From 97bee00b0013d6bc2b67f11f8a4aef3a1b6ad1dc Mon Sep 17 00:00:00 2001
From: bp117 <bindupriya117@gmail.com>
Date: Tue, 13 Feb 2024 19:11:14 +0530
Subject: [PATCH] Add files via upload

---
 DATA_P~1_IPY (1).ipynb | 5605 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 5605 insertions(+)
 create mode 100644 DATA_P~1_IPY (1).ipynb

diff --git a/DATA_P~1_IPY (1).ipynb b/DATA_P~1_IPY (1).ipynb
new file mode 100644
index 0000000..6c3b993
--- /dev/null
+++ b/DATA_P~1_IPY (1).ipynb	
@@ -0,0 +1,5605 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "NbwcYaVVfMKG"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np # linear algebra\n",
+        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
+        "import math\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "import shap as shap\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "f8TlbG-RfXUD",
+        "outputId": "44abddf7-badc-4db6-ec2e-fb23affd7d5d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting shap\n",
+            "  Downloading shap-0.44.1-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (535 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m535.7/535.7 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from shap) (1.23.5)\n",
+            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from shap) (1.11.4)\n",
+            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from shap) (1.2.2)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from shap) (1.5.3)\n",
+            "Requirement already satisfied: tqdm>=4.27.0 in /usr/local/lib/python3.10/dist-packages (from shap) (4.66.1)\n",
+            "Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.10/dist-packages (from shap) (23.2)\n",
+            "Collecting slicer==0.0.7 (from shap)\n",
+            "  Downloading slicer-0.0.7-py3-none-any.whl (14 kB)\n",
+            "Requirement already satisfied: numba in /usr/local/lib/python3.10/dist-packages (from shap) (0.58.1)\n",
+            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.10/dist-packages (from shap) (2.2.1)\n",
+            "Requirement already satisfied: llvmlite<0.42,>=0.41.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba->shap) (0.41.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->shap) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->shap) (2023.4)\n",
+            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->shap) (1.3.2)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->shap) (3.2.0)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->shap) (1.16.0)\n",
+            "Installing collected packages: slicer, shap\n",
+            "Successfully installed shap-0.44.1 slicer-0.0.7\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install shap"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "omD5f7RHfa-n",
+        "outputId": "3326ae8b-d0e2-403f-f660-9bda73c1ea9c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of train samples are (12906, 83)\n",
+            "Number of test samples are (5792, 82)\n"
+          ]
+        }
+      ],
+      "source": [
+        "#load the dataset\n",
+        "train = pd.read_csv(\"/content/training.csv\")\n",
+        "test = pd.read_csv(\"/content/test.csv\")\n",
+        "print(\"Number of train samples are\",train.shape)\n",
+        "print(\"Number of test samples are\",test.shape)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 342
+        },
+        "id": "dDXeXuvifrLh",
+        "outputId": "f5970af8-df68-4210-baf5-52fc7c176c27"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   patient_id patient_race  payer_type patient_state  patient_zip3  \\\n",
+              "0      475714          NaN    MEDICAID            CA           924   \n",
+              "1      349367        White  COMMERCIAL            CA           928   \n",
+              "2      138632        White  COMMERCIAL            TX           760   \n",
+              "3      617843        White  COMMERCIAL            CA           926   \n",
+              "4      817482          NaN  COMMERCIAL            ID           836   \n",
+              "\n",
+              "   patient_age patient_gender    bmi breast_cancer_diagnosis_code  \\\n",
+              "0           84              F    NaN                       C50919   \n",
+              "1           62              F  28.49                       C50411   \n",
+              "2           43              F  38.09                       C50112   \n",
+              "3           45              F    NaN                       C50212   \n",
+              "4           55              F    NaN                         1749   \n",
+              "\n",
+              "                        breast_cancer_diagnosis_desc  ...   disabled  \\\n",
+              "0  Malignant neoplasm of unsp site of unspecified...  ...  12.871429   \n",
+              "1  Malig neoplm of upper-outer quadrant of right ...  ...   8.957576   \n",
+              "2  Malignant neoplasm of central portion of left ...  ...  11.253333   \n",
+              "3  Malig neoplasm of upper-inner quadrant of left...  ...   8.845238   \n",
+              "4  Malignant neoplasm of breast (female), unspeci...  ...  15.276000   \n",
+              "\n",
+              "     poverty limited_english commute_time health_uninsured    veteran  \\\n",
+              "0  22.542857       10.100000    27.814286        11.200000   3.500000   \n",
+              "1  10.109091        8.057576    30.606061         7.018182   4.103030   \n",
+              "2   9.663333        3.356667    31.394915        15.066667   7.446667   \n",
+              "3   8.688095        5.280952    27.561905         4.404762   4.809524   \n",
+              "4  11.224000        1.946000    26.170213        12.088000  13.106000   \n",
+              "\n",
+              "       Ozone      PM25        N02  DiagPeriodL90D  \n",
+              "0  52.237210  8.650555  18.606528               1  \n",
+              "1  42.301121  8.487175  20.113179               1  \n",
+              "2  40.108207  7.642753  14.839351               1  \n",
+              "3  42.070075  7.229393  15.894123               0  \n",
+              "4  41.356058  4.110749  11.722197               0  \n",
+              "\n",
+              "[5 rows x 83 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-cda82860-bf1c-4968-910e-9ca501d3484e\" 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>patient_id</th>\n",
+              "      <th>patient_race</th>\n",
+              "      <th>payer_type</th>\n",
+              "      <th>patient_state</th>\n",
+              "      <th>patient_zip3</th>\n",
+              "      <th>patient_age</th>\n",
+              "      <th>patient_gender</th>\n",
+              "      <th>bmi</th>\n",
+              "      <th>breast_cancer_diagnosis_code</th>\n",
+              "      <th>breast_cancer_diagnosis_desc</th>\n",
+              "      <th>...</th>\n",
+              "      <th>disabled</th>\n",
+              "      <th>poverty</th>\n",
+              "      <th>limited_english</th>\n",
+              "      <th>commute_time</th>\n",
+              "      <th>health_uninsured</th>\n",
+              "      <th>veteran</th>\n",
+              "      <th>Ozone</th>\n",
+              "      <th>PM25</th>\n",
+              "      <th>N02</th>\n",
+              "      <th>DiagPeriodL90D</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>475714</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>MEDICAID</td>\n",
+              "      <td>CA</td>\n",
+              "      <td>924</td>\n",
+              "      <td>84</td>\n",
+              "      <td>F</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C50919</td>\n",
+              "      <td>Malignant neoplasm of unsp site of unspecified...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>12.871429</td>\n",
+              "      <td>22.542857</td>\n",
+              "      <td>10.100000</td>\n",
+              "      <td>27.814286</td>\n",
+              "      <td>11.200000</td>\n",
+              "      <td>3.500000</td>\n",
+              "      <td>52.237210</td>\n",
+              "      <td>8.650555</td>\n",
+              "      <td>18.606528</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>349367</td>\n",
+              "      <td>White</td>\n",
+              "      <td>COMMERCIAL</td>\n",
+              "      <td>CA</td>\n",
+              "      <td>928</td>\n",
+              "      <td>62</td>\n",
+              "      <td>F</td>\n",
+              "      <td>28.49</td>\n",
+              "      <td>C50411</td>\n",
+              "      <td>Malig neoplm of upper-outer quadrant of right ...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>8.957576</td>\n",
+              "      <td>10.109091</td>\n",
+              "      <td>8.057576</td>\n",
+              "      <td>30.606061</td>\n",
+              "      <td>7.018182</td>\n",
+              "      <td>4.103030</td>\n",
+              "      <td>42.301121</td>\n",
+              "      <td>8.487175</td>\n",
+              "      <td>20.113179</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>138632</td>\n",
+              "      <td>White</td>\n",
+              "      <td>COMMERCIAL</td>\n",
+              "      <td>TX</td>\n",
+              "      <td>760</td>\n",
+              "      <td>43</td>\n",
+              "      <td>F</td>\n",
+              "      <td>38.09</td>\n",
+              "      <td>C50112</td>\n",
+              "      <td>Malignant neoplasm of central portion of left ...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>11.253333</td>\n",
+              "      <td>9.663333</td>\n",
+              "      <td>3.356667</td>\n",
+              "      <td>31.394915</td>\n",
+              "      <td>15.066667</td>\n",
+              "      <td>7.446667</td>\n",
+              "      <td>40.108207</td>\n",
+              "      <td>7.642753</td>\n",
+              "      <td>14.839351</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>617843</td>\n",
+              "      <td>White</td>\n",
+              "      <td>COMMERCIAL</td>\n",
+              "      <td>CA</td>\n",
+              "      <td>926</td>\n",
+              "      <td>45</td>\n",
+              "      <td>F</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C50212</td>\n",
+              "      <td>Malig neoplasm of upper-inner quadrant of left...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>8.845238</td>\n",
+              "      <td>8.688095</td>\n",
+              "      <td>5.280952</td>\n",
+              "      <td>27.561905</td>\n",
+              "      <td>4.404762</td>\n",
+              "      <td>4.809524</td>\n",
+              "      <td>42.070075</td>\n",
+              "      <td>7.229393</td>\n",
+              "      <td>15.894123</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>817482</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>COMMERCIAL</td>\n",
+              "      <td>ID</td>\n",
+              "      <td>836</td>\n",
+              "      <td>55</td>\n",
+              "      <td>F</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1749</td>\n",
+              "      <td>Malignant neoplasm of breast (female), unspeci...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>15.276000</td>\n",
+              "      <td>11.224000</td>\n",
+              "      <td>1.946000</td>\n",
+              "      <td>26.170213</td>\n",
+              "      <td>12.088000</td>\n",
+              "      <td>13.106000</td>\n",
+              "      <td>41.356058</td>\n",
+              "      <td>4.110749</td>\n",
+              "      <td>11.722197</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 83 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cda82860-bf1c-4968-910e-9ca501d3484e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\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",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\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-cda82860-bf1c-4968-910e-9ca501d3484e 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-cda82860-bf1c-4968-910e-9ca501d3484e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-1be99240-afc2-416f-bc9d-cb8dd8b99d7d\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1be99240-afc2-416f-bc9d-cb8dd8b99d7d')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "    <g>\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\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-1be99240-afc2-416f-bc9d-cb8dd8b99d7d button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ],
+      "source": [
+        "train.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 662
+        },
+        "id": "REmSQMB5fxD4",
+        "outputId": "67fbe4b9-620e-41b4-ebe8-4088deb4a32f"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "          patient_id  patient_zip3   patient_age          bmi    population  \\\n",
+              "count   12906.000000  12906.000000  12906.000000  3941.000000  12905.000000   \n",
+              "mean   547381.196033    573.754300     59.183326    28.984539  20744.441237   \n",
+              "std    260404.959974    275.447534     13.335216     5.696906  13886.903756   \n",
+              "min    100063.000000    101.000000     18.000000    14.000000    635.545455   \n",
+              "25%    321517.000000    331.000000     50.000000    24.660000   9463.896552   \n",
+              "50%    543522.000000    554.000000     59.000000    28.190000  19154.190480   \n",
+              "75%    772671.750000    846.000000     67.000000    32.920000  30021.278690   \n",
+              "max    999896.000000    999.000000     91.000000    85.000000  71374.131580   \n",
+              "\n",
+              "            density    age_median  age_under_10  age_10_to_19       age_20s  \\\n",
+              "count  12905.000000  12905.000000  12905.000000  12905.000000  12905.000000   \n",
+              "mean    1581.950419     40.502259     11.122784     12.945265     13.290376   \n",
+              "std     2966.305306      4.036963      1.512376      1.923974      3.354103   \n",
+              "min        0.916667     20.600000      0.000000      6.314286      5.925000   \n",
+              "25%      171.857143     37.129825     10.160000     11.741176     11.013415   \n",
+              "50%      700.337500     40.639344     11.039216     12.923944     12.538095   \n",
+              "75%     1666.515385     42.934783     12.190000     14.019767     14.971053   \n",
+              "max    21172.000000     54.570000     17.675000     35.300000     62.100000   \n",
+              "\n",
+              "       ...      disabled       poverty  limited_english  commute_time  \\\n",
+              "count  ...  12905.000000  12902.000000     12902.000000  12905.000000   \n",
+              "mean   ...     13.335299     13.406950         4.474956     27.978387   \n",
+              "std    ...      3.690949      5.222495         4.837085      5.083939   \n",
+              "min    ...      4.600000      3.433333         0.000000     12.460784   \n",
+              "25%    ...     10.270492      9.663333         0.994444     24.933333   \n",
+              "50%    ...     12.884000     12.177778         2.747222     27.788235   \n",
+              "75%    ...     15.555405     16.635556         5.976000     30.709375   \n",
+              "max    ...     35.155556     38.347826        26.755000     48.020000   \n",
+              "\n",
+              "       health_uninsured       veteran         Ozone          PM25  \\\n",
+              "count      12905.000000  12905.000000  12877.000000  12877.000000   \n",
+              "mean           8.575284      7.083376     39.822352      7.475221   \n",
+              "std            4.203482      3.109022      3.559492      1.516499   \n",
+              "min            2.440000      1.200000     30.939316      2.636008   \n",
+              "25%            5.618750      4.929688     37.698880      6.651215   \n",
+              "50%            7.465714      6.847059     39.108249      7.686577   \n",
+              "75%           10.617442      8.620000     41.136513      8.276922   \n",
+              "max           27.566102     25.200000     52.237210     11.169408   \n",
+              "\n",
+              "                N02  DiagPeriodL90D  \n",
+              "count  12877.000000    12906.000000  \n",
+              "mean      16.098988        0.624516  \n",
+              "std        5.842501        0.484266  \n",
+              "min        2.760371        0.000000  \n",
+              "25%       11.280694        0.000000  \n",
+              "50%       15.589148        1.000000  \n",
+              "75%       20.801880        1.000000  \n",
+              "max       31.504775        1.000000  \n",
+              "\n",
+              "[8 rows x 72 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-24095d94-d09a-4ee5-83c0-92e4de1cc3ef\" 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>patient_id</th>\n",
+              "      <th>patient_zip3</th>\n",
+              "      <th>patient_age</th>\n",
+              "      <th>bmi</th>\n",
+              "      <th>population</th>\n",
+              "      <th>density</th>\n",
+              "      <th>age_median</th>\n",
+              "      <th>age_under_10</th>\n",
+              "      <th>age_10_to_19</th>\n",
+              "      <th>age_20s</th>\n",
+              "      <th>...</th>\n",
+              "      <th>disabled</th>\n",
+              "      <th>poverty</th>\n",
+              "      <th>limited_english</th>\n",
+              "      <th>commute_time</th>\n",
+              "      <th>health_uninsured</th>\n",
+              "      <th>veteran</th>\n",
+              "      <th>Ozone</th>\n",
+              "      <th>PM25</th>\n",
+              "      <th>N02</th>\n",
+              "      <th>DiagPeriodL90D</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12906.000000</td>\n",
+              "      <td>12906.000000</td>\n",
+              "      <td>12906.000000</td>\n",
+              "      <td>3941.000000</td>\n",
+              "      <td>12905.000000</td>\n",
+              "      <td>12905.000000</td>\n",
+              "      <td>12905.000000</td>\n",
+              "      <td>12905.000000</td>\n",
+              "      <td>12905.000000</td>\n",
+              "      <td>12905.000000</td>\n",
+              "      <td>...</td>\n",
+              "      <td>12905.000000</td>\n",
+              "      <td>12902.000000</td>\n",
+              "      <td>12902.000000</td>\n",
+              "      <td>12905.000000</td>\n",
+              "      <td>12905.000000</td>\n",
+              "      <td>12905.000000</td>\n",
+              "      <td>12877.000000</td>\n",
+              "      <td>12877.000000</td>\n",
+              "      <td>12877.000000</td>\n",
+              "      <td>12906.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>547381.196033</td>\n",
+              "      <td>573.754300</td>\n",
+              "      <td>59.183326</td>\n",
+              "      <td>28.984539</td>\n",
+              "      <td>20744.441237</td>\n",
+              "      <td>1581.950419</td>\n",
+              "      <td>40.502259</td>\n",
+              "      <td>11.122784</td>\n",
+              "      <td>12.945265</td>\n",
+              "      <td>13.290376</td>\n",
+              "      <td>...</td>\n",
+              "      <td>13.335299</td>\n",
+              "      <td>13.406950</td>\n",
+              "      <td>4.474956</td>\n",
+              "      <td>27.978387</td>\n",
+              "      <td>8.575284</td>\n",
+              "      <td>7.083376</td>\n",
+              "      <td>39.822352</td>\n",
+              "      <td>7.475221</td>\n",
+              "      <td>16.098988</td>\n",
+              "      <td>0.624516</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>260404.959974</td>\n",
+              "      <td>275.447534</td>\n",
+              "      <td>13.335216</td>\n",
+              "      <td>5.696906</td>\n",
+              "      <td>13886.903756</td>\n",
+              "      <td>2966.305306</td>\n",
+              "      <td>4.036963</td>\n",
+              "      <td>1.512376</td>\n",
+              "      <td>1.923974</td>\n",
+              "      <td>3.354103</td>\n",
+              "      <td>...</td>\n",
+              "      <td>3.690949</td>\n",
+              "      <td>5.222495</td>\n",
+              "      <td>4.837085</td>\n",
+              "      <td>5.083939</td>\n",
+              "      <td>4.203482</td>\n",
+              "      <td>3.109022</td>\n",
+              "      <td>3.559492</td>\n",
+              "      <td>1.516499</td>\n",
+              "      <td>5.842501</td>\n",
+              "      <td>0.484266</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>100063.000000</td>\n",
+              "      <td>101.000000</td>\n",
+              "      <td>18.000000</td>\n",
+              "      <td>14.000000</td>\n",
+              "      <td>635.545455</td>\n",
+              "      <td>0.916667</td>\n",
+              "      <td>20.600000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>6.314286</td>\n",
+              "      <td>5.925000</td>\n",
+              "      <td>...</td>\n",
+              "      <td>4.600000</td>\n",
+              "      <td>3.433333</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>12.460784</td>\n",
+              "      <td>2.440000</td>\n",
+              "      <td>1.200000</td>\n",
+              "      <td>30.939316</td>\n",
+              "      <td>2.636008</td>\n",
+              "      <td>2.760371</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>321517.000000</td>\n",
+              "      <td>331.000000</td>\n",
+              "      <td>50.000000</td>\n",
+              "      <td>24.660000</td>\n",
+              "      <td>9463.896552</td>\n",
+              "      <td>171.857143</td>\n",
+              "      <td>37.129825</td>\n",
+              "      <td>10.160000</td>\n",
+              "      <td>11.741176</td>\n",
+              "      <td>11.013415</td>\n",
+              "      <td>...</td>\n",
