_i = \\frac{1}{|C_i|} \\sum_{x \\in C_i} x$$\n",
-        "- $\\|x - \\mu_i\\|^2$ \u00e8 la distanza euclidea al quadrato tra il punto $x$ e il centroide $\\mu_i$.\n",
+        "- $\\|x - \\mu_i\\|^2$ è la distanza euclidea al quadrato tra il punto $x$ e il centroide $\\mu_i$.\n",
         "\n",
         "> [!IMPORTANT]\n",
         "> **Il Limite del Parametro K a Priori:**\n",
-        "> Uno dei principali limiti del K-Means \u00e8 che il numero di cluster $K$ deve essere specificato dall'utente prima dell'addestramento. Nella pratica, la scelta di $K$ non \u00e8 banale e si ricorre a metodi diagnostici come il **Metodo del Gomito (Elbow Method)** o il **Punteggio di Silhouette (Silhouette Score)**."
+        "> Uno dei principali limiti del K-Means è che il numero di cluster $K$ deve essere specificato dall'utente prima dell'addestramento. Nella pratica, la scelta di $K$ non è banale e si ricorre a metodi diagnostici come il **Metodo del Gomito (Elbow Method)** o il **Punteggio di Silhouette (Silhouette Score)**."
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 1,
       "metadata": {
         "id": "tgsrHVBQmOy4"
       },
@@ -82,26 +64,26 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "plt.rcParams[\"figure.figsize\"] = (16,10)\n",
-        "sns.set_theme()"
-      ],
+      "execution_count": 2,
       "metadata": {
         "id": "SJtejcsByqdR"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "plt.rcParams[\"figure.figsize\"] = (16,10)\n",
+        "sns.set_theme()"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "RANDOM_SEED = 2"
-      ],
+      "execution_count": 3,
       "metadata": {
         "id": "Gd7CdwVbyt54"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "RANDOM_SEED = 2"
+      ]
     },
     {
       "cell_type": "markdown",
@@ -112,28 +94,22 @@
         "Prima di iniziare, importiamo i moduli necessari:\n",
         "- **`numpy`** e **`pandas`**: essenziali per la manipolazione di array e dati tabellari.\n",
         "- **`sklearn.datasets.make_blobs`**: una comoda utility per generare cluster artificiali isotropi con distribuzione gaussiana, ideale per testare algoritmi di clustering.\n",
-        "- **`matplotlib.pyplot`** e **`seaborn`**: per la creazione di grafici di qualit\u00e0.\n",
+        "- **`matplotlib.pyplot`** e **`seaborn`**: per la creazione di grafici di qualità.\n",
         "- **`time`**: per misurare e confrontare i tempi di addestramento dei modelli."
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Generiamo i dati"
-      ],
       "metadata": {
         "id": "EyyfBGiREUPz"
-      }
+      },
+      "source": [
+        "### Generiamo i dati"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "X, _ = make_blobs(n_samples=100, n_features=2, centers=3, cluster_std=0.5, random_state=RANDOM_SEED)\n",
-        "X[:,0] = (X[:,0]-X[:,0].min())*20\n",
-        "X[:,1] = (X[:,1]-X[:,1].min())*6 \n",
-        "\n",
-        "sns.scatterplot(x=X[:,0], y=X[:,1], s=100)"
-      ],
+      "execution_count": 4,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -142,28 +118,34 @@
         "id": "HPgylM5jx4Ur",
         "outputId": "f30be827-1bf2-4602-d89a-6da5d4f2df1e"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5d68d5e610>"
+              "<Axes: >"
             ]
           },
+          "execution_count": 4,
           "metadata": {},
-          "execution_count": 6
+          "output_type": "execute_result"
         },
         {
-          "output_type": "display_data",
           "data": {
-            "image/png": 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             "text/plain": [
-              "<Figure size 1152x720 with 1 Axes>"
+              "<Figure size 1600x1000 with 1 Axes>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "X, _ = make_blobs(n_samples=100, n_features=2, centers=3, cluster_std=0.5, random_state=RANDOM_SEED)\n",
+        "X[:,0] = (X[:,0]-X[:,0].min())*20\n",
+        "X[:,1] = (X[:,1]-X[:,1].min())*6 \n",
+        "\n",
+        "sns.scatterplot(x=X[:,0], y=X[:,1], s=100)"
       ]
     },
     {
@@ -176,24 +158,24 @@
         "1. Generiamo cluster ideali con `make_blobs`.\n",
         "2. Applichiamo una riscalatura asimmetrica: le feature della prima colonna ($X_0$) vengono moltiplicate per $20$, mentre quelle della seconda colonna ($X_1$) per $6$.\n",
         "\n",
-        "**Perch\u00e9 questa riscalatura asimmetrica \u00e8 cruciale dal punto di vista didattico?**\n",
+        "**Perché questa riscalatura asimmetrica è cruciale dal punto di vista didattico?**\n",
         "K-Means calcola la distanza euclidea tra i punti dati e i centroidi:\n",
         "$$d(p, q) = \\sqrt{(p_1 - q_1)^2 + (p_2 - q_2)^2}$$\n",
         "\n",
-        "Se le feature hanno scale o intervalli di valori molto differenti (ad esempio, se $X_0$ spazia da 0 a 150 e $X_1$ spazia da 0 a 10), la feature con l'intervallo pi\u00f9 ampio dominer\u00e0 completamente il calcolo della distanza. Di conseguenza, il clustering avverr\u00e0 quasi esclusivamente lungo l'asse della feature dominante, ignorando l'altra.\n",
+        "Se le feature hanno scale o intervalli di valori molto differenti (ad esempio, se $X_0$ spazia da 0 a 150 e $X_1$ spazia da 0 a 10), la feature con l'intervallo più ampio dominerà completamente il calcolo della distanza. Di conseguenza, il clustering avverrà quasi esclusivamente lungo l'asse della feature dominante, ignorando l'altra.\n",
         "\n",
         "> [!TIP]\n",
-        "> Nella pratica reale con K-Means, \u00e8 **sempre fortemente raccomandato** applicare una standardizzazione (es. con `StandardScaler` di scikit-learn) o una normalizzazione MinMax prima dell'addestramento, in modo che ogni feature contribuisca equamente alla definizione dei cluster."
+        "> Nella pratica reale con K-Means, è **sempre fortemente raccomandato** applicare una standardizzazione (es. con `StandardScaler` di scikit-learn) o una normalizzazione MinMax prima dell'addestramento, in modo che ogni feature contribuisca equamente alla definizione dei cluster."
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Creiamo il modello"
-      ],
       "metadata": {
         "id": "6VN5bmI-Gsd0"
-      }
+      },
+      "source": [
+        "### Creiamo il modello"
+      ]
     },
     {
       "cell_type": "markdown",
@@ -204,7 +186,7 @@
         "L'addestramento del K-Means (noto anche come algoritmo di Lloyd) procede iterativamente alternando due fasi principali fino alla convergenza:\n",
         "\n",
         "1. **Fase di Assegnazione (Expectation)**:\n",
-        "   Ogni punto dati $x$ viene assegnato al cluster del centroide pi\u00f9 vicino, calcolando la distanza euclidea minima:\n",
+        "   Ogni punto dati $x$ viene assegnato al cluster del centroide più vicino, calcolando la distanza euclidea minima:\n",
         "   $$S_i^{(t)} = \\left\\{ x : \\|x - \\mu_i^{(t)}\\|^2 \\le \\|x - \\mu_j^{(t)}\\|^2 \\quad \\forall j, 1 \\le j \\le K \\right\\}$$\n",
         "   Dove $S_i^{(t)}$ rappresenta il gruppo di punti assegnati al centroide $\\mu_i$ all'iterazione $t$.\n",
         "\n",
@@ -212,47 +194,42 @@
         "   I centroidi vengono ricalcolati determinando la media geometrica di tutti i punti assegnati a ciascun cluster nella fase precedente:\n",
         "   $$\\mu_i^{(t+1)} = \\frac{1}{|S_i^{(t)}|} \\sum_{x \\in S_i^{(t)}} x$$\n",
         "\n",
-        "Questo ciclo si ripete fino a quando la posizione dei centroidi non cambia pi\u00f9 in modo significativo o viene raggiunto il numero massimo di iterazioni preimpostato.\n",
+        "Questo ciclo si ripete fino a quando la posizione dei centroidi non cambia più in modo significativo o viene raggiunto il numero massimo di iterazioni preimpostato.\n",
         "\n",
         "---\n",
         "\n",
         "#### Il Problema dei Minimi Locali e l'Inizializzazione dei Centroidi\n",
         "\n",
-        "Poich\u00e9 la funzione obiettivo (Inerzia) non \u00e8 convessa, K-Means \u00e8 estremamente sensibile alla scelta delle posizioni iniziali dei centroidi. Un'inizializzazione sfortunata pu\u00f2 intrappolare l'algoritmo in **minimi locali subottimali**.\n",
+        "Poiché la funzione obiettivo (Inerzia) non è convessa, K-Means è estremamente sensibile alla scelta delle posizioni iniziali dei centroidi. Un'inizializzazione sfortunata può intrappolare l'algoritmo in **minimi locali subottimali**.\n",
         "\n",
         "Per mitigare questo problema, esistono due strategie principali impostabili tramite il parametro `init`:\n",
         "\n",
         "- **Inizializzazione Casuale (`init=\"random\"`)**:\n",
-        "  I centroidi iniziali vengono scelti in modo casuale estraendo $K$ campioni dal dataset. Questo approccio \u00e8 soggetto a forte variabilit\u00e0 del risultato finale.\n",
+        "  I centroidi iniziali vengono scelti in modo casuale estraendo $K$ campioni dal dataset. Questo approccio è soggetto a forte variabilità del risultato finale.\n",
         "  \n",
         "- **Inizializzazione Intelligente (`init=\"k-means++\"`)**:\n",
-        "  Sceglie il primo centroide in modo casuale e i successivi con una probabilit\u00e0 proporzionale alla distanza al quadrato dal centroide pi\u00f9 vicino gi\u00e0 selezionato. Questo garantisce che i centroidi iniziali siano ben distanziati nello spazio, riducendo drasticamente il numero di iterazioni necessarie per convergere e migliorando la stabilit\u00e0 globale del clustering.\n",
+        "  Sceglie il primo centroide in modo casuale e i successivi con una probabilità proporzionale alla distanza al quadrato dal centroide più vicino già selezionato. Questo garantisce che i centroidi iniziali siano ben distanziati nello spazio, riducendo drasticamente il numero di iterazioni necessarie per convergere e migliorando la stabilità globale del clustering.\n",
         "\n",
         "> [!NOTE]\n",
-        "> In Scikit-learn (dalla versione 0.24 in poi), l'inizializzazione predefinita \u00e8 impostata su `'k-means++'`. Nei blocchi di codice successivi confronteremo l'addestramento con inizializzazione implicita ed esplicita."
+        "> In Scikit-learn (dalla versione 0.24 in poi), l'inizializzazione predefinita è impostata su `'k-means++'`. Nei blocchi di codice successivi confronteremo l'addestramento con inizializzazione implicita ed esplicita."
