diff --git a/2022 Edition/3. Classical Algorithms/Algorytmy_Klasyczne.ipynb b/2022 Edition/3. Classical Algorithms/Algorytmy_Klasyczne.ipynb
new file mode 100644
index 0000000..2f93962
--- /dev/null
+++ b/2022 Edition/3. Classical Algorithms/Algorytmy_Klasyczne.ipynb	
@@ -0,0 +1,2497 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LaGQ5E_FJbbb"
+      },
+      "source": [
+        "<img src=\"https://drive.google.com/uc?export=view&id=1uDiRDsBQS2OijoIu83KvnqRfAOlToUk2\" style=\"width: 200px;\"/>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aMESP4RAJbbc"
+      },
+      "source": [
+        "# Classical Algorithms\n",
+        "##### Marek Jeliński\n",
+        "\n",
+        "Description:\n",
+        "- The goal of this notebook is to get familiar with classical algorithms.\n",
+        "- This notebook covers topics like Linear Regression, Logistic Regression, Decision Trees, KNN and SVM.\n",
+        "- You will learn how simple it is to create simple model with the help of scikit-learn.\n",
+        "\n",
+        "<a href=\"https://githubtocolab.com/Gradient-PG/gradient-live-session/blob/main/2022%20Edition/3.%20Classical%20Algorithms/Algorytmy_Klasyczne.ipynb">Run in Google Colab</a>\n",
+        "## Table of Contents \n",
+        "**[1. Linear Regression](#1.-Section-1)<br>**\n",
+        "&nbsp;&nbsp;&nbsp;&nbsp;[1.1 Data Initialization](#1.1-Subsection-1.1)<br>\n",
+        "&nbsp;&nbsp;&nbsp;&nbsp;[1.2 Loss and prediction function](#1.2-Subsection-1.2)<br>\n",
+        "&nbsp;&nbsp;&nbsp;&nbsp;[1.3 Model Training](#1.3-Subsection-1.3)<br>\n",
+        "&nbsp;&nbsp;&nbsp;&nbsp;[1.4 Results](#1.4-Subsection-1.4)<br>\n",
+        "**[2. Logistic Regression](#2.-Section-2)<br>**\n",
+        "&nbsp;&nbsp;&nbsp;&nbsp;[2.1 Data](#2.1-Subsection-2.1)<br>\n",
+        "&nbsp;&nbsp;&nbsp;&nbsp;[2.2 Logitic Regression Model](#2.2-Subsection-2.2)<br>\n",
+        "&nbsp;&nbsp;&nbsp;&nbsp;[2.3 Evaluation](#2.3-Subsection-2.3)<br>\n",
+        "**[3. Decision Trees](#3.-Section-3)<br>**\n",
+        "&nbsp;&nbsp;&nbsp;&nbsp;[3.1 Data](#3.1-Subsection-3.1)<br>\n",
+        "&nbsp;&nbsp;&nbsp;&nbsp;[3.2 Decision Tree Model](#3.2-Subsection-3.2)<br>\n",
+        "&nbsp;&nbsp;&nbsp;&nbsp;[3.3 Evaluation](#3.3-Subsection-3.3)<br>\n",
+        "**[4. K-Nearest Neigbor](#4.-Section-4)<br>**\n",
+        "&nbsp;&nbsp;&nbsp;&nbsp;[4.1 Data](#4.1-Subsection-4.1)<br>\n",
+        "&nbsp;&nbsp;&nbsp;&nbsp;[4.2 KNN Model](#4.2-Subsection-4.2)<br>\n",
+        "&nbsp;&nbsp;&nbsp;&nbsp;[4.3 Evaluation](#4.3-Subsection-4.3)<br>\n",
+        "**[4. SVM](#5.-Section-5)<br>**\n",
+        "&nbsp;&nbsp;&nbsp;&nbsp;[5.1 Data](#5.1-Subsection-5.1)<br>\n",
+        "&nbsp;&nbsp;&nbsp;&nbsp;[5.2 Functions for mapping decision boundaries](#5.2-Subsection-5.2)<br>\n",
+        "&nbsp;&nbsp;&nbsp;&nbsp;[5.3 PCA dimensionality reduction](#5.3-Subsection-5.3)<br>\n",
+        "&nbsp;&nbsp;&nbsp;&nbsp;[5.4 Support Vector Machine (SVM) linear model](#5.4-Subsection-5.4)<br>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4dARF7A3Jbbd"
+      },
+      "source": [
+        "---\n",
+        "<a name=\"1.-Section-1\"></a>\n",
+        "# 1. Linear Regression"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The formula for linear regression with a single independent variable is,\n",
+        "\n",
+        "  y = W*x + b\n",
+        "\n",
+        "where,<br>\n",
+        "y is the dependent variable,<br>\n",
+        "x is the independent variable,<br>\n",
+        "W is the weight, and<br>\n",
+        "b is the bias.<br>\n",
+        "\n",
+        "We will be using TensorFlow to find the value of weight w and bias b that can best predict the value y given x."
+      ],
+      "metadata": {
+        "id": "5blX8Rzbbtdg"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Importing necessary libraries"
+      ],
+      "metadata": {
+        "id": "NCRfzS6pZJCL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 64,
+      "metadata": {
+        "id": "nFq3I0maJbbd"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import tensorflow as tf\n",
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n5asnwU1Jbbe"
+      },
+      "source": [
+        "<a name=\"1.1-Subsection-1.1\"></a>\n",
+        "## 1.1 Data Initialization"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HezOBd-lJbbe"
+      },
+      "source": [
+        "Generate some random data for training the Linear Regression Model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 65,
+      "metadata": {
+        "id": "dOzcSiAGJbbe"
+      },
+      "outputs": [],
+      "source": [
+        "# the training dataset\n",
+        "x_train = np.linspace(0, 10, 100)\n",
+        "y_train = x_train + np.random.normal(0,1,100)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Visualization of the training data."