+              "      <td>10.270492</td>\n",
+              "      <td>9.663333</td>\n",
+              "      <td>0.994444</td>\n",
+              "      <td>24.933333</td>\n",
+              "      <td>5.618750</td>\n",
+              "      <td>4.929688</td>\n",
+              "      <td>37.698880</td>\n",
+              "      <td>6.651215</td>\n",
+              "      <td>11.280694</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>543522.000000</td>\n",
+              "      <td>554.000000</td>\n",
+              "      <td>59.000000</td>\n",
+              "      <td>28.190000</td>\n",
+              "      <td>19154.190480</td>\n",
+              "      <td>700.337500</td>\n",
+              "      <td>40.639344</td>\n",
+              "      <td>11.039216</td>\n",
+              "      <td>12.923944</td>\n",
+              "      <td>12.538095</td>\n",
+              "      <td>...</td>\n",
+              "      <td>12.884000</td>\n",
+              "      <td>12.177778</td>\n",
+              "      <td>2.747222</td>\n",
+              "      <td>27.788235</td>\n",
+              "      <td>7.465714</td>\n",
+              "      <td>6.847059</td>\n",
+              "      <td>39.108249</td>\n",
+              "      <td>7.686577</td>\n",
+              "      <td>15.589148</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>772671.750000</td>\n",
+              "      <td>846.000000</td>\n",
+              "      <td>67.000000</td>\n",
+              "      <td>32.920000</td>\n",
+              "      <td>30021.278690</td>\n",
+              "      <td>1666.515385</td>\n",
+              "      <td>42.934783</td>\n",
+              "      <td>12.190000</td>\n",
+              "      <td>14.019767</td>\n",
+              "      <td>14.971053</td>\n",
+              "      <td>...</td>\n",
+              "      <td>15.555405</td>\n",
+              "      <td>16.635556</td>\n",
+              "      <td>5.976000</td>\n",
+              "      <td>30.709375</td>\n",
+              "      <td>10.617442</td>\n",
+              "      <td>8.620000</td>\n",
+              "      <td>41.136513</td>\n",
+              "      <td>8.276922</td>\n",
+              "      <td>20.801880</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>999896.000000</td>\n",
+              "      <td>999.000000</td>\n",
+              "      <td>91.000000</td>\n",
+              "      <td>85.000000</td>\n",
+              "      <td>71374.131580</td>\n",
+              "      <td>21172.000000</td>\n",
+              "      <td>54.570000</td>\n",
+              "      <td>17.675000</td>\n",
+              "      <td>35.300000</td>\n",
+              "      <td>62.100000</td>\n",
+              "      <td>...</td>\n",
+              "      <td>35.155556</td>\n",
+              "      <td>38.347826</td>\n",
+              "      <td>26.755000</td>\n",
+              "      <td>48.020000</td>\n",
+              "      <td>27.566102</td>\n",
+              "      <td>25.200000</td>\n",
+              "      <td>52.237210</td>\n",
+              "      <td>11.169408</td>\n",
+              "      <td>31.504775</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 72 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-24095d94-d09a-4ee5-83c0-92e4de1cc3ef')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\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-24095d94-d09a-4ee5-83c0-92e4de1cc3ef 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-24095d94-d09a-4ee5-83c0-92e4de1cc3ef');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-6629aa0b-e946-49f7-b58e-5331d6ca2b63\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6629aa0b-e946-49f7-b58e-5331d6ca2b63')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\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-6629aa0b-e946-49f7-b58e-5331d6ca2b63 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_adcceb61-da37-44d0-b72c-4361619b196b\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\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-generate: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-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate: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",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('train_descriptive_stats')\"\n",
+              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_adcceb61-da37-44d0-b72c-4361619b196b button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('train_descriptive_stats');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "          patient_id  patient_zip3  patient_age          bmi    population  \\\n",
+              "count    5792.000000   5792.000000  5792.000000  1777.000000   5792.000000   \n",
+              "mean   549946.787983    570.217887    59.274862    28.900073  20266.872152   \n",
+              "std    260988.833446    275.314510    13.076323     5.609491  13694.738314   \n",
+              "min    100266.000000    101.000000    18.000000    14.000000    829.515152   \n",
+              "25%    320284.500000    329.000000    50.000000    24.600000   8863.550000   \n",
+              "50%    553042.000000    554.000000    59.000000    28.150000  18711.016665   \n",
+              "75%    778552.250000    836.000000    67.000000    32.860000  28996.772730   \n",
+              "max    999890.000000    996.000000    91.000000    43.900000  71374.131580   \n",
+              "\n",
+              "            density   age_median  age_under_10  age_10_to_19      age_20s  \\\n",
+              "count   5792.000000  5792.000000   5792.000000   5792.000000  5792.000000   \n",
+              "mean    1510.471786    40.575472     11.119250     12.919191    13.218348   \n",
+              "std     2883.409750     4.029852      1.478579      1.909698     3.294389   \n",
+              "min        0.821739    20.600000      0.000000      6.314286     5.925000   \n",
+              "25%      161.925000    37.190476     10.160000     11.726471    10.996226   \n",
+              "50%      626.236667    40.640909     11.039216     12.923944    12.531646   \n",
+              "75%     1612.851111    43.085938     12.143396     13.904830    14.937500   \n",
+              "max    21172.000000    54.570000     16.481818     35.300000    62.100000   \n",
+              "\n",
+              "       ...     hispanic     disabled      poverty  limited_english  \\\n",
+              "count  ...  5792.000000  5792.000000  5791.000000      5791.000000   \n",
+              "mean   ...    18.036444    13.483574    13.435855         4.292997   \n",
+              "std    ...    16.716396     3.693358     5.105505         4.673928   \n",
+              "min    ...     0.194444     4.600000     3.433333         0.000000   \n",
+              "25%    ...     4.698529    10.388889     9.995000         0.878049   \n",
+              "50%    ...    11.842623    13.093333    12.218182         2.690196   \n",
+              "75%    ...    27.594872    15.802128    16.444444         5.863830   \n",
+              "max    ...    91.005085    35.155556    38.347826        22.591667   \n",
+              "\n",
+              "       commute_time  health_uninsured      veteran        Ozone         PM25  \\\n",
+              "count   5792.000000       5792.000000  5792.000000  5778.000000  5778.000000   \n",
+              "mean      27.915686          8.636586     7.171897    39.788771     7.435363   \n",
+              "std        4.997893          4.142770     3.107104     3.486773     1.528655   \n",
+              "min       13.722078          2.440000     1.200000    30.939316     2.636008   \n",
+              "25%       24.932500          5.772727     5.078571    37.722740     6.590523   \n",
+              "50%       27.561905          7.468000     7.070370    39.127948     7.666953   \n",
+              "75%       30.709375         10.820000     8.774510    41.075217     8.276922   \n",
+              "max       48.020000         27.566102    21.426667    52.237210    11.169408   \n",
+              "\n",
+              "               N02  \n",
+              "count  5778.000000  \n",
+              "mean     15.936645  \n",
+              "std       5.894659  \n",
+              "min       2.760371  \n",
+              "25%      11.100666  \n",
+              "50%      15.246437  \n",
+              "75%      20.348878  \n",
+              "max      31.504775  \n",
+              "\n",
+              "[8 rows x 71 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4aeef046-a631-46e9-abe3-47608442f8b7\" 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>patient_id</th>\n",
+              "      <th>patient_zip3</th>\n",
+              "      <th>patient_age</th>\n",
+              "      <th>bmi</th>\n",
+              "      <th>population</th>\n",
+              "      <th>density</th>\n",
+              "      <th>age_median</th>\n",
+              "      <th>age_under_10</th>\n",
+              "      <th>age_10_to_19</th>\n",
+              "      <th>age_20s</th>\n",
+              "      <th>...</th>\n",
+              "      <th>hispanic</th>\n",
+              "      <th>disabled</th>\n",
+              "      <th>poverty</th>\n",
+              "      <th>limited_english</th>\n",
+              "      <th>commute_time</th>\n",
+              "      <th>health_uninsured</th>\n",
+              "      <th>veteran</th>\n",
+              "      <th>Ozone</th>\n",
+              "      <th>PM25</th>\n",
+              "      <th>N02</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5792.000000</td>\n",
+              "      <td>5792.000000</td>\n",
+              "      <td>5792.000000</td>\n",
+              "      <td>1777.000000</td>\n",
+              "      <td>5792.000000</td>\n",
+              "      <td>5792.000000</td>\n",
+              "      <td>5792.000000</td>\n",
+              "      <td>5792.000000</td>\n",
+              "      <td>5792.000000</td>\n",
+              "      <td>5792.000000</td>\n",
+              "      <td>...</td>\n",
+              "      <td>5792.000000</td>\n",
+              "      <td>5792.000000</td>\n",
+              "      <td>5791.000000</td>\n",
+              "      <td>5791.000000</td>\n",
+              "      <td>5792.000000</td>\n",
+              "      <td>5792.000000</td>\n",
+              "      <td>5792.000000</td>\n",
+              "      <td>5778.000000</td>\n",
+              "      <td>5778.000000</td>\n",
+              "      <td>5778.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>549946.787983</td>\n",
+              "      <td>570.217887</td>\n",
+              "      <td>59.274862</td>\n",
+              "      <td>28.900073</td>\n",
+              "      <td>20266.872152</td>\n",
+              "      <td>1510.471786</td>\n",
+              "      <td>40.575472</td>\n",
+              "      <td>11.119250</td>\n",
+              "      <td>12.919191</td>\n",
+              "      <td>13.218348</td>\n",
+              "      <td>...</td>\n",
+              "      <td>18.036444</td>\n",
+              "      <td>13.483574</td>\n",
+              "      <td>13.435855</td>\n",
+              "      <td>4.292997</td>\n",
+              "      <td>27.915686</td>\n",
+              "      <td>8.636586</td>\n",
+              "      <td>7.171897</td>\n",
+              "      <td>39.788771</td>\n",
+              "      <td>7.435363</td>\n",
+              "      <td>15.936645</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>260988.833446</td>\n",
+              "      <td>275.314510</td>\n",
+              "      <td>13.076323</td>\n",
+              "      <td>5.609491</td>\n",
+              "      <td>13694.738314</td>\n",
+              "      <td>2883.409750</td>\n",
+              "      <td>4.029852</td>\n",
+              "      <td>1.478579</td>\n",
+              "      <td>1.909698</td>\n",
+              "      <td>3.294389</td>\n",
+              "      <td>...</td>\n",
+              "      <td>16.716396</td>\n",
+              "      <td>3.693358</td>\n",
+              "      <td>5.105505</td>\n",
+              "      <td>4.673928</td>\n",
+              "      <td>4.997893</td>\n",
+              "      <td>4.142770</td>\n",
+              "      <td>3.107104</td>\n",
+              "      <td>3.486773</td>\n",
+              "      <td>1.528655</td>\n",
+              "      <td>5.894659</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>100266.000000</td>\n",
+              "      <td>101.000000</td>\n",
+              "      <td>18.000000</td>\n",
+              "      <td>14.000000</td>\n",
+              "      <td>829.515152</td>\n",
+              "      <td>0.821739</td>\n",
+              "      <td>20.600000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>6.314286</td>\n",
+              "      <td>5.925000</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.194444</td>\n",
+              "      <td>4.600000</td>\n",
+              "      <td>3.433333</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>13.722078</td>\n",
+              "      <td>2.440000</td>\n",
+              "      <td>1.200000</td>\n",
+              "      <td>30.939316</td>\n",
+              "      <td>2.636008</td>\n",
+              "      <td>2.760371</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>320284.500000</td>\n",
+              "      <td>329.000000</td>\n",
+              "      <td>50.000000</td>\n",
+              "      <td>24.600000</td>\n",
+              "      <td>8863.550000</td>\n",
+              "      <td>161.925000</td>\n",
+              "      <td>37.190476</td>\n",
+              "      <td>10.160000</td>\n",
+              "      <td>11.726471</td>\n",
+              "      <td>10.996226</td>\n",
+              "      <td>...</td>\n",
+              "      <td>4.698529</td>\n",
+              "      <td>10.388889</td>\n",
+              "      <td>9.995000</td>\n",
+              "      <td>0.878049</td>\n",
+              "      <td>24.932500</td>\n",
+              "      <td>5.772727</td>\n",
+              "      <td>5.078571</td>\n",
+              "      <td>37.722740</td>\n",
+              "      <td>6.590523</td>\n",
+              "      <td>11.100666</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>553042.000000</td>\n",
+              "      <td>554.000000</td>\n",
+              "      <td>59.000000</td>\n",
+              "      <td>28.150000</td>\n",
+              "      <td>18711.016665</td>\n",
+              "      <td>626.236667</td>\n",
+              "      <td>40.640909</td>\n",
+              "      <td>11.039216</td>\n",
+              "      <td>12.923944</td>\n",
+              "      <td>12.531646</td>\n",
+              "      <td>...</td>\n",
+              "      <td>11.842623</td>\n",
+              "      <td>13.093333</td>\n",
+              "      <td>12.218182</td>\n",
+              "      <td>2.690196</td>\n",
+              "      <td>27.561905</td>\n",
+              "      <td>7.468000</td>\n",
+              "      <td>7.070370</td>\n",
+              "      <td>39.127948</td>\n",
+              "      <td>7.666953</td>\n",
+              "      <td>15.246437</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>778552.250000</td>\n",
+              "      <td>836.000000</td>\n",
+              "      <td>67.000000</td>\n",
+              "      <td>32.860000</td>\n",
+              "      <td>28996.772730</td>\n",
+              "      <td>1612.851111</td>\n",
+              "      <td>43.085938</td>\n",
+              "      <td>12.143396</td>\n",
+              "      <td>13.904830</td>\n",
+              "      <td>14.937500</td>\n",
+              "      <td>...</td>\n",
+              "      <td>27.594872</td>\n",
+              "      <td>15.802128</td>\n",
+              "      <td>16.444444</td>\n",
+              "      <td>5.863830</td>\n",
+              "      <td>30.709375</td>\n",
+              "      <td>10.820000</td>\n",
+              "      <td>8.774510</td>\n",
+              "      <td>41.075217</td>\n",
+              "      <td>8.276922</td>\n",
+              "      <td>20.348878</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>999890.000000</td>\n",
+              "      <td>996.000000</td>\n",
+              "      <td>91.000000</td>\n",
+              "      <td>43.900000</td>\n",
+              "      <td>71374.131580</td>\n",
+              "      <td>21172.000000</td>\n",
+              "      <td>54.570000</td>\n",
+              "      <td>16.481818</td>\n",
+              "      <td>35.300000</td>\n",
+              "      <td>62.100000</td>\n",
+              "      <td>...</td>\n",
+              "      <td>91.005085</td>\n",
+              "      <td>35.155556</td>\n",
+              "      <td>38.347826</td>\n",
+              "      <td>22.591667</td>\n",
+              "      <td>48.020000</td>\n",
+              "      <td>27.566102</td>\n",
+              "      <td>21.426667</td>\n",
+              "      <td>52.237210</td>\n",
+              "      <td>11.169408</td>\n",
+              "      <td>31.504775</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 71 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4aeef046-a631-46e9-abe3-47608442f8b7')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\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-4aeef046-a631-46e9-abe3-47608442f8b7 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-4aeef046-a631-46e9-abe3-47608442f8b7');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-2cf56290-aa28-4955-91ec-33e173d6dc9e\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2cf56290-aa28-4955-91ec-33e173d6dc9e')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\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-2cf56290-aa28-4955-91ec-33e173d6dc9e button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_cf0cbaf3-c3a3-4145-94a4-2cc7b24e9457\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\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-generate: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-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate: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",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('test_descriptive_stats')\"\n",
+              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_cf0cbaf3-c3a3-4145-94a4-2cc7b24e9457 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('test_descriptive_stats');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Descriptive statistics for the training set\n",
+        "train_descriptive_stats = train.describe()\n",
+        "\n",
+        "# Descriptive statistics for the test set\n",
+        "test_descriptive_stats = test.describe()\n",
+        "\n",
+        "# Display the results\n",
+        "display(train_descriptive_stats)\n",
+        "\n",
+        "display(test_descriptive_stats)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EPiuK-27f2mX",
+        "outputId": "f31c7d64-192d-4bfd-9192-dac95d6666dd"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Data types and their corresponding count of unique values:\n",
+            "float64: 68\n",
+            "object: 11\n",
+            "int64: 4\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Get the data types of each column\n",
+        "data_types = train.dtypes\n",
+        "\n",
+        "# Count the number of unique values for each data type\n",
+        "unique_counts = data_types.value_counts()\n",
+        "\n",
+        "# Print the results\n",
+        "print(\"Data types and their corresponding count of unique values:\")\n",
+        "for data_type, count in unique_counts.items():\n",
+        "    print(f\"{data_type}: {count}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CxeE47hLf3Kn",
+        "outputId": "9ac9048d-fbb1-45dd-b6f1-5fe8f97ed5fc"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Missing values in the training data:\n",
+            "patient_race                         6385\n",
+            "payer_type                           1803\n",
+            "patient_state                          51\n",
+            "bmi                                  8965\n",
+            "metastatic_first_novel_treatment    12882\n",
+            "                                    ...  \n",
+            "health_uninsured                        1\n",
+            "veteran                                 1\n",
+            "Ozone                                  29\n",
+            "PM25                                   29\n",
+            "N02                                    29\n",
+            "Length: 75, dtype: int64\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Check for missing values in the training data\n",
+        "missing_values_train = train.isnull().sum()\n",
+        "print(\"Missing values in the training data:\")\n",
+        "print(missing_values_train[missing_values_train > 0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "7K3j8SnYgIfH",
+        "outputId": "454f8215-6b67-459e-a566-192a72cf7501"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Missing values in training Data')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2500x1100 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "names = train.columns\n",
+        "plt.figure(figsize = (25,11))\n",
+        "sns.heatmap(train.isna().values, xticklabels=train.columns)\n",
+        "plt.title(\"Missing values in training Data\", size=20)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QP0ARMRYgI7m",
+        "outputId": "7442d74e-3b88-4e8b-f2ce-d99a5d4386d3"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1mmetastatic_first_novel_treatment\u001b[0m has \u001b[1m\u001b[93m12882\u001b[0m missing values, which is \u001b[1m\u001b[93m99.81%\u001b[0m of the column.\n",
+            "\u001b[1mmetastatic_first_novel_treatment_type\u001b[0m has \u001b[1m\u001b[93m12882\u001b[0m missing values, which is \u001b[1m\u001b[93m99.81%\u001b[0m of the column.\n",
+            "\u001b[1mbmi\u001b[0m has \u001b[1m\u001b[93m8965\u001b[0m missing values, which is \u001b[1m\u001b[93m69.46%\u001b[0m of the column.\n",