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "5KNkfle1QTVZ"
+      },
+      "outputs": [],
       "source": [
         "from sklearn.cluster import KMeans\n",
         "from scipy.cluster.vq import vq\n",
         "from time import time"
-      ],
-      "metadata": {
-        "id": "5KNkfle1QTVZ"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "kmeans = KMeans(n_clusters=3, random_state=RANDOM_SEED)\n",
-        "tick = time()\n",
-        "kmeans.fit(X)\n",
-        "print(f\"Modello addestrato in {time()-tick:.5f} secondi\" )"
-      ],
+      "execution_count": 6,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -260,25 +237,25 @@
         "id": "6h4lHyBu6a-8",
         "outputId": "5798d71d-8899-4823-9bc9-a5cc86f4d23f"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "Modello addestrato in 0.26826 secondi\n"
+            "Modello addestrato in 0.01350 secondi\n"
           ]
         }
-      ]
-    },
-    {
-      "cell_type": "code",
+      ],
       "source": [
-        "kmeans = KMeans(n_clusters=3, random_state=RANDOM_SEED, init=\"k-means++\")\n",
+        "kmeans = KMeans(n_clusters=3, random_state=RANDOM_SEED)\n",
         "tick = time()\n",
         "kmeans.fit(X)\n",
         "print(f\"Modello addestrato in {time()-tick:.5f} secondi\" )"
-      ],
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -286,15 +263,20 @@
         "id": "4kqwsGLXGzOK",
         "outputId": "fd432154-ba33-4908-8ac3-9a2f647a3803"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "Modello addestrato in 0.03150 secondi\n"
+            "Modello addestrato in 0.00145 secondi\n"
           ]
         }
+      ],
+      "source": [
+        "kmeans = KMeans(n_clusters=3, random_state=RANDOM_SEED, init=\"k-means++\")\n",
+        "tick = time()\n",
+        "kmeans.fit(X)\n",
+        "print(f\"Modello addestrato in {time()-tick:.5f} secondi\" )"
       ]
     },
     {
@@ -303,17 +285,17 @@
       "source": [
         "> [!NOTE]\n",
         "> **Analisi dei Tempi di Addestramento:**\n",
-        "> Se notate una differenza di tempo tra il primo addestramento ($\\approx 0.019$ s) e il secondo ($\\approx 0.001$ s), non \u00e8 dovuta all'efficienza intrinseca del parametro `init=\"k-means++\"` (che in realt\u00e0 \u00e8 attivo in entrambi i casi per default). In Python, la prima esecuzione di un metodo di una libreria complessa come `scikit-learn` comporta un overhead iniziale dovuto al caricamento in memoria dei moduli sottostanti e alla compilazione JIT o inizializzazione di thread-pool C. Le successive esecuzioni beneficiano della cache e dei moduli gi\u00e0 pronti in memoria."
+        "> Se notate una differenza di tempo tra il primo addestramento ($\\approx 0.019$ s) e il secondo ($\\approx 0.001$ s), non è dovuta all'efficienza intrinseca del parametro `init=\"k-means++\"` (che in realtà è attivo in entrambi i casi per default). In Python, la prima esecuzione di un metodo di una libreria complessa come `scikit-learn` comporta un overhead iniziale dovuto al caricamento in memoria dei moduli sottostanti e alla compilazione JIT o inizializzazione di thread-pool C. Le successive esecuzioni beneficiano della cache e dei moduli già pronti in memoria."
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Valutiamo il modello"
-      ],
       "metadata": {
         "id": "qebcXu68nBKe"
-      }
+      },
+      "source": [
+        "### Valutiamo il modello"
+      ]
     },
     {
       "cell_type": "markdown",
@@ -321,21 +303,21 @@
       "source": [
         "### Metriche di Valutazione: Distorsione vs Inerzia\n",
         "\n",
-        "Poich\u00e9 nel clustering non disponiamo di etichette reali (ground truth) per calcolare metriche classiche come accuratezza o precisione, dobbiamo valutare la qualit\u00e0 della segmentazione analizzando la compattezza dei cluster ottenuti.\n",
+        "Poiché nel clustering non disponiamo di etichette reali (ground truth) per calcolare metriche classiche come accuratezza o precisione, dobbiamo valutare la qualità della segmentazione analizzando la compattezza dei cluster ottenuti.\n",
         "\n",
         "Utilizziamo due metriche fondamentali basate sulle distanze euclidee:\n",
         "\n",
         "1. **Distorsione (Distortion)**:\n",
         "   Rappresenta la **media** delle distanze euclidee al quadrato tra ciascun punto e il rispettivo centroide assegnato.\n",
         "   $$\\text{Distortion} = \\frac{1}{N} \\sum_{i=1}^{K} \\sum_{x \\in C_i} \\|x - \\mu_i\\|^2$$\n",
-        "   Dove $N$ \u00e8 il numero totale di campioni nel dataset.\n",
+        "   Dove $N$ è il numero totale di campioni nel dataset.\n",
         "\n",
         "2. **Inerzia (Inertia o WCSS)**:\n",
         "   Rappresenta la **somma** totale delle distanze euclidee al quadrato di tutti i punti dai propri centroidi.\n",
         "   $$\\text{Inertia} = \\sum_{i=1}^{K} \\sum_{x \\in C_i} \\|x - \\mu_i\\|^2$$\n",
         "\n",
         "#### Relazione Matematica\n",
-        "\u00c8 evidente che l'Inerzia \u00e8 direttamente proporzionale alla Distorsione tramite la dimensione del dataset $N$:\n",
+        "È evidente che l'Inerzia è direttamente proporzionale alla Distorsione tramite la dimensione del dataset $N$:\n",
         "$$\\text{Inertia} = N \\times \\text{Distortion}$$\n",
         "\n",
         "Scikit-learn calcola automaticamente l'inerzia durante il fitting del modello e la memorizza nell'attributo `inertia_` dell'oggetto `KMeans`.\n",
@@ -349,26 +331,23 @@
         "- **Inerzia Manuale**: $\\approx 10741.0407$\n",
         "- **Inerzia Nativa (`kmeans.inertia_`)**: $\\approx 10741.0407$\n",
         "\n",
-        "Ci\u00f2 dimostra empiricamente la formula di conversione e la correttezza del calcolo nativo di Scikit-learn."
+        "Ciò dimostra empiricamente la formula di conversione e la correttezza del calcolo nativo di Scikit-learn."
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "from scipy.spatial.distance import cdist\n"
-      ],
+      "execution_count": 8,
       "metadata": {
         "id": "mypcuh_Zi6W5"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "from scipy.spatial.distance import cdist\n"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "distorsion = sum(np.square(np.min(cdist(X, kmeans.cluster_centers_, 'euclidean'), axis=1))) / X.shape[0]\n",
-        "distorsion"
-      ],
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -376,26 +355,26 @@
         "id": "hpeZbLfei7Iq",
         "outputId": "2d729af7-f17b-4c1c-e1a1-647033419c74"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "107.41040694974288"
+              "np.float64(107.41040694974288)"
             ]
           },
+          "execution_count": 9,
           "metadata": {},
-          "execution_count": 24
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "distorsion = sum(np.square(np.min(cdist(X, kmeans.cluster_centers_, 'euclidean'), axis=1))) / X.shape[0]\n",
+        "distorsion"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "inertia = sum(np.square(np.min(cdist(X, kmeans.cluster_centers_, 'euclidean'), axis=1)))\n",
-        "inertia"
-      ],
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -403,25 +382,26 @@
         "id": "ORNsLlSWjATb",
         "outputId": "96e9083e-4ef1-4cd4-8b05-4552c8bcdbd6"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "10741.040694974288"
+              "np.float64(10741.040694974288)"
             ]
           },
+          "execution_count": 10,
           "metadata": {},
-          "execution_count": 23
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "inertia = sum(np.square(np.min(cdist(X, kmeans.cluster_centers_, 'euclidean'), axis=1)))\n",
+        "inertia"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "kmeans.inertia_"
-      ],
+      "execution_count": 11,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -429,73 +409,75 @@
         "id": "SREihFzLk2if",
         "outputId": "55f64715-e11d-43b2-ae32-c4fc08f5d591"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "10741.040694974286"
             ]
           },
+          "execution_count": 11,
           "metadata": {},
-          "execution_count": 14
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "kmeans.inertia_"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### Visualizziamo i cluster"
-      ],
       "metadata": {
         "id": "mQsRbyfXGxBl"
-      }
+      },
+      "source": [
+        "### Visualizziamo i cluster"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "y_kmeans = kmeans.predict(X)"
-      ],
+      "execution_count": 12,
       "metadata": {
         "id": "NLtSCWakQXMo"
       },
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "y_kmeans = kmeans.predict(X)"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "L = {0:\"Donne single\",1:\"Neo pap\u00e0\",2:\"Neo mamme\"}\n",
-        "vfunc = np.vectorize(lambda x: L[x])\n",
-        "labels = vfunc(y_kmeans)\n",
-        "sns.scatterplot(x=X[:,0], y=X[:,1], hue=labels, s=100)\n",
-        "plt.xlabel(\"Spesa in birra\")\n",
-        "plt.ylabel(\"Spesa in pannolini\")\n",
-        "\n",
-        "centers = kmeans.cluster_centers_\n",
-        "plt.scatter(centers[:, 0], centers[:, 1], c='red', s=200, alpha=0.5);"
-      ],
+      "execution_count": 13,
       "metadata": {
-        "id": "MHn6MP929PHc",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 611
         },
+        "id": "MHn6MP929PHc",
         "outputId": "af11e73e-319d-48a1-8198-772997dcccfe"
       },
-      "execution_count": null,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "image/png": 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\n",
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",
             "text/plain": [
-              "<Figure size 1152x720 with 1 Axes>"
+              "<Figure size 1600x1000 with 1 Axes>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "L = {0:\"Donne single\",1:\"Neo papà\",2:\"Neo mamme\"}\n",
+        "vfunc = np.vectorize(lambda x: L[x])\n",
+        "labels = vfunc(y_kmeans)\n",
+        "sns.scatterplot(x=X[:,0], y=X[:,1], hue=labels, s=100)\n",
+        "plt.xlabel(\"Spesa in birra\")\n",
+        "plt.ylabel(\"Spesa in pannolini\")\n",
+        "\n",
+        "centers = kmeans.cluster_centers_\n",
+        "plt.scatter(centers[:, 0], centers[:, 1], c='red', s=200, alpha=0.5);"
       ]
     },
     {
@@ -507,17 +489,45 @@
         "Il grafico soprastante mostra il risultato finale dell'algoritmo K-Means applicato ai dati di spesa di 100 clienti. Possiamo interpretare i tre cluster identificati come segue:\n",
         "\n",
         "1. **Neo mamme (Cluster con alta spesa in pannolini, bassa spesa in birra)**:\n",
-        "   Questo gruppo di clienti mostra un comportamento d'acquisto focalizzato sui beni di prima necessit\u00e0 per neonati. Le strategie di marketing potrebbero includere offerte per latte in polvere o omogeneizzati.\n",
+        "   Questo gruppo di clienti mostra un comportamento d'acquisto focalizzato sui beni di prima necessità per neonati. Le strategie di marketing potrebbero includere offerte per latte in polvere o omogeneizzati.\n",
         "\n",
-        "2. **Neo pap\u00e0 (Cluster con alta spesa in pannolini, alta spesa in birra)**:\n",
-        "   Una celebre leggenda metropolitana del marketing narra che i padri inviati a comprare pannolini tendano ad associare l'acquisto a una gratificazione personale, come la birra. Questo cluster riflette esattamente tale pattern di co-acquisto. Le promozioni potrebbero posizionare questi prodotti in prossimit\u00e0 nel punto vendita.\n",
+        "2. **Neo papà (Cluster con alta spesa in pannolini, alta spesa in birra)**:\n",
+        "   Una celebre leggenda metropolitana del marketing narra che i padri inviati a comprare pannolini tendano ad associare l'acquisto a una gratificazione personale, come la birra. Questo cluster riflette esattamente tale pattern di co-acquisto. Le promozioni potrebbero posizionare questi prodotti in prossimità nel punto vendita.\n",
         "\n",
         "3. **Donne single (Cluster con bassa spesa in pannolini, spesa in birra moderata/alta)**:\n",
-        "   Clienti senza figli neonati, orientati a consumi differenti. La spesa in pannolini \u00e8 nulla o trascurabile, mentre quella per bevande/socializzazione \u00e8 rilevante.\n",
+        "   Clienti senza figli neonati, orientati a consumi differenti. La spesa in pannolini è nulla o trascurabile, mentre quella per bevande/socializzazione è rilevante.\n",
         "\n",
         "#### Il Ruolo dei Centroidi (Punti Rossi)\n",
-        "I centroidi rappresentano il \"profilo medio\" o tipico di ciascun segmento di clientela. Ad esempio, il centroide delle \"Neo mamme\" ci dice qual \u00e8 la spesa media in birra e pannolini di tutto quel gruppo, agendo da punto di riferimento per future campagne di targeting personalizzato."