+      ],
+      "metadata": {
+        "id": "IqPOCwQdcqwq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.scatter(x_train, y_train)\n",
+        "plt.xlabel('x')\n",
+        "plt.ylabel('y')\n",
+        "plt.title(\"Training Data\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        },
+        "id": "iiskjdZZcst3",
+        "outputId": "ecd8a390-b740-4604-9dfa-9dbf2243e688"
+      },
+      "execution_count": 66,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "19Jl8QeHJbbe"
+      },
+      "source": [
+        "<a name=\"1.2-Subsection-1.2\"></a>\n",
+        "## 1.2 Loss and prediction function\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let’s define the weight and bias. This is usually set randomly."
+      ],
+      "metadata": {
+        "id": "ByxGnM5xoiPL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# declare weights\n",
+        "weight = tf.Variable(0.)\n",
+        "bias = tf.Variable(0.)"
+      ],
+      "metadata": {
+        "id": "lUCBW977oljY"
+      },
+      "execution_count": 67,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "After this, let’s define the linear regression function to get predicted values of y, or y_pred."
+      ],
+      "metadata": {
+        "id": "o-DZAJMLouPu"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define linear regression expression y\n",
+        "def linreg(x):\n",
+        "  y = weight*x + bias\n",
+        "  return y"
+      ],
+      "metadata": {
+        "id": "QSzZ8xZ3oww7"
+      },
+      "execution_count": 68,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now, define the loss function, which in this case is MSE."
+      ],
+      "metadata": {
+        "id": "bpdIQ1x6o3y3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define loss function (MSE)\n",
+        "def squared_error(y_pred, y_true):\n",
+        "  return tf.reduce_mean(tf.square(y_pred - y_true))"
+      ],
+      "metadata": {
+        "id": "q9ltFjGEo4p_"
+      },
+      "execution_count": 69,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PcxhFSBnfpsh"
+      },
+      "source": [
+        "<a name=\"1.3-Subsection-1.3\"></a>\n",
+        "## 1.3 Model Training\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now that you have all functions defined, the next step is to train the model.\n",
+        "<br>\n",
+        "We will be using gradient tape here to keep track of the loss after every epoch and then to differentiate that loss with respect to the weight and bias to get gradients.\n",
+        "<br>\n",
+        "This gradient will then be multiplied with the learning rate and subtracted from the existing value of parameters to get new optimized parameter values."
+      ],
+      "metadata": {
+        "id": "Xz8-U4EMfpsi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 70,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nsEM0fTtfpsi",
+        "outputId": "edfdfc52-d55b-46a5-a360-2146a9c6e9b9"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch count 0: Loss value: 36.891902923583984\n",
+            "Epoch count 1: Loss value: 4.526712894439697\n",
+            "Epoch count 2: Loss value: 1.3166717290878296\n",
+            "Epoch count 3: Loss value: 0.9982717037200928\n",
+            "Epoch count 4: Loss value: 0.966667890548706\n",
+            "Epoch count 5: Loss value: 0.9635093808174133\n",
+            "Epoch count 6: Loss value: 0.9631720781326294\n",
+            "Epoch count 7: Loss value: 0.9631150960922241\n",
+            "Epoch count 8: Loss value: 0.9630857706069946\n",
+            "Epoch count 9: Loss value: 0.9630596041679382\n",
+            "Epoch count 10: Loss value: 0.9630341529846191\n",
+            "Epoch count 11: Loss value: 0.9630088806152344\n",
+            "Epoch count 12: Loss value: 0.9629837274551392\n",
+            "Epoch count 13: Loss value: 0.9629590511322021\n",
+            "Epoch count 14: Loss value: 0.9629345536231995\n",
+            "Epoch count 15: Loss value: 0.9629100561141968\n",
+            "Epoch count 16: Loss value: 0.9628860950469971\n",
+            "Epoch count 17: Loss value: 0.9628622531890869\n",
+            "Epoch count 18: Loss value: 0.9628385901451111\n",
+            "Epoch count 19: Loss value: 0.9628151655197144\n",
+            "Epoch count 20: Loss value: 0.9627922177314758\n",
+            "Epoch count 21: Loss value: 0.9627692699432373\n",
+            "Epoch count 22: Loss value: 0.9627465009689331\n",
+            "Epoch count 23: Loss value: 0.9627242088317871\n",
+            "Epoch count 24: Loss value: 0.9627017974853516\n",
+            "Epoch count 25: Loss value: 0.962679922580719\n",
+            "Epoch count 26: Loss value: 0.9626581072807312\n",
+            "Epoch count 27: Loss value: 0.9626364707946777\n",
+            "Epoch count 28: Loss value: 0.9626151323318481\n",
+            "Epoch count 29: Loss value: 0.9625939726829529\n",
+            "Epoch count 30: Loss value: 0.9625731706619263\n",
+            "Epoch count 31: Loss value: 0.9625524282455444\n",
+            "Epoch count 32: Loss value: 0.9625318646430969\n",
+            "Epoch count 33: Loss value: 0.962511420249939\n",
+            "Epoch count 34: Loss value: 0.9624913930892944\n",
+            "Epoch count 35: Loss value: 0.9624714851379395\n",
+            "Epoch count 36: Loss value: 0.9624517560005188\n",
+            "Epoch count 37: Loss value: 0.9624321460723877\n",