+            "\u001b[1mpatient_race\u001b[0m has \u001b[1m\u001b[93m6385\u001b[0m missing values, which is \u001b[1m\u001b[93m49.47%\u001b[0m of the column.\n",
+            "\u001b[1mpayer_type\u001b[0m has \u001b[1m\u001b[93m1803\u001b[0m missing values, which is \u001b[1m\u001b[93m13.97%\u001b[0m of the column.\n",
+            "\u001b[1mRegion\u001b[0m has \u001b[1m\u001b[93m52\u001b[0m missing values, which is \u001b[1m\u001b[93m0.40%\u001b[0m of the column.\n",
+            "\u001b[1mDivision\u001b[0m has \u001b[1m\u001b[93m52\u001b[0m missing values, which is \u001b[1m\u001b[93m0.40%\u001b[0m of the column.\n",
+            "\u001b[1mpatient_state\u001b[0m has \u001b[1m\u001b[93m51\u001b[0m missing values, which is \u001b[1m\u001b[93m0.40%\u001b[0m of the column.\n",
+            "\u001b[1mPM25\u001b[0m has \u001b[1m\u001b[93m29\u001b[0m missing values, which is \u001b[1m\u001b[93m0.22%\u001b[0m of the column.\n",
+            "\u001b[1mOzone\u001b[0m has \u001b[1m\u001b[93m29\u001b[0m missing values, which is \u001b[1m\u001b[93m0.22%\u001b[0m of the column.\n",
+            "\u001b[1mN02\u001b[0m has \u001b[1m\u001b[93m29\u001b[0m missing values, which is \u001b[1m\u001b[93m0.22%\u001b[0m of the column.\n",
+            "\u001b[1mincome_household_75_to_100\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mincome_household_150_over\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mincome_household_15_to_20\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mincome_household_20_to_25\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mincome_household_25_to_35\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mincome_household_35_to_50\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mincome_household_50_to_75\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mincome_household_100_to_150\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mincome_household_six_figure\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mincome_household_under_5\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mhome_ownership\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mhome_value\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mrent_median\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mrent_burden\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mfarmer\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mself_employed\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mincome_household_5_to_10\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mincome_household_10_to_15\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mincome_household_median\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mfamily_dual_income\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mlimited_english\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mpoverty\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mfamily_size\u001b[0m has \u001b[1m\u001b[93m4\u001b[0m missing values, which is \u001b[1m\u001b[93m0.03%\u001b[0m of the column.\n",
+            "\u001b[1mrace_native\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mrace_white\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mlabor_force_participation\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1munemployment_rate\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mpopulation\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mdensity\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mveteran\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mhealth_uninsured\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mcommute_time\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1meducation_college_or_above\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mrace_pacific\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mrace_black\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mdisabled\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mhispanic\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mrace_asian\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mrace_multiple\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mrace_other\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1meducation_stem_degree\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mage_under_10\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1meducation_graduate\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1meducation_bachelors\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mage_20s\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mage_30s\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mage_40s\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mage_50s\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mage_60s\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mage_70s\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mage_over_80\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mmale\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mfemale\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mmarried\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mdivorced\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mnever_married\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mwidowed\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mage_median\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mincome_individual_median\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mage_10_to_19\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1meducation_less_highschool\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1meducation_highschool\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1meducation_some_college\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n",
+            "\u001b[1mhousing_units\u001b[0m has \u001b[1m\u001b[93m1\u001b[0m missing values, which is \u001b[1m\u001b[93m0.01%\u001b[0m of the column.\n"
+          ]
+        }
+      ],
+      "source": [
+        "ORANGE, BOLD, RESET = '\\033[93m', '\\033[1m', '\\033[0m'\n",
+        "#check the details of missing value in each feature\n",
+        "missing_info = train.isna().mean() * 100\n",
+        "missing_info = missing_info[missing_info > 0].sort_values(ascending=False)\n",
+        "\n",
+        "for column, missing_percentage in missing_info.items():\n",
+        "    print(f\"{BOLD}{column}{RESET} has {BOLD}{ORANGE}{train[column].isna().sum()}{RESET} missing values, which is {BOLD}{ORANGE}{missing_percentage:.2f}%{RESET} of the column.\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pDlcnSGEgbGJ",
+        "outputId": "e775312c-f934-4c60-dede-5d2caa692d96"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "the columns with > 45% missing values are:\n",
+            "['patient_race' 'bmi' 'metastatic_first_novel_treatment'\n",
+            " 'metastatic_first_novel_treatment_type']\n",
+            "Number of train samples are (12906, 78)\n",
+            "Number of test samples are (5792, 77)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# drop columns with high percentage of missing values\n",
+        "lst = train.isna().sum() / len(train)\n",
+        "p = pd.DataFrame(lst)\n",
+        "p.reset_index(inplace=True)\n",
+        "p.columns = ['a', 'b']\n",
+        "low_count = p[p['b'] > 0.45]\n",
+        "todelete = low_count['a'].values\n",
+        "# print the columns that we chosen to delete.\n",
+        "print(\"\\nthe columns with > 45% missing values are:\")\n",
+        "print(todelete)\n",
+        "train.drop(todelete, axis=1, inplace=True)\n",
+        "test.drop(todelete, axis=1, inplace=True)\n",
+        "\n",
+        "train.drop([\"patient_gender\"],axis =1, inplace=True)\n",
+        "test.drop([\"patient_gender\"],axis =1, inplace=True)\n",
+        "print(\"Number of train samples are\",train.shape)\n",
+        "print(\"Number of test samples are\",test.shape)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "yugBxzc4gl5R"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.impute import SimpleImputer\n",
+        "\n",
+        "# Identify columns with missing values\n",
+        "missing_columns = [col for col in train.columns if train[col].isnull().any()]\n",
+        "\n",
+        "# Count the number of missing values per column\n",
+        "missingvalues_count = train.isna().sum()\n",
+        "\n",
+        "# Create a DataFrame with columns and their corresponding missing values count (excluding columns with no missing values)\n",
+        "missingValues_df = pd.DataFrame(missingvalues_count.rename('Null Values Count')).loc[missingvalues_count.ne(0)]\n",
+        "\n",
+        "# Identify categorical and numerical features\n",
+        "categorical_features = train.select_dtypes(include=['object']).columns\n",
+        "numerical_features = train.select_dtypes('number')\n",
+        "\n",
+        "# Define a function to find the intersection of two lists\n",
+        "def intersection(lst1, lst2):\n",
+        "    lst3 = [value for value in lst1 if value in lst2]\n",
+        "    return lst3\n",
+        "\n",
+        "# Find missing columns that are categorical or numerical\n",
+        "cat_miss = intersection(missing_columns, categorical_features)\n",
+        "num_miss = intersection(missing_columns, numerical_features)\n",
+        "\n",
+        "# Impute missing values in categorical features\n",
+        "if len(cat_miss) > 0:\n",
+        "    cat_imputer = SimpleImputer(missing_values=np.nan, strategy=\"constant\", fill_value=\"unknown\")\n",
+        "    train[cat_miss] = cat_imputer.fit_transform(train[cat_miss])\n",
+        "    test[cat_miss] = cat_imputer.fit_transform(test[cat_miss])\n",
+        "\n",
+        "# Use Simple Imputer for numerical features (using median as an example)\n",
+        "num_imputer = SimpleImputer(missing_values=np.nan, strategy=\"median\")\n",
+        "train[num_miss] = num_imputer.fit_transform(train[num_miss])\n",
+        "test[num_miss] = num_imputer.fit_transform(test[num_miss])\n",
+        "\n",
+        "# Uncomment the following lines to use KNN imputer\n",
+        "# from sklearn.impute import KNNImputer\n",
+        "# knn_imputer = KNNImputer(n_neighbors=10, weights=\"uniform\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lN4r5ET7jN5-",
+        "outputId": "713650e9-70a1-4fcd-dcae-a3188aa706dc"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "patient_id        0\n",
+            "payer_type        0\n",
+            "patient_state     0\n",
+            "patient_zip3      0\n",
+            "patient_age       0\n",
+            "                 ..\n",
+            "veteran           0\n",
+            "Ozone             0\n",
+            "PM25              0\n",
+            "N02               0\n",
+            "DiagPeriodL90D    0\n",
+            "Length: 78, dtype: int64\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(train.isnull().sum())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "k5cOjDF0jUqw",
+        "outputId": "c0665311-1f0c-4353-e45c-38a87c735389"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "\n",
+            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+            "\n",
+            "\n",
+            "\n",
+            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 3000x600 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "\n",
+            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+            "\n",
+            "\n",
+            "\n",
+            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 3000x600 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "\n",
+            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+            "\n",
+            "\n",
+            "\n",
+            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 3000x600 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "\n",
+            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+            "\n",
+            "\n",
+            "\n",
+            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 3000x600 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "def plot_categorical_distribution(train, test, categorical_features):\n",
+        "    for feature in categorical_features:\n",
+        "        plt.figure(figsize=(30, 6))\n",
+        "        plt.subplot(1, 2, 1)\n",
+        "        sns.countplot(data=train, x=feature, palette='viridis')\n",
+        "        plt.title(f'Training Set - {feature} Distribution')\n",
+        "\n",
+        "        plt.subplot(1, 2, 2)\n",
+        "        sns.countplot(data=test, x=feature, palette='viridis')\n",
+        "        plt.title(f'Test Set - {feature} Distribution')\n",
+        "\n",
+        "        # Set rotation for x-axis labels\n",
+        "        for label in plt.gca().get_xticklabels():\n",
+        "            label.set_rotation(90)\n",
+        "\n",
+        "        plt.show()\n",
+        "\n",
+        "# List of categorical features\n",
+        "categorical_features_to_check = ['payer_type', 'patient_state', 'Region', 'Division']\n",
+        "\n",
+        "# Plot the distribution\n",
+        "plot_categorical_distribution(train, test, categorical_features_to_check)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qlaK-LS1jZnv",
+        "outputId": "1df4a893-8d3a-495c-b207-21b8cca86d9e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of numerical features: 70\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Identify the target column\n",
+        "target_column = 'DiagPeriodL90D'\n",
+        "\n",
+        "# Select numerical features from the training set\n",
+        "numerical_features = train.select_dtypes('number').columns.tolist()\n",
+        "\n",
+        "# Exclude the target column from the list of numerical features\n",
+        "numerical_features.remove(target_column)\n",
+        "\n",
+        "num_features_count = len(numerical_features)\n",
+        "print(f\"Number of numerical features: {num_features_count}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "IpnWbQFVjjB3",
+        "outputId": "edad566c-4817-4205-9168-8921f34ae56b"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x1500 with 70 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "def visualize_outliers(data, numerical_features, rows, cols):\n",
+        "    fig, axes = plt.subplots(rows, cols, figsize=(20, 15))\n",
+        "\n",
+        "    # Flatten the axes array for ease of indexing\n",
+        "    axes = axes.flatten()\n",
+        "\n",
+        "    for i, feature in enumerate(numerical_features):\n",
+        "        sns.boxplot(x=data[feature], ax=axes[i])\n",
+        "        #axes[i].set_title(f'Box Plot for {feature}')\n",
+        "        axes[i].set_title(f'{feature}')\n",
+        "\n",
+        "    # Remove any empty subplots\n",
+        "    for j in range(i + 1, len(axes)):\n",
+        "        fig.delaxes(axes[j])\n",
+        "\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "# Specify the number of rows and columns for the grid\n",
+        "rows = 10  # You can adjust this based on the number of features\n",
+        "cols = 7\n",
+        "\n",
+        "# Visualize outliers in the training set\n",
+        "visualize_outliers(train, numerical_features, rows, cols)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pdJUM3NejjnO",
+        "outputId": "c6a2fbb5-5573-48b1-b9be-dc050b402631"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "X does not have valid feature names, but IsolationForest was fitted with feature names\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Removed Rows (Outliers):\n",
+            "       patient_id  patient_zip3  patient_age    population       density  \\\n",
+            "107        661451           104           47  58934.160000  16936.584000   \n",
+            "146        846369           246           43   2498.360000     29.392000   \n",
+            "153        915900           941           50  29859.758620   7971.313793   \n",
+            "236        225060           200           60  25266.518520   6156.214815   \n",
+            "380        839781           409           54   2111.100000     18.683333   \n",
+            "...           ...           ...          ...           ...           ...   \n",
+            "12234      266381           414           61   3986.666667     14.750000   \n",
+            "12438      980020           941           44  29859.758620   7971.313793   \n",
+            "12699      735740           409           59   2111.100000     18.683333   \n",
+            "12726      269858           803           64  21435.000000   1413.250000   \n",
+            "12823      884163           947           51  14753.800000   4547.400000   \n",
+            "\n",
+            "       age_median  age_under_10  age_10_to_19    age_20s    age_30s  ...  \\\n",
+            "107     36.216000     13.464000     13.308000  15.064000  13.668000  ...   \n",
+            "146     47.970833      8.679167      9.354167  10.354167   9.508333  ...   \n",
+            "153     38.992593      7.829630      7.711111  16.492593  21.148148  ...   \n",
+            "236     33.053846      8.061538     16.830769  22.807692  17.650000  ...   \n",
+            "380     42.676923      9.877778     12.855556  10.596296  11.522222  ...   \n",
+            "...           ...           ...           ...        ...        ...  ...   \n",
+            "12234   47.283333      9.766667     10.550000   8.383333   9.900000  ...   \n",
+            "12438   38.992593      7.829630      7.711111  16.492593  21.148148  ...   \n",
+            "12699   42.676923      9.877778     12.855556  10.596296  11.522222  ...   \n",
+            "12726   32.000000      6.216667     21.150000  26.000000  10.600000  ...   \n",
+            "12823   36.710000      7.710000     15.930000  21.770000  12.720000  ...   \n",
+            "\n",
+            "        disabled    poverty  limited_english  commute_time  health_uninsured  \\\n",
+            "107    16.100000  25.028000        15.920000     44.944000          7.468000   \n",
+            "146    30.145833  23.245833         0.095833     27.826316          8.204167   \n",
+            "153    10.329630  12.177778        11.629630     32.244444          4.070370   \n",
+            "236     9.400000  19.016667         3.195455     26.684615          2.876923   \n",
+            "380    22.925926  33.737037         0.000000     28.057895          4.718519   \n",
+            "...          ...        ...              ...           ...               ...   \n",
+            "12234  31.300000  28.900000         0.000000     34.400000          6.383333   \n",
+            "12438  10.329630  12.177778        11.629630     32.244444          4.070370   \n",
+            "12699  22.925926  33.737037         0.000000     28.057895          4.718519   \n",
+            "12726   6.566667  17.700000         1.580000     19.816667          3.683333   \n",
+            "12823   8.810000  16.220000         3.290000     30.140000          3.020000   \n",
+            "\n",
+            "        veteran      Ozone      PM25        N02  DiagPeriodL90D  \n",
+            "107    2.520000  36.555194  7.426677  29.089314               0  \n",
+            "146    8.108333  40.629513  6.149121   8.550782               1  \n",
+            "153    3.296296  34.654293  7.290648  14.669646               0  \n",
+            "236    3.507692  39.565648  7.716444  16.868950               1  \n",
+            "380    3.777778  40.601291  6.781696   4.987938               0  \n",
+            "...         ...        ...       ...        ...             ...  \n",
+            "12234  5.000000  41.140709  6.702363   5.235343               0  \n",
+            "12438  3.296296  34.654293  7.290648  14.669646               0  \n",
+            "12699  3.777778  40.601291  6.781696   4.987938               1  \n",
+            "12726  3.216667  47.489305  4.342268  23.053861               1  \n",
+            "12823  2.750000  30.939316  6.243063  18.201716               0  \n",
+            "\n",
+            "[130 rows x 71 columns]\n"
+          ]
+        }
+      ],
+      "source": [
+        "#outlier detection\n",
+        "\n",
+        "#### If you want to inspect outliers or use some type of flag features if the sample is an outlier\n",
+        "from sklearn.neighbors import LocalOutlierFactor\n",
+        "from sklearn.ensemble import IsolationForest\n",
+        "\n",
+        "#iso = LocalOutlierFactor(n_neighbors=35, contamination=0.01)\n",
+        "iso = IsolationForest(contamination=0.01)\n",
+        "num_train = train.select_dtypes('number')\n",
+        "outliers = iso.fit_predict(num_train)\n",
+        "# select all rows that are not outliers\n",
+        "# Identify the indices of outliers in the original dataset\n",
+        "outlier_indices = num_train.index[outliers == -1]\n",
+        "\n",
+        "# Display the rows that were removed\n",
+        "removed_rows = num_train.loc[outlier_indices]\n",
+        "print(\"Removed Rows (Outliers):\")\n",
+        "print(removed_rows)\n",
+        "train = train[outliers!=-1]\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "a5MBWw6Ujpcj",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2ae229ca-72a1-496c-b021-dfbde3d988a2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "\n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "\n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "\n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "\n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "\n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "\n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
+          ]
+        }
+      ],
+      "source": [
+        "#encoding\n",
+        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+        "label_encoder = LabelEncoder()\n",
+        "categorical_features = ['payer_type', 'patient_state', 'Region', 'Division', 'breast_cancer_diagnosis_code',\n",
+        "       'breast_cancer_diagnosis_desc', 'metastatic_cancer_diagnosis_code']\n",
+        "for col in categorical_features:\n",
+        "    train[col] = label_encoder.fit_transform(train[col])\n",