+        "I centroidi rappresentano il \"profilo medio\" o tipico di ciascun segmento di clientela. Ad esempio, il centroide delle \"Neo mamme\" ci dice qual è la spesa media in birra e pannolini di tutto quel gruppo, agendo da punto di riferimento per future campagne di targeting personalizzato."
       ]
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "colab": {
+      "authorship_tag": "ABX9TyNh7IFD30OZ7Qg+83hbXdZ5",
+      "collapsed_sections": [],
+      "include_colab_link": true,
+      "name": "kmeans.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/Progetto Finale - Cross Selling di Polizze/health_insurance_cross_sell_prediction.ipynb b/Progetto Finale - Cross Selling di Polizze/health_insurance_cross_sell_prediction.ipynb
index 5b55858..a165d84 100644
--- a/Progetto Finale - Cross Selling di Polizze/health_insurance_cross_sell_prediction.ipynb	
+++ b/Progetto Finale - Cross Selling di Polizze/health_insurance_cross_sell_prediction.ipynb	
@@ -1,30 +1,10 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "health_insurance_cross_sell_prediction.ipynb",
-      "provenance": [],
-      "collapsed_sections": [
-        "TLo1H9qcEo7X"
-      ],
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    },
-    "accelerator": "GPU"
-  },
   "cells": [
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/ProfAI/machine-learning-fondamenti/blob/main/Progetto%20Finale%20-%20Cross%20Selling%20di%20Polizze/health_insurance_cross_sell_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -32,6 +12,9 @@
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "C-_t2lBQvESK"
+      },
       "source": [
         "# Previsione di opportunit\u00e0 di Cross Sell di assicurazioni\n",
         "\n",
@@ -61,10 +44,7 @@
         "\n",
         "\n",
         "[LINK AL DATASET (Richiede un'account su Kaggle)](https://www.kaggle.com/anmolkumar/health-insurance-cross-sell-prediction)"
-      ],
-      "metadata": {
-        "id": "C-_t2lBQvESK"
-      }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -81,12 +61,587 @@
     },
     {
       "cell_type": "code",
-      "source": [],
+      "execution_count": null,
       "metadata": {
-        "id": "CXRcDInkuh-m"
+        "id": "download_cell"
       },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import kagglehub\n",
+        "\n",
+        "# Configura il percorso locale del dataset gi\u00e0 scaricato per il fallback\n",
+        "cached_path = \"/home/rares/.cache/kagglehub/datasets/anmolkumar/health-insurance-cross-sell-prediction/versions/1\"\n",
+        "\n",
+        "try:\n",
+        "    # Tenta il download con kagglehub\n",
+        "    path = kagglehub.dataset_download(\"anmolkumar/health-insurance-cross-sell-prediction\")\n",
+        "    print(\"Dataset scaricato tramite kagglehub in:\", path)\n",
+        "except Exception as e:\n",
+        "    print(f\"Impossibile scaricare tramite kagglehub ({e}). Uso del percorso locale di fallback...\")\n",
+        "    if os.path.exists(cached_path):\n",
+        "        path = cached_path\n",
+        "        print(\"Fallback sul percorso locale del dataset:\", path)\n",
+        "    else:\n",
+        "        raise FileNotFoundError(\"Dataset non trovato localmente. Assicurati che sia presente in ~/.cache/kagglehub.\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 1. Analisi Esplorativa dei Dati (EDA)\n",
+        "\n",
+        "Iniziamo importando le librerie fondamentali (`pandas`, `numpy`, `matplotlib`, `seaborn`) e caricando il dataset di training per comprenderne la struttura, le tipologie dei dati e verificare l'eventuale presenza di valori nulli o outlier."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "# Impostiamo il seed per la riproducibilit\u00e0 didattica\n",
+        "RANDOM_SEED = 42\n",
+        "np.random.seed(RANDOM_SEED)\n",
+        "\n",
+        "# Configurazione dello stile dei grafici\n",
+        "sns.set_theme(style=\"whitegrid\")\n",
+        "plt.rcParams[\"figure.figsize\"] = (10, 6)\n",
+        "\n",
+        "# Caricamento dei dataset\n",
+        "train_df = pd.read_csv(os.path.join(path, \"train.csv\"))\n",
+        "test_df = pd.read_csv(os.path.join(path, \"test.csv\"))\n",
+        "\n",
+        "print(f\"Dimensioni del dataset di Train: {train_df.shape}\")\n",
+        "print(f\"Dimensioni del dataset di Test: {test_df.shape}\")\n",
+        "train_df.head()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Controllo dei Valori Mancanti e Tipi di Dati\n",
+        "\n",
+        "Prima di procedere ad analisi statistiche pi\u00f9 approfondite, \u00e8 fondamentale verificare se vi sono valori mancanti (NaN) che richiederebbero tecniche di imputazione, e controllare i tipi di dati associati a ciascuna colonna."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"Informazioni generali sul dataset di Train:\")\n",
+        "print(train_df.info())\n",
+        "\n",
+        "print(\"\\nValori mancanti per ciascuna colonna del Train:\")\n",
+        "print(train_df.isnull().sum())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Analisi del Sbilanciamento del Target (`Response`)\n",
+        "\n",
+        "Il target del nostro modello \u00e8 la colonna **Response** (1 = interessato ad acquistare l'assicurazione veicolo, 0 = non interessato).\n",
+        "\n",
+        "Nei problemi reali di cross-selling o rilevamento frodi, le classi sono tipicamente **fortemente sbilanciate** (imbalanced). \u00c8 essenziale misurare questa proporzione: se addestrassimo un classificatore su dati sbilanciati senza opportuni accorgimenti, il modello potrebbe massimizzare l'accuratezza banale predicendo sempre la classe maggioritaria (in questo caso `0`), risultando per\u00f2 del tutto inutile per le decisioni aziendali. Visualizziamo la distribuzione delle classi."
+      ]
+    },
+    {
+      "cell_type": "code",
       "execution_count": null,
-      "outputs": []
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "response_counts = train_df['Response'].value_counts()\n",
+        "response_pct = train_df['Response'].value_counts(normalize=True) * 100\n",
+        "\n",
+        "print(\"Frequenza assoluta delle classi:\")\n",
+        "print(response_counts)\n",
+        "print(\"\\nPercentuale relativa delle classi:\")\n",
+        "print(response_pct)\n",
+        "\n",
+        "plt.figure(figsize=(6, 5))\n",
+        "sns.countplot(x='Response', data=train_df, hue='Response', palette='viridis', legend=False)\n",
+        "plt.title(\"Distribuzione della Risposta (Target)\")\n",
+        "plt.xlabel(\"Risposta (0 = Non Interessato, 1 = Interessato)\")\n",
+        "plt.ylabel(\"Numero di Clienti\")\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Relazione tra Feature Categoriche e Target\n",
+        "\n",
+        "Analizziamo le principali feature categoriche per verificare l'esistenza di pattern evidenti:\n",
+        "- `Gender` (Sesso dell'acquirente)\n",
+        "- `Driving_License` (Possesso della patente)\n",
+        "- `Previously_Insured` (Se possiede gi\u00e0 un veicolo assicurato)\n",
+        "- `Vehicle_Age` (Et\u00e0 del veicolo)\n",
+        "- `Vehicle_Damage` (Danni passati al veicolo)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "categorical_cols = ['Gender', 'Driving_License', 'Previously_Insured', 'Vehicle_Age', 'Vehicle_Damage']\n",
+        "\n",
+        "fig, axes = plt.subplots(3, 2, figsize=(14, 16))\n",
+        "axes = axes.flatten()\n",
+        "\n",
+        "for i, col in enumerate(categorical_cols):\n",
+        "    sns.countplot(ax=axes[i], x=col, hue='Response', data=train_df, palette='muted')\n",
+        "    axes[i].set_title(f\"Distribuzione di {col} rispetto al Target\")\n",
+        "    axes[i].set_xlabel(col)\n",
+        "    axes[i].set_ylabel(\"Frequenza\")\n",
+        "\n",
+        "# Rimuoviamo l'ultimo subplot vuoto per motivi estetici\n",
+        "fig.delaxes(axes[-1])\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Analisi delle Feature Numeriche Continue\n",
+        "\n",
+        "Ora esaminiamo le variabili continue:\n",
+        "- `Age`: Et\u00e0 dell'assicurato.\n",
+        "- `Annual_Premium`: Premio annuale da pagare.\n",
+        "- `Vintage`: Giorni trascorsi dall'inizio del rapporto di clientela.\n",
+        "\n",
+        "Visualizziamo prima le statistiche descrittive generali e poi le densit\u00e0 di distribuzione (KDE) condizionate alla classe target."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "numerical_cols = ['Age', 'Annual_Premium', 'Vintage']\n",
+        "train_df[numerical_cols].describe()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Le densit\u00e0 condizionate ci aiutano a verificare se ci sono differenze significative nella distribuzione delle feature continue tra chi risponde positivamente (`Response=1`) e chi negativamente (`Response=0`)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
+        "\n",
+        "for i, col in enumerate(numerical_cols):\n",
+        "    if col == 'Annual_Premium':\n",
+        "        # La feature Annual_Premium ha outlier estremi a destra. Per visualizzare\n",
+        "        # la densit\u00e0 principale limitiamo l'asse x a 100.000\n",
+        "        sns.kdeplot(ax=axes[i], x=col, hue='Response', data=train_df, fill=True, common_norm=False, palette='crest')\n",
+        "        axes[i].set_xlim(0, 100000)\n",
+        "    else:\n",
+        "        sns.kdeplot(ax=axes[i], x=col, hue='Response', data=train_df, fill=True, common_norm=False, palette='crest')\n",
+        "    axes[i].set_title(f\"Densit\u00e0 di {col} per classe Target\")\n",
+        "    axes[i].set_xlabel(col)\n",
+        "    axes[i].set_ylabel(\"Densit\u00e0\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 2. Preprocessing dei Dati e Prevenzione del Data Leakage\n",
+        "\n",
+        "### \u26a0\ufe0f Che cos'\u00e8 il Data Leakage?\n",
+        "Il **Data Leakage** si verifica quando le informazioni provenienti dal validation o dal test set \"fuggono\" nel training set durante le fasi di preprocessing. Ad esempio, se calcoliamo la media e la deviazione standard per standardizzare (`StandardScaler`) l'intero dataset *prima* di suddividerlo, la media del training conterr\u00e0 informazioni del test. Questo falsa la stima dell'errore di generalizzazione del modello durante la validazione.\n",
+        "\n",
+        "### Best Practices\n",
+        "1. **Dividere il dataset subito**: Eseguiamo il `train_test_split` prima di qualsiasi manipolazione.\n",
+        "2. **Fittare i transformer solo sul training**: Calcoliamo i parametri di scaling (`fit_transform`) esclusivamente su `X_train` e usiamoli per trasformare (`transform`) `X_val` e `X_test`.\n",
+        "3. **Encoding delle variabili categoriche**:\n",
+        "   - `Gender`: Mappatura binaria (`Male` -> 1, `Female` -> 0).\n",
+        "   - `Vehicle_Damage`: Mappatura binaria (`Yes` -> 1, `No` -> 0).\n",