+            "Epoch count 38: Loss value: 0.9624127745628357\n",
+            "Epoch count 39: Loss value: 0.9623937010765076\n",
+            "Epoch count 40: Loss value: 0.9623748064041138\n",
+            "Epoch count 41: Loss value: 0.9623559713363647\n",
+            "Epoch count 42: Loss value: 0.9623374938964844\n",
+            "Epoch count 43: Loss value: 0.9623189568519592\n",
+            "Epoch count 44: Loss value: 0.9623008966445923\n",
+            "Epoch count 45: Loss value: 0.9622827768325806\n",
+            "Epoch count 46: Loss value: 0.9622648358345032\n",
+            "Epoch count 47: Loss value: 0.9622472524642944\n",
+            "Epoch count 48: Loss value: 0.9622296094894409\n",
+            "Epoch count 49: Loss value: 0.9622123837471008\n",
+            "Epoch count 50: Loss value: 0.9621952772140503\n",
+            "Epoch count 51: Loss value: 0.9621782898902893\n",
+            "Epoch count 52: Loss value: 0.9621614813804626\n",
+            "Epoch count 53: Loss value: 0.962144672870636\n",
+            "Epoch count 54: Loss value: 0.9621282815933228\n",
+            "Epoch count 55: Loss value: 0.9621119499206543\n",
+            "Epoch count 56: Loss value: 0.9620958566665649\n",
+            "Epoch count 57: Loss value: 0.9620797038078308\n",
+            "Epoch count 58: Loss value: 0.9620639085769653\n",
+            "Epoch count 59: Loss value: 0.9620482921600342\n",
+            "Epoch count 60: Loss value: 0.9620327949523926\n",
+            "Epoch count 61: Loss value: 0.9620173573493958\n",
+            "Epoch count 62: Loss value: 0.9620020985603333\n",
+            "Epoch count 63: Loss value: 0.9619871377944946\n",
+            "Epoch count 64: Loss value: 0.9619720578193665\n",
+            "Epoch count 65: Loss value: 0.9619573354721069\n",
+            "Epoch count 66: Loss value: 0.961942732334137\n",
+            "Epoch count 67: Loss value: 0.961928129196167\n",
+            "Epoch count 68: Loss value: 0.9619138836860657\n",
+            "Epoch count 69: Loss value: 0.9618996381759644\n",
+            "Epoch count 70: Loss value: 0.9618855118751526\n",
+            "Epoch count 71: Loss value: 0.9618716239929199\n",
+            "Epoch count 72: Loss value: 0.9618577361106873\n",
+            "Epoch count 73: Loss value: 0.9618443250656128\n",
+            "Epoch count 74: Loss value: 0.9618306756019592\n",
+            "Epoch count 75: Loss value: 0.9618172645568848\n",
+            "Epoch count 76: Loss value: 0.9618040323257446\n",
+            "Epoch count 77: Loss value: 0.9617909789085388\n",
+            "Epoch count 78: Loss value: 0.9617780447006226\n",
+            "Epoch count 79: Loss value: 0.9617652297019958\n",
+            "Epoch count 80: Loss value: 0.9617524147033691\n",
+            "Epoch count 81: Loss value: 0.9617398977279663\n",
+            "Epoch count 82: Loss value: 0.9617273807525635\n",
+            "Epoch count 83: Loss value: 0.9617149233818054\n",
+            "Epoch count 84: Loss value: 0.9617027044296265\n",
+            "Epoch count 85: Loss value: 0.9616905450820923\n",
+            "Epoch count 86: Loss value: 0.9616785645484924\n",
+            "Epoch count 87: Loss value: 0.9616668224334717\n",
+            "Epoch count 88: Loss value: 0.9616549611091614\n",
+            "Epoch count 89: Loss value: 0.9616432785987854\n",
+            "Epoch count 90: Loss value: 0.9616317749023438\n",
+            "Epoch count 91: Loss value: 0.9616202712059021\n",
+            "Epoch count 92: Loss value: 0.9616090655326843\n",
+            "Epoch count 93: Loss value: 0.9615979194641113\n",
+            "Epoch count 94: Loss value: 0.9615868926048279\n",
+            "Epoch count 95: Loss value: 0.9615759253501892\n",
+            "Epoch count 96: Loss value: 0.9615650177001953\n",
+            "Epoch count 97: Loss value: 0.9615542888641357\n",
+            "Epoch count 98: Loss value: 0.9615436792373657\n",
+            "Epoch count 99: Loss value: 0.9615331292152405\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Learning rate\n",
+        "learning_rate = 0.01\n",
+        "\n",
+        "# Number of loops for training through all your data to update the parameters\n",
+        "training_epochs = 100\n",
+        "\n",
+        "# train model\n",
+        "for epoch in range(training_epochs):\n",
+        "\n",
+        "  # Compute loss within Gradient Tape context\n",
+        "  with tf.GradientTape() as tape:\n",
+        "    y_predicted = linreg(x_train)\n",
+        "    loss = squared_error(y_predicted, y_train)\n",
+        "\n",
+        "  # Get gradients\n",
+        "  gradients = tape.gradient(loss, [weight,bias])\n",
+        "\n",
+        "  # Adjust weights\n",
+        "  weight.assign_sub(gradients[0]*learning_rate)\n",
+        "  bias.assign_sub(gradients[1]*learning_rate)\n",
+        "\n",
+        "  # Print output\n",
+        "  print(f\"Epoch count {epoch}: Loss value: {loss.numpy()}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wi0papoCgx-S"
+      },
+      "source": [
+        "<a name=\"1.4-Subsection-1.4\"></a>\n",
+        "## 1.4 Results\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The training process is now complete. You can see that the model reached near minimum loss in the 6th epoch itself. Thanks to gradient tape. Let’s see the final value of weight and bias."