+        "    test[col] = label_encoder.fit_transform(test[col])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "ufhTUT8VjsUm"
+      },
+      "outputs": [],
+      "source": [
+        "#remove columns with low variation\n",
+        "\n",
+        "def remove_feature_with_low_var(df, threshold=0.1):\n",
+        "    for col in df.columns:\n",
+        "        if df[col].std() < threshold:\n",
+        "            df = df.drop([col], axis=1)\n",
+        "    return df\n",
+        "\n",
+        "\n",
+        "#remove correlations\n",
+        "numerical_features = train.select_dtypes('number').columns\n",
+        "num_feature = [col for col in numerical_features]\n",
+        "drop_columns = []\n",
+        "# Create correlation matrix\n",
+        "corr_matrix = train[num_feature].corr().abs()\n",
+        "# Select upper triangle of correlation matrix\n",
+        "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool_))\n",
+        "\n",
+        "# Find index of feature columns with correlation greater than 0.98\n",
+        "to_drop = [column for column in upper.columns if any(upper[column] > 0.98)]\n",
+        "train.drop(to_drop, inplace=True, axis=1)\n",
+        "test.drop(to_drop, inplace=True, axis=1)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "uCcwHMj0jyEI"
+      },
+      "outputs": [],
+      "source": [
+        "#Separating the target variable\n",
+        "\n",
+        "target = train[\"DiagPeriodL90D\"]\n",
+        "train = train.drop([\"DiagPeriodL90D\"],axis =1)\n",
+        "temp_test = test\n",
+        "column_names = train.columns\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "NnqW5lESj6Eg"
+      },
+      "outputs": [],
+      "source": [
+        "## Standardize the data\n",
+        "#scaling\n",
+        "scaler = StandardScaler()\n",
+        "train = scaler.fit_transform(train)\n",
+        "test_scaled = scaler.transform(test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "Ft0Qajmqj8WO",
+        "outputId": "539088b9-461f-47ff-ca59-2e88801940a3"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from sklearn.linear_model import LassoCV\n",
+        "lasso = LassoCV().fit(train, target)\n",
+        "importance = np.abs(lasso.coef_)\n",
+        "feature_names = np.array(column_names)\n",
+        "\n",
+        "feat_ind = np.argsort(importance)[::-1]\n",
+        "totlasso = 20\n",
+        "feature_imp = pd.DataFrame(columns=['Value','Feature'])\n",
+        "feature_imp.loc[:,'Value'] = importance\n",
+        "feature_imp.loc[:,'Feature'] = column_names\n",
+        "\n",
+        "feature_imp = pd.DataFrame(sorted(zip(importance, column_names)), columns=['Value','Feature'])\n",
+        "data = feature_imp.sort_values(by=\"Value\", ascending=False)\n",
+        "data = data.iloc[0:30, :]\n",
+        "plt.figure(figsize=(10, 10))\n",
+        "sns.barplot(x=\"Value\", y=\"Feature\", data=data)\n",
+        "plt.title('Lasso Features')\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "#if we want to use only the selected fetures by LASSO to train the classifiers\n",
+        "#X_train = pd.DataFrame(train,columns = column_names)\n",
+        "#train = X_train.iloc[:, feat_ind]\n",
+        "#X_test = pd.DataFrame(test, columns = column_names)\n",
+        "#test = X_test.iloc[:, feat_ind]\n",
+        "#column_names = train.columns\n",
+        "\n",
+        "\n",
+        "## a different feature selection method, forward sequential feature selector\n",
+        "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "# Sequential Forward Selection(sfs)\n",
+        "sfs = SFS(LinearRegression(),\n",
+        "          k_features=30,\n",
+        "          forward=True,\n",
+        "          floating=False,\n",
+        "          scoring = 'r2',\n",
+        "          cv = 0)\n",
+        "\n",
+        "#sfs.fit(train, target)\n",
+        "#sfs.k_feature_names_\n",
+        "\n",
+        "#if we want to use only the selected fetures by SFS to train the classifiers\n",
+        "#train = pd.DataFrame(train, columns = column_names)\n",
+        "#test = pd.DataFrame(test, columns = column_names)\n",
+        "#train = train[list(sfs.k_feature_names_)]\n",
+        "#test = test[list(sfs.k_feature_names_)]\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 210
+        },
+        "id": "_KFQaEM-kc8Z",
+        "outputId": "8b9f0904-13c4-46db-9e96-3f30984f923f"
+      },
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'X_train' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-24-d4b49df44f51>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mneighbors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mclassifier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_neighbors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'minkowski'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
+        "classifier.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cRk-135wmobW"
+      },
+      "outputs": [],
+      "source": [
+        "y_pred = classifier.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "my6TJERgmu4J",
+        "outputId": "59ee01aa-2b47-464d-decf-f86db06d736f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[1 1 0 ... 1 1 1]\n",
+            "[1 1 0 ... 1 1 1]\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(y_pred)\n",
+        "# Convert probabilities to binary predictions using a threshold of 0.5\n",
+        "y_binary_predictions = (y_pred >= 0.5).astype(int)\n",
+        "print(y_binary_predictions)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_KqmlPA1nAZB",
+        "outputId": "a375afae-91e8-4b1d-de92-f1d52732cb95"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[[ 434  500]\n",
+            " [ 296 1326]]\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "0.6885758998435054"
+            ]
+          },
+          "execution_count": 84,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
+        "cm = confusion_matrix(y_test, y_pred)\n",
+        "print(cm)\n",
+        "accuracy_score(y_test, y_pred)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EuSr82kSnFTW",
+        "outputId": "0b81c262-180c-4313-9f1d-19f9f59117ee"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Confusion Matrix:\n",
+            "[[ 434  500]\n",
+            " [ 296 1326]]\n",
+            "\n",
+            "Classification Report:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.59      0.46      0.52       934\n",
+            "           1       0.73      0.82      0.77      1622\n",
+            "\n",
+            "    accuracy                           0.69      2556\n",
+            "   macro avg       0.66      0.64      0.65      2556\n",
+            "weighted avg       0.68      0.69      0.68      2556\n",
+            "\n",
+            "\n",
+            "Individual Metrics:\n",
+            "Accuracy: 0.6885758998435054\n",
+            "Precision: 0.7261774370208105\n",
+            "Recall: 0.8175092478421702\n",
+            "F1 Score: 0.7691415313225057\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print confusion matrix\n",
+        "print(\"Confusion Matrix:\")\n",
+        "print(confusion_matrix(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Evaluate the model using classification report\n",
+        "print(\"\\nClassification Report:\")\n",
+        "print(classification_report(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Calculate and print individual metrics\n",
+        "accuracy = accuracy_score(y_test,y_binary_predictions)\n",
+        "precision = precision_score(y_test, y_binary_predictions)\n",
+        "recall = recall_score(y_test, y_binary_predictions)\n",
+        "f1 = f1_score(y_test, y_binary_predictions)\n",
+        "\n",
+        "print(\"\\nIndividual Metrics:\")\n",
+        "print(\"Accuracy:\", accuracy)\n",
+        "print(\"Precision:\", precision)\n",
+        "print(\"Recall:\", recall)\n",
+        "print(\"F1 Score:\", f1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 735
+        },
+        "id": "wbVJIE2KnRrl",
+        "outputId": "f95b445f-1cd2-42ea-bb74-8b2746966a16"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "AUC: 0.6410886710302928\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import roc_auc_score, roc_curve\n",
+        "# Assuming y_binary_predictions are the predicted binary labels for your test set\n",
+        "\n",
+        "# Calculate AUC\n",
+        "auc = roc_auc_score(y_test, y_binary_predictions)\n",
+        "print(\"AUC:\", auc)\n",
+        "\n",
+        "# Plot ROC curve\n",
+        "fpr, tpr, thresholds = roc_curve(y_test, y_binary_predictions)\n",
+        "plt.figure(figsize=(8, 8))\n",
+        "plt.plot(fpr, tpr, label=f'AUC = {auc:.2f}')\n",
+        "plt.plot([0, 1], [0, 1], 'k--', label='Random')\n",
+        "plt.xlabel('False Positive Rate')\n",
+        "plt.ylabel('True Positive Rate')\n",
+        "plt.title('ROC Curve')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 366
+        },
+        "id": "7ss0AgTjnXK8",
+        "outputId": "c3e3aadc-b80b-4bb1-fb0b-c393e80fbc01"
+      },
+      "outputs": [
+        {
+          "ename": "TypeError",
+          "evalue": "The passed model is not callable and cannot be analyzed directly with the given masker! Model: KNeighborsClassifier()",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-87-3c87e09b908f>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mX_test_ft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumn_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mexplainer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExplainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mshap_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexplainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test_ft\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/shap/explainers/_explainer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, masker, link, algorithm, output_names, feature_names, linearize_link, seed, **kwargs)\u001b[0m\n\u001b[1;32m    172\u001b[0m                 \u001b[0;31m# if we get here then we don't know how to handle what was given to us\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The passed model is not callable and cannot be analyzed directly with the given masker! Model: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m             \u001b[0;31m# build the right subclass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mTypeError\u001b[0m: The passed model is not callable and cannot be analyzed directly with the given masker! Model: KNeighborsClassifier()"
+          ]
+        }
+      ],
+      "source": [
+        "#explanations code from https://www.kaggle.com/shreyasajal/wids-datathon-2022-explainable-ai-walkthrough\n",
+        "\n",
+        "X_test_ft = pd.DataFrame(X_test, columns=column_names)\n",
+        "\n",
+        "explainer = shap.Explainer(classifier)\n",
+        "shap_values = explainer(X_test_ft)\n",
+        "\n",
+        "shap.summary_plot(shap_values, X_test_ft,plot_type=\"bar\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 193
+        },
+        "id": "iUFBgAd8n5Qf",
+        "outputId": "0ff43c1c-a569-42cd-8e34-3400fc6f0cef"
+      },
+      "outputs": [
+        {
+          "ename": "NameError",
+          "evalue": "name 'shap_values' is not defined",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-88-5267e20d64dd>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# visualize the first prediction's explanation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mshap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplots\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaterfall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshap_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m: name 'shap_values' is not defined"
+          ]
+        }
+      ],
+      "source": [
+        "# visualize the first prediction's explanation\n",
+        "shap.plots.waterfall(shap_values[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 245
+        },
+        "id": "DhcQicnPnSVr",
+        "outputId": "31f16953-2b56-42e2-9b09-04d440a9b693"
+      },
+      "outputs": [
+        {
+          "ename": "NameError",
+          "evalue": "name 'predictions' is not defined",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-89-d8ff8382bfc6>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Create a DataFrame for submission\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msubmission\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'patient_id'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'patient_id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'DiagPeriodL90D'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Save to CSV\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msubmission\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"submission.csv\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'predictions' is not defined"
+          ]
+        }
+      ],
+      "source": [
+        "# Create a DataFrame for submission\n",
+        "submission = pd.DataFrame({'patient_id': test['patient_id'], 'DiagPeriodL90D': predictions})\n",
+        "\n",
+        "# Save to CSV\n",
+        "submission.to_csv(\"submission.csv\", index=False)\n",
+        "\n",
+        "# Check the shape of the DataFrame\n",
+        "print(\"Shape of submission:\", submission.shape)\n",
+        "\n",
+        "# Display the first few rows of the DataFrame\n",
+        "submission.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OlqghpsBmQ-j",
+        "outputId": "d5e6d262-11ce-4b93-e32b-58e6b9fcd1c1"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting catboost\n",
+            "  Downloading catboost-1.2.2-cp310-cp310-manylinux2014_x86_64.whl (98.7 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.7/98.7 MB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost) (0.20.1)\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from catboost) (3.7.1)\n",
+            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.23.5)\n",
+            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.5.3)\n",
+            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from catboost) (1.11.4)\n",
+            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from catboost) (5.15.0)\n",
+            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from catboost) (1.16.0)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2023.4)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.2.0)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (4.48.1)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.4.5)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (23.2)\n",
+            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (9.4.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (3.1.1)\n",
+            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->catboost) (8.2.3)\n",
+            "Installing collected packages: catboost\n",
+            "Successfully installed catboost-1.2.2\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import classification_report, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "import xgboost\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "!pip install catboost\n",
+        "import catboost\n",
+        "import lightgbm as lightgbm"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "WTYU0nmOmhpN"
+      },
+      "outputs": [],
+      "source": [
+        "#splitting into training and validation sets\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(train, target, test_size = 0.2, random_state = 50)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "opdGH9MImUDc",
+        "outputId": "414a0a1d-5426-4adc-dbd6-904ac00879e6"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Collecting catboost\n",
+            "  Downloading catboost-1.2.2-cp310-cp310-manylinux2014_x86_64.whl (98.7 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.7/98.7 MB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost) (0.20.1)\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from catboost) (3.7.1)\n",
+            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.23.5)\n",
+            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.5.3)\n",
+            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from catboost) (1.11.4)\n",
+            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from catboost) (5.15.0)\n",
+            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from catboost) (1.16.0)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2023.4)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.2.0)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (4.48.1)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.4.5)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (23.2)\n",
+            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (9.4.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (3.1.1)\n",
+            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->catboost) (8.2.3)\n",
+            "Installing collected packages: catboost\n",
+            "Successfully installed catboost-1.2.2\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install catboost"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 197
+        },
+        "id": "PQDR_A3Qk3Gf",
+        "outputId": "004c5dbd-02e5-428e-ec06-e6b307bd1c6e"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "lbfgs failed to converge (status=1):\n",
+            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+            "\n",
+            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+            "Please also refer to the documentation for alternative solver options:\n",
+            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=0)</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "LogisticRegression(random_state=0)"
+            ]
+          },
+          "execution_count": 90,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.linear_model import LogisticRegression\n",
+        "classifier = LogisticRegression(random_state = 0)\n",
+        "classifier.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 75
+        },
+        "id": "hYnwSRzDlEVX",
+        "outputId": "7e87f9e0-61b7-4c13-a413-5de60a30d966"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=0)</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "SVC(kernel='linear', random_state=0)"
+            ]
+          },
+          "execution_count": 91,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.svm import SVC\n",
+        "classifier = SVC(kernel = 'linear', random_state = 0)\n",
+        "classifier.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EcpUOYdApeWI"
+      },
+      "outputs": [],
+      "source": [
+        "y_pred = classifier.predict(X_test)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UcJdGO7ipn7e",
+        "outputId": "d1cf11a6-7b08-4e01-f9bc-d3f7d0116ab3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[1 1 1 ... 1 1 1]\n",
+            "[1 1 1 ... 1 1 1]\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(y_pred)\n",
+        "# Convert probabilities to binary predictions using a threshold of 0.5\n",
+        "y_binary_predictions = (y_pred >= 0.5).astype(int)\n",
+        "print(y_binary_predictions)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7yFsmHxQps8x",
+        "outputId": "5a9d4b0c-5ed1-4e25-ce52-8efc881ba82d"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Confusion Matrix:\n",
+            "[[ 548  386]\n",
+            " [ 156 1466]]\n",
+            "\n",
+            "Classification Report:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.78      0.59      0.67       934\n",
+            "           1       0.79      0.90      0.84      1622\n",
+            "\n",
+            "    accuracy                           0.79      2556\n",
+            "   macro avg       0.78      0.75      0.76      2556\n",
+            "weighted avg       0.79      0.79      0.78      2556\n",
+            "\n",
+            "\n",
+            "Individual Metrics:\n",
+            "Accuracy: 0.7879499217527387\n",
+            "Precision: 0.7915766738660908\n",
+            "Recall: 0.903822441430333\n",
+            "F1 Score: 0.8439838802533104\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print confusion matrix\n",
+        "print(\"Confusion Matrix:\")\n",
+        "print(confusion_matrix(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Evaluate the model using classification report\n",
+        "print(\"\\nClassification Report:\")\n",
+        "print(classification_report(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Calculate and print individual metrics\n",
+        "accuracy = accuracy_score(y_test,y_binary_predictions)\n",
+        "precision = precision_score(y_test, y_binary_predictions)\n",
+        "recall = recall_score(y_test, y_binary_predictions)\n",
+        "f1 = f1_score(y_test, y_binary_predictions)\n",
+        "\n",
+        "print(\"\\nIndividual Metrics:\")\n",
+        "print(\"Accuracy:\", accuracy)\n",
+        "print(\"Precision:\", precision)\n",
+        "print(\"Recall:\", recall)\n",
+        "print(\"F1 Score:\", f1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 735
+        },
+        "id": "Iw-FcKt_pzMf",
+        "outputId": "f627e68f-dbf1-413b-c19e-e965afd2c478"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "AUC: 0.7452731050834749\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import roc_auc_score, roc_curve\n",
+        "# Assuming y_binary_predictions are the predicted binary labels for your test set\n",
+        "\n",
+        "# Calculate AUC\n",
+        "auc = roc_auc_score(y_test, y_binary_predictions)\n",
+        "print(\"AUC:\", auc)\n",
+        "\n",
+        "# Plot ROC curve\n",
+        "fpr, tpr, thresholds = roc_curve(y_test, y_binary_predictions)\n",
+        "plt.figure(figsize=(8, 8))\n",
+        "plt.plot(fpr, tpr, label=f'AUC = {auc:.2f}')\n",
+        "plt.plot([0, 1], [0, 1], 'k--', label='Random')\n",
+        "plt.xlabel('False Positive Rate')\n",
+        "plt.ylabel('True Positive Rate')\n",
+        "plt.title('ROC Curve')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 366
+        },