+        "   - `Vehicle_Age`: Mappatura ordinale poich\u00e9 c'\u00e8 una chiara gerarchia (`< 1 Year` -> 0, `1-2 Year` -> 1, `> 2 Years` -> 2)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "# Rimuoviamo la feature non informativa 'id' e separiamo il target\n",
+        "X = train_df.drop(columns=['id', 'Response'])\n",
+        "y = train_df['Response']\n",
+        "\n",
+        "# Suddividiamo in Train e Validation Set (80/20) con stratificazione per preservare la proporzione delle classi\n",
+        "X_train, X_val, y_train, y_val = train_test_split(\n",
+        "    X, y, test_size=0.2, random_state=RANDOM_SEED, stratify=y\n",
+        ")\n",
+        "\n",
+        "print(f\"Dimensioni di X_train: {X_train.shape}, y_train: {y_train.shape}\")\n",
+        "print(f\"Dimensioni di X_val: {X_val.shape}, y_val: {y_val.shape}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Scriviamo ora una funzione di preprocessing riutilizzabile che automatizza la codifica e lo scaling delle feature continue, assicurando di non incappare nel data leakage."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import StandardScaler\n",
+        "\n",
+        "def preprocess_data(X_data, scaler=None, is_train=True):\n",
+        "    X_processed = X_data.copy()\n",
+        "    \n",
+        "    # 1. Encoding Binario per Gender e Vehicle_Damage\n",
+        "    X_processed['Gender'] = X_processed['Gender'].map({'Male': 1, 'Female': 0})\n",
+        "    X_processed['Vehicle_Damage'] = X_processed['Vehicle_Damage'].map({'Yes': 1, 'No': 0})\n",
+        "    \n",
+        "    # 2. Ordinal Encoding per Vehicle_Age\n",
+        "    vehicle_age_map = {'< 1 Year': 0, '1-2 Year': 1, '> 2 Years': 2}\n",
+        "    X_processed['Vehicle_Age'] = X_processed['Vehicle_Age'].map(vehicle_age_map)\n",
+        "    \n",
+        "    # 3. Standardizzazione dei valori numerici continui\n",
+        "    cols_to_scale = ['Age', 'Annual_Premium', 'Vintage']\n",
+        "    \n",
+        "    if is_train:\n",
+        "        scaler = StandardScaler()\n",
+        "        X_processed[cols_to_scale] = scaler.fit_transform(X_processed[cols_to_scale])\n",
+        "        return X_processed, scaler\n",
+        "    else:\n",
+        "        if scaler is None:\n",
+        "            raise ValueError(\"Uno scaler fittato deve essere fornito durante la fase di validazione/test.\")\n",
+        "        X_processed[cols_to_scale] = scaler.transform(X_processed[cols_to_scale])\n",
+        "        return X_processed\n",
+        "\n",
+        "# Applichiamo il preprocessing a Train e Validation set\n",
+        "X_train_preprocessed, scaler = preprocess_data(X_train, is_train=True)\n",
+        "X_val_preprocessed = preprocess_data(X_val, scaler=scaler, is_train=False)\n",
+        "\n",
+        "print(\"Esempio di dati preprocessati:\")\n",
+        "X_train_preprocessed.head()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 3. Selezione del Modello e Valutazione delle Performance\n",
+        "\n",
+        "### Come gestire lo sbilanciamento delle classi?\n",
+        "Per contrastare lo sbilanciamento delle classi, utilizzeremo due approcci:\n",
+        "1. **Pesi delle Classi Bilanciati**: Gli algoritmi scikit-learn supportano l'argomento `class_weight='balanced'`, che assegna penalit\u00e0 pi\u00f9 elevate agli errori sulla classe minoritaria. La formula per il peso della classe $j$ \u00e8:\n",
+        "   $$w_j = \\frac{N}{\\text{n\\_classes} \\times N_j}$$\n",
+        "   dove $N$ \u00e8 il numero totale di campioni e $N_j$ \u00e8 il numero di campioni della classe $j$.\n",
+        "2. **Valutazione con metriche robuste**: L'accuratezza \u00e8 fuorviante. Utilizzeremo:\n",
+        "   - **ROC AUC**: Misura la capacit\u00e0 globale di discriminazione a qualsiasi soglia di classificazione.\n",
+        "   - **F1-Score**: La media armonica di Precision e Recall.\n",
+        "     $$F_1 = 2 \\times \\frac{\\text{Precision} \\times \\text{Recall}}{\\text{Precision} + \\text{Recall}}$$\n",
+        "\n",
+        "Confronteremo tre modelli in Cross-Validation:\n",
+        "- **Logistic Regression** (Baseline lineare)\n",
+        "- **HistGradientBoostingClassifier** (Algoritmo ensemble ad alte performance, ideale per dataset massivi)\n",
+        "- **Random Forest Classifier** (Robusto modello ensemble basato su bagging)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.ensemble import HistGradientBoostingClassifier\n",
+        "from sklearn.model_selection import cross_val_score\n",
+        "from sklearn.metrics import classification_report, roc_auc_score, f1_score\n",
+        "\n",
+        "# Definiamo i modelli da confrontare, testando sia la versione standard che quella bilanciata\n",
+        "models = {\n",
+        "    \"Logistic Regression (Sbilanciata)\": LogisticRegression(max_iter=1000, random_state=RANDOM_SEED),\n",
+        "    \"Logistic Regression (Bilanciata)\": LogisticRegression(max_iter=1000, class_weight='balanced', random_state=RANDOM_SEED),\n",
+        "    \"HistGradientBoosting (Sbilanciata)\": HistGradientBoostingClassifier(random_state=RANDOM_SEED),\n",
+        "    \"HistGradientBoosting (Bilanciata)\": HistGradientBoostingClassifier(class_weight='balanced', random_state=RANDOM_SEED)\n",
+        "}\n",
+        "\n",
+        "# Eseguiamo la cross-validation con 3 folds usando la metrica ROC AUC\n",
+        "for name, model in models.items():\n",
+        "    print(f\"Valutazione in Cross-Validation per {name}...\")\n",
+        "    scores = cross_val_score(model, X_train_preprocessed, y_train, cv=3, scoring='roc_auc', n_jobs=-1)\n",
+        "    print(f\"  ROC AUC medio: {scores.mean():.4f} (+/- {scores.std():.4f})\\n\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Ora addestriamo ciascun modello sull'intero `X_train_preprocessed` e ne verifichiamo la performance sul validation set per confrontare i report di classificazione e le metriche di precisione e recall dettagliate."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "results = {}\n",
+        "\n",
+        "for name, model in models.items():\n",
+        "    model.fit(X_train_preprocessed, y_train)\n",
+        "    y_pred = model.predict(X_val_preprocessed)\n",
+        "    y_pred_proba = model.predict_proba(X_val_preprocessed)[:, 1]\n",
+        "    \n",
+        "    auc = roc_auc_score(y_val, y_pred_proba)\n",
+        "    f1 = f1_score(y_val, y_pred)\n",
+        "    \n",
+        "    results[name] = {\n",
+        "        \"y_pred\": y_pred,\n",
+        "        \"y_pred_proba\": y_pred_proba,\n",
+        "        \"ROC AUC\": auc,\n",
+        "        \"F1 Score\": f1\n",
+        "    }\n",
+        "    \n",
+        "    print(f\"=== REPORT DI CLASSIFICAZIONE PER: {name} ===\")\n",
+        "    print(f\"ROC AUC: {auc:.4f} | F1-Score: {f1:.4f}\")\n",
+        "    print(classification_report(y_val, y_pred))\n",
+        "    print(\"-\" * 60)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Confronto con Random Forest\n",
+        "\n",
+        "Aggiungiamo un modello basato su foresta casuale (`RandomForestClassifier`) bilanciata. Per contenere l'uso di risorse computazionali e velocizzare l'esecuzione, impostiamo `max_depth=12` e `n_estimators=100`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "\n",
+        "print(\"Addestramento di Random Forest (Bilanciato) in corso...\")\n",
+        "rf_model = RandomForestClassifier(\n",
+        "    n_estimators=100,\n",
+        "    max_depth=12,\n",
+        "    class_weight='balanced',\n",
+        "    random_state=RANDOM_SEED,\n",
+        "    n_jobs=-1\n",
+        ")\n",
+        "rf_model.fit(X_train_preprocessed, y_train)\n",
+        "\n",
+        "y_pred_rf = rf_model.predict(X_val_preprocessed)\n",
+        "y_pred_proba_rf = rf_model.predict_proba(X_val_preprocessed)[:, 1]\n",
+        "\n",
+        "auc_rf = roc_auc_score(y_val, y_pred_proba_rf)\n",
+        "f1_rf = f1_score(y_val, y_pred_rf)\n",
+        "\n",
+        "results[\"Random Forest (Bilanciato)\"] = {\n",
+        "    \"y_pred\": y_pred_rf,\n",
+        "    \"y_pred_proba\": y_pred_proba_rf,\n",
+        "    \"ROC AUC\": auc_rf,\n",
+        "    \"F1 Score\": f1_rf\n",
+        "}\n",
+        "\n",
+        "print(\"=== REPORT DI CLASSIFICAZIONE PER: Random Forest (Bilanciato) ===\")\n",
+        "print(f\"ROC AUC: {auc_rf:.4f} | F1-Score: {f1_rf:.4f}\")\n",
+        "print(classification_report(y_val, y_pred_rf))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 4. Visualizzazione Grafica delle Curve di Performance\n",
+        "\n",
+        "Confrontiamo graficamente le curve **ROC** e **Precision-Recall** per i tre modelli principali bilanciati. Questo ci permetter\u00e0 di confrontare il comportamento dei modelli al variare delle soglie di decisione commerciale."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import roc_curve, precision_recall_curve\n",
+        "\n",
+        "plt.figure(figsize=(14, 6))\n",
+        "\n",
+        "# 1. ROC Curve\n",
+        "plt.subplot(1, 2, 1)\n",
+        "for name in [\"Logistic Regression (Bilanciata)\", \"HistGradientBoosting (Bilanciata)\", \"Random Forest (Bilanciato)\"]:\n",
+        "    fpr, tpr, _ = roc_curve(y_val, results[name][\"y_pred_proba\"])\n",
+        "    plt.plot(fpr, tpr, label=f\"{name} (AUC = {results[name]['ROC AUC']:.3f})\")\n",
+        "plt.plot([0, 1], [0, 1], 'k--', label=\"Classificatore Casuale (AUC = 0.500)\")\n",
+        "plt.xlabel(\"False Positive Rate (FPR)\")\n",
+        "plt.ylabel(\"True Positive Rate (TPR / Recall)\")\n",
+        "plt.title(\"Curve ROC a confronto\")\n",
+        "plt.legend(loc=\"lower right\")\n",
+        "\n",
+        "# 2. Precision-Recall Curve\n",
+        "plt.subplot(1, 2, 2)\n",
+        "for name in [\"Logistic Regression (Bilanciata)\", \"HistGradientBoosting (Bilanciata)\", \"Random Forest (Bilanciato)\"]:\n",
+        "    precision, recall, _ = precision_recall_curve(y_val, results[name][\"y_pred_proba\"])\n",
+        "    plt.plot(recall, precision, label=f\"{name} (F1 = {results[name]['F1 Score']:.3f})\")\n",
+        "plt.xlabel(\"Recall (Sensibilit\u00e0)\")\n",
+        "plt.ylabel(\"Precision (Precisione)\")\n",
+        "plt.title(\"Curve Precision-Recall a confronto\")\n",
+        "plt.legend(loc=\"lower left\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Analisi delle Matrici di Confusione\n",
+        "\n",
+        "Confrontiamo la Matrice di Confusione tra la versione **Sbilanciata** (standard) e quella **Bilanciata** (pesata) di `HistGradientBoosting`. Questo contrasto evidenzier\u00e0 graficamente come varia la capacit\u00e0 del modello di intercettare i clienti interessati."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+        "\n",
+        "# Modello Sbilanciato\n",
+        "cm_unbal = confusion_matrix(y_val, results[\"HistGradientBoosting (Sbilanciata)\"][\"y_pred\"])\n",
+        "disp_unbal = ConfusionMatrixDisplay(confusion_matrix=cm_unbal, display_labels=[\"Non Inter.\", \"Interessato\"])\n",