+      ],
+      "metadata": {
+        "id": "KWeVXYbAgx-g"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 71,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Y409YmhEgx-g",
+        "outputId": "fe58b5c3-4a4c-4d62-a03a-e39168f397a2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1.0189908\n",
+            "0.110945135\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(weight.numpy())\n",
+        "print(bias.numpy())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The final step of this linear regression model is to plot the best fit line based on our final optimized parameter values."
+      ],
+      "metadata": {
+        "id": "P156Q5gCpmdj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plotting the Results\n",
+        "plt.plot(x_train, y_train, 'ro', label ='Original data',color=\"blue\")\n",
+        "plt.plot(x_train, linreg(x_train), label ='Fitted line')\n",
+        "plt.title('Linear Regression Result')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 281
+        },
+        "id": "yzozONLjiLze",
+        "outputId": "a436e6c7-10db-4c03-b01d-e2db726d9d50"
+      },
+      "execution_count": 72,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "---\n",
+        "<a name=\"2.-Section-2\"></a>\n",
+        "# 2. Logistic Regression"
+      ],
+      "metadata": {
+        "id": "Fnjc7n0SpEtL"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Importing necessary libraries"
+      ],
+      "metadata": {
+        "id": "VauaNPlGtYAx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import random\n",
+        "import warnings\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.datasets import load_iris\n",
+        "from sklearn import datasets\n",
+        "import seaborn as sns\n",
+        "\n",
+        "%matplotlib inline"
+      ],
+      "metadata": {
+        "id": "EeFy9RyUpGs9"
+      },
+      "execution_count": 73,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<a name=\"2.1-Subsection-2.1\"></a>\n",
+        "## 2.1 Data"
+      ],
+      "metadata": {
+        "id": "e5pTdqBJtgSV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "d = sns.load_dataset(\"iris\")\n",
+        "d.head(5)"
+      ],
+      "metadata": {
+        "id": "wMSS6QA5pW94",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "outputId": "39ec7c49-1156-491f-8806-0620697d4b9f"
+      },
+      "execution_count": 74,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   sepal_length  sepal_width  petal_length  petal_width species\n",
+              "0           5.1          3.5           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          3.1           1.5          0.2  setosa\n",
+              "4           5.0          3.6           1.4          0.2  setosa"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-511e53b1-d803-4c75-ba91-cd6c4001f72b\">\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>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>3.5</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>3.1</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",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-511e53b1-d803-4c75-ba91-cd6c4001f72b')\"\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-511e53b1-d803-4c75-ba91-cd6c4001f72b 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-511e53b1-d803-4c75-ba91-cd6c4001f72b');\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",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 74
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Visualize the data"
+      ],
+      "metadata": {
+        "id": "5plt7BJAtrBE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "sns.pairplot(d,hue = 'species')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 743
+        },
+        "id": "d3N20AC9r6Xc",
+        "outputId": "1f6f4be7-2d72-4384-b609-942f39f2a213"
+      },
+      "execution_count": 75,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<seaborn.axisgrid.PairGrid at 0x7f2da930d9a0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 75
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 804.75x720 with 20 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Preparing data for our model"
+      ],
+      "metadata": {
+        "id": "32L3T070t6Ez"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X = d.drop(['species'], axis = 1)\n",
+        "Y = d['species']"
+      ],
+      "metadata": {
+        "id": "w7vcyoSgpxWE"
+      },
+      "execution_count": 76,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Splitting data into two sets"
+      ],
+      "metadata": {
+        "id": "6c9ixdxzt5Hn"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train, x_test, y_train, y_test = train_test_split(X,Y, test_size = 0.2)\n",
+        "print(x_train.shape, y_train.shape)\n",
+        "print(x_test.shape, y_test.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "F5-9Gd6eqh9n",
+        "outputId": "a46f4ebb-abd5-4fec-d6e4-7e20e868babd"
+      },
+      "execution_count": 77,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(120, 4) (120,)\n",
+            "(30, 4) (30,)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<a name=\"2.2-Subsection-2.2\"></a>\n",
+        "## 2.2 Logitic Regression Model\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "754pxrF8uFPs"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.linear_model import LogisticRegression\n",
+        "model = LogisticRegression()\n",
+        "model.fit(x_train, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "X_klcb9dqjLo",
+        "outputId": "93f51ec4-880e-44b4-97c0-5feb4bf124e0"
+      },
+      "execution_count": 78,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LogisticRegression()"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 78
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<a name=\"2.3-Subsection-2.3\"></a>\n",
+        "## 2.3 Evaluation"
+      ],
+      "metadata": {
+        "id": "NG-M7DkLuo04"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.score(x_test, y_test)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ks4KSeMQqlL5",
+        "outputId": "3099b1f8-4913-4608-b0dd-4ee43c7ec4b2"
+      },
+      "execution_count": 79,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.9"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 79
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn import metrics\n",
+        "print(\"Model Accuracy :\", metrics.accuracy_score(y_test, model.predict(x_test)))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0ONK4Lo1qm-T",
+        "outputId": "c7917fa2-e1b7-4d6d-f3a4-3a55ceae3c51"
+      },
+      "execution_count": 80,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model Accuracy : 0.9\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import confusion_matrix\n",