+        "id": "-_OkFKU-p5n4",
+        "outputId": "c206e61d-080e-40b0-fa1a-6ac978d5eaa3"
+      },
+      "outputs": [
+        {
+          "ename": "TypeError",
+          "evalue": "The passed model is not callable and cannot be analyzed directly with the given masker! Model: SVC(kernel='linear', random_state=0)",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-97-3c87e09b908f>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mX_test_ft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumn_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mexplainer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExplainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mshap_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexplainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test_ft\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/shap/explainers/_explainer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, masker, link, algorithm, output_names, feature_names, linearize_link, seed, **kwargs)\u001b[0m\n\u001b[1;32m    172\u001b[0m                 \u001b[0;31m# if we get here then we don't know how to handle what was given to us\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The passed model is not callable and cannot be analyzed directly with the given masker! Model: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m             \u001b[0;31m# build the right subclass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mTypeError\u001b[0m: The passed model is not callable and cannot be analyzed directly with the given masker! Model: SVC(kernel='linear', random_state=0)"
+          ]
+        }
+      ],
+      "source": [
+        "#explanations code from https://www.kaggle.com/shreyasajal/wids-datathon-2022-explainable-ai-walkthrough\n",
+        "\n",
+        "X_test_ft = pd.DataFrame(X_test, columns=column_names)\n",
+        "\n",
+        "explainer = shap.Explainer(classifier)\n",
+        "shap_values = explainer(X_test_ft)\n",
+        "\n",
+        "shap.summary_plot(shap_values, X_test_ft,plot_type=\"bar\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 75
+        },
+        "id": "e2z2Oc2XlKtP",
+        "outputId": "c4f96697-1b63-43c1-cadb-1b105547babf"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_estimators=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_estimators=10)</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "RandomForestRegressor(n_estimators=10)"
+            ]
+          },
+          "execution_count": 98,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "regressor = RandomForestRegressor(n_estimators = 10)\n",
+        "regressor.fit(X_train,y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MSuRfISXqEBG",
+        "outputId": "4127b8a6-389c-4aa9-df0a-f8ff87e135ec"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[0.5 1.  0.9 ... 0.9 1.  0.3]\n",
+            "[1 1 1 ... 1 1 0]\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_pred = regressor.predict(X_test)\n",
+        "print(y_pred)\n",
+        "# Convert probabilities to binary predictions using a threshold of 0.5\n",
+        "y_binary_predictions = (y_pred >= 0.5).astype(int)\n",
+        "print(y_binary_predictions)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tUh2h8SpqS08",
+        "outputId": "68f717cd-f773-42a5-eacc-40602db92028"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Confusion Matrix:\n",
+            "[[ 522  412]\n",
+            " [ 145 1477]]\n",
+            "\n",
+            "Classification Report:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.78      0.56      0.65       934\n",
+            "           1       0.78      0.91      0.84      1622\n",
+            "\n",
+            "    accuracy                           0.78      2556\n",
+            "   macro avg       0.78      0.73      0.75      2556\n",
+            "weighted avg       0.78      0.78      0.77      2556\n",
+            "\n",
+            "\n",
+            "Individual Metrics:\n",
+            "Accuracy: 0.7820813771517997\n",
+            "Precision: 0.7818951826363155\n",
+            "Recall: 0.9106041923551171\n",
+            "F1 Score: 0.8413557391056679\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print confusion matrix\n",
+        "print(\"Confusion Matrix:\")\n",
+        "print(confusion_matrix(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Evaluate the model using classification report\n",
+        "print(\"\\nClassification Report:\")\n",
+        "print(classification_report(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Calculate and print individual metrics\n",
+        "accuracy = accuracy_score(y_test,y_binary_predictions)\n",
+        "precision = precision_score(y_test, y_binary_predictions)\n",
+        "recall = recall_score(y_test, y_binary_predictions)\n",
+        "f1 = f1_score(y_test, y_binary_predictions)\n",
+        "\n",
+        "print(\"\\nIndividual Metrics:\")\n",
+        "print(\"Accuracy:\", accuracy)\n",
+        "print(\"Precision:\", precision)\n",
+        "print(\"Recall:\", recall)\n",
+        "print(\"F1 Score:\", f1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 735
+        },
+        "id": "LYwZMlhTqS8O",
+        "outputId": "88cfa2f0-f198-4acb-c281-c1eafc22545a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "AUC: 0.7347453509955457\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "\n",
+        "\n",
+        "from sklearn.metrics import roc_auc_score, roc_curve\n",
+        "# Assuming y_binary_predictions are the predicted binary labels for your test set\n",
+        "\n",
+        "# Calculate AUC\n",
+        "auc = roc_auc_score(y_test, y_binary_predictions)\n",
+        "print(\"AUC:\", auc)\n",
+        "\n",
+        "# Plot ROC curve\n",
+        "fpr, tpr, thresholds = roc_curve(y_test, y_binary_predictions)\n",
+        "plt.figure(figsize=(8, 8))\n",
+        "plt.plot(fpr, tpr, label=f'AUC = {auc:.2f}')\n",
+        "plt.plot([0, 1], [0, 1], 'k--', label='Random')\n",
+        "plt.xlabel('False Positive Rate')\n",
+        "plt.ylabel('True Positive Rate')\n",
+        "plt.title('ROC Curve')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ptbCcMmGl-CA"
+      },
+      "outputs": [],
+      "source": [
+        "#model training and testing\n",
+        "#Xgboost classifier\n",
+        "xgboost_model = xgboost.XGBRegressor(n_estimators=1000, learning_rate=0.05, max_depth=10) #gamma=0, subsample=0.75, colsample_bytree=0.4,\n",
+        "xgboost_model.fit(X_train,y_train)\n",
+        "y_pred = xgboost_model.predict(X_test)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QzRBUCloqj1D",
+        "outputId": "ca3155a4-f16b-4357-f70e-fbb9dbef9318"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[0.37068444 0.89117306 0.7838869  ... 0.8576379  0.8134752  0.598153  ]\n",
+            "[0 1 1 ... 1 1 1]\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(y_pred)\n",
+        "# Convert probabilities to binary predictions using a threshold of 0.5\n",
+        "y_binary_predictions = (y_pred >= 0.5).astype(int)\n",
+        "print(y_binary_predictions)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DPas3ZLSqniZ",
+        "outputId": "3952b689-ecaa-485c-e593-6089791bcfe6"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Confusion Matrix:\n",
+            "[[ 569  365]\n",
+            " [ 194 1428]]\n",
+            "\n",
+            "Classification Report:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.75      0.61      0.67       934\n",
+            "           1       0.80      0.88      0.84      1622\n",
+            "\n",
+            "    accuracy                           0.78      2556\n",
+            "   macro avg       0.77      0.74      0.75      2556\n",
+            "weighted avg       0.78      0.78      0.78      2556\n",
+            "\n",
+            "\n",
+            "Individual Metrics:\n",
+            "Accuracy: 0.7812989045383412\n",
+            "Precision: 0.7964305633017289\n",
+            "Recall: 0.8803945745992602\n",
+            "F1 Score: 0.8363103953147877\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print confusion matrix\n",
+        "print(\"Confusion Matrix:\")\n",
+        "print(confusion_matrix(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Evaluate the model using classification report\n",
+        "print(\"\\nClassification Report:\")\n",
+        "print(classification_report(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Calculate and print individual metrics\n",
+        "accuracy = accuracy_score(y_test,y_binary_predictions)\n",
+        "precision = precision_score(y_test, y_binary_predictions)\n",
+        "recall = recall_score(y_test, y_binary_predictions)\n",
+        "f1 = f1_score(y_test, y_binary_predictions)\n",
+        "\n",
+        "print(\"\\nIndividual Metrics:\")\n",
+        "print(\"Accuracy:\", accuracy)\n",
+        "print(\"Precision:\", precision)\n",
+        "print(\"Recall:\", recall)\n",
+        "print(\"F1 Score:\", f1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 735
+        },
+        "id": "WVu4d1g5qpWp",
+        "outputId": "75ecba7a-d4fb-4a98-ace7-9151b568143c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "AUC: 0.7448011416893516\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import roc_auc_score, roc_curve\n",
+        "# Assuming y_binary_predictions are the predicted binary labels for your test set\n",
+        "\n",
+        "# Calculate AUC\n",
+        "auc = roc_auc_score(y_test, y_binary_predictions)\n",
+        "print(\"AUC:\", auc)\n",
+        "\n",
+        "# Plot ROC curve\n",
+        "fpr, tpr, thresholds = roc_curve(y_test, y_binary_predictions)\n",
+        "plt.figure(figsize=(8, 8))\n",
+        "plt.plot(fpr, tpr, label=f'AUC = {auc:.2f}')\n",
+        "plt.plot([0, 1], [0, 1], 'k--', label='Random')\n",
+        "plt.xlabel('False Positive Rate')\n",
+        "plt.ylabel('True Positive Rate')\n",
+        "plt.title('ROC Curve')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 390
+        },
+        "id": "nDasu-S4qrZC",
+        "outputId": "7ccec125-8e48-4135-e9e3-362d2a89ffd2"
+      },
+      "outputs": [
+        {
+          "ename": "KeyboardInterrupt",
+          "evalue": "",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-106-4a867bfcb5c5>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mexplainer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExplainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxgboost_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mshap_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexplainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test_ft\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mshap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshap_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test_ft\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mplot_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"bar\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/shap/explainers/_tree.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, X, y, interactions, check_additivity)\u001b[0m\n\u001b[1;32m    244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0minteractions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 246\u001b[0;31m             \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshap_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrom_call\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_additivity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_additivity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapproximate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapproximate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    247\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    248\u001b[0m                 \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# put outputs at the end\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/shap/explainers/_tree.py\u001b[0m in \u001b[0;36mshap_values\u001b[0;34m(self, X, y, tree_limit, approximate, check_additivity, from_call)\u001b[0m\n\u001b[1;32m    403\u001b[0m                     \u001b[0mtree_limit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    404\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 405\u001b[0;31m                     phi = self.model.original_model.predict(\n\u001b[0m\u001b[1;32m    406\u001b[0m                         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miteration_range\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtree_limit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred_contribs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    407\u001b[0m                         \u001b[0mapprox_contribs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mapproximate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, data, output_margin, pred_leaf, pred_contribs, approx_contribs, pred_interactions, validate_features, training, iteration_range, strict_shape)\u001b[0m\n\u001b[1;32m   2296\u001b[0m         \u001b[0mdims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc_bst_ulong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2297\u001b[0m         _check_call(\n\u001b[0;32m-> 2298\u001b[0;31m             _LIB.XGBoosterPredictFromDMatrix(\n\u001b[0m\u001b[1;32m   2299\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2300\u001b[0m                 \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ],
+      "source": [
+        "#explanations code from https://www.kaggle.com/shreyasajal/wids-datathon-2022-explainable-ai-walkthrough\n",
+        "\n",
+        "X_test_ft = pd.DataFrame(X_test, columns=column_names)\n",
+        "\n",
+        "explainer = shap.Explainer(xgboost_model)\n",
+        "shap_values = explainer(X_test_ft)\n",
+        "\n",
+        "shap.summary_plot(shap_values, X_test_ft,plot_type=\"bar\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9fbwQ3l7qthn"
+      },
+      "outputs": [],
+      "source": [
+        "# visualize the first prediction's explanation\n",
+        "shap.plots.waterfall(shap_values[0])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 75
+        },
+        "id": "aFF_Vgvjq8el",
+        "outputId": "b6982014-0712-45b4-aa11-811cce7dc4ff"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=0)</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "SVC(random_state=0)"
+            ]
+          },
+          "execution_count": 107,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.svm import SVC\n",
+        "classifier = SVC(kernel = 'rbf', random_state = 0)\n",
+        "classifier.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "xjEVpZpVrzzX",
+        "outputId": "786418b9-7f65-444c-88cc-62476b9f3ae1"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[1 1 1 ... 1 1 1]\n",
+            "[1 1 1 ... 1 1 1]\n",
+            "Confusion Matrix:\n",
+            "[[ 534  400]\n",
+            " [ 145 1477]]\n",
+            "\n",
+            "Classification Report:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.79      0.57      0.66       934\n",
+            "           1       0.79      0.91      0.84      1622\n",
+            "\n",
+            "    accuracy                           0.79      2556\n",
+            "   macro avg       0.79      0.74      0.75      2556\n",
+            "weighted avg       0.79      0.79      0.78      2556\n",
+            "\n",
+            "\n",
+            "Individual Metrics:\n",
+            "Accuracy: 0.7867762128325508\n",
+            "Precision: 0.786893979754928\n",
+            "Recall: 0.9106041923551171\n",
+            "F1 Score: 0.8442412117747927\n",
+            "AUC: 0.7411693338649247\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "y_pred = classifier.predict(X_test)\n",
+        "print(y_pred)\n",
+        "# Convert probabilities to binary predictions using a threshold of 0.5\n",
+        "y_binary_predictions = (y_pred >= 0.5).astype(int)\n",
+        "print(y_binary_predictions)\n",
+        "# Print confusion matrix\n",
+        "print(\"Confusion Matrix:\")\n",
+        "print(confusion_matrix(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Evaluate the model using classification report\n",
+        "print(\"\\nClassification Report:\")\n",
+        "print(classification_report(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Calculate and print individual metrics\n",
+        "accuracy = accuracy_score(y_test,y_binary_predictions)\n",
+        "precision = precision_score(y_test, y_binary_predictions)\n",
+        "recall = recall_score(y_test, y_binary_predictions)\n",
+        "f1 = f1_score(y_test, y_binary_predictions)\n",
+        "\n",
+        "print(\"\\nIndividual Metrics:\")\n",
+        "print(\"Accuracy:\", accuracy)\n",
+        "print(\"Precision:\", precision)\n",
+        "print(\"Recall:\", recall)\n",
+        "print(\"F1 Score:\", f1)\n",
+        "from sklearn.metrics import roc_auc_score, roc_curve\n",
+        "# Assuming y_binary_predictions are the predicted binary labels for your test set\n",
+        "\n",
+        "# Calculate AUC\n",
+        "auc = roc_auc_score(y_test, y_binary_predictions)\n",
+        "print(\"AUC:\", auc)\n",
+        "\n",
+        "# Plot ROC curve\n",
+        "fpr, tpr, thresholds = roc_curve(y_test, y_binary_predictions)\n",
+        "plt.figure(figsize=(8, 8))\n",
+        "plt.plot(fpr, tpr, label=f'AUC = {auc:.2f}')\n",
+        "plt.plot([0, 1], [0, 1], 'k--', label='Random')\n",
+        "plt.xlabel('False Positive Rate')\n",
+        "plt.ylabel('True Positive Rate')\n",
+        "plt.title('ROC Curve')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 75
+        },
+        "id": "abaHS7G4rCyN",
+        "outputId": "b00d8b00-d47d-4f82-a4b9-5756cf15371e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "GaussianNB()"
+            ]
+          },
+          "execution_count": 109,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.naive_bayes import GaussianNB\n",
+        "classifier = GaussianNB()\n",
+        "classifier.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "Er8uTYhBr_Vz",
+        "outputId": "62fc0f46-0f8b-42e6-ca97-dfb5625a609a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[1 1 1 ... 1 1 1]\n",
+            "[1 1 1 ... 1 1 1]\n",
+            "Confusion Matrix:\n",
+            "[[ 589  345]\n",
+            " [ 367 1255]]\n",
+            "\n",
+            "Classification Report:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.62      0.63      0.62       934\n",
+            "           1       0.78      0.77      0.78      1622\n",
+            "\n",
+            "    accuracy                           0.72      2556\n",
+            "   macro avg       0.70      0.70      0.70      2556\n",
+            "weighted avg       0.72      0.72      0.72      2556\n",
+            "\n",
+            "\n",
+            "Individual Metrics:\n",
+            "Accuracy: 0.7214397496087637\n",
+            "Precision: 0.784375\n",
+            "Recall: 0.7737361282367448\n",
+            "F1 Score: 0.7790192427063936\n",
+            "AUC: 0.7021785566237257\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "y_pred = classifier.predict(X_test)\n",
+        "print(y_pred)\n",
+        "# Convert probabilities to binary predictions using a threshold of 0.5\n",
+        "y_binary_predictions = (y_pred >= 0.5).astype(int)\n",
+        "print(y_binary_predictions)\n",
+        "# Print confusion matrix\n",
+        "print(\"Confusion Matrix:\")\n",
+        "print(confusion_matrix(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Evaluate the model using classification report\n",
+        "print(\"\\nClassification Report:\")\n",
+        "print(classification_report(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Calculate and print individual metrics\n",
+        "accuracy = accuracy_score(y_test,y_binary_predictions)\n",
+        "precision = precision_score(y_test, y_binary_predictions)\n",
+        "recall = recall_score(y_test, y_binary_predictions)\n",
+        "f1 = f1_score(y_test, y_binary_predictions)\n",
+        "\n",
+        "print(\"\\nIndividual Metrics:\")\n",
+        "print(\"Accuracy:\", accuracy)\n",
+        "print(\"Precision:\", precision)\n",
+        "print(\"Recall:\", recall)\n",
+        "print(\"F1 Score:\", f1)\n",
+        "from sklearn.metrics import roc_auc_score, roc_curve\n",
+        "# Assuming y_binary_predictions are the predicted binary labels for your test set\n",
+        "\n",
+        "# Calculate AUC\n",
+        "auc = roc_auc_score(y_test, y_binary_predictions)\n",
+        "print(\"AUC:\", auc)\n",
+        "\n",
+        "# Plot ROC curve\n",
+        "fpr, tpr, thresholds = roc_curve(y_test, y_binary_predictions)\n",
+        "plt.figure(figsize=(8, 8))\n",
+        "plt.plot(fpr, tpr, label=f'AUC = {auc:.2f}')\n",
+        "plt.plot([0, 1], [0, 1], 'k--', label='Random')\n",
+        "plt.xlabel('False Positive Rate')\n",
+        "plt.ylabel('True Positive Rate')\n",
+        "plt.title('ROC Curve')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 249
+        },
+        "id": "pIgjixWCrG8c",
+        "outputId": "fd3eaf89-2b5d-49c4-b694-59f80c8ba26c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+              "              colsample_bylevel=None, colsample_bynode=None,\n",
+              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "              gamma=None, grow_policy=None, importance_type=None,\n",
+              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+              "              colsample_bylevel=None, colsample_bynode=None,\n",
+              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "              gamma=None, grow_policy=None, importance_type=None,\n",
+              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+              "              colsample_bylevel=None, colsample_bynode=None,\n",
+              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "              gamma=None, grow_policy=None, importance_type=None,\n",
+              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "              num_parallel_tree=None, random_state=None, ...)"