+        "disp_unbal.plot(ax=axes[0], cmap='Blues', values_format='d')\n",
+        "axes[0].set_title(\"HistGradientBoosting (Sbilanciata)\\nSoglia di decisione standard = 0.5\")\n",
+        "\n",
+        "# Modello Bilanciato\n",
+        "cm_bal = confusion_matrix(y_val, results[\"HistGradientBoosting (Bilanciata)\"][\"y_pred\"])\n",
+        "disp_bal = ConfusionMatrixDisplay(confusion_matrix=cm_bal, display_labels=[\"Non Inter.\", \"Interessato\"])\n",
+        "disp_bal.plot(ax=axes[1], cmap='Greens', values_format='d')\n",
+        "axes[1].set_title(\"HistGradientBoosting (Bilanciata)\\nClass Weight bilanciati\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### \ud83d\udca1 Interpretazione Aziendale delle Metriche\n",
+        "\n",
+        "- **Modello Sbilanciato (Soglia standard 0.5)**: Prevede quasi sempre `0` a causa del forte sbilanciamento. Sebbene l'accuratezza totale sia elevata (~88%), commette un numero enorme di falsi negativi (manca quasi tutti i clienti interessati). Dal punto di vista del business, questa soluzione non genera valore perch\u00e9 non individua le opportunit\u00e0 di cross-selling.\n",
+        "- **Modello Bilanciato**: Penalizzando gli errori sulla classe minoritaria, il classificatore abbassa la soglia per etichettare un cliente come `1`. Questo aumenta in modo sensibile il **Recall** (identificando la stragrande maggioranza dei potenziali clienti interessati), pur accettando una precisione pi\u00f9 bassa (pi\u00f9 falsi positivi, ovvero contatti a vuoto).\n",
+        "\n",
+        "### Come sfruttare le probabilit\u00e0 predette? (Leads Prioritization)\n",
+        "Nello scenario industriale reale, il team di marketing non contatter\u00e0 i clienti usando una decisione rigida (0/1). Invece:\n",
+        "1. Estrarr\u00e0 la probabilit\u00e0 di acquisto per ogni cliente tramite `predict_proba`.\n",
+        "2. Ordiner\u00e0 i contatti dal pi\u00f9 propense al meno propense (ranking di decrescente probabilit\u00e0).\n",
+        "3. Decider\u00e0 di contattare ad esempio solo il primo 20% dei clienti. Poich\u00e9 il modello `HistGradientBoosting` ha un ROC AUC di ~0.85, questo top 20% conterr\u00e0 la stragrande maggioranza di tutti i potenziali acquirenti, permettendo di risparmiare l'80% del budget di marketing a fronte di una perdita minima di opportunit\u00e0 di vendita."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Applichiamo lo stesso preprocessing del training set al test set ufficiale\n",
+        "# Nota: Passiamo scaler=scaler e is_train=False per prevenire data leakage!\n",
+        "X_test_preprocessed = preprocess_data(test_df.drop(columns=['id']), scaler=scaler, is_train=False)\n",
+        "\n",
+        "# Selezioniamo il miglior modello addestrato (HistGradientBoosting Bilanciato)\n",
+        "best_model = models[\"HistGradientBoosting (Bilanciata)\"]\n",
+        "\n",
+        "# Generiamo probabilit\u00e0 e classi predette\n",
+        "test_predictions_proba = best_model.predict_proba(X_test_preprocessed)[:, 1]\n",
+        "test_predictions_binary = best_model.predict(X_test_preprocessed)\n",
+        "\n",
+        "# Costruiamo il DataFrame di submission/predizioni finali\n",
+        "submission_df = pd.DataFrame({\n",
+        "    'id': test_df['id'],\n",
+        "    'Response_Probability': test_predictions_proba,\n",
+        "    'Response_Class': test_predictions_binary\n",
+        "})\n",
+        "\n",
+        "# Salviamo le predizioni in formato CSV\n",
+        "output_csv_path = os.path.join(path, \"test_predictions.csv\")\n",
+        "submission_df.to_csv(output_csv_path, index=False)\n",
+        "\n",
+        "print(f\"Predizioni esportate con successo in: {output_csv_path}\")\n",
+        "print(f\"Dimensioni file esportato: {submission_df.shape}\")\n",
+        "submission_df.head()\n"
+      ]
     }
-  ]
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [
+        "TLo1H9qcEo7X"
+      ],
+      "include_colab_link": true,
+      "name": "health_insurance_cross_sell_prediction.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
\ No newline at end of file

From 40055d62684c0b86bf7eea073fca1facdaf97fa8 Mon Sep 17 00:00:00 2001
From: Spatariu Rares <81376159+SpatariuRares@users.noreply.github.com>
Date: Wed, 1 Jul 2026 23:24:10 +0200
Subject: [PATCH 8/9] fix: correct encoding errors and italian character
 accents throughout the notebook

---
 ...alth_insurance_cross_sell_prediction.ipynb | 90 +++++++++----------
 1 file changed, 45 insertions(+), 45 deletions(-)

diff --git a/Progetto Finale - Cross Selling di Polizze/health_insurance_cross_sell_prediction.ipynb b/Progetto Finale - Cross Selling di Polizze/health_insurance_cross_sell_prediction.ipynb
index a165d84..3e9ecf6 100644
--- a/Progetto Finale - Cross Selling di Polizze/health_insurance_cross_sell_prediction.ipynb	
+++ b/Progetto Finale - Cross Selling di Polizze/health_insurance_cross_sell_prediction.ipynb	
@@ -16,30 +16,30 @@
         "id": "C-_t2lBQvESK"
       },
       "source": [
-        "# Previsione di opportunit\u00e0 di Cross Sell di assicurazioni\n",
+        "# Previsione di opportunità di Cross Sell di assicurazioni\n",
         "\n",
-        "Il cliente \u00e8 una compagnia di assicurazioni che ha fornito un'assicurazione sanitaria ai suoi clienti, adesso hanno bisogno del tuo aiuto per costruire un modello predittivo in grado di prevedere se gli assicurati dell'anno passato potrebbero essere interessati ad acquistare anche un'assicurazione per il proprio veicolo.\n",
+        "Il cliente è una compagnia di assicurazioni che ha fornito un'assicurazione sanitaria ai suoi clienti, adesso hanno bisogno del tuo aiuto per costruire un modello predittivo in grado di prevedere se gli assicurati dell'anno passato potrebbero essere interessati ad acquistare anche un'assicurazione per il proprio veicolo.\n",
         "\n",
-        "Il dataset \u00e8 composto dalle seguenti propriet\u00e0:\n",
+        "Il dataset è composto dalle seguenti proprietà:\n",
         "- **id**: id univoco dell'acquirente.\n",
         "- **Gender**: sesso dell'acquirente.\n",
-        "- **Age**: et\u00e0 dell'acquirente.\n",
+        "- **Age**: età dell'acquirente.\n",
         "- **Driving_License**: 1 se l'utente ha la patente di guida, 0 altrimenti.\n",
         "- **Region_Code**: codice univoco della regione dell'acquirente.\n",
-        "- **Previously_Insured**: 1 se l'utente ha gi\u00e0 un veicolo assicurato, 0 altrimenti.\n",
-        "- **Vehicle_Age**: et\u00e0 del veicolo\n",
+        "- **Previously_Insured**: 1 se l'utente ha già un veicolo assicurato, 0 altrimenti.\n",
+        "- **Vehicle_Age**: età del veicolo\n",
         "- **Vehicle_Damage**: 1 se l'utente ha danneggiato il veicolo in passato, 0 altrimenti.\n",
         "- **Annual_Premium**: la cifra che l'utente deve pagare come premio durante l'anno.\n",
         "- **Policy_Sales_Channel**: codice anonimizzato del canale utilizzato per la proposta (es. per email, per telefono, di persona, ecc...)\n",
-        "- **Vintage**: numero di giorni dalla quale l'utente \u00e8 cliente dell'azienda.\n",
-        "- **Response**: 1 se l'acquirente ha risposto positivametne alla proposta di vendit\u00e0, 0 altrimenti.\n",
+        "- **Vintage**: numero di giorni dalla quale l'utente è cliente dell'azienda.\n",
+        "- **Response**: 1 se l'acquirente ha risposto positivametne alla proposta di vendità, 0 altrimenti.\n",
         "\n",
-        "L'obiettivo del modello \u00e8 prevedere il valore di **Response**.\n",
+        "L'obiettivo del modello è prevedere il valore di **Response**.\n",
         "\n",
         "**Tip**\n",
         "Fai attenzione alla distribuzione delle classi, dai uno sguardo a [questo approfondimento](https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/). In caso di classi sbilanciate puoi provare a:\n",
         "\n",
-        "- Penalizzare la classe pi\u00f9 frequente (ricorda l'argomento class_weight)\n",
+        "- Penalizzare la classe più frequente (ricorda l'argomento class_weight)\n",
         "- Utilizzare [l'oversampling o l'undersampling](https://machinelearningmastery.com/random-oversampling-and-undersampling-for-imbalanced-classification/).\n",
         "\n",
         "\n",
@@ -50,13 +50,13 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Il **Progetto Finale** rappresenta un caso d'uso end-to-end industriale: stimare la propensione dei clienti che possiedono gi\u00e0 una polizza sanitaria ad acquistare un'assicurazione per il proprio veicolo (cross-selling).\n",
+        "Il **Progetto Finale** rappresenta un caso d'uso end-to-end industriale: stimare la propensione dei clienti che possiedono già una polizza sanitaria ad acquistare un'assicurazione per il proprio veicolo (cross-selling).\n",
         "\n",
         "Questo progetto mette a frutto tutte le fasi tipiche di una pipeline reale di Data Science:\n",
         "1. **Exploratory Data Analysis (EDA)**: Comprendere la distribuzione del target, le correlazioni e identificare potenziali problemi (es. forte sbilanciamento delle classi).\n",
         "2. **Data Preprocessing**: Trattamento dei dati mancanti, encoding di variabili categoriche nominali/ordinali e scaling delle feature numeriche.\n",
         "3. **Model Selection & Validation**: Addestramento e ottimizzazione di diversi modelli di classificazione (es. regressione logistica, alberi decisionali, ensemble) tramite tecniche robuste come la Cross-Validation.\n",
-        "4. **Evaluation**: Scelta delle metriche pi\u00f9 opportune (es. ROC AUC, F1-Score) per gestire lo sbilanciamento delle classi e guidare le decisioni commerciali.\n"
+        "4. **Evaluation**: Scelta delle metriche più opportune (es. ROC AUC, F1-Score) per gestire lo sbilanciamento delle classi e guidare le decisioni commerciali.\n"
       ]
     },
     {
@@ -70,7 +70,7 @@
         "import os\n",
         "import kagglehub\n",
         "\n",
-        "# Configura il percorso locale del dataset gi\u00e0 scaricato per il fallback\n",
+        "# Configura il percorso locale del dataset già scaricato per il fallback\n",
         "cached_path = \"/home/rares/.cache/kagglehub/datasets/anmolkumar/health-insurance-cross-sell-prediction/versions/1\"\n",
         "\n",
         "try:\n",
@@ -106,7 +106,7 @@
         "import matplotlib.pyplot as plt\n",
         "import seaborn as sns\n",
         "\n",
-        "# Impostiamo il seed per la riproducibilit\u00e0 didattica\n",
+        "# Impostiamo il seed per la riproducibilità didattica\n",
         "RANDOM_SEED = 42\n",
         "np.random.seed(RANDOM_SEED)\n",
         "\n",
@@ -129,7 +129,7 @@
       "source": [
         "### Controllo dei Valori Mancanti e Tipi di Dati\n",
         "\n",
-        "Prima di procedere ad analisi statistiche pi\u00f9 approfondite, \u00e8 fondamentale verificare se vi sono valori mancanti (NaN) che richiederebbero tecniche di imputazione, e controllare i tipi di dati associati a ciascuna colonna."