+        "cm = confusion_matrix(y_test, model.predict(x_test))\n",
+        "cm"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cgJYJgthqo-7",
+        "outputId": "08052afa-7850-48ed-f0d8-e4aae14d71b0"
+      },
+      "execution_count": 81,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[11,  0,  0],\n",
+              "       [ 0, 10,  3],\n",
+              "       [ 0,  0,  6]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 81
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Plot confusion matrix"
+      ],
+      "metadata": {
+        "id": "RAG-9qbRvdXZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "plt.figure(figsize = (10,7))\n",
+        "sns.heatmap(cm, annot = True)\n",
+        "plt.xlabel(\"Predicted\")\n",
+        "plt.ylabel(\"Truth\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 459
+        },
+        "id": "rsDlyqXYqrxx",
+        "outputId": "69b4df1b-7547-4ee6-8fbc-04d0ab9aac76"
+      },
+      "execution_count": 82,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(69.0, 0.5, 'Truth')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 82
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x504 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "---\n",
+        "<a name=\"3.-Section-3\"></a>\n",
+        "# 3. Decision Trees"
+      ],
+      "metadata": {
+        "id": "qcwMTzNxrqMv"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Importing necessary libraries"
+      ],
+      "metadata": {
+        "id": "ZzrEOltGwbFm"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np \n",
+        "import pandas as pd \n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns \n",
+        "\n",
+        "from sklearn import tree\n",
+        "\n",
+        "%matplotlib inline"
+      ],
+      "metadata": {
+        "id": "64Ed_bHgrvr7"
+      },
+      "execution_count": 83,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<a name=\"3.1-Subsection-3.1\"></a>\n",
+        "## 3.1 Data"
+      ],
+      "metadata": {
+        "id": "SWaoRHm9wcwP"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Load data"
+      ],
+      "metadata": {
+        "id": "hKc7qysWwqC_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data = sns.load_dataset(\"iris\")\n",
+        "data.head(5)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "MC0A9IiUsvFF",
+        "outputId": "7f1c2df2-75fb-4cfa-85a6-bc07fd17ee63"
+      },
+      "execution_count": 84,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   sepal_length  sepal_width  petal_length  petal_width species\n",
+              "0           5.1          3.5           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          3.1           1.5          0.2  setosa\n",
+              "4           5.0          3.6           1.4          0.2  setosa"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-2a407f9c-bace-4409-834d-f0a8af829a85\">\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>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>3.5</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>3.1</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",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a407f9c-bace-4409-834d-f0a8af829a85')\"\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-2a407f9c-bace-4409-834d-f0a8af829a85 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-2a407f9c-bace-4409-834d-f0a8af829a85');\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",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 84
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Preparing data for our model"
+      ],
+      "metadata": {
+        "id": "KvoJqwU9w0Q6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "target_col = ['species']\n",
+        "X = data.drop(['species'], axis=1)\n",
+        "\n",
+        "y = data['species']"
+      ],
+      "metadata": {
+        "id": "zNO1YPX3tAyo"
+      },
+      "execution_count": 85,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Splitting data into two sets"
+      ],
+      "metadata": {
+        "id": "KGWMO-LtwpRt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 42)"
+      ],
+      "metadata": {
+        "id": "sPyBD_gWtOyH"
+      },
+      "execution_count": 86,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<a name=\"3.2-Subsection-3.2\"></a>\n",
+        "## 3.2 Decision Tree Model\n"
+      ],
+      "metadata": {
+        "id": "RjEa79kQw11x"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.tree import DecisionTreeClassifier"
+      ],
+      "metadata": {
+        "id": "JwGGuw0ltR95"
+      },
+      "execution_count": 87,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Training model"
+      ],
+      "metadata": {
+        "id": "_NatidspxdZQ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "clf_gini = DecisionTreeClassifier(criterion='gini', max_depth=3, random_state=0)\n",
+        "clf_gini.fit(X_train, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gQ8xdCDctSb0",
+        "outputId": "40de799d-ac91-401f-97e0-700f9ba6b184"
+      },
+      "execution_count": 88,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "DecisionTreeClassifier(max_depth=3, random_state=0)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 88
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<a name=\"3.3-Subsection-3.3\"></a>\n",
+        "## 3.3 Evaluation\n"
+      ],
+      "metadata": {
+        "id": "DC6T_KyrxgCQ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Predicting classification on test set"
+      ],
+      "metadata": {
+        "id": "NzTbqJBmxCet"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_gini = clf_gini.predict(X_test)\n",
+        "y_pred_gini"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OY6XdmwOtUzt",
+        "outputId": "6570844f-a96f-4854-b897-920a650fb7bc"
+      },
+      "execution_count": 89,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['versicolor', 'setosa', 'virginica', 'versicolor', 'versicolor',\n",
+              "       'setosa', 'versicolor', 'virginica', 'versicolor', 'versicolor',\n",
+              "       'virginica', 'setosa', 'setosa', 'setosa', 'setosa', 'versicolor',\n",
+              "       'virginica', 'versicolor', 'versicolor', 'virginica', 'setosa',\n",
+              "       'virginica', 'setosa', 'virginica', 'virginica', 'virginica',\n",
+              "       'virginica', 'virginica', 'setosa', 'setosa', 'setosa', 'setosa',\n",
+              "       'versicolor', 'setosa', 'setosa', 'virginica', 'versicolor',\n",
+              "       'setosa', 'setosa', 'setosa', 'virginica', 'versicolor',\n",
+              "       'versicolor', 'setosa', 'setosa', 'versicolor', 'versicolor',\n",
+              "       'virginica', 'versicolor', 'virginica'], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 89