+            ]
+          },
+          "execution_count": 38,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from xgboost import XGBClassifier\n",
+        "classifier = XGBClassifier()\n",
+        "classifier.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "7KOjdxHusARn",
+        "outputId": "fe0c52c6-af5c-4ec2-d322-eda6f99af2c9"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[0 1 0 ... 0 1 1]\n",
+            "[0 1 0 ... 0 1 1]\n",
+            "Confusion Matrix:\n",
+            "[[ 579  392]\n",
+            " [ 142 1443]]\n",
+            "\n",
+            "Classification Report:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.80      0.60      0.68       971\n",
+            "           1       0.79      0.91      0.84      1585\n",
+            "\n",
+            "    accuracy                           0.79      2556\n",
+            "   macro avg       0.79      0.75      0.76      2556\n",
+            "weighted avg       0.79      0.79      0.78      2556\n",
+            "\n",
+            "\n",
+            "Individual Metrics:\n",
+            "Accuracy: 0.7910798122065728\n",
+            "Precision: 0.7863760217983651\n",
+            "Recall: 0.910410094637224\n",
+            "F1 Score: 0.8438596491228071\n",
+            "AUC: 0.7533512883072835\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "y_pred = classifier.predict(X_test)\n",
+        "print(y_pred)\n",
+        "# Convert probabilities to binary predictions using a threshold of 0.5\n",
+        "y_binary_predictions = (y_pred >= 0.5).astype(int)\n",
+        "print(y_binary_predictions)\n",
+        "# Print confusion matrix\n",
+        "print(\"Confusion Matrix:\")\n",
+        "print(confusion_matrix(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Evaluate the model using classification report\n",
+        "print(\"\\nClassification Report:\")\n",
+        "print(classification_report(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Calculate and print individual metrics\n",
+        "accuracy = accuracy_score(y_test,y_binary_predictions)\n",
+        "precision = precision_score(y_test, y_binary_predictions)\n",
+        "recall = recall_score(y_test, y_binary_predictions)\n",
+        "f1 = f1_score(y_test, y_binary_predictions)\n",
+        "\n",
+        "print(\"\\nIndividual Metrics:\")\n",
+        "print(\"Accuracy:\", accuracy)\n",
+        "print(\"Precision:\", precision)\n",
+        "print(\"Recall:\", recall)\n",
+        "print(\"F1 Score:\", f1)\n",
+        "from sklearn.metrics import roc_auc_score, roc_curve\n",
+        "# Assuming y_binary_predictions are the predicted binary labels for your test set\n",
+        "\n",
+        "# Calculate AUC\n",
+        "auc = roc_auc_score(y_test, y_binary_predictions)\n",
+        "print(\"AUC:\", auc)\n",
+        "\n",
+        "# Plot ROC curve\n",
+        "fpr, tpr, thresholds = roc_curve(y_test, y_binary_predictions)\n",
+        "plt.figure(figsize=(8, 8))\n",
+        "plt.plot(fpr, tpr, label=f'AUC = {auc:.2f}')\n",
+        "plt.plot([0, 1], [0, 1], 'k--', label='Random')\n",
+        "plt.xlabel('False Positive Rate')\n",
+        "plt.ylabel('True Positive Rate')\n",
+        "plt.title('ROC Curve')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 210
+        },
+        "id": "j-l42BXQsDL6",
+        "outputId": "c6b48d1e-cf16-4836-b474-b8534c05f353"
+      },
+      "outputs": [
+        {
+          "ename": "TypeError",
+          "evalue": "BaseLibSVM.fit() missing 1 required positional argument: 'y'",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-43-f76130a1e348>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msvm\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSVC\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mclassifier1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'rbf'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mclassifier1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;31mTypeError\u001b[0m: BaseLibSVM.fit() missing 1 required positional argument: 'y'"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.svm import SVC\n",
+        "classifier1 = SVC(kernel = 'rbf', random_state = 0)\n",
+        "classifier1.fit( y_pred)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 245
+        },
+        "id": "5mHZoYKGmF99",
+        "outputId": "c7076c90-f0e9-4490-92a2-571022ab3949"
+      },
+      "outputs": [
+        {
+          "ename": "NameError",
+          "evalue": "name 'xgboost' is not defined",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-60-9ca2ccc261b5>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#model training and testing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#Xgboost classifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mxgboost_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgboost\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mXGBRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearning_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#gamma=0, subsample=0.75, colsample_bytree=0.4,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mxgboost_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgboost_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'xgboost' is not defined"
+          ]
+        }
+      ],
+      "source": [
+        "#model training and testing\n",
+        "#Xgboost classifier\n",
+        "xgboost_model = xgboost.XGBRegressor(n_estimators=1000, learning_rate=0.05, max_depth=10) #gamma=0, subsample=0.75, colsample_bytree=0.4,\n",
+        "xgboost_model.fit(X_train,y_train)\n",
+        "y_pred = xgboost_model.predict(X_test)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 75
+        },
+        "id": "zRkCU33irVpO",
+        "outputId": "d386aadc-5000-485e-fb4b-6aaf9e5ba7dd"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_estimators=10, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_estimators=10, random_state=0)</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=0)"
+            ]
+          },
+          "execution_count": 113,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
+        "classifier.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "tdS0uarusEd-",
+        "outputId": "29ea1f53-3ebc-4f07-e4bc-dc2b9638163a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[1 1 0 ... 1 1 1]\n",
+            "[1 1 0 ... 1 1 1]\n",
+            "Confusion Matrix:\n",
+            "[[ 579  355]\n",
+            " [ 295 1327]]\n",
+            "\n",
+            "Classification Report:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.66      0.62      0.64       934\n",
+            "           1       0.79      0.82      0.80      1622\n",
+            "\n",
+            "    accuracy                           0.75      2556\n",
+            "   macro avg       0.73      0.72      0.72      2556\n",
+            "weighted avg       0.74      0.75      0.74      2556\n",
+            "\n",
+            "\n",
+            "Individual Metrics:\n",
+            "Accuracy: 0.7456964006259781\n",
+            "Precision: 0.7889417360285375\n",
+            "Recall: 0.8181257706535142\n",
+            "F1 Score: 0.8032687651331719\n",
+            "AUC: 0.7190200587742946\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "y_pred = classifier.predict(X_test)\n",
+        "print(y_pred)\n",
+        "# Convert probabilities to binary predictions using a threshold of 0.5\n",
+        "y_binary_predictions = (y_pred >= 0.5).astype(int)\n",
+        "print(y_binary_predictions)\n",
+        "# Print confusion matrix\n",
+        "print(\"Confusion Matrix:\")\n",
+        "print(confusion_matrix(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Evaluate the model using classification report\n",
+        "print(\"\\nClassification Report:\")\n",
+        "print(classification_report(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Calculate and print individual metrics\n",
+        "accuracy = accuracy_score(y_test,y_binary_predictions)\n",
+        "precision = precision_score(y_test, y_binary_predictions)\n",
+        "recall = recall_score(y_test, y_binary_predictions)\n",
+        "f1 = f1_score(y_test, y_binary_predictions)\n",
+        "\n",
+        "print(\"\\nIndividual Metrics:\")\n",
+        "print(\"Accuracy:\", accuracy)\n",
+        "print(\"Precision:\", precision)\n",
+        "print(\"Recall:\", recall)\n",
+        "print(\"F1 Score:\", f1)\n",
+        "from sklearn.metrics import roc_auc_score, roc_curve\n",
+        "# Assuming y_binary_predictions are the predicted binary labels for your test set\n",
+        "\n",
+        "# Calculate AUC\n",
+        "auc = roc_auc_score(y_test, y_binary_predictions)\n",
+        "print(\"AUC:\", auc)\n",
+        "\n",
+        "# Plot ROC curve\n",
+        "fpr, tpr, thresholds = roc_curve(y_test, y_binary_predictions)\n",
+        "plt.figure(figsize=(8, 8))\n",
+        "plt.plot(fpr, tpr, label=f'AUC = {auc:.2f}')\n",
+        "plt.plot([0, 1], [0, 1], 'k--', label='Random')\n",
+        "plt.xlabel('False Positive Rate')\n",
+        "plt.ylabel('True Positive Rate')\n",
+        "plt.title('ROC Curve')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 75
+        },
+        "id": "ij96_oa-rcuv",
+        "outputId": "b73e5cb6-0de5-426d-d0f3-380090d25dac"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_estimators=10, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_estimators=10, random_state=0)</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "RandomForestRegressor(n_estimators=10, random_state=0)"
+            ]
+          },
+          "execution_count": 116,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "regressor = RandomForestRegressor(n_estimators = 10, random_state = 0)\n",
+        "regressor.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "c1jf4c7OsFaf",
+        "outputId": "112e9954-63dd-4966-cd8a-7f146d39635d"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[0.7 0.8 0.3 ... 0.6 1.  0.7]\n",
+            "[1 1 0 ... 1 1 1]\n",
+            "Confusion Matrix:\n",
+            "[[ 533  401]\n",
+            " [ 144 1478]]\n",
+            "\n",
+            "Classification Report:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.79      0.57      0.66       934\n",
+            "           1       0.79      0.91      0.84      1622\n",
+            "\n",
+            "    accuracy                           0.79      2556\n",
+            "   macro avg       0.79      0.74      0.75      2556\n",
+            "weighted avg       0.79      0.79      0.78      2556\n",
+            "\n",
+            "\n",
+            "Individual Metrics:\n",
+            "Accuracy: 0.7867762128325508\n",
+            "Precision: 0.7865886109632784\n",
+            "Recall: 0.9112207151664612\n",
+            "F1 Score: 0.8443301913738932\n",
+            "AUC: 0.7409422633648152\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "y_pred = regressor.predict(X_test)\n",
+        "print(y_pred)\n",
+        "# Convert probabilities to binary predictions using a threshold of 0.5\n",
+        "y_binary_predictions = (y_pred >= 0.5).astype(int)\n",
+        "print(y_binary_predictions)\n",
+        "# Print confusion matrix\n",
+        "print(\"Confusion Matrix:\")\n",
+        "print(confusion_matrix(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Evaluate the model using classification report\n",
+        "print(\"\\nClassification Report:\")\n",
+        "print(classification_report(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Calculate and print individual metrics\n",
+        "accuracy = accuracy_score(y_test,y_binary_predictions)\n",
+        "precision = precision_score(y_test, y_binary_predictions)\n",
+        "recall = recall_score(y_test, y_binary_predictions)\n",
+        "f1 = f1_score(y_test, y_binary_predictions)\n",
+        "\n",
+        "print(\"\\nIndividual Metrics:\")\n",
+        "print(\"Accuracy:\", accuracy)\n",
+        "print(\"Precision:\", precision)\n",
+        "print(\"Recall:\", recall)\n",
+        "print(\"F1 Score:\", f1)\n",
+        "from sklearn.metrics import roc_auc_score, roc_curve\n",
+        "# Assuming y_binary_predictions are the predicted binary labels for your test set\n",
+        "\n",
+        "# Calculate AUC\n",
+        "auc = roc_auc_score(y_test, y_binary_predictions)\n",
+        "print(\"AUC:\", auc)\n",
+        "\n",
+        "# Plot ROC curve\n",
+        "fpr, tpr, thresholds = roc_curve(y_test, y_binary_predictions)\n",
+        "plt.figure(figsize=(8, 8))\n",
+        "plt.plot(fpr, tpr, label=f'AUC = {auc:.2f}')\n",
+        "plt.plot([0, 1], [0, 1], 'k--', label='Random')\n",
+        "plt.xlabel('False Positive Rate')\n",
+        "plt.ylabel('True Positive Rate')\n",
+        "plt.title('ROC Curve')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 646
+        },
+        "id": "kyqabGXCu4Qp",
+        "outputId": "e4c06fc7-00b4-4d98-9e60-f7cdfc3f7ecc"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+            "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+            "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+            "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+            "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+            "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+            "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+            "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+            "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+            "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from sklearn.cluster import KMeans\n",
+        "wcss = []\n",
+        "for i in range(1, 11):\n",
+        "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
+        "    kmeans.fit(X_train, y_train)\n",
+        "    wcss.append(kmeans.inertia_)\n",
+        "plt.plot(range(1, 11), wcss)\n",
+        "plt.title('The Elbow Method')\n",
+        "plt.xlabel('Number of clusters')\n",
+        "plt.ylabel('WCSS')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-0KUuJrAvMwS"
+      },
+      "outputs": [],
+      "source": [
+        "kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wIRsJZ2VvO_X",
+        "outputId": "03966553-674f-4287-831c-d858f6d05014"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_kmeans = kmeans.fit_predict(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 803
+        },
+        "id": "iNLVY8dUvak4",
+        "outputId": "7bd5ab91-bc28-4f89-94f2-dce6b4275abd"
+      },
+      "outputs": [
+        {
+          "ename": "KeyError",
+          "evalue": "'key of type tuple not found and not a MultiIndex'",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-128-e2a46d110fb1>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_kmeans\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_kmeans\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'cyan'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Cluster 4'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_kmeans\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_kmeans\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'magenta'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Cluster 5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_kmeans\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_kmeans\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'red'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Cluster 1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_kmeans\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_kmeans\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'blue'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Cluster 2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_kmeans\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_kmeans\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'green'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Cluster 3'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1005\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1007\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1008\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1009\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_with\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1020\u001b[0m             )\n\u001b[1;32m   1021\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1023\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_values_tuple\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1058\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1059\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1060\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"key of type tuple not found and not a MultiIndex\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1061\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1062\u001b[0m         \u001b[0;31m# If key is contained, would have returned by now\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyError\u001b[0m: 'key of type tuple not found and not a MultiIndex'"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.scatter(X_train[y_kmeans == 0, 0], X_train[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
+        "plt.scatter(X_train[y_kmeans == 1, 0], X_train[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
+        "plt.scatter(X_train[y_kmeans == 2, 0],X_train[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
+        "plt.scatter(X_train[y_kmeans == 3, 0], X_train[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
+        "plt.scatter(X_train[y_kmeans == 4, 0], X_train[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n",
+        "plt.scatter(y_train[y_kmeans == 0, 0], y_train[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
+        "plt.scatter(y_train[y_kmeans == 1, 0], y_train[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
+        "plt.scatter(y_train[y_kmeans == 2, 0],y_train[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
+        "plt.scatter(y_train[y_kmeans == 3, 0], y_train[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
+        "plt.scatter(y_train[y_kmeans == 4, 0], y_train[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n",
+        "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n",
+        "plt.title('Clusters of customers')\n",
+        "plt.xlabel('Annual Income (k$)')\n",
+        "plt.ylabel('Spending Score (1-100)')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "id": "UdJQNxV7wHFC"
+      },
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf\n",
+        "ann = tf.keras.models.Sequential()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lLBqZ9a7NcAX"
+      },
+      "outputs": [],
+      "source": [
+        "ann.add(tf.keras.layers.Dense(units=4096, activation='relu'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ann.add(tf.keras.layers.Dense(units=2048, activation='relu'))"
+      ],
+      "metadata": {
+        "id": "ZMZMLdD37NHG"
+      },
+      "execution_count": 28,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ann.add(tf.keras.layers.Dense(units=1024, activation='relu'))"
+      ],
+      "metadata": {
+        "id": "d9oFgctM7QZe"
+      },
+      "execution_count": 29,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "id": "6GlKHAsINoEO"
+      },
+      "outputs": [],
+      "source": [
+        "ann.add(tf.keras.layers.Dense(units=512, activation='relu'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "id": "x9D-OR_PPlqM"
+      },
+      "outputs": [],
+      "source": [
+        "ann.add(tf.keras.layers.Dense(units=256, activation='relu'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "id": "7vXYbyx4PoYe"
+      },
+      "outputs": [],
+      "source": [
+        "ann.add(tf.keras.layers.Dense(units=128, activation='relu'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "id": "TQxT1AduPqLU"
+      },
+      "outputs": [],
+      "source": [
+        "ann.add(tf.keras.layers.Dense(units=64, activation='relu'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "id": "hnlW7BV4Pr38"
+      },
+      "outputs": [],
+      "source": [
+        "ann.add(tf.keras.layers.Dense(units=32, activation='relu'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "id": "ECCj-dr_NoNI"
+      },
+      "outputs": [],
+      "source": [
+        "ann.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "id": "iQHw4DvKNt5F"
+      },
+      "outputs": [],
+      "source": [
+        "ann.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dBWyng8vNwbb",
+        "outputId": "6a4555a5-5901-4d11-81de-4ad376198938"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/150\n",
+            "1278/1278 [==============================] - 50s 37ms/step - loss: 0.5540 - accuracy: 0.7550\n",
+            "Epoch 2/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.5105 - accuracy: 0.7886\n",
+            "Epoch 3/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.5061 - accuracy: 0.7892\n",
+            "Epoch 4/150\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.5034 - accuracy: 0.7895\n",
+            "Epoch 5/150\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.4926 - accuracy: 0.7962\n",
+            "Epoch 6/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.4863 - accuracy: 0.7977\n",
+            "Epoch 7/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.4876 - accuracy: 0.8016\n",