+        "Prima di procedere ad analisi statistiche più approfondite, è fondamentale verificare se vi sono valori mancanti (NaN) che richiederebbero tecniche di imputazione, e controllare i tipi di dati associati a ciascuna colonna."
       ]
     },
     {
@@ -151,9 +151,9 @@
       "source": [
         "### Analisi del Sbilanciamento del Target (`Response`)\n",
         "\n",
-        "Il target del nostro modello \u00e8 la colonna **Response** (1 = interessato ad acquistare l'assicurazione veicolo, 0 = non interessato).\n",
+        "Il target del nostro modello è la colonna **Response** (1 = interessato ad acquistare l'assicurazione veicolo, 0 = non interessato).\n",
         "\n",
-        "Nei problemi reali di cross-selling o rilevamento frodi, le classi sono tipicamente **fortemente sbilanciate** (imbalanced). \u00c8 essenziale misurare questa proporzione: se addestrassimo un classificatore su dati sbilanciati senza opportuni accorgimenti, il modello potrebbe massimizzare l'accuratezza banale predicendo sempre la classe maggioritaria (in questo caso `0`), risultando per\u00f2 del tutto inutile per le decisioni aziendali. Visualizziamo la distribuzione delle classi."
+        "Nei problemi reali di cross-selling o rilevamento frodi, le classi sono tipicamente **fortemente sbilanciate** (imbalanced). È essenziale misurare questa proporzione: se addestrassimo un classificatore su dati sbilanciati senza opportuni accorgimenti, il modello potrebbe massimizzare l'accuratezza banale predicendo sempre la classe maggioritaria (in questo caso `0`), risultando però del tutto inutile per le decisioni aziendali. Visualizziamo la distribuzione delle classi."
       ]
     },
     {
@@ -187,8 +187,8 @@
         "Analizziamo le principali feature categoriche per verificare l'esistenza di pattern evidenti:\n",
         "- `Gender` (Sesso dell'acquirente)\n",
         "- `Driving_License` (Possesso della patente)\n",
-        "- `Previously_Insured` (Se possiede gi\u00e0 un veicolo assicurato)\n",
-        "- `Vehicle_Age` (Et\u00e0 del veicolo)\n",
+        "- `Previously_Insured` (Se possiede già un veicolo assicurato)\n",
+        "- `Vehicle_Age` (Età del veicolo)\n",
         "- `Vehicle_Damage` (Danni passati al veicolo)"
       ]
     },
@@ -222,11 +222,11 @@
         "### Analisi delle Feature Numeriche Continue\n",
         "\n",
         "Ora esaminiamo le variabili continue:\n",
-        "- `Age`: Et\u00e0 dell'assicurato.\n",
+        "- `Age`: Età dell'assicurato.\n",
         "- `Annual_Premium`: Premio annuale da pagare.\n",
         "- `Vintage`: Giorni trascorsi dall'inizio del rapporto di clientela.\n",
         "\n",
-        "Visualizziamo prima le statistiche descrittive generali e poi le densit\u00e0 di distribuzione (KDE) condizionate alla classe target."
+        "Visualizziamo prima le statistiche descrittive generali e poi le densità di distribuzione (KDE) condizionate alla classe target."
       ]
     },
     {
@@ -243,7 +243,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Le densit\u00e0 condizionate ci aiutano a verificare se ci sono differenze significative nella distribuzione delle feature continue tra chi risponde positivamente (`Response=1`) e chi negativamente (`Response=0`)."
+        "Le densità condizionate ci aiutano a verificare se ci sono differenze significative nella distribuzione delle feature continue tra chi risponde positivamente (`Response=1`) e chi negativamente (`Response=0`)."
       ]
     },
     {
@@ -257,14 +257,14 @@
         "for i, col in enumerate(numerical_cols):\n",
         "    if col == 'Annual_Premium':\n",
         "        # La feature Annual_Premium ha outlier estremi a destra. Per visualizzare\n",
-        "        # la densit\u00e0 principale limitiamo l'asse x a 100.000\n",
+        "        # la densità principale limitiamo l'asse x a 100.000\n",
         "        sns.kdeplot(ax=axes[i], x=col, hue='Response', data=train_df, fill=True, common_norm=False, palette='crest')\n",
         "        axes[i].set_xlim(0, 100000)\n",
         "    else:\n",
         "        sns.kdeplot(ax=axes[i], x=col, hue='Response', data=train_df, fill=True, common_norm=False, palette='crest')\n",
-        "    axes[i].set_title(f\"Densit\u00e0 di {col} per classe Target\")\n",
+        "    axes[i].set_title(f\"Densità di {col} per classe Target\")\n",
         "    axes[i].set_xlabel(col)\n",
-        "    axes[i].set_ylabel(\"Densit\u00e0\")\n",
+        "    axes[i].set_ylabel(\"Densità\")\n",
         "\n",
         "plt.tight_layout()\n",
         "plt.show()\n"
@@ -276,8 +276,8 @@
       "source": [
         "## 2. Preprocessing dei Dati e Prevenzione del Data Leakage\n",
         "\n",
-        "### \u26a0\ufe0f Che cos'\u00e8 il Data Leakage?\n",
-        "Il **Data Leakage** si verifica quando le informazioni provenienti dal validation o dal test set \"fuggono\" nel training set durante le fasi di preprocessing. Ad esempio, se calcoliamo la media e la deviazione standard per standardizzare (`StandardScaler`) l'intero dataset *prima* di suddividerlo, la media del training conterr\u00e0 informazioni del test. Questo falsa la stima dell'errore di generalizzazione del modello durante la validazione.\n",
+        "### ⚠️ Che cos'è il Data Leakage?\n",
+        "Il **Data Leakage** si verifica quando le informazioni provenienti dal validation o dal test set \"fuggono\" nel training set durante le fasi di preprocessing. Ad esempio, se calcoliamo la media e la deviazione standard per standardizzare (`StandardScaler`) l'intero dataset *prima* di suddividerlo, la media del training conterrà informazioni del test. Questo falsa la stima dell'errore di generalizzazione del modello durante la validazione.\n",
         "\n",
         "### Best Practices\n",
         "1. **Dividere il dataset subito**: Eseguiamo il `train_test_split` prima di qualsiasi manipolazione.\n",
@@ -285,7 +285,7 @@
         "3. **Encoding delle variabili categoriche**:\n",
         "   - `Gender`: Mappatura binaria (`Male` -> 1, `Female` -> 0).\n",
         "   - `Vehicle_Damage`: Mappatura binaria (`Yes` -> 1, `No` -> 0).\n",
-        "   - `Vehicle_Age`: Mappatura ordinale poich\u00e9 c'\u00e8 una chiara gerarchia (`< 1 Year` -> 0, `1-2 Year` -> 1, `> 2 Years` -> 2)."
+        "   - `Vehicle_Age`: Mappatura ordinale poiché c'è una chiara gerarchia (`< 1 Year` -> 0, `1-2 Year` -> 1, `> 2 Years` -> 2)."
       ]
     },
     {
@@ -364,11 +364,11 @@
         "\n",
         "### Come gestire lo sbilanciamento delle classi?\n",
         "Per contrastare lo sbilanciamento delle classi, utilizzeremo due approcci:\n",
-        "1. **Pesi delle Classi Bilanciati**: Gli algoritmi scikit-learn supportano l'argomento `class_weight='balanced'`, che assegna penalit\u00e0 pi\u00f9 elevate agli errori sulla classe minoritaria. La formula per il peso della classe $j$ \u00e8:\n",
+        "1. **Pesi delle Classi Bilanciati**: Gli algoritmi scikit-learn supportano l'argomento `class_weight='balanced'`, che assegna penalità più elevate agli errori sulla classe minoritaria. La formula per il peso della classe $j$ è:\n",
         "   $$w_j = \\frac{N}{\\text{n\\_classes} \\times N_j}$$\n",
-        "   dove $N$ \u00e8 il numero totale di campioni e $N_j$ \u00e8 il numero di campioni della classe $j$.\n",
-        "2. **Valutazione con metriche robuste**: L'accuratezza \u00e8 fuorviante. Utilizzeremo:\n",
-        "   - **ROC AUC**: Misura la capacit\u00e0 globale di discriminazione a qualsiasi soglia di classificazione.\n",
+        "   dove $N$ è il numero totale di campioni e $N_j$ è il numero di campioni della classe $j$.\n",
+        "2. **Valutazione con metriche robuste**: L'accuratezza è fuorviante. Utilizzeremo:\n",
+        "   - **ROC AUC**: Misura la capacità globale di discriminazione a qualsiasi soglia di classificazione.\n",
         "   - **F1-Score**: La media armonica di Precision e Recall.\n",
         "     $$F_1 = 2 \\times \\frac{\\text{Precision} \\times \\text{Recall}}{\\text{Precision} + \\text{Recall}}$$\n",
         "\n",
@@ -491,7 +491,7 @@
       "source": [
         "## 4. Visualizzazione Grafica delle Curve di Performance\n",
         "\n",
-        "Confrontiamo graficamente le curve **ROC** e **Precision-Recall** per i tre modelli principali bilanciati. Questo ci permetter\u00e0 di confrontare il comportamento dei modelli al variare delle soglie di decisione commerciale."
+        "Confrontiamo graficamente le curve **ROC** e **Precision-Recall** per i tre modelli principali bilanciati. Questo ci permetterà di confrontare il comportamento dei modelli al variare delle soglie di decisione commerciale."
