From 18d4d9f0ee97bbef8c973d1afa169fcc8769b74f Mon Sep 17 00:00:00 2001
From: pedrogzz18 <89173994+pedrogzz18@users.noreply.github.com>
Date: Sat, 27 Apr 2024 20:30:32 -0600
Subject: [PATCH] neural network for ASL classifiaction

---
 NN_ASL.ipynb | 1344 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1344 insertions(+)
 create mode 100644 NN_ASL.ipynb

diff --git a/NN_ASL.ipynb b/NN_ASL.ipynb
new file mode 100644
index 0000000..341e4ca
--- /dev/null
+++ b/NN_ASL.ipynb
@@ -0,0 +1,1344 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/pedrogzz18/ASL-Model/blob/nn/NN_ASL.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import string\n",
+        "letters = list(string.ascii_uppercase)\n",
+        "letters"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "aKh81whPBVoF",
+        "outputId": "72846ca9-ad45-4986-f19e-0ca37c6d3d0a"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['A',\n",
+              " 'B',\n",
+              " 'C',\n",
+              " 'D',\n",
+              " 'E',\n",
+              " 'F',\n",
+              " 'G',\n",
+              " 'H',\n",
+              " 'I',\n",
+              " 'J',\n",
+              " 'K',\n",
+              " 'L',\n",
+              " 'M',\n",
+              " 'N',\n",
+              " 'O',\n",
+              " 'P',\n",
+              " 'Q',\n",
+              " 'R',\n",
+              " 'S',\n",
+              " 'T',\n",
+              " 'U',\n",
+              " 'V',\n",
+              " 'W',\n",
+              " 'X',\n",
+              " 'Y',\n",
+              " 'Z']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "numbers = [str(num) for num in range(0, 10)]\n",
+        "numbers"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6Qw32Ab3BWct",
+        "outputId": "3d6b0329-022b-4c28-ae0e-c4619994f2eb"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class_names = letters + numbers\n",
+        "class_names"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gWKw2PHVBY71",
+        "outputId": "e8ace820-2056-44b1-ca8b-7d0e4c75cef1"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['A',\n",
+              " 'B',\n",
+              " 'C',\n",
+              " 'D',\n",
+              " 'E',\n",
+              " 'F',\n",
+              " 'G',\n",
+              " 'H',\n",
+              " 'I',\n",
+              " 'J',\n",
+              " 'K',\n",
+              " 'L',\n",
+              " 'M',\n",
+              " 'N',\n",
+              " 'O',\n",
+              " 'P',\n",
+              " 'Q',\n",
+              " 'R',\n",
+              " 'S',\n",
+              " 'T',\n",
+              " 'U',\n",
+              " 'V',\n",
+              " 'W',\n",
+              " 'X',\n",
+              " 'Y',\n",
+              " 'Z',\n",
+              " '0',\n",
+              " '1',\n",
+              " '2',\n",
+              " '3',\n",
+              " '4',\n",
+              " '5',\n",
+              " '6',\n",
+              " '7',\n",
+              " '8',\n",
+              " '9']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "NaL78iXS8c1x"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.pipeline import make_pipeline\n",
+        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
+        "import pandas as pd\n",
+        "from sklearn.metrics import accuracy_score"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 342
+        },
+        "id": "eLd9Znj-8gh-",
+        "outputId": "0abb326c-c87a-43d6-8d2e-075bd32d8181"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "  class        x1        y1            z1   v1        x2        y2        z2  \\\n",
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+              "1     A  0.582702  0.602976 -4.789155e-07  0.0  0.514781  0.599014  0.001139   \n",
+              "2     A  0.568719  0.601903 -4.693277e-07  0.0  0.502747  0.596997  0.000186   \n",
+              "3     A  0.559268  0.600854 -4.881787e-07  0.0  0.491037  0.593645  0.006739   \n",
+              "4     A  0.548125  0.613788 -4.889142e-07  0.0  0.476834  0.605301  0.008348   \n",
+              "\n",
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+              "4  0.0  0.434377  ...  0.007032  0.0  0.514065  0.463542  0.014416  0.0   \n",
+              "\n",
+              "        x21       y21       z21  v21  \n",
+              "0  0.584027  0.460907  0.033485  0.0  \n",
+              "1  0.570349  0.470588  0.025043  0.0  \n",
+              "2  0.558533  0.469868  0.014419  0.0  \n",
+              "3  0.547573  0.469695  0.022285  0.0  \n",
+              "4  0.530254  0.481641  0.025965  0.0  \n",
+              "\n",
+              "[5 rows x 85 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8b77f0ac-8516-48a8-aa79-d2f042d95928\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "  <thead>\n",
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+              "      <td>-4.789155e-07</td>\n",
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+              "      <td>0.601903</td>\n",
+              "      <td>-4.693277e-07</td>\n",
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+              "      <td>0.0</td>\n",
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+              "      <th>3</th>\n",
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+              "<p>5 rows × 85 columns</p>\n",
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+              "\n",
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+              "  <style>\n",
+              "    .colab-df-container {\n",
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+              "    .colab-df-convert {\n",
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+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "                                                    [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",
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+              "  }\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",
+              "\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-cdaedf9a-214b-4771-ac0f-10e1ccc5403b button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ],
+      "source": [
+        "df = pd.read_csv('coords.csv')\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "f6cy9XuW89Pf"
+      },
+      "outputs": [],