+            "Epoch 8/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.4844 - accuracy: 0.8006\n",
+            "Epoch 9/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.4788 - accuracy: 0.8017\n",
+            "Epoch 10/150\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.4748 - accuracy: 0.8051\n",
+            "Epoch 11/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.4701 - accuracy: 0.8046\n",
+            "Epoch 12/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.4684 - accuracy: 0.8059\n",
+            "Epoch 13/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.4651 - accuracy: 0.8049\n",
+            "Epoch 14/150\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.4618 - accuracy: 0.8073\n",
+            "Epoch 15/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.4616 - accuracy: 0.8063\n",
+            "Epoch 16/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.4586 - accuracy: 0.8078\n",
+            "Epoch 17/150\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.4534 - accuracy: 0.8069\n",
+            "Epoch 18/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.4502 - accuracy: 0.8084\n",
+            "Epoch 19/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.4426 - accuracy: 0.8097\n",
+            "Epoch 20/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.4407 - accuracy: 0.8096\n",
+            "Epoch 21/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.4349 - accuracy: 0.8136\n",
+            "Epoch 22/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.4301 - accuracy: 0.8123\n",
+            "Epoch 23/150\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.4245 - accuracy: 0.8136\n",
+            "Epoch 24/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.4170 - accuracy: 0.8163\n",
+            "Epoch 25/150\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.4128 - accuracy: 0.8197\n",
+            "Epoch 26/150\n",
+            "1278/1278 [==============================] - 45s 36ms/step - loss: 0.4074 - accuracy: 0.8223\n",
+            "Epoch 27/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.4003 - accuracy: 0.8227\n",
+            "Epoch 28/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.3915 - accuracy: 0.8272\n",
+            "Epoch 29/150\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.3855 - accuracy: 0.8317\n",
+            "Epoch 30/150\n",
+            "1278/1278 [==============================] - 45s 36ms/step - loss: 0.3810 - accuracy: 0.8329\n",
+            "Epoch 31/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.3713 - accuracy: 0.8355\n",
+            "Epoch 32/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.3651 - accuracy: 0.8389\n",
+            "Epoch 33/150\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.3661 - accuracy: 0.8384\n",
+            "Epoch 34/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.3528 - accuracy: 0.8391\n",
+            "Epoch 35/150\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.3468 - accuracy: 0.8460\n",
+            "Epoch 36/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.3396 - accuracy: 0.8483\n",
+            "Epoch 37/150\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.3390 - accuracy: 0.8494\n",
+            "Epoch 38/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.3336 - accuracy: 0.8524\n",
+            "Epoch 39/150\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.3254 - accuracy: 0.8558\n",
+            "Epoch 40/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.3220 - accuracy: 0.8567\n",
+            "Epoch 41/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.3121 - accuracy: 0.8628\n",
+            "Epoch 42/150\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.3092 - accuracy: 0.8637\n",
+            "Epoch 43/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.3033 - accuracy: 0.8649\n",
+            "Epoch 44/150\n",
+            "1278/1278 [==============================] - 45s 36ms/step - loss: 0.2982 - accuracy: 0.8689\n",
+            "Epoch 45/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2954 - accuracy: 0.8698\n",
+            "Epoch 46/150\n",
+            "1278/1278 [==============================] - 45s 36ms/step - loss: 0.2962 - accuracy: 0.8702\n",
+            "Epoch 47/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2824 - accuracy: 0.8736\n",
+            "Epoch 48/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2808 - accuracy: 0.8714\n",
+            "Epoch 49/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2789 - accuracy: 0.8750\n",
+            "Epoch 50/150\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.2666 - accuracy: 0.8813\n",
+            "Epoch 51/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2591 - accuracy: 0.8818\n",
+            "Epoch 52/150\n",
+            "1278/1278 [==============================] - 45s 36ms/step - loss: 0.2569 - accuracy: 0.8866\n",
+            "Epoch 53/150\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.2581 - accuracy: 0.8841\n",
+            "Epoch 54/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2494 - accuracy: 0.8886\n",
+            "Epoch 55/150\n",
+            "1278/1278 [==============================] - 45s 36ms/step - loss: 0.2439 - accuracy: 0.8926\n",
+            "Epoch 56/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2450 - accuracy: 0.8927\n",
+            "Epoch 57/150\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.2304 - accuracy: 0.8932\n",
+            "Epoch 58/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2393 - accuracy: 0.8942\n",
+            "Epoch 59/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2312 - accuracy: 0.8996\n",
+            "Epoch 60/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2258 - accuracy: 0.9002\n",
+            "Epoch 61/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2346 - accuracy: 0.8973\n",
+            "Epoch 62/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2315 - accuracy: 0.8979\n",
+            "Epoch 63/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2325 - accuracy: 0.9007\n",
+            "Epoch 64/150\n",
+            "1278/1278 [==============================] - 45s 36ms/step - loss: 0.2188 - accuracy: 0.9052\n",
+            "Epoch 65/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2210 - accuracy: 0.9049\n",
+            "Epoch 66/150\n",
+            "1278/1278 [==============================] - 45s 36ms/step - loss: 0.2138 - accuracy: 0.9066\n",
+            "Epoch 67/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2083 - accuracy: 0.9064\n",
+            "Epoch 68/150\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.2155 - accuracy: 0.9087\n",
+            "Epoch 69/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2129 - accuracy: 0.9101\n",
+            "Epoch 70/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1991 - accuracy: 0.9114\n",
+            "Epoch 71/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1934 - accuracy: 0.9150\n",
+            "Epoch 72/150\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.1911 - accuracy: 0.9156\n",
+            "Epoch 73/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1916 - accuracy: 0.9175\n",
+            "Epoch 74/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1899 - accuracy: 0.9166\n",
+            "Epoch 75/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1899 - accuracy: 0.9171\n",
+            "Epoch 76/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1982 - accuracy: 0.9159\n",
+            "Epoch 77/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1865 - accuracy: 0.9201\n",
+            "Epoch 78/150\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.1735 - accuracy: 0.9244\n",
+            "Epoch 79/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1807 - accuracy: 0.9233\n",
+            "Epoch 80/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2108 - accuracy: 0.9216\n",
+            "Epoch 81/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1827 - accuracy: 0.9190\n",
+            "Epoch 82/150\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.1669 - accuracy: 0.9253\n",
+            "Epoch 83/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1657 - accuracy: 0.9255\n",
+            "Epoch 84/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1808 - accuracy: 0.9212\n",
+            "Epoch 85/150\n",
+            "1278/1278 [==============================] - 45s 36ms/step - loss: 0.1775 - accuracy: 0.9226\n",
+            "Epoch 86/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1644 - accuracy: 0.9279\n",
+            "Epoch 87/150\n",
+            "1278/1278 [==============================] - 45s 36ms/step - loss: 0.1508 - accuracy: 0.9335\n",
+            "Epoch 88/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1704 - accuracy: 0.9288\n",
+            "Epoch 89/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1566 - accuracy: 0.9317\n",
+            "Epoch 90/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1603 - accuracy: 0.9336\n",
+            "Epoch 91/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1706 - accuracy: 0.9274\n",
+            "Epoch 92/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1524 - accuracy: 0.9326\n",
+            "Epoch 93/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1578 - accuracy: 0.9345\n",
+            "Epoch 94/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1483 - accuracy: 0.9371\n",
+            "Epoch 95/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1486 - accuracy: 0.9357\n",
+            "Epoch 96/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1509 - accuracy: 0.9344\n",
+            "Epoch 97/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1440 - accuracy: 0.9348\n",
+            "Epoch 98/150\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.1404 - accuracy: 0.9375\n",
+            "Epoch 99/150\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.1497 - accuracy: 0.9350\n",
+            "Epoch 100/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1522 - accuracy: 0.9355\n",
+            "Epoch 101/150\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.1497 - accuracy: 0.9395\n",
+            "Epoch 102/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1421 - accuracy: 0.9400\n",
+            "Epoch 103/150\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.1333 - accuracy: 0.9429\n",
+            "Epoch 104/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1373 - accuracy: 0.9410\n",
+            "Epoch 105/150\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.1389 - accuracy: 0.9411\n",
+            "Epoch 106/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2240 - accuracy: 0.9292\n",
+            "Epoch 107/150\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.1400 - accuracy: 0.9431\n",
+            "Epoch 108/150\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.1264 - accuracy: 0.9455\n",
+            "Epoch 109/150\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.1308 - accuracy: 0.9440\n",
+            "Epoch 110/150\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.1338 - accuracy: 0.9428\n",
+            "Epoch 111/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1381 - accuracy: 0.9400\n",
+            "Epoch 112/150\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.1389 - accuracy: 0.9442\n",
+            "Epoch 113/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1409 - accuracy: 0.9454\n",
+            "Epoch 114/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1290 - accuracy: 0.9459\n",
+            "Epoch 115/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1249 - accuracy: 0.9472\n",
+            "Epoch 116/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2133 - accuracy: 0.9464\n",
+            "Epoch 117/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1225 - accuracy: 0.9464\n",
+            "Epoch 118/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1303 - accuracy: 0.9440\n",
+            "Epoch 119/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1297 - accuracy: 0.9472\n",
+            "Epoch 120/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1154 - accuracy: 0.9502\n",
+            "Epoch 121/150\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.1243 - accuracy: 0.9458\n",
+            "Epoch 122/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1605 - accuracy: 0.9364\n",
+            "Epoch 123/150\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.1302 - accuracy: 0.9443\n",
+            "Epoch 124/150\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.1304 - accuracy: 0.9472\n",
+            "Epoch 125/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1182 - accuracy: 0.9509\n",
+            "Epoch 126/150\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.1311 - accuracy: 0.9492\n",
+            "Epoch 127/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1092 - accuracy: 0.9520\n",
+            "Epoch 128/150\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.1137 - accuracy: 0.9540\n",
+            "Epoch 129/150\n",
+            "1278/1278 [==============================] - 48s 37ms/step - loss: 0.1280 - accuracy: 0.9481\n",
+            "Epoch 130/150\n",
+            "1278/1278 [==============================] - 49s 38ms/step - loss: 0.1110 - accuracy: 0.9530\n",
+            "Epoch 131/150\n",
+            "1278/1278 [==============================] - 48s 38ms/step - loss: 0.1287 - accuracy: 0.9490\n",
+            "Epoch 132/150\n",
+            "1278/1278 [==============================] - 47s 37ms/step - loss: 0.1100 - accuracy: 0.9539\n",
+            "Epoch 133/150\n",
+            "1278/1278 [==============================] - 48s 37ms/step - loss: 0.1181 - accuracy: 0.9533\n",
+            "Epoch 134/150\n",
+            "1278/1278 [==============================] - 48s 37ms/step - loss: 0.1179 - accuracy: 0.9510\n",
+            "Epoch 135/150\n",
+            "1278/1278 [==============================] - 45s 36ms/step - loss: 0.1204 - accuracy: 0.9508\n",
+            "Epoch 136/150\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.1229 - accuracy: 0.9527\n",
+            "Epoch 137/150\n",
+            "1278/1278 [==============================] - 47s 37ms/step - loss: 0.1298 - accuracy: 0.9475\n",
+            "Epoch 138/150\n",
+            "1278/1278 [==============================] - 47s 36ms/step - loss: 0.1275 - accuracy: 0.9501\n",
+            "Epoch 139/150\n",
+            "1278/1278 [==============================] - 47s 37ms/step - loss: 0.1108 - accuracy: 0.9567\n",
+            "Epoch 140/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0986 - accuracy: 0.9583\n",
+            "Epoch 141/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1038 - accuracy: 0.9556\n",
+            "Epoch 142/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1048 - accuracy: 0.9568\n",
+            "Epoch 143/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1215 - accuracy: 0.9513\n",
+            "Epoch 144/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1141 - accuracy: 0.9531\n",
+            "Epoch 145/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1177 - accuracy: 0.9534\n",
+            "Epoch 146/150\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.1208 - accuracy: 0.9539\n",
+            "Epoch 147/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1176 - accuracy: 0.9570\n",
+            "Epoch 148/150\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0960 - accuracy: 0.9575\n",
+            "Epoch 149/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1002 - accuracy: 0.9585\n",
+            "Epoch 150/150\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0989 - accuracy: 0.9577\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x780e400db5b0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ],
+      "source": [
+        "ann.fit(X_train, y_train, batch_size = 8, epochs = 150)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "id": "Hu9Ne3M8O1_l",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0fb37239-d34d-4756-a2d9-5d7f4f6295fb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "80/80 [==============================] - 1s 8ms/step\n",
+            "[[0.9947471 ]\n",
+            " [1.        ]\n",
+            " [0.9804536 ]\n",
+            " ...\n",
+            " [0.0085172 ]\n",
+            " [0.18620111]\n",
+            " [0.99931806]]\n",
+            "[[1]\n",
+            " [1]\n",
+            " [1]\n",
+            " ...\n",
+            " [0]\n",
+            " [0]\n",
+            " [1]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_pred = ann.predict(X_test)\n",
+        "print(y_pred)\n",
+        "# Convert probabilities to binary predictions using a threshold of 0.5\n",
+        "y_binary_predictions = (y_pred >= 0.5).astype(int)\n",
+        "print(y_binary_predictions)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "id": "LLA6oVmpO6wB",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0e1669da-255a-4078-ac68-72a6b55d96f7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Confusion Matrix:\n",
+            "[[ 598  369]\n",
+            " [ 364 1225]]\n",
+            "\n",
+            "Classification Report:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.62      0.62      0.62       967\n",
+            "           1       0.77      0.77      0.77      1589\n",
+            "\n",
+            "    accuracy                           0.71      2556\n",
+            "   macro avg       0.70      0.69      0.69      2556\n",
+            "weighted avg       0.71      0.71      0.71      2556\n",
+            "\n",
+            "\n",
+            "Individual Metrics:\n",
+            "Accuracy: 0.7132237871674492\n",
+            "Precision: 0.7685069008782937\n",
+            "Recall: 0.7709251101321586\n",
+            "F1 Score: 0.7697141061891297\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print confusion matrix\n",
+        "print(\"Confusion Matrix:\")\n",
+        "print(confusion_matrix(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Evaluate the model using classification report\n",
+        "print(\"\\nClassification Report:\")\n",
+        "print(classification_report(y_test, y_binary_predictions))\n",
+        "\n",
+        "# Calculate and print individual metrics\n",
+        "accuracy = accuracy_score(y_test,y_binary_predictions)\n",
+        "precision = precision_score(y_test, y_binary_predictions)\n",
+        "recall = recall_score(y_test, y_binary_predictions)\n",
+        "f1 = f1_score(y_test, y_binary_predictions)\n",
+        "\n",
+        "print(\"\\nIndividual Metrics:\")\n",
+        "print(\"Accuracy:\", accuracy)\n",
+        "print(\"Precision:\", precision)\n",
+        "print(\"Recall:\", recall)\n",
+        "print(\"F1 Score:\", f1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "id": "PgurXL6OO8yA",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 735
+        },
+        "outputId": "f9bc41b1-29a0-426a-8a6b-ff7f5dc54870"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "AUC: 0.6946662779202675\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import roc_auc_score, roc_curve\n",
+        "# Assuming y_binary_predictions are the predicted binary labels for your test set\n",
+        "\n",
+        "# Calculate AUC\n",
+        "auc = roc_auc_score(y_test, y_binary_predictions)\n",
+        "print(\"AUC:\", auc)\n",
+        "\n",
+        "# Plot ROC curve\n",
+        "fpr, tpr, thresholds = roc_curve(y_test, y_binary_predictions)\n",
+        "plt.figure(figsize=(8, 8))\n",
+        "plt.plot(fpr, tpr, label=f'AUC = {auc:.2f}')\n",
+        "plt.plot([0, 1], [0, 1], 'k--', label='Random')\n",
+        "plt.xlabel('False Positive Rate')\n",
+        "plt.ylabel('True Positive Rate')\n",
+        "plt.title('ROC Curve')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ann.fit(X_train, y_train, batch_size = 8, epochs = 50)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6v4Ob6g45ztK",
+        "outputId": "74250b52-7444-4bdf-b928-3610e1630ba5"
+      },
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/50\n",
+            "1278/1278 [==============================] - 49s 38ms/step - loss: 0.1009 - accuracy: 0.9595\n",
+            "Epoch 2/50\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.1033 - accuracy: 0.9597\n",
+            "Epoch 3/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0943 - accuracy: 0.9582\n",
+            "Epoch 4/50\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.0863 - accuracy: 0.9623\n",
+            "Epoch 5/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0982 - accuracy: 0.9588\n",
+            "Epoch 6/50\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.0946 - accuracy: 0.9591\n",
+            "Epoch 7/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0975 - accuracy: 0.9592\n",
+            "Epoch 8/50\n",
+            "1278/1278 [==============================] - 47s 36ms/step - loss: 0.1190 - accuracy: 0.9568\n",
+            "Epoch 9/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0816 - accuracy: 0.9637\n",
+            "Epoch 10/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0849 - accuracy: 0.9611\n",
+            "Epoch 11/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.1035 - accuracy: 0.9584\n",