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import accuracy_score\n",
+        "\n",
+        "print('Model accuracy score with criterion gini index: {0:0.4f}'. format(accuracy_score(y_test, y_pred_gini)))# y_pred_gini are the predicted class labels in the test-set."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vUrCl2fftXlq",
+        "outputId": "10901b66-a8a5-4bb9-84c8-ff4f9c805fc2"
+      },
+      "execution_count": 90,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model accuracy score with criterion gini index: 0.9800\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Predicting classification on train set"
+      ],
+      "metadata": {
+        "id": "2QNpWeGcxXd7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Compare the train-set and test-set accuracy\n",
+        "y_pred_train_gini = clf_gini.predict(X_train)\n",
+        "\n",
+        "y_pred_train_gini"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yjNgSsOstZVt",
+        "outputId": "d45845f7-e7a0-423f-c6b7-774ebcca9bba"
+      },
+      "execution_count": 91,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['versicolor', 'virginica', 'versicolor', 'setosa', 'virginica',\n",
+              "       'versicolor', 'setosa', 'setosa', 'setosa', 'versicolor',\n",
+              "       'virginica', 'setosa', 'setosa', 'setosa', 'versicolor', 'setosa',\n",
+              "       'versicolor', 'virginica', 'setosa', 'versicolor', 'virginica',\n",
+              "       'setosa', 'virginica', 'virginica', 'versicolor', 'versicolor',\n",
+              "       'virginica', 'versicolor', 'setosa', 'versicolor', 'virginica',\n",
+              "       'setosa', 'setosa', 'versicolor', 'versicolor', 'setosa',\n",
+              "       'virginica', 'setosa', 'setosa', 'virginica', 'versicolor',\n",
+              "       'virginica', 'versicolor', 'virginica', 'virginica', 'versicolor',\n",
+              "       'setosa', 'setosa', 'versicolor', 'virginica', 'setosa', 'setosa',\n",
+              "       'setosa', 'versicolor', 'virginica', 'setosa', 'virginica',\n",
+              "       'virginica', 'setosa', 'versicolor', 'versicolor', 'virginica',\n",
+              "       'versicolor', 'virginica', 'setosa', 'virginica', 'versicolor',\n",
+              "       'virginica', 'versicolor', 'versicolor', 'versicolor', 'setosa',\n",
+              "       'versicolor', 'versicolor', 'setosa', 'versicolor', 'virginica',\n",
+              "       'virginica', 'setosa', 'versicolor', 'virginica', 'virginica',\n",
+              "       'setosa', 'virginica', 'setosa', 'versicolor', 'virginica',\n",
+              "       'virginica', 'versicolor', 'virginica', 'versicolor', 'versicolor',\n",
+              "       'virginica', 'virginica', 'setosa', 'versicolor', 'versicolor',\n",
+              "       'setosa', 'versicolor', 'virginica'], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 91
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Training-set accuracy score: {0:0.4f}'. format(accuracy_score(y_train, y_pred_train_gini)))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "piP0-7SOtbbN",
+        "outputId": "c17b4d75-d1ef-49b2-e017-afd3840cd165"
+      },
+      "execution_count": 92,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training-set accuracy score: 0.9700\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Comparing accuracy"
+      ],
+      "metadata": {
+        "id": "mW8Y8w1Qxnr3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Check for overfitting and underfitting\n",
+        "\n",
+        "print('Training set score: {:.4f}'.format(clf_gini.score(X_train, y_train)))\n",
+        "\n",
+        "print('Test set score: {:.4f}'.format(clf_gini.score(X_test, y_test)))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "__-jj7g9tcqU",
+        "outputId": "8352163d-1afa-4a79-a8fa-d66e7d165de0"
+      },
+      "execution_count": 93,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training set score: 0.9700\n",
+            "Test set score: 0.9800\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Plotting decision tree"
+      ],
+      "metadata": {
+        "id": "Cs9HSA_1xsTF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(12,8))\n",
+        "_ = tree.plot_tree(clf_gini.fit(X_train, y_train),\n",
+        "               feature_names=list(data.columns[:-1]),  \n",
+        "               class_names=data[\"species\"].unique(),\n",
+        "               filled=True) "
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 466
+        },
+        "id": "12LvbUALtfMa",
+        "outputId": "e473fedd-6af4-4795-cbc9-22f7fbfe63fb"
+      },
+      "execution_count": 94,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 864x576 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "---\n",
+        "<a name=\"4.-Section-4\"></a>\n",
+        "# 4. K-Nearest Neigbor "
+      ],
+      "metadata": {
+        "id": "L3gpxVautmIT"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Importing necessary libraries"
+      ],
+      "metadata": {
+        "id": "TbGXDneQz3sJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "import pandas as pd"
+      ],
+      "metadata": {
+        "id": "IoHZTuXXtrf3"
+      },
+      "execution_count": 95,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<a name=\"4.1-Subsection-4.1\"></a>\n",
+        "## 4.1 Data"
+      ],
+      "metadata": {
+        "id": "Vs_jO7ajz72x"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Load data"
+      ],
+      "metadata": {
+        "id": "Dt-DXzz0z_vm"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "iris = sns.load_dataset(\"iris\")"
+      ],
+      "metadata": {
+        "id": "IhSMICmqz-wP"
+      },
+      "execution_count": 96,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Visualize data"
+      ],
+      "metadata": {
+        "id": "oxtIdqwZ0Bbv"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.pairplot(iris, hue=\"species\", corner=True, kind='reg')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 743
+        },
+        "id": "cyj1HuAawsoB",
+        "outputId": "8857c81f-bb55-4e93-f8d7-a1dee2d42c3f"
+      },
+      "execution_count": 97,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<seaborn.axisgrid.PairGrid at 0x7f2da9390220>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 97
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 804.75x720 with 14 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Preparing data for our model"
+      ],
+      "metadata": {
+        "id": "DipuRlWT0WjF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train = iris.sample(frac=0.7, random_state=500) # random state is a seed value\n",
+        "test = iris.drop(train.index)\n",