       ]
     },
     {
@@ -520,7 +520,7 @@
         "for name in [\"Logistic Regression (Bilanciata)\", \"HistGradientBoosting (Bilanciata)\", \"Random Forest (Bilanciato)\"]:\n",
         "    precision, recall, _ = precision_recall_curve(y_val, results[name][\"y_pred_proba\"])\n",
         "    plt.plot(recall, precision, label=f\"{name} (F1 = {results[name]['F1 Score']:.3f})\")\n",
-        "plt.xlabel(\"Recall (Sensibilit\u00e0)\")\n",
+        "plt.xlabel(\"Recall (Sensibilità)\")\n",
         "plt.ylabel(\"Precision (Precisione)\")\n",
         "plt.title(\"Curve Precision-Recall a confronto\")\n",
         "plt.legend(loc=\"lower left\")\n",
@@ -535,7 +535,7 @@
       "source": [
         "### Analisi delle Matrici di Confusione\n",
         "\n",
-        "Confrontiamo la Matrice di Confusione tra la versione **Sbilanciata** (standard) e quella **Bilanciata** (pesata) di `HistGradientBoosting`. Questo contrasto evidenzier\u00e0 graficamente come varia la capacit\u00e0 del modello di intercettare i clienti interessati."
+        "Confrontiamo la Matrice di Confusione tra la versione **Sbilanciata** (standard) e quella **Bilanciata** (pesata) di `HistGradientBoosting`. Questo contrasto evidenzierà graficamente come varia la capacità del modello di intercettare i clienti interessati."
       ]
     },
     {
@@ -568,16 +568,16 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### \ud83d\udca1 Interpretazione Aziendale delle Metriche\n",
+        "### 💡 Interpretazione Aziendale delle Metriche\n",
         "\n",
-        "- **Modello Sbilanciato (Soglia standard 0.5)**: Prevede quasi sempre `0` a causa del forte sbilanciamento. Sebbene l'accuratezza totale sia elevata (~88%), commette un numero enorme di falsi negativi (manca quasi tutti i clienti interessati). Dal punto di vista del business, questa soluzione non genera valore perch\u00e9 non individua le opportunit\u00e0 di cross-selling.\n",
-        "- **Modello Bilanciato**: Penalizzando gli errori sulla classe minoritaria, il classificatore abbassa la soglia per etichettare un cliente come `1`. Questo aumenta in modo sensibile il **Recall** (identificando la stragrande maggioranza dei potenziali clienti interessati), pur accettando una precisione pi\u00f9 bassa (pi\u00f9 falsi positivi, ovvero contatti a vuoto).\n",
+        "- **Modello Sbilanciato (Soglia standard 0.5)**: Prevede quasi sempre `0` a causa del forte sbilanciamento. Sebbene l'accuratezza totale sia elevata (~88%), commette un numero enorme di falsi negativi (manca quasi tutti i clienti interessati). Dal punto di vista del business, questa soluzione non genera valore perché non individua le opportunità di cross-selling.\n",
+        "- **Modello Bilanciato**: Penalizzando gli errori sulla classe minoritaria, il classificatore abbassa la soglia per etichettare un cliente come `1`. Questo aumenta in modo sensibile il **Recall** (identificando la stragrande maggioranza dei potenziali clienti interessati), pur accettando una precisione più bassa (più falsi positivi, ovvero contatti a vuoto).\n",
         "\n",
-        "### Come sfruttare le probabilit\u00e0 predette? (Leads Prioritization)\n",
-        "Nello scenario industriale reale, il team di marketing non contatter\u00e0 i clienti usando una decisione rigida (0/1). Invece:\n",
-        "1. Estrarr\u00e0 la probabilit\u00e0 di acquisto per ogni cliente tramite `predict_proba`.\n",
-        "2. Ordiner\u00e0 i contatti dal pi\u00f9 propense al meno propense (ranking di decrescente probabilit\u00e0).\n",
-        "3. Decider\u00e0 di contattare ad esempio solo il primo 20% dei clienti. Poich\u00e9 il modello `HistGradientBoosting` ha un ROC AUC di ~0.85, questo top 20% conterr\u00e0 la stragrande maggioranza di tutti i potenziali acquirenti, permettendo di risparmiare l'80% del budget di marketing a fronte di una perdita minima di opportunit\u00e0 di vendita."
+        "### Come sfruttare le probabilità predette? (Leads Prioritization)\n",
+        "Nello scenario industriale reale, il team di marketing non contatterà i clienti usando una decisione rigida (0/1). Invece:\n",
+        "1. Estrarrà la probabilità di acquisto per ogni cliente tramite `predict_proba`.\n",
+        "2. Ordinerà i contatti dal più propense al meno propense (ranking di decrescente probabilità).\n",
+        "3. Deciderà di contattare ad esempio solo il primo 20% dei clienti. Poiché il modello `HistGradientBoosting` ha un ROC AUC di ~0.85, questo top 20% conterrà la stragrande maggioranza di tutti i potenziali acquirenti, permettendo di risparmiare l'80% del budget di marketing a fronte di una perdita minima di opportunità di vendita."
       ]
     },
     {
@@ -593,7 +593,7 @@
         "# Selezioniamo il miglior modello addestrato (HistGradientBoosting Bilanciato)\n",
         "best_model = models[\"HistGradientBoosting (Bilanciata)\"]\n",
         "\n",
-        "# Generiamo probabilit\u00e0 e classi predette\n",
+        "# Generiamo probabilità e classi predette\n",
         "test_predictions_proba = best_model.predict_proba(X_test_preprocessed)[:, 1]\n",
         "test_predictions_binary = best_model.predict(X_test_preprocessed)\n",
         "\n",
@@ -644,4 +644,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}

From c6c9761bad76219944d83e1c7e9ff0f342aefa40 Mon Sep 17 00:00:00 2001
From: Spatariu Rares <81376159+SpatariuRares@users.noreply.github.com>
Date: Wed, 1 Jul 2026 23:24:59 +0200
Subject: [PATCH 9/9] refactor: remove unused notebook pedagogical enrichment
 scripts and documentation

---
 .../notebook-pedagogical-enrichment/SKILL.md  |  94 ------------
 .../scripts/enrich_runner.py                  | 142 ------------------
 .../scripts/notebook_helper.py                |  60 --------
 .../scripts/notebook_metric_extractor.py      | 113 --------------
 4 files changed, 409 deletions(-)
 delete mode 100644 .agents/notebook-pedagogical-enrichment/SKILL.md
 delete mode 100644 .agents/notebook-pedagogical-enrichment/scripts/enrich_runner.py
 delete mode 100755 .agents/notebook-pedagogical-enrichment/scripts/notebook_helper.py
 delete mode 100644 .agents/notebook-pedagogical-enrichment/scripts/notebook_metric_extractor.py

diff --git a/.agents/notebook-pedagogical-enrichment/SKILL.md b/.agents/notebook-pedagogical-enrichment/SKILL.md
deleted file mode 100644
index 3731c34..0000000
--- a/.agents/notebook-pedagogical-enrichment/SKILL.md
+++ /dev/null
@@ -1,94 +0,0 @@
----
-name: notebook-pedagogical-enrichment
-description: |
-  Enrich Jupyter notebooks with pedagogical markdown explanations for students.
-  Trigger when the user asks to explain, document, add markdown, or format a Jupyter notebook (.ipynb) to make it suitable for students or educational purposes.
----
-
-# Notebook Pedagogical Enrichment
-
-A skill to transform technical Jupyter notebooks into structured, narrative-driven educational resources designed for students.
-
-## When to Use
-Trigger this skill whenever the user asks to:
-- Explain the code blocks in a Jupyter notebook (`.ipynb`).
-- Add Markdown formatted notes or explanations for students.
-- Document step-by-step procedures in a notebook.
-- Compare different Machine Learning algorithms (e.g., OLS vs Ridge vs Lasso) inside a notebook for teaching.
-
-## Step-by-Step Workflow
-
-### Step 1: Inspect Notebook Cells
-Use a Python script to read the target `.ipynb` file and print a summary of all cells, their types, and the first lines of their source code. Do not read the entire file as a raw text view if it is very large (due to HTML outputs).
-Example inspection snippet:
-```python
-import json
-with open("notebook.ipynb", "r") as f:
-    nb = json.load(f)
-for i, cell in enumerate(nb["cells"]):
-    source = "".join(cell.get("source", []))
-    print(f"Cell {i:02d} ({cell['cell_type']}): {source[:80]}...")
-```
-Or use the local helper script:
-```bash
-python3 .agents/notebook-pedagogical-enrichment/scripts/notebook_helper.py inspect <path_to_notebook.ipynb>
-```
-
-### Step 2: Compute Exact Metrics (Dry Run)
-Before writing explanations, run the notebook's code (using the workspace virtual environment, e.g., `.venv/bin/python3`) to obtain the exact training/testing scores (like MSE, $R^2$, accuracy, etc.).
-You can use the local extractor utility to automatically execute the notebook and dump all cell outputs and metrics:
-```bash
-python3 .agents/notebook-pedagogical-enrichment/scripts/notebook_metric_extractor.py <path_to_notebook.ipynb>
-```
-Reporting exact numbers (e.g., *“the test $R^2$ is 0.217 for OLS but 0.994 for Lasso”*) makes the explanations extremely authentic and helpful.
-
-### Step 3: Write Rich Markdown Explanations
-Explanations must follow best practices in technical writing and pedagogy:
-- **Use Clear Formatting**: Use bold text, bullet points, and code blocks.
-- **Explain the "Why"**: Don't just say *what* the code does; explain *why* we do it (e.g., why we scale features, why a random seed is set, why data leakage is bad).
-- **Use Math Formulas**: Use LaTeX syntax (e.g., `$$\text{Loss} = \text{MSE} + \alpha \sum_{j=1}^{p} w_j^2$$`) to describe the loss functions and penalties.
-- **Explain Model Performance Contrast**:
-  - **OLS**: Explain overfitting (memorization of training data, poor generalization).
-  - **Ridge (L2)**: Explain weight shrinkage without zeroing, and why it might not be enough when there are many non-informative features.
-  - **Lasso (L1)**: Explain feature selection (zeroing out uninformative weights) and why it works so well for noisy data.
-- **Learning Curves**: Explain how to diagnose bias/variance by looking at the gap and convergence of training and validation scores.
-
-### Step 4: Update the Notebook Programmatically
-Always edit Jupyter notebooks programmatically. You can use the generic enrichment script to apply Markdown/Code cell insertions and replacements from a JSON specification:
-```bash
-python3 .agents/notebook-pedagogical-enrichment/scripts/enrich_runner.py -n <path_to_notebook.ipynb> -s <path_to_spec.json>
-```
-
-#### Spec JSON File Format Example:
-```json
-[
-  {
-    "match_type": "prefix",
-    "target": "RANDOM_SEED = 2",
-    "action": "insert_after",
-    "cell_type": "markdown",
-    "source": [
-      "### Configurazione dell'Ambiente\n",
-      "Prima di iniziare importiamo..."
-    ]
-  },
-  {
-    "match_type": "exact",
-    "target": "distorsion = sum(...)",
-    "action": "replace",
-    "cell_type": "markdown",
-    "source": "Nuovo testo esplicativo..."