+            "Epoch 12/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0993 - accuracy: 0.9575\n",
+            "Epoch 13/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0983 - accuracy: 0.9625\n",
+            "Epoch 14/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0959 - accuracy: 0.9614\n",
+            "Epoch 15/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.1100 - accuracy: 0.9580\n",
+            "Epoch 16/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0936 - accuracy: 0.9611\n",
+            "Epoch 17/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0868 - accuracy: 0.9634\n",
+            "Epoch 18/50\n",
+            "1278/1278 [==============================] - 45s 36ms/step - loss: 0.0874 - accuracy: 0.9631\n",
+            "Epoch 19/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0953 - accuracy: 0.9630\n",
+            "Epoch 20/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0925 - accuracy: 0.9626\n",
+            "Epoch 21/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0770 - accuracy: 0.9664\n",
+            "Epoch 22/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1016 - accuracy: 0.9613\n",
+            "Epoch 23/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.1044 - accuracy: 0.9586\n",
+            "Epoch 24/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1025 - accuracy: 0.9614\n",
+            "Epoch 25/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0878 - accuracy: 0.9623\n",
+            "Epoch 26/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0852 - accuracy: 0.9641\n",
+            "Epoch 27/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0787 - accuracy: 0.9666\n",
+            "Epoch 28/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0992 - accuracy: 0.9608\n",
+            "Epoch 29/50\n",
+            "1278/1278 [==============================] - 43s 34ms/step - loss: 0.0992 - accuracy: 0.9616\n",
+            "Epoch 30/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0955 - accuracy: 0.9635\n",
+            "Epoch 31/50\n",
+            "1278/1278 [==============================] - 43s 34ms/step - loss: 0.0769 - accuracy: 0.9690\n",
+            "Epoch 32/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0854 - accuracy: 0.9624\n",
+            "Epoch 33/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.1010 - accuracy: 0.9586\n",
+            "Epoch 34/50\n",
+            "1278/1278 [==============================] - 43s 34ms/step - loss: 0.0730 - accuracy: 0.9686\n",
+            "Epoch 35/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.1217 - accuracy: 0.9582\n",
+            "Epoch 36/50\n",
+            "1278/1278 [==============================] - 43s 34ms/step - loss: 0.0812 - accuracy: 0.9648\n",
+            "Epoch 37/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0827 - accuracy: 0.9696\n",
+            "Epoch 38/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0690 - accuracy: 0.9685\n",
+            "Epoch 39/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0847 - accuracy: 0.9673\n",
+            "Epoch 40/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0926 - accuracy: 0.9620\n",
+            "Epoch 41/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1105 - accuracy: 0.9586\n",
+            "Epoch 42/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0720 - accuracy: 0.9729\n",
+            "Epoch 43/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0735 - accuracy: 0.9693\n",
+            "Epoch 44/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0826 - accuracy: 0.9667\n",
+            "Epoch 45/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0730 - accuracy: 0.9699\n",
+            "Epoch 46/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0891 - accuracy: 0.9646\n",
+            "Epoch 47/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1164 - accuracy: 0.9570\n",
+            "Epoch 48/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0839 - accuracy: 0.9652\n",
+            "Epoch 49/50\n",
+            "1278/1278 [==============================] - 51s 40ms/step - loss: 0.0757 - accuracy: 0.9673\n",
+            "Epoch 50/50\n",
+            "1278/1278 [==============================] - 63s 49ms/step - loss: 0.0934 - accuracy: 0.9666\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x780e38a15d80>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 41
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ann.fit(X_train, y_train, batch_size = 8, epochs = 50)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rjmuw09DGMcE",
+        "outputId": "504e8190-de5f-44f3-8a86-f6648d50eaea"
+      },
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/50\n",
+            "1278/1278 [==============================] - 64s 50ms/step - loss: 0.0854 - accuracy: 0.9660\n",
+            "Epoch 2/50\n",
+            "1278/1278 [==============================] - 43s 34ms/step - loss: 0.0908 - accuracy: 0.9638\n",
+            "Epoch 3/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0770 - accuracy: 0.9690\n",
+            "Epoch 4/50\n",
+            "1278/1278 [==============================] - 43s 34ms/step - loss: 0.0868 - accuracy: 0.9660\n",
+            "Epoch 5/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0903 - accuracy: 0.9655\n",
+            "Epoch 6/50\n",
+            "1278/1278 [==============================] - 43s 34ms/step - loss: 0.0791 - accuracy: 0.9680\n",
+            "Epoch 7/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0769 - accuracy: 0.9656\n",
+            "Epoch 8/50\n",
+            "1278/1278 [==============================] - 43s 34ms/step - loss: 0.0840 - accuracy: 0.9681\n",
+            "Epoch 9/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0938 - accuracy: 0.9648\n",
+            "Epoch 10/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1191 - accuracy: 0.9606\n",
+            "Epoch 11/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1228 - accuracy: 0.9598\n",
+            "Epoch 12/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1203 - accuracy: 0.9636\n",
+            "Epoch 13/50\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.0786 - accuracy: 0.9667\n",
+            "Epoch 14/50\n",
+            "1278/1278 [==============================] - 47s 37ms/step - loss: 0.0765 - accuracy: 0.9673\n",
+            "Epoch 15/50\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.0823 - accuracy: 0.9670\n",
+            "Epoch 16/50\n",
+            "1278/1278 [==============================] - 43s 34ms/step - loss: 0.0901 - accuracy: 0.9666\n",
+            "Epoch 17/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0945 - accuracy: 0.9657\n",
+            "Epoch 18/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0877 - accuracy: 0.9659\n",
+            "Epoch 19/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0763 - accuracy: 0.9702\n",
+            "Epoch 20/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0979 - accuracy: 0.9631\n",
+            "Epoch 21/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.2273 - accuracy: 0.9614\n",
+            "Epoch 22/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0997 - accuracy: 0.9578\n",
+            "Epoch 23/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0777 - accuracy: 0.9687\n",
+            "Epoch 24/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0693 - accuracy: 0.9728\n",
+            "Epoch 25/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0864 - accuracy: 0.9699\n",
+            "Epoch 26/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0730 - accuracy: 0.9695\n",
+            "Epoch 27/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0909 - accuracy: 0.9667\n",
+            "Epoch 28/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0732 - accuracy: 0.9691\n",
+            "Epoch 29/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.1225 - accuracy: 0.9619\n",
+            "Epoch 30/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.1205 - accuracy: 0.9677\n",
+            "Epoch 31/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0821 - accuracy: 0.9686\n",
+            "Epoch 32/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0874 - accuracy: 0.9694\n",
+            "Epoch 33/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0876 - accuracy: 0.9669\n",
+            "Epoch 34/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0999 - accuracy: 0.9647\n",
+            "Epoch 35/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0739 - accuracy: 0.9690\n",
+            "Epoch 36/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.1265 - accuracy: 0.9561\n",
+            "Epoch 37/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0884 - accuracy: 0.9701\n",
+            "Epoch 38/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.5848 - accuracy: 0.9627\n",
+            "Epoch 39/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0723 - accuracy: 0.9719\n",
+            "Epoch 40/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0788 - accuracy: 0.9707\n",
+            "Epoch 41/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0676 - accuracy: 0.9735\n",
+            "Epoch 42/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0817 - accuracy: 0.9702\n",
+            "Epoch 43/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0852 - accuracy: 0.9737\n",
+            "Epoch 44/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0676 - accuracy: 0.9735\n",
+            "Epoch 45/50\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0700 - accuracy: 0.9719\n",
+            "Epoch 46/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0893 - accuracy: 0.9693\n",
+            "Epoch 47/50\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1342 - accuracy: 0.9627\n",
+            "Epoch 48/50\n",
+            "1278/1278 [==============================] - 45s 36ms/step - loss: 0.0724 - accuracy: 0.9722\n",
+            "Epoch 49/50\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0665 - accuracy: 0.9736\n",
+            "Epoch 50/50\n",
+            "1278/1278 [==============================] - 45s 36ms/step - loss: 0.0716 - accuracy: 0.9761\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x780e3a3477c0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 42
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ann.fit(X_train, y_train, batch_size = 8, epochs = 100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FGBXE4t_Pjl-",
+        "outputId": "a14807b6-fd0c-480f-f074-0955e77a8c24"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/100\n",
+            "1278/1278 [==============================] - 56s 44ms/step - loss: 0.1746 - accuracy: 0.9606\n",
+            "Epoch 2/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0701 - accuracy: 0.9758\n",
+            "Epoch 3/100\n",
+            "1278/1278 [==============================] - 45s 36ms/step - loss: 0.0657 - accuracy: 0.9765\n",
+            "Epoch 4/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0866 - accuracy: 0.9697\n",
+            "Epoch 5/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0642 - accuracy: 0.9772\n",
+            "Epoch 6/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0698 - accuracy: 0.9727\n",
+            "Epoch 7/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0878 - accuracy: 0.9698\n",
+            "Epoch 8/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0759 - accuracy: 0.9703\n",
+            "Epoch 9/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.1211 - accuracy: 0.9601\n",
+            "Epoch 10/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0848 - accuracy: 0.9716\n",
+            "Epoch 11/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0860 - accuracy: 0.9716\n",
+            "Epoch 12/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0694 - accuracy: 0.9746\n",
+            "Epoch 13/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0758 - accuracy: 0.9740\n",
+            "Epoch 14/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0774 - accuracy: 0.9719\n",
+            "Epoch 15/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.1319 - accuracy: 0.9681\n",
+            "Epoch 16/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0836 - accuracy: 0.9714\n",
+            "Epoch 17/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0763 - accuracy: 0.9729\n",
+            "Epoch 18/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.1054 - accuracy: 0.9673\n",
+            "Epoch 19/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0722 - accuracy: 0.9739\n",
+            "Epoch 20/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0742 - accuracy: 0.9736\n",
+            "Epoch 21/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0995 - accuracy: 0.9709\n",
+            "Epoch 22/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0716 - accuracy: 0.9758\n",
+            "Epoch 23/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0689 - accuracy: 0.9733\n",
+            "Epoch 24/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0712 - accuracy: 0.9761\n",
+            "Epoch 25/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0825 - accuracy: 0.9695\n",
+            "Epoch 26/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0751 - accuracy: 0.9727\n",
+            "Epoch 27/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0777 - accuracy: 0.9711\n",
+            "Epoch 28/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0625 - accuracy: 0.9768\n",
+            "Epoch 29/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0805 - accuracy: 0.9710\n",
+            "Epoch 30/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0748 - accuracy: 0.9741\n",
+            "Epoch 31/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.3663 - accuracy: 0.9595\n",
+            "Epoch 32/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0673 - accuracy: 0.9776\n",
+            "Epoch 33/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0595 - accuracy: 0.9803\n",
+            "Epoch 34/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0660 - accuracy: 0.9774\n",
+            "Epoch 35/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0983 - accuracy: 0.9699\n",
+            "Epoch 36/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0677 - accuracy: 0.9776\n",
+            "Epoch 37/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0714 - accuracy: 0.9768\n",
+            "Epoch 38/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.1309 - accuracy: 0.9675\n",
+            "Epoch 39/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0726 - accuracy: 0.9750\n",
+            "Epoch 40/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0617 - accuracy: 0.9788\n",
+            "Epoch 41/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0769 - accuracy: 0.9726\n",
+            "Epoch 42/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0899 - accuracy: 0.9706\n",
+            "Epoch 43/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0687 - accuracy: 0.9750\n",
+            "Epoch 44/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0713 - accuracy: 0.9743\n",
+            "Epoch 45/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0677 - accuracy: 0.9751\n",
+            "Epoch 46/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0864 - accuracy: 0.9693\n",
+            "Epoch 47/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0856 - accuracy: 0.9760\n",
+            "Epoch 48/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0596 - accuracy: 0.9785\n",
+            "Epoch 49/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0575 - accuracy: 0.9792\n",
+            "Epoch 50/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0630 - accuracy: 0.9752\n",
+            "Epoch 51/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1602 - accuracy: 0.9618\n",
+            "Epoch 52/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0719 - accuracy: 0.9744\n",
+            "Epoch 53/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0616 - accuracy: 0.9756\n",
+            "Epoch 54/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0802 - accuracy: 0.9719\n",
+            "Epoch 55/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0662 - accuracy: 0.9781\n",
+            "Epoch 56/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0793 - accuracy: 0.9754\n",
+            "Epoch 57/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0553 - accuracy: 0.9807\n",
+            "Epoch 58/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0595 - accuracy: 0.9773\n",
+            "Epoch 59/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0617 - accuracy: 0.9784\n",
+            "Epoch 60/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0811 - accuracy: 0.9732\n",
+            "Epoch 61/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1342 - accuracy: 0.9733\n",
+            "Epoch 62/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.5055 - accuracy: 0.9756\n",
+            "Epoch 63/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0778 - accuracy: 0.9739\n",
+            "Epoch 64/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0641 - accuracy: 0.9797\n",
+            "Epoch 65/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0494 - accuracy: 0.9816\n",
+            "Epoch 66/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0484 - accuracy: 0.9828\n",
+            "Epoch 67/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0555 - accuracy: 0.9800\n",
+            "Epoch 68/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0656 - accuracy: 0.9769\n",
+            "Epoch 69/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0647 - accuracy: 0.9782\n",
+            "Epoch 70/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.1844 - accuracy: 0.9701\n",
+            "Epoch 71/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0802 - accuracy: 0.9753\n",
+            "Epoch 72/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0776 - accuracy: 0.9787\n",
+            "Epoch 73/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0664 - accuracy: 0.9780\n",
+            "Epoch 74/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0693 - accuracy: 0.9776\n",
+            "Epoch 75/100\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.0654 - accuracy: 0.9776\n",
+            "Epoch 76/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0796 - accuracy: 0.9731\n",
+            "Epoch 77/100\n",
+            "1278/1278 [==============================] - 45s 36ms/step - loss: 0.0669 - accuracy: 0.9776\n",
+            "Epoch 78/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1043 - accuracy: 0.9746\n",
+            "Epoch 79/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0745 - accuracy: 0.9761\n",
+            "Epoch 80/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0607 - accuracy: 0.9783\n",
+            "Epoch 81/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1329 - accuracy: 0.9703\n",
+            "Epoch 82/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0637 - accuracy: 0.9788\n",
+            "Epoch 83/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1249 - accuracy: 0.9759\n",
+            "Epoch 84/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0777 - accuracy: 0.9785\n",
+            "Epoch 85/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0508 - accuracy: 0.9822\n",
+            "Epoch 86/100\n",
+            "1278/1278 [==============================] - 44s 34ms/step - loss: 0.0611 - accuracy: 0.9788\n",
+            "Epoch 87/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0800 - accuracy: 0.9743\n",
+            "Epoch 88/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0663 - accuracy: 0.9762\n",
+            "Epoch 89/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0710 - accuracy: 0.9752\n",
+            "Epoch 90/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.1253 - accuracy: 0.9737\n",
+            "Epoch 91/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0702 - accuracy: 0.9758\n",
+            "Epoch 92/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0709 - accuracy: 0.9750\n",
+            "Epoch 93/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0905 - accuracy: 0.9714\n",
+            "Epoch 94/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.0877 - accuracy: 0.9737\n",
+            "Epoch 95/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0896 - accuracy: 0.9728\n",
+            "Epoch 96/100\n",
+            "1278/1278 [==============================] - 44s 35ms/step - loss: 0.1044 - accuracy: 0.9748\n",
+            "Epoch 97/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0531 - accuracy: 0.9799\n",
+            "Epoch 98/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0523 - accuracy: 0.9801\n",
+            "Epoch 99/100\n",
+            "1278/1278 [==============================] - 46s 36ms/step - loss: 0.0625 - accuracy: 0.9781\n",
+            "Epoch 100/100\n",
+            "1278/1278 [==============================] - 45s 35ms/step - loss: 0.0655 - accuracy: 0.9782\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x780e3afdcc40>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 43
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file