+      "source": [
+        "df.dropna(axis = 0, inplace = True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "dHl84A5m8g7u"
+      },
+      "outputs": [],
+      "source": [
+        "X = df.drop('class', axis = 1)\n",
+        "y = df['class']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "IK7_nzOj8g_N"
+      },
+      "outputs": [],
+      "source": [
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42, stratify = y)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "FNot1_Oe9vqY"
+      },
+      "outputs": [],
+      "source": [
+        "from tensorflow.keras import Sequential\n",
+        "from tensorflow.keras.layers import InputLayer, Dense"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "VVm4I2yaAHZO"
+      },
+      "outputs": [],
+      "source": [
+        "def create_asl_nn():\n",
+        "  model = Sequential([\n",
+        "      Dense(input_shape=(84,), units=84, activation='relu'),\n",
+        "      Dense(units=32, activation='relu'),\n",
+        "      Dense(units=16, activation='relu'),\n",
+        "      Dense(units=len(class_names), activation='softmax')\n",
+        "  ])\n",
+        "  model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=[\"accuracy\"])\n",
+        "  return model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from scikeras.wrappers import KerasClassifier\n",
+        "from sklearn.model_selection import StratifiedKFold, cross_val_score\n",
+        "from tensorflow.keras.callbacks import EarlyStopping\n",
+        "\n",
+        "trans = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
+        "clf = KerasClassifier(create_asl_nn, epochs=100, batch_size=200, shuffle=True, verbose=1, validation_split=0.2 ,callbacks=[EarlyStopping(min_delta=0.001, patience=5)])\n",
+        "clf = make_pipeline(StandardScaler(), clf)"
+      ],
+      "metadata": {
+        "id": "uhwMyVvnB1ct"
+      },
+      "execution_count": 32,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "label_encoder = LabelEncoder()\n",
+        "y_train_n = label_encoder.fit_transform(y_train)\n",
+        "clf.fit(X_train, y_train_n)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "bFwiDde4Du9N",
+        "outputId": "b032b294-195a-49da-b668-77b065f03ff3"
+      },
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/100\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - accuracy: 0.1050 - loss: 3.4341 - val_accuracy: 0.2773 - val_loss: 2.7735\n",
+            "Epoch 2/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3401 - loss: 2.4661 - val_accuracy: 0.6292 - val_loss: 1.4795\n",
+            "Epoch 3/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6920 - loss: 1.2431 - val_accuracy: 0.8196 - val_loss: 0.7415\n",
+            "Epoch 4/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8494 - loss: 0.6514 - val_accuracy: 0.9066 - val_loss: 0.4452\n",
+            "Epoch 5/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9175 - loss: 0.4033 - val_accuracy: 0.9418 - val_loss: 0.3008\n",
+            "Epoch 6/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9400 - loss: 0.2783 - val_accuracy: 0.9493 - val_loss: 0.2159\n",
+            "Epoch 7/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9538 - loss: 0.2002 - val_accuracy: 0.9642 - val_loss: 0.1642\n",
+            "Epoch 8/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9672 - loss: 0.1555 - val_accuracy: 0.9692 - val_loss: 0.1320\n",
+            "Epoch 9/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9731 - loss: 0.1168 - val_accuracy: 0.9801 - val_loss: 0.1081\n",
+            "Epoch 10/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9777 - loss: 0.0995 - val_accuracy: 0.9816 - val_loss: 0.0925\n",
+            "Epoch 11/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9831 - loss: 0.0827 - val_accuracy: 0.9831 - val_loss: 0.0814\n",
+            "Epoch 12/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9854 - loss: 0.0739 - val_accuracy: 0.9896 - val_loss: 0.0703\n",
+            "Epoch 13/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9842 - loss: 0.0669 - val_accuracy: 0.9896 - val_loss: 0.0647\n",
+            "Epoch 14/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9888 - loss: 0.0564 - val_accuracy: 0.9906 - val_loss: 0.0613\n",
+            "Epoch 15/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9920 - loss: 0.0479 - val_accuracy: 0.9916 - val_loss: 0.0538\n",
+            "Epoch 16/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9909 - loss: 0.0434 - val_accuracy: 0.9881 - val_loss: 0.0568\n",
+            "Epoch 17/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9920 - loss: 0.0393 - val_accuracy: 0.9906 - val_loss: 0.0513\n",
+            "Epoch 18/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9938 - loss: 0.0336 - val_accuracy: 0.9911 - val_loss: 0.0493\n",
+            "Epoch 19/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9931 - loss: 0.0341 - val_accuracy: 0.9925 - val_loss: 0.0438\n",
+            "Epoch 20/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9957 - loss: 0.0254 - val_accuracy: 0.9911 - val_loss: 0.0397\n",
+            "Epoch 21/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9962 - loss: 0.0222 - val_accuracy: 0.9920 - val_loss: 0.0398\n",
+            "Epoch 22/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9959 - loss: 0.0265 - val_accuracy: 0.9920 - val_loss: 0.0412\n",
+            "Epoch 23/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9962 - loss: 0.0229 - val_accuracy: 0.9916 - val_loss: 0.0417\n",
+            "Epoch 24/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9968 - loss: 0.0192 - val_accuracy: 0.9920 - val_loss: 0.0353\n",
+            "Epoch 25/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9970 - loss: 0.0187 - val_accuracy: 0.9925 - val_loss: 0.0363\n",
+            "Epoch 26/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9975 - loss: 0.0169 - val_accuracy: 0.9925 - val_loss: 0.0362\n",
+            "Epoch 27/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9976 - loss: 0.0158 - val_accuracy: 0.9940 - val_loss: 0.0333\n",
+            "Epoch 28/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9975 - loss: 0.0144 - val_accuracy: 0.9916 - val_loss: 0.0371\n",