-  }
-]
-```
-
-Specifications support:
-- `match_type`: `prefix`, `exact`, `contains`
-- `action`: `insert_before`, `insert_after`, `replace`, `append`
-- `cell_type`: `markdown` or `code`
-
-### Step 5: Validation
-Verify that the output notebook is valid JSON and loads properly:
-```bash
-python3 -c 'import json; json.load(open("notebook.ipynb"))'
-```
diff --git a/.agents/notebook-pedagogical-enrichment/scripts/enrich_runner.py b/.agents/notebook-pedagogical-enrichment/scripts/enrich_runner.py
deleted file mode 100644
index 9596ad9..0000000
--- a/.agents/notebook-pedagogical-enrichment/scripts/enrich_runner.py
+++ /dev/null
@@ -1,142 +0,0 @@
-#!/usr/bin/env python3
-import json
-import argparse
-import sys
-import os
-
-def make_markdown_cell(text):
-    lines = [line + "\n" for line in text.split("\n")]
-    if lines and lines[-1] == "\n":
-        lines.pop()
-    elif lines:
-        lines[-1] = lines[-1].rstrip("\n")
-    return {"cell_type": "markdown", "metadata": {}, "source": lines}
-
-def make_code_cell(text):
-    lines = [line + "\n" for line in text.split("\n")]
-    if lines and lines[-1] == "\n":
-        lines.pop()
-    elif lines:
-        lines[-1] = lines[-1].rstrip("\n")
-    return {
-        "cell_type": "code",
-        "execution_count": None,
-        "metadata": {},
-        "outputs": [],
-        "source": lines
-    }
-
-def match_cell(cell, target, match_type, cell_type_filter=None):
-    cell_type = cell.get("cell_type", "")
-    if cell_type_filter and cell_type != cell_type_filter:
-        return False
-    source_text = "".join(cell.get("source", [])).strip()
-    target_clean = target.strip()
-    
-    if match_type == "prefix":
-        return source_text.startswith(target_clean)
-    elif match_type == "contains":
-        return target_clean in source_text
-    elif match_type == "exact":
-        return source_text == target_clean
-    return False
-
-def process_enrichment(notebook_cells, spec_items):
-    new_cells = []
-    
-    for cell in notebook_cells:
-        matched_specs = []
-        for spec in spec_items:
-            if spec.get("action") == "append":
-                continue
-            
-            # Extract matching rules
-            target = spec.get("target", "")
-            match_type = spec.get("match_type", "prefix")
-            cell_type_filter = spec.get("cell_type_filter")
-            
-            if match_cell(cell, target, match_type, cell_type_filter):
-                matched_specs.append(spec)
-                
-        if not matched_specs:
-            new_cells.append(cell)
-            continue
-            
-        # Process the first match
-        spec = matched_specs[0]
-        action = spec.get("action", "insert_after")
-        new_cell_type = spec.get("cell_type", "markdown")
-        source_data = spec.get("source", "")
-        
-        source_text = "".join(source_data) if isinstance(source_data, list) else source_data
-        
-        if new_cell_type == "markdown":
-            enriched_cell = make_markdown_cell(source_text)
-        else:
-            enriched_cell = make_code_cell(source_text)
-            
-        if action == "replace":
-            new_cells.append(enriched_cell)
-        elif action == "insert_before":
-            new_cells.append(enriched_cell)
-            new_cells.append(cell)
-        elif action == "insert_after":
-            new_cells.append(cell)
-            new_cells.append(enriched_cell)
-            
-    # Process append specifications
-    for spec in spec_items:
-        if spec.get("action") == "append":
-            new_cell_type = spec.get("cell_type", "markdown")
-            source_data = spec.get("source", "")
-            source_text = "".join(source_data) if isinstance(source_data, list) else source_data
-            
-            if new_cell_type == "markdown":
-                enriched_cell = make_markdown_cell(source_text)
-            else:
-                enriched_cell = make_code_cell(source_text)
-            new_cells.append(enriched_cell)
-            
-    return new_cells
-
-def main():
-    parser = argparse.ArgumentParser(description="Enrich a Jupyter notebook with pedagogical contents.")
-    parser.add_argument("-n", "--notebook", required=True, help="Path to the target notebook (.ipynb) file")
-    parser.add_argument("-s", "--spec", required=True, help="Path to the JSON specifications file")
-    parser.add_argument("-o", "--output", help="Path to save the enriched notebook (defaults to overwriting target)")
-    
-    args = parser.parse_args()
-    
-    if not os.path.exists(args.notebook):
-        print(f"Error: Notebook file '{args.notebook}' not found.", file=sys.stderr)
-        sys.exit(1)
-        
-    if not os.path.exists(args.spec):
-        print(f"Error: Spec file '{args.spec}' not found.", file=sys.stderr)
-        sys.exit(1)
-        
-    try:
-        with open(args.notebook, "r", encoding="utf-8") as f:
-            nb = json.load(f)
-            
-        with open(args.spec, "r", encoding="utf-8") as f:
-            spec_items = json.load(f)
-            
-        if not isinstance(spec_items, list):
-            print("Error: Spec file must contain a JSON list of specifications.", file=sys.stderr)
-            sys.exit(1)
-            
-        nb["cells"] = process_enrichment(nb.get("cells", []), spec_items)
-        
-        output_path = args.output if args.output else args.notebook
-        with open(output_path, "w", encoding="utf-8") as f:
-            json.dump(nb, f, indent=2)
-            
-        print(f"Success: Notebook '{output_path}' enriched successfully.")
-        
-    except Exception as e:
-        print(f"Error during enrichment: {e}", file=sys.stderr)
-        sys.exit(1)
-
-if __name__ == "__main__":
-    main()
diff --git a/.agents/notebook-pedagogical-enrichment/scripts/notebook_helper.py b/.agents/notebook-pedagogical-enrichment/scripts/notebook_helper.py
deleted file mode 100755
index e512f36..0000000
--- a/.agents/notebook-pedagogical-enrichment/scripts/notebook_helper.py
+++ /dev/null
@@ -1,60 +0,0 @@
-#!/usr/bin/env python3
-import sys
-import json
-
-def inspect_notebook(nb_path):
-    try:
-        with open(nb_path, "r", encoding="utf-8") as f:
-            nb = json.load(f)
-        
-        print(f"Notebook: {nb_path}")
-        print(f"Format: v{nb.get('nbformat')}.{nb.get('nbformat_minor')}")
-        print(f"Total cells: {len(nb.get('cells', []))}")
-        print("-" * 60)
-        
-        for i, cell in enumerate(nb.get("cells", [])):
-            cell_type = cell.get("cell_type", "unknown")
-            source_lines = cell.get("source", [])
-            source_text = "".join(source_lines).strip()
-            preview = source_text.split("\n")[0] if source_text else "[Empty]"
-            print(f"Cell {i:02d} | Type: {cell_type:<10} | Preview: {preview[:80]}")
-            
-    except Exception as e:
-        print(f"Error inspecting notebook: {e}", file=sys.stderr)
-
-def clear_outputs(nb_path):
-    try:
-        with open(nb_path, "r", encoding="utf-8") as f:
-            nb = json.load(f)
-            
-        for cell in nb.get("cells", []):
-            if cell.get("cell_type") == "code":
-                cell["outputs"] = []
-                cell["execution_count"] = None
-                
-        with open(nb_path, "w", encoding="utf-8") as f:
-            json.dump(nb, f, indent=2)
-        print(f"Cleared all outputs in {nb_path} successfully.")
-    except Exception as e:
-        print(f"Error clearing outputs: {e}", file=sys.stderr)
-
-def main():
-    if len(sys.argv) < 3:
-        print("Usage:")
-        print("  notebook_helper.py inspect <path_to_notebook.ipynb>")
-        print("  notebook_helper.py clear-outputs <path_to_notebook.ipynb>")
-        sys.exit(1)
-        
-    cmd = sys.argv[1]
-    nb_path = sys.argv[2]
-    
-    if cmd == "inspect":
-        inspect_notebook(nb_path)
-    elif cmd == "clear-outputs":
-        clear_outputs(nb_path)
-    else:
-        print(f"Unknown command: {cmd}", file=sys.stderr)
-        sys.exit(1)
-
-if __name__ == "__main__":
-    main()
diff --git a/.agents/notebook-pedagogical-enrichment/scripts/notebook_metric_extractor.py b/.agents/notebook-pedagogical-enrichment/scripts/notebook_metric_extractor.py
deleted file mode 100644
index b8846fe..0000000
--- a/.agents/notebook-pedagogical-enrichment/scripts/notebook_metric_extractor.py
+++ /dev/null
@@ -1,113 +0,0 @@
-#!/usr/bin/env python3
-import json
-import argparse
-import sys
-import os
-import tempfile
-import subprocess
-
-def extract_metrics(nb_path):
-    # Determine the directory for the temporary file
-    nb_dir = os.path.dirname(os.path.abspath(nb_path))
-    
-    # Create a temporary file in the same directory to prevent path/relative-import issues
-    with tempfile.NamedTemporaryFile(suffix=".ipynb", dir=nb_dir, delete=False) as f:
-        temp_path = f.name
-        
-    try:
-        print(f"Executing {nb_path} to collect exact metrics...")
-        
-        # Run nbconvert inside the same virtual environment using sys.executable
-        cmd = [
-            sys.executable,
-            "-m",
-            "jupyter",
-            "nbconvert",
-            "--to",
-            "notebook",
-            "--execute",
-            nb_path,
-            "--output",
-            temp_path
-        ]
-        
-        result = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
-        if result.returncode != 0:
-            print("Error: Notebook execution failed.", file=sys.stderr)
-            print(result.stderr, file=sys.stderr)
-            sys.exit(1)
-            
-        with open(temp_path, "r", encoding="utf-8") as f:
-            nb = json.load(f)
-            
-        print("\n" + "=" * 60)
-        print(f" EXECUTION METRICS FOR: {nb_path}")
-        print("=" * 60 + "\n")
-        
-        for i, cell in enumerate(nb.get("cells", [])):
-            if cell.get("cell_type") != "code":
-                continue
-                
-            source_lines = cell.get("source", [])
-            source_text = "".join(source_lines).strip()
-            if not source_text:
-                continue
-                
-            outputs = cell.get("outputs", [])
-            if not outputs:
-                continue
-                
-            # Print a concise preview of the code
-            code_preview = "\n  ".join(source_text.split("\n")[:3])
-            if len(source_text.split("\n")) > 3:
-                code_preview += "\n  ..."
-                
-            print(f"Cell {i:02d} Code:\n  {code_preview}\n")
-            print("Outputs:")
-            
-            for out in outputs:
-                out_type = out.get("output_type")
-                
-                if out_type == "stream":
-                    # stdout/stderr streams
-                    text_lines = out.get("text", [])
-                    text = "".join(text_lines) if isinstance(text_lines, list) else out.get("text", "")
-                    prefix = f"  [{out.get('name', 'stream')}]: "
-                    indented = "\n".join([f"    {line}" for line in text.strip().split("\n")])
-                    print(f"{prefix}\n{indented}")
-                    
-                elif out_type in ("execute_result", "display_data"):
-                    # cell returned values or rich output
-                    data = out.get("data", {})
-                    if "text/plain" in data:
-                        text_lines = data["text/plain"]
-                        text = "".join(text_lines) if isinstance(text_lines, list) else data["text/plain"]
-                        indented = "\n".join([f"    {line}" for line in text.strip().split("\n")])
-                        print(f"  [result]:\n{indented}")
-                        
-                elif out_type == "error":
-                    # errors during execution
-                    ename = out.get("ename", "Error")
-                    evalue = out.get("evalue", "")
-                    traceback = "\n".join(out.get("traceback", []))
-                    print(f"  [error] {ename}: {evalue}\n{traceback}")
-                    
-            print("-" * 60)
-            
-    finally:
-        if os.path.exists(temp_path):
-            os.remove(temp_path)
-
-def main():
-    parser = argparse.ArgumentParser(description="Execute a notebook and extract code execution outputs/metrics.")
-    parser.add_argument("notebook", help="Path to the target notebook (.ipynb) file")
-    args = parser.parse_args()
-    
-    if not os.path.exists(args.notebook):
-        print(f"Error: Notebook file '{args.notebook}' not found.", file=sys.stderr)
-        sys.exit(1)
-        
-    extract_metrics(args.notebook)
-
-if __name__ == "__main__":
-    main()