+        "X_train = train.drop(labels='species', axis=1)\n",
+        "y_train = train.species\n",
+        "X_test = test.drop(labels='species', axis=1)\n",
+        "y_test = test.species"
+      ],
+      "metadata": {
+        "id": "ifu8FXPYxFo-"
+      },
+      "execution_count": 98,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<a name=\"4.2-Subsection-4.2\"></a>\n",
+        "## 4.2 KNN Model\n"
+      ],
+      "metadata": {
+        "id": "x1ew89gK0axi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "knn = KNeighborsClassifier(n_neighbors=3)"
+      ],
+      "metadata": {
+        "id": "NOrZk-doxjPp"
+      },
+      "execution_count": 99,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Training our model"
+      ],
+      "metadata": {
+        "id": "wpnARVDr0f9P"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "knn.fit(X_train,y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "v79-Z9n1xjrt",
+        "outputId": "ea2e4f72-007e-42aa-aee6-7b0073409d2a"
+      },
+      "execution_count": 100,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "KNeighborsClassifier(n_neighbors=3)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 100
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<a name=\"4.3-Subsection-4.3\"></a>\n",
+        "## 4.3 Evaluation\n"
+      ],
+      "metadata": {
+        "id": "E_uV4OLd0jfO"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Check score of our model"
+      ],
+      "metadata": {
+        "id": "0WIzmG8i0562"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "knn.score(X_test,y_test)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pyV7PPZf0q1b",
+        "outputId": "7fd6cc34-882b-4e63-e1a4-b618754a3b4c"
+      },
+      "execution_count": 101,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.9555555555555556"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 101
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Make classification predictions"
+      ],
+      "metadata": {
+        "id": "LHm03JFA072I"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "results = knn.predict(X_test)"
+      ],
+      "metadata": {
+        "id": "AI18vwHrxtxw"
+      },
+      "execution_count": 102,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(pd.crosstab(y_test, results, rownames=['Real'], colnames=['Predicted'], margins=True, margins_name='total'))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZqFp9huxx3o3",
+        "outputId": "b32a80df-ee58-475a-b2b6-64dad90d68b9"
+      },
+      "execution_count": 103,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted   setosa  versicolor  virginica  total\n",
+            "Real                                            \n",
+            "setosa          12           0          0     12\n",
+            "versicolor       0          20          2     22\n",
+            "virginica        0           0         11     11\n",
+            "total           12          20         13     45\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Confusion matrix"
+      ],
+      "metadata": {
+        "id": "AhvZIRpg0wTM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import confusion_matrix\n",
+        "cm = confusion_matrix(y_test, results)\n",
+        "cm"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MkITt2sjy2Bs",
+        "outputId": "2e45cabb-501a-4851-c81c-a8eaa6e8dd9e"
+      },
+      "execution_count": 104,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[12,  0,  0],\n",
+              "       [ 0, 20,  2],\n",
+              "       [ 0,  0, 11]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 104
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "plt.figure(figsize = (10,7))\n",
+        "sns.heatmap(cm, annot = True)\n",
+        "plt.xlabel(\"Predicted\")\n",
+        "plt.ylabel(\"Truth\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 463
+        },
+        "id": "FyZ633jdy94z",
+        "outputId": "f40e1e82-bdfb-4caa-dfd6-daea5d6c722d"
+      },
+      "execution_count": 105,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(69.0, 0.5, 'Truth')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 105
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x504 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "---\n",
+        "<a name=\"5.-Section-5\"></a>\n",
+        "# 5. SVM"
+      ],
+      "metadata": {
+        "id": "XmrLceltzCVE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Importing necessary libraries"
+      ],
+      "metadata": {
+        "id": "ZaOj05NDmwMt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from sklearn.datasets import load_iris"
+      ],
+      "metadata": {
+        "id": "hO3jIsMNmx73"
+      },
+      "execution_count": 106,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<a name=\"5.1-Subsection-5.1\"></a>\n",
+        "## 5.1 Data"
+      ],
+      "metadata": {
+        "id": "uJVVh9F2mkT6"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Load data"
+      ],
+      "metadata": {
+        "id": "SWBX2L-vm-to"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "iris = load_iris()\n",
+        "df_data = pd.DataFrame(data= np.c_[iris['data'], iris['target']],\n",
+        "                     columns= ['SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm','Species'])\n",
+        "df_data.head(5)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "0kyy2vbyE6wG",
+        "outputId": "5b27dace-99f5-4f07-b43d-98a4d75f2f83"
+      },
+      "execution_count": 107,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
+              "0            5.1           3.5            1.4           0.2      0.0\n",
+              "1            4.9           3.0            1.4           0.2      0.0\n",
+              "2            4.7           3.2            1.3           0.2      0.0\n",
+              "3            4.6           3.1            1.5           0.2      0.0\n",
+              "4            5.0           3.6            1.4           0.2      0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-dcb8974e-0927-45ab-9c36-7a843f05889a\">\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>SepalLengthCm</th>\n",
+              "      <th>SepalWidthCm</th>\n",
+              "      <th>PetalLengthCm</th>\n",
+              "      <th>PetalWidthCm</th>\n",
+              "      <th>Species</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>5.1</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>0.0</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>0.0</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>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4.6</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>0.0</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>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dcb8974e-0927-45ab-9c36-7a843f05889a')\"\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-dcb8974e-0927-45ab-9c36-7a843f05889a 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-dcb8974e-0927-45ab-9c36-7a843f05889a');\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",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 107
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Splitting data into two sets"
+      ],
+      "metadata": {
+        "id": "rHWFaRgZnBuP"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "X = df_data.drop(labels=['Species'],axis=1).values # 移除Species並取得剩下欄位資料\n",
+        "y = df_data['Species'].values\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)\n",
+        "\n",
+        "print('train shape:', X_train.shape)\n",
+        "print('test shape:', X_test.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zL9K7NqYE_pG",
+        "outputId": "faac2a66-1cbd-499f-e9c7-6a887471f375"
+      },
+      "execution_count": 108,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "train shape: (105, 4)\n",
+            "test shape: (45, 4)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<a name=\"5.2-Subsection-5.2\"></a>\n",
+        "## 5.2 Functions for mapping decision boundaries\n"
+      ],
+      "metadata": {
+        "id": "aH1qtJV_pA85"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Function for the meshgrid"
+      ],
+      "metadata": {
+        "id": "UgFIqYnbp-q1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def make_meshgrid(x, y, h=.02):\n",
+        "    \"\"\"Create a mesh of points to plot in\n",
+        "\n",
+        "    Parameters\n",
+        "    ----------\n",
+        "    x: data to base x-axis meshgrid on\n",
+        "    y: data to base y-axis meshgrid on\n",
+        "    h: stepsize for meshgrid, optional\n",
+        "\n",
+        "    Returns\n",
+        "    -------\n",
+        "    xx, yy : ndarray\n",
+        "    \"\"\"\n",
+        "    x_min, x_max = x.min() - 1, x.max() + 1\n",
+        "    y_min, y_max = y.min() - 1, y.max() + 1\n",
+        "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
+        "                         np.arange(y_min, y_max, h))\n",
+        "    return xx, yy"
+      ],
+      "metadata": {
+        "id": "kCP6gtJEFCdZ"
+      },
+      "execution_count": 109,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Function for ploting the entire meshgrid"
+      ],
+      "metadata": {
+        "id": "SZmHDy_FrFPW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def plot_contours(ax, clf, xx, yy, **params):\n",
+        "    \"\"\"Plot the decision boundaries for a classifier.\n",
+        "\n",
+        "    Parameters\n",
+        "    ----------\n",
+        "    ax: matplotlib axes object\n",
+        "    clf: a classifier\n",
+        "    xx: meshgrid ndarray\n",
+        "    yy: meshgrid ndarray\n",
+        "    params: dictionary of params to pass to contourf, optional\n",
+        "    \"\"\"\n",
+        "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+        "    Z = Z.reshape(xx.shape)\n",
+        "    out = ax.contourf(xx, yy, Z, **params)\n",
+        "    return out"
+      ],
+      "metadata": {
+        "id": "OA4UjqsRrI46"
+      },
+      "execution_count": 110,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<a name=\"5.3-Subsection-5.3\"></a>\n",
+        "## 5.3 PCA dimensionality reduction\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "TU9bgm7Iq8nh"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The original iris 4 features have been reduced to 2 dimensions to facilitate visualisation."
+      ],
+      "metadata": {
+        "id": "a3syioUYrWRJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.decomposition import PCA\n",
+        "pca = PCA(n_components=2, iterated_power=1)\n",
+        "train_reduced = pca.fit_transform(X_train)"
+      ],
+      "metadata": {
+        "id": "INIfGuEnFD8D"
+      },
+      "execution_count": 111,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<a name=\"5.4-Subsection-5.4\"></a>\n",
+        "## 5.4 Support Vector Machine (SVM) linear model\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "U-Ki7_C7rg1Z"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Parameters:\n",
+        "- C: Limit the complexity of the model to prevent overfitting.\n",
+        "- max_iter: Maximum number of iterations, default 1000."
+      ],
+      "metadata": {
+        "id": "lxxPIfx9r_1F"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn import svm\n",
+        "\n",
+        "# Build linearSvc model\n",
+        "linearSvcModel=svm.LinearSVC(C=1, max_iter=10000)\n",
+        "# Train model\n",
+        "linearSvcModel.fit(train_reduced, y_train)\n",
+        "# Using training data to predict classification\n",
+        "predicted=linearSvcModel.predict(train_reduced)\n",
+        "# Calculate Accuracy\n",
+        "accuracy = linearSvcModel.score(train_reduced, y_train)\n",
+        "#Plotting\n",
+        "X0, X1 = train_reduced[:, 0], train_reduced[:, 1]\n",
+        "xx, yy = make_meshgrid(X0, X1)\n",
+        "plot_contours(plt, linearSvcModel, xx, yy,\n",
+        "                  cmap=plt.cm.coolwarm, alpha=0.8)\n",
+        "plt.scatter(X0, X1, c=y_train, cmap=plt.cm.coolwarm, s=20, edgecolors='k')\n",
+        "plt.xlabel('Sepal length')\n",
+        "plt.ylabel('Sepal width')\n",
+        "plt.title('LinearSVC (linear kernel)'+ '\\n' + 'Accuracy:%.2f'%accuracy)"
+      ],
+      "metadata": {
+        "id": "e0PbQ-e9FFk3",
+        "outputId": "ad84be99-5e0b-466a-bf10-b71796b62561",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 327
+        }
+      },
+      "execution_count": 112,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'LinearSVC (linear kernel)\\nAccuracy:0.96')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 112
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "---\n",
+        "\n",
+        "This notebook is a part of introductory machine learning course, led by the [Gradient](https://gradient.eti.pg.gda.pl/) science club in the 2022/2023 school year.\n",
+        "\n",
+        "<img src=\"https://drive.google.com/uc?export=view&id=1P7SX6J_GWlS1ENxVaZCcBO0BxHP6ULEg\" width=\"1050\"/>"
+      ],
+      "metadata": {
+        "id": "xZ23d5lsPloz"
+      }
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3.8.5 32-bit",
+      "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": "3.8.5"
+    },
+    "orig_nbformat": 4,
+    "vscode": {
+      "interpreter": {
+        "hash": "298c87bb46c0ece7abcceec7d9fcd87435e079314f0dee0953f90f5b1b40d0e0"
+      }
+    },
+    "colab": {
+      "provenance": [],
+      "collapsed_sections": [
+        "PcxhFSBnfpsh"
+      ]
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file