+            "Epoch 29/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9976 - loss: 0.0154 - val_accuracy: 0.9925 - val_loss: 0.0327\n",
+            "Epoch 30/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9985 - loss: 0.0128 - val_accuracy: 0.9935 - val_loss: 0.0329\n",
+            "Epoch 31/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9935 - loss: 0.0232 - val_accuracy: 0.9925 - val_loss: 0.0360\n",
+            "Epoch 32/100\n",
+            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9980 - loss: 0.0125 - val_accuracy: 0.9930 - val_loss: 0.0326\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
+              "                ('kerasclassifier',\n",
+              "                 KerasClassifier(batch_size=200, callbacks=[<keras.src.callbacks.early_stopping.EarlyStopping object at 0x7850864e63b0>], epochs=100, model=<function create_asl_nn at 0x78508e5b2440>, validation_split=0.2))])"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-3 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\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",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 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-container-id-3 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-container-id-3 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-container-id-3 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-container-id-3 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-container-id-3 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-3 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-container-id-3 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-container-id-3 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 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-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-3 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 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-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-3 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-container-id-3 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-container-id-3 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-container-id-3 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-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-3 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-container-id-3 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-container-id-3 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-container-id-3 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-3 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-container-id-3 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-3 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 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-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\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",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\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",
+              "  color: var(--sklearn-color-background);\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",
+              "  color: var(--sklearn-color-background);\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-container-id-3 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-background);\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-container-id-3 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\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-container-id-3 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-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;kerasclassifier&#x27;,\n",
+              "                 KerasClassifier(batch_size=200, callbacks=[&lt;keras.src.callbacks.early_stopping.EarlyStopping object at 0x7850864e63b0&gt;], epochs=100, model=&lt;function create_asl_nn at 0x78508e5b2440&gt;, validation_split=0.2))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;kerasclassifier&#x27;,\n",
+              "                 KerasClassifier(batch_size=200, callbacks=[&lt;keras.src.callbacks.early_stopping.EarlyStopping object at 0x7850864e63b0&gt;], epochs=100, model=&lt;function create_asl_nn at 0x78508e5b2440&gt;, validation_split=0.2))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">KerasClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>KerasClassifier(\n",
+              "\tmodel=&lt;function create_asl_nn at 0x78508e5b2440&gt;\n",
+              "\tbuild_fn=None\n",
+              "\twarm_start=False\n",
+              "\trandom_state=None\n",
+              "\toptimizer=rmsprop\n",
+              "\tloss=None\n",
+              "\tmetrics=None\n",
+              "\tbatch_size=200\n",
+              "\tvalidation_batch_size=None\n",
+              "\tverbose=1\n",
+              "\tcallbacks=[&lt;keras.src.callbacks.early_stopping.EarlyStopping object at 0x7850864e63b0&gt;]\n",
+              "\tvalidation_split=0.2\n",
+              "\tshuffle=True\n",
+              "\trun_eagerly=False\n",
+              "\tepochs=100\n",
+              "\tclass_weight=None\n",
+              ")</pre></div> </div></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "preds = clf.predict(X_test)\n",
+        "preds = label_encoder.inverse_transform(y=preds)\n",
+        "acc = accuracy_score(y_test, preds)\n",
+        "print('NN', acc)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Alf_C1_IGBXO",
+        "outputId": "ddcb883d-aeea-4745-8cd7-07cdd941a216"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
+            "NN 0.9935034802784223\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "label_encoder.inverse_transform(clf.predict(pd.DataFrame([X_test.iloc[0][:]])))[0]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 53
+        },
+        "id": "ZV4oBtDdWr7N",
+        "outputId": "27809c60-f232-44d2-bb89-c53493e58e7b"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'I'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "max(clf.predict_proba(pd.DataFrame([X_test.iloc[0][:]])))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eEqBQ2sXKZum",
+        "outputId": "ffe5932a-c614-4656-fe82-836d981dcbda"
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.9972435"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyNhSUKc6l0Z7z413uYq6c11",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file