From 438e87e8d4ddb09b251fff54b4af07c9e635961f Mon Sep 17 00:00:00 2001
From: manishrana <you@example.com>
Date: Sun, 2 Jun 2024 14:31:43 +0530
Subject: [PATCH] diabetesprediction

---
 .../Diabetes.ipynb                            | 3756 +++++++++++++++++
 .../deeplearning.pkl                          |  Bin 0 -> 88576 bytes
 .../diabetes.csv                              |  769 ++++
 .../lr .pkl                                   |  Bin 0 -> 772 bytes
 .../requirements.txt                          |   27 +
 .../scaler .pkl                               |  Bin 0 -> 833 bytes
 6 files changed, 4552 insertions(+)
 create mode 100644 Diabetes prediction (Machine Learning Algorithms)/Diabetes.ipynb
 create mode 100644 Diabetes prediction (Machine Learning Algorithms)/deeplearning.pkl
 create mode 100644 Diabetes prediction (Machine Learning Algorithms)/diabetes.csv
 create mode 100644 Diabetes prediction (Machine Learning Algorithms)/lr .pkl
 create mode 100644 Diabetes prediction (Machine Learning Algorithms)/requirements.txt
 create mode 100644 Diabetes prediction (Machine Learning Algorithms)/scaler .pkl

diff --git a/Diabetes prediction (Machine Learning Algorithms)/Diabetes.ipynb b/Diabetes prediction (Machine Learning Algorithms)/Diabetes.ipynb
new file mode 100644
index 000000000..6376e18a6
--- /dev/null
+++ b/Diabetes prediction (Machine Learning Algorithms)/Diabetes.ipynb	
@@ -0,0 +1,3756 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "_xysq3dyorE3"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.metrics import accuracy_score, confusion_matrix, precision_recall_fscore_support\n",
+        "from sklearn.naive_bayes import GaussianNB\n",
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "from sklearn import tree\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.svm import SVC\n",
+        "import pickle as pkl"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "D8QN__pWoclf",
+        "outputId": "a7cb9a03-182d-4375-ac02-1067b91997b0"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "HUt1kptjorFB",
+        "outputId": "5a72c66e-a5d4-48ee-b942-03e178578d1e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
+              "0              6      148             72             35        0  33.6   \n",
+              "1              1       85             66             29        0  26.6   \n",
+              "2              8      183             64              0        0  23.3   \n",
+              "3              1       89             66             23       94  28.1   \n",
+              "4              0      137             40             35      168  43.1   \n",
+              "..           ...      ...            ...            ...      ...   ...   \n",
+              "763           10      101             76             48      180  32.9   \n",
+              "764            2      122             70             27        0  36.8   \n",
+              "765            5      121             72             23      112  26.2   \n",
+              "766            1      126             60              0        0  30.1   \n",
+              "767            1       93             70             31        0  30.4   \n",
+              "\n",
+              "     DiabetesPedigreeFunction  Age  Outcome  \n",
+              "0                       0.627   50        1  \n",
+              "1                       0.351   31        0  \n",
+              "2                       0.672   32        1  \n",
+              "3                       0.167   21        0  \n",
+              "4                       2.288   33        1  \n",
+              "..                        ...  ...      ...  \n",
+              "763                     0.171   63        0  \n",
+              "764                     0.340   27        0  \n",
+              "765                     0.245   30        0  \n",
+              "766                     0.349   47        1  \n",
+              "767                     0.315   23        0  \n",
+              "\n",
+              "[768 rows x 9 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ed9b50de-cbbc-4758-8cd4-b199720fa028\" 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",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Pregnancies</th>\n",
+              "      <th>Glucose</th>\n",
+              "      <th>BloodPressure</th>\n",
+              "      <th>SkinThickness</th>\n",
+              "      <th>Insulin</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>DiabetesPedigreeFunction</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Outcome</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
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+              "      <td>148</td>\n",
+              "      <td>72</td>\n",
+              "      <td>35</td>\n",
+              "      <td>0</td>\n",
+              "      <td>33.6</td>\n",
+              "      <td>0.627</td>\n",
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+              "    </tr>\n",
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+              "      <td>85</td>\n",
+              "      <td>66</td>\n",
+              "      <td>29</td>\n",
+              "      <td>0</td>\n",
+              "      <td>26.6</td>\n",
+              "      <td>0.351</td>\n",
+              "      <td>31</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
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+              "      <td>64</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>0.672</td>\n",
+              "      <td>32</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>89</td>\n",
+              "      <td>66</td>\n",
+              "      <td>23</td>\n",
+              "      <td>94</td>\n",
+              "      <td>28.1</td>\n",
+              "      <td>0.167</td>\n",
+              "      <td>21</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
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+              "      <td>40</td>\n",
+              "      <td>35</td>\n",
+              "      <td>168</td>\n",
+              "      <td>43.1</td>\n",
+              "      <td>2.288</td>\n",
+              "      <td>33</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>763</th>\n",
+              "      <td>10</td>\n",
+              "      <td>101</td>\n",
+              "      <td>76</td>\n",
+              "      <td>48</td>\n",
+              "      <td>180</td>\n",
+              "      <td>32.9</td>\n",
+              "      <td>0.171</td>\n",
+              "      <td>63</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>764</th>\n",
+              "      <td>2</td>\n",
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+              "      <td>70</td>\n",
+              "      <td>27</td>\n",
+              "      <td>0</td>\n",
+              "      <td>36.8</td>\n",
+              "      <td>0.340</td>\n",
+              "      <td>27</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>765</th>\n",
+              "      <td>5</td>\n",
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+              "      <td>72</td>\n",
+              "      <td>23</td>\n",
+              "      <td>112</td>\n",
+              "      <td>26.2</td>\n",
+              "      <td>0.245</td>\n",
+              "      <td>30</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>766</th>\n",
+              "      <td>1</td>\n",
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+              "      <td>60</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>30.1</td>\n",
+              "      <td>0.349</td>\n",
+              "      <td>47</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>767</th>\n",
+              "      <td>1</td>\n",
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+              "      <td>70</td>\n",
+              "      <td>31</td>\n",
+              "      <td>0</td>\n",
+              "      <td>30.4</td>\n",
+              "      <td>0.315</td>\n",
+              "      <td>23</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>768 rows × 9 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-ed9b50de-cbbc-4758-8cd4-b199720fa028')\"\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-ed9b50de-cbbc-4758-8cd4-b199720fa028 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-ed9b50de-cbbc-4758-8cd4-b199720fa028');\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-1d70e28b-7e70-4adf-981f-b28b9e9a4f01\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1d70e28b-7e70-4adf-981f-b28b9e9a4f01')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\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-1d70e28b-7e70-4adf-981f-b28b9e9a4f01 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_8a30caf1-4a74-4697-be8d-8b5d8c1ccade\">\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('df')\"\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",
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+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_8a30caf1-4a74-4697-be8d-8b5d8c1ccade button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.884160320375446,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3313285950127749,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
+      "source": [
+        "df = pd.read_csv('/content/drive/MyDrive/GoogleGirlsScript/usingDeepLearning#57/diabetes.csv')\n",
+        "df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "BeY3HFdQoasq"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BdJ3aJ_OorFF",
+        "outputId": "75d9a867-6871-4e81-c049-a47dff96293b"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(768, 9)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ],
+      "source": [
+        "df.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fihiwcbnorFH",
+        "outputId": "c24c2c44-9f7d-4feb-bb1f-96ea9826658e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Pregnancies                 0\n",
+              "Glucose                     0\n",
+              "BloodPressure               0\n",
+              "SkinThickness               0\n",
+              "Insulin                     0\n",
+              "BMI                         0\n",
+              "DiabetesPedigreeFunction    0\n",
+              "Age                         0\n",
+              "Outcome                     0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ],
+      "source": [
+        "df.isnull().sum()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KmaSBszGorFI"
+      },
+      "source": [
+        "- There are no null values"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uUNO-wqHorFN",
+        "outputId": "796f5291-1648-4ace-e30a-2df5a77e3e5a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 768 entries, 0 to 767\n",
+            "Data columns (total 9 columns):\n",
+            " #   Column                    Non-Null Count  Dtype  \n",
+            "---  ------                    --------------  -----  \n",
+            " 0   Pregnancies               768 non-null    int64  \n",
+            " 1   Glucose                   768 non-null    int64  \n",
+            " 2   BloodPressure             768 non-null    int64  \n",
+            " 3   SkinThickness             768 non-null    int64  \n",
+            " 4   Insulin                   768 non-null    int64  \n",
+            " 5   BMI                       768 non-null    float64\n",
+            " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
+            " 7   Age                       768 non-null    int64  \n",
+            " 8   Outcome                   768 non-null    int64  \n",
+            "dtypes: float64(2), int64(7)\n",
+            "memory usage: 54.1 KB\n"
+          ]
+        }
+      ],
+      "source": [
+        "df.info()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HVXP1TllorFO"
+      },
+      "source": [
+        "- Statistical analysis of the data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 320
+        },
+        "id": "ciL295UdorFP",
+        "outputId": "6aacc5bf-ce61-4d2f-9550-9835fdc6ee0e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
+              "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
+              "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
+              "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
+              "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
+              "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
+              "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
+              "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
+              "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
+              "\n",
+              "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
+              "count  768.000000                768.000000  768.000000  768.000000  \n",
+              "mean    31.992578                  0.471876   33.240885    0.348958  \n",
+              "std      7.884160                  0.331329   11.760232    0.476951  \n",
+              "min      0.000000                  0.078000   21.000000    0.000000  \n",
+              "25%     27.300000                  0.243750   24.000000    0.000000  \n",
+              "50%     32.000000                  0.372500   29.000000    0.000000  \n",
+              "75%     36.600000                  0.626250   41.000000    1.000000  \n",
+              "max     67.100000                  2.420000   81.000000    1.000000  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d80cb37e-4367-4e36-9019-30f1e1b6198e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "\n",
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+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Pregnancies</th>\n",
+              "      <th>Glucose</th>\n",
+              "      <th>BloodPressure</th>\n",
+              "      <th>SkinThickness</th>\n",
+              "      <th>Insulin</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>DiabetesPedigreeFunction</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Outcome</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>768.000000</td>\n",
+              "      <td>768.000000</td>\n",
+              "      <td>768.000000</td>\n",
+              "      <td>768.000000</td>\n",
+              "      <td>768.000000</td>\n",
+              "      <td>768.000000</td>\n",
+              "      <td>768.000000</td>\n",
+              "      <td>768.000000</td>\n",
+              "      <td>768.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>3.845052</td>\n",
+              "      <td>120.894531</td>\n",
+              "      <td>69.105469</td>\n",
+              "      <td>20.536458</td>\n",
+              "      <td>79.799479</td>\n",
+              "      <td>31.992578</td>\n",
+              "      <td>0.471876</td>\n",
+              "      <td>33.240885</td>\n",
+              "      <td>0.348958</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>3.369578</td>\n",
+              "      <td>31.972618</td>\n",
+              "      <td>19.355807</td>\n",
+              "      <td>15.952218</td>\n",
+              "      <td>115.244002</td>\n",
+              "      <td>7.884160</td>\n",
+              "      <td>0.331329</td>\n",
+              "      <td>11.760232</td>\n",
+              "      <td>0.476951</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.078000</td>\n",
+              "      <td>21.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>99.000000</td>\n",
+              "      <td>62.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>27.300000</td>\n",
+              "      <td>0.243750</td>\n",
+              "      <td>24.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>117.000000</td>\n",
+              "      <td>72.000000</td>\n",
+              "      <td>23.000000</td>\n",
+              "      <td>30.500000</td>\n",
+              "      <td>32.000000</td>\n",
+              "      <td>0.372500</td>\n",
+              "      <td>29.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>6.000000</td>\n",
+              "      <td>140.250000</td>\n",
+              "      <td>80.000000</td>\n",
+              "      <td>32.000000</td>\n",
+              "      <td>127.250000</td>\n",
+              "      <td>36.600000</td>\n",
+              "      <td>0.626250</td>\n",
+              "      <td>41.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>17.000000</td>\n",
+              "      <td>199.000000</td>\n",
+              "      <td>122.000000</td>\n",
+              "      <td>99.000000</td>\n",
+              "      <td>846.000000</td>\n",
+              "      <td>67.100000</td>\n",
+              "      <td>2.420000</td>\n",
+              "      <td>81.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
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+              "\n",
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+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "      --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-9b28f681-4370-4763-8564-3fa60bde1da1 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 269.85223453356366,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.8450520833333335,\n          3.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 243.73802348295857,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          120.89453125,\n          117.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 252.8525053581062,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          69.10546875,\n          72.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 263.7684730531098,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          768.0,\n          20.536458333333332,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 350.26059167945886,\n        \"min\": 0.0,\n        \"max\": 846.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          768.0,\n          79.79947916666667,\n          127.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 262.05117817552093,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          31.992578124999998,\n          32.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.3005221658502,\n        \"min\": 0.078,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.47187630208333325,\n          0.3725,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 260.1941178528413,\n        \"min\": 11.760231540678685,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          33.240885416666664,\n          29.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.3865920388932,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3489583333333333,\n          1.0,\n          0.47695137724279896\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ],
+      "source": [
+        "df.describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cdwmFGxuorFQ",
+        "outputId": "b2bc5cbd-8e7a-4844-ba38-cc6c7bf47b82"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Outcome\n",
+              "0    500\n",
+              "1    268\n",
+              "Name: count, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ],
+      "source": [
+        "target_vals = df['Outcome'].value_counts()\n",
+        "target_vals"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fCrSeve-orFQ"
+      },
+      "source": [
+        "- 0 --> Non-diabetic patients\n",
+        "- 1 --> Diabetic patients"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 163
+        },
+        "id": "o703kKZJorFR",
+        "outputId": "2c4cd095-419f-40d7-97fc-a29d4ef50808"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
+              "Outcome                                                                      \n",
+              "0           3.298000  109.980000      68.184000      19.664000   68.792000   \n",
+              "1           4.865672  141.257463      70.824627      22.164179  100.335821   \n",
+              "\n",
+              "               BMI  DiabetesPedigreeFunction        Age  \n",
+              "Outcome                                                  \n",
+              "0        30.304200                  0.429734  31.190000  \n",
+              "1        35.142537                  0.550500  37.067164  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d91ee73a-443d-4ce3-82a4-ba8e170572b6\" 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>Pregnancies</th>\n",
+              "      <th>Glucose</th>\n",
+              "      <th>BloodPressure</th>\n",
+              "      <th>SkinThickness</th>\n",
+              "      <th>Insulin</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>DiabetesPedigreeFunction</th>\n",
+              "      <th>Age</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Outcome</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>3.298000</td>\n",
+              "      <td>109.980000</td>\n",
+              "      <td>68.184000</td>\n",
+              "      <td>19.664000</td>\n",
+              "      <td>68.792000</td>\n",
+              "      <td>30.304200</td>\n",
+              "      <td>0.429734</td>\n",
+              "      <td>31.190000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>4.865672</td>\n",
+              "      <td>141.257463</td>\n",
+              "      <td>70.824627</td>\n",
+              "      <td>22.164179</td>\n",
+              "      <td>100.335821</td>\n",
+              "      <td>35.142537</td>\n",
+              "      <td>0.550500</td>\n",
+              "      <td>37.067164</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d91ee73a-443d-4ce3-82a4-ba8e170572b6')\"\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-d91ee73a-443d-4ce3-82a4-ba8e170572b6 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-d91ee73a-443d-4ce3-82a4-ba8e170572b6');\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-43c5f51d-495d-469a-b17f-db778adf2431\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-43c5f51d-495d-469a-b17f-db778adf2431')\"\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-43c5f51d-495d-469a-b17f-db778adf2431 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.108511248584296,\n        \"min\": 3.298,\n        \"max\": 4.865671641791045,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4.865671641791045,\n          3.298\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.116505963980842,\n        \"min\": 109.98,\n        \"max\": 141.25746268656715,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          141.25746268656715,\n          109.98\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8672051632998017,\n        \"min\": 68.184,\n        \"max\": 70.82462686567165,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          70.82462686567165,\n          68.184\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7678935989570275,\n        \"min\": 19.664,\n        \"max\": 22.16417910447761,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          22.16417910447761,\n          19.664\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.304849659757796,\n        \"min\": 68.792,\n        \"max\": 100.33582089552239,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          100.33582089552239,\n          68.792\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.4212211239962618,\n        \"min\": 30.3042,\n        \"max\": 35.14253731343284,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          35.14253731343284,\n          30.3042\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08539445753677459,\n        \"min\": 0.429734,\n        \"max\": 0.5505,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.5505,\n          0.429734\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.155782645191446,\n        \"min\": 31.19,\n        \"max\": 37.06716417910448,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          37.06716417910448,\n          31.19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ],
+      "source": [
+        "df.groupby('Outcome').mean()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2BxMy1vqorFR"
+      },
+      "source": [
+        "- Above table shows that the mean of all the features is higher for diabetic patients than non-diabetic patients. This means that the diabetic patients have higher values of all the features than non-diabetic patients.\n",
+        "\n",
+        "- Moreover, people with diabetes have much higher value of glucose and insulin than people without diabetes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 789
+        },
+        "id": "1TtNV4pforFS",
+        "outputId": "6c9ee2b2-b2a5-4152-940a-b1f963accd7d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 900x700 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(9, 7))\n",
+        "sns.heatmap(df.corr(), annot=True, fmt='.2%')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xUE9vRwmorFT"
+      },
+      "source": [
+        "- All the columns are moderately correlated"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eA85S9RDorFT",
+        "outputId": "6ea9b51e-e2e6-48c4-9c11-7821cc05bc60"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Percentage of patients without diabetes:  65.0\n",
+            "Percentage of patients with diabetes:  35.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Percentage of patients without diabetes: \" , round(target_vals[0]/len(df.Outcome), 2)*100)\n",
+        "print(\"Percentage of patients with diabetes: \" , round(target_vals[1]/len(df.Outcome), 2)*100)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lIxzQRyporFU"
+      },
+      "source": [
+        "- Separating data and labels"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "AARphY9IorFU",
+        "outputId": "a391d88c-2cfa-47a6-da7f-fe7a3ca57193"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
+              "0            6      148             72             35        0  33.6   \n",
+              "1            1       85             66             29        0  26.6   \n",
+              "2            8      183             64              0        0  23.3   \n",
+              "3            1       89             66             23       94  28.1   \n",
+              "4            0      137             40             35      168  43.1   \n",
+              "\n",
+              "   DiabetesPedigreeFunction  Age  \n",
+              "0                     0.627   50  \n",
+              "1                     0.351   31  \n",
+              "2                     0.672   32  \n",
+              "3                     0.167   21  \n",
+              "4                     2.288   33  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-54d183c2-cb4e-480a-a686-2594977d098f\" 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",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Pregnancies</th>\n",
+              "      <th>Glucose</th>\n",
+              "      <th>BloodPressure</th>\n",
+              "      <th>SkinThickness</th>\n",
+              "      <th>Insulin</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>DiabetesPedigreeFunction</th>\n",
+              "      <th>Age</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>6</td>\n",
+              "      <td>148</td>\n",
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+              "      <td>33.6</td>\n",
+              "      <td>0.627</td>\n",
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+              "    <tr>\n",
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+              "      <td>0.672</td>\n",
+              "      <td>32</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>89</td>\n",
+              "      <td>66</td>\n",
+              "      <td>23</td>\n",
+              "      <td>94</td>\n",
+              "      <td>28.1</td>\n",
+              "      <td>0.167</td>\n",
+              "      <td>21</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>137</td>\n",
+              "      <td>40</td>\n",
+              "      <td>35</td>\n",
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+              "      <td>43.1</td>\n",
+              "      <td>2.288</td>\n",
+              "      <td>33</td>\n",
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+              "  </tbody>\n",
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+              "      background-color: #E8F0FE;\n",
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+              "\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-54d183c2-cb4e-480a-a686-2594977d098f 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-54d183c2-cb4e-480a-a686-2594977d098f');\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-581127b0-50ca-4cfe-b854-a5799c66cc39\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-581127b0-50ca-4cfe-b854-a5799c66cc39')\"\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-581127b0-50ca-4cfe-b854-a5799c66cc39 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "x",
+              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.884160320375446,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3313285950127749,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ],
+      "source": [
+        "x = df.drop(columns='Outcome', axis = 1)\n",
+        "x.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2dNL6pyIorFW",
+        "outputId": "e989c4e2-5065-44e3-9890-6c6927950f07"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    1\n",
+              "1    0\n",
+              "2    1\n",
+              "3    0\n",
+              "4    1\n",
+              "Name: Outcome, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ],
+      "source": [
+        "y = df['Outcome']\n",
+        "y.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GUAd8V1eorFX"
+      },
+      "source": [
+        "- Data Standardization"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "Dn-QB_I9orFX"
+      },
+      "outputs": [],
+      "source": [
+        "scalar = StandardScaler()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 75
+        },
+        "id": "AdQqYF3worFY",
+        "outputId": "3a327e76-b1f1-4199-f9d6-a28f7de6d682"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "StandardScaler()"
+            ],
+            "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>StandardScaler()</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\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ],
+      "source": [
+        "scalar.fit(x)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WFTGIN4forFY",
+        "outputId": "4d1afdfc-4c27-4c6d-ef17-b5117d637f40"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 0.63994726,  0.84832379,  0.14964075, ...,  0.20401277,\n",
+              "         0.46849198,  1.4259954 ],\n",
+              "       [-0.84488505, -1.12339636, -0.16054575, ..., -0.68442195,\n",
+              "        -0.36506078, -0.19067191],\n",
+              "       [ 1.23388019,  1.94372388, -0.26394125, ..., -1.10325546,\n",
+              "         0.60439732, -0.10558415],\n",
+              "       ...,\n",
+              "       [ 0.3429808 ,  0.00330087,  0.14964075, ..., -0.73518964,\n",
+              "        -0.68519336, -0.27575966],\n",
+              "       [-0.84488505,  0.1597866 , -0.47073225, ..., -0.24020459,\n",
+              "        -0.37110101,  1.17073215],\n",
+              "       [-0.84488505, -0.8730192 ,  0.04624525, ..., -0.20212881,\n",
+              "        -0.47378505, -0.87137393]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ],
+      "source": [
+        "standardized_data = scalar.transform(x)\n",
+        "standardized_data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "7vB_DPlCorFZ"
+      },
+      "outputs": [],
+      "source": [
+        "pkl.dump(scalar, open('scaler.pkl', 'wb'))\n",
+        "# scalar"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "CB3ncD4uorFZ"
+      },
+      "outputs": [],
+      "source": [
+        "X = standardized_data\n",
+        "Y = df['Outcome']"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w3cbbrCtorFa"
+      },
+      "source": [
+        "- train test split\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "1F3fIAqiorFa"
+      },
+      "outputs": [],
+      "source": [
+        "train_x, test_x, train_y, test_y = train_test_split(X, Y, test_size = 0.2, stratify=y, random_state = 2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iBV6DvZUorFb",
+        "outputId": "d852117d-756f-4828-ef2a-571dcf2541ca"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(614, 8)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ],
+      "source": [
+        "train_x.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZhCqN3HxorFb",
+        "outputId": "0ca3b923-be5d-462c-a2d5-d43eb69a1ce9"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(154, 8)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ],
+      "source": [
+        "test_x.shape\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Dn_y08auorFb",
+        "outputId": "182d4799-fc86-43a9-9b4d-8045fd12cfd8"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(614,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ],
+      "source": [
+        "train_y.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "smtK6hNcorFc",
+        "outputId": "cdb709fc-5c76-428e-9977-e0743ddace5f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(154,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ],
+      "source": [
+        "\n",
+        "test_y.shape\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dc-wOJ15orFc"
+      },
+      "source": [
+        "- Model Training"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OvmfkyZPorFd"
+      },
+      "source": [
+        "- LOGISTIC REGRESSION\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 75
+        },
+        "id": "ryLcXQROorFd",
+        "outputId": "688808f9-0a4a-4633-b055-bbc4a31b98cb"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LogisticRegression()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ],
+      "source": [
+        "lr = LogisticRegression()\n",
+        "lr = lr.fit(train_x, train_y)\n",
+        "lr"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "id": "R1Rsh35EorFe"
+      },
+      "outputs": [],
+      "source": [
+        "logistic_train_pred = lr.predict(train_x)\n",
+        "logistic_test_pred = lr.predict(test_x)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gbZRKj2corFe",
+        "outputId": "655c338b-493f-405f-d46d-06bf7e18e0c5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Logistic Regression Training Accuracy:  79.0\n",
+            "Logistic Regression Testing Accuracy:  76.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Logistic Regression Training Accuracy: \", round(accuracy_score(train_y, logistic_train_pred), 2)*100)\n",
+        "print(\"Logistic Regression Testing Accuracy: \", round(accuracy_score(test_y, logistic_test_pred), 2)*100)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "id": "nIhDu4FNorFe"
+      },
+      "outputs": [],
+      "source": [
+        "pkl.dump(lr, open('lr.pkl', 'wb'))\n",
+        "# lr"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jVEVi1bRorFf"
+      },
+      "source": [
+        "- KNN"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 75
+        },
+        "id": "Nu9NIqDSorFf",
+        "outputId": "c19119fc-00c9-47a0-aa3c-1a6de3e00975"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "KNeighborsClassifier(n_neighbors=50)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=50)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=50)</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ],
+      "source": [
+        "knn = KNeighborsClassifier(n_neighbors=50)\n",
+        "knn.fit(train_x, train_y)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "id": "-IPaLZFWorFg"
+      },
+      "outputs": [],
+      "source": [
+        "knn_train_pred = knn.predict(train_x)\n",
+        "knn_test_pred = knn.predict(test_x)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "y15OM1KvorFs",
+        "outputId": "19a7b526-29da-4f32-dfa4-fd39b4e471c2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "KNN Training Accuracy:  77.0\n",
+            "KNN Testing Accuracy:  73.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"KNN Training Accuracy: \", round(accuracy_score(train_y, knn_train_pred), 2)*100)\n",
+        "print(\"KNN Testing Accuracy: \", round(accuracy_score(test_y, knn_test_pred), 2)*100)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d_a9xpevorFt"
+      },
+      "source": [
+        "- Decision Trees"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 75
+        },
+        "id": "8jucMN6BorFt",
+        "outputId": "3fdbe7b9-c563-444e-9bb3-9be693a2c4a9"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "DecisionTreeClassifier(random_state=91)"
+            ],
+            "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>DecisionTreeClassifier(random_state=91)</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\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=91)</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ],
+      "source": [
+        "max_accuracy = 0\n",
+        "for i in range(2000):\n",
+        "    clf = DecisionTreeClassifier(random_state = i)\n",
+        "    clf.fit(train_x, train_y)\n",
+        "    pred = clf.predict(test_x)\n",
+        "    current_accuracy = round(accuracy_score(pred, test_y)*100,2)\n",
+        "    if(current_accuracy > max_accuracy):\n",
+        "        max_accuracy = current_accuracy\n",
+        "        best_random_state = i\n",
+        "\n",
+        "# print(\"Best Random State: \", best_random_state)\n",
+        "# print(\"Best Accuracy: \", max_accuracy)\n",
+        "\n",
+        "clf = DecisionTreeClassifier(random_state = best_random_state)\n",
+        "clf.fit(train_x, train_y)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "KhTgPuOOorFu",
+        "outputId": "6adc42e0-b5d5-4e34-ed92-26d5c485dfda"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[Text(0.453125, 0.96875, 'x[1] <= 0.207\\ngini = 0.454\\nsamples = 614\\nvalue = [400, 214]'),\n",
+              " Text(0.20545212765957446, 0.90625, 'x[7] <= -0.403\\ngini = 0.296\\nsamples = 388\\nvalue = [318, 70]'),\n",
+              " Text(0.10638297872340426, 0.84375, 'x[5] <= 1.702\\ngini = 0.151\\nsamples = 219\\nvalue = [201, 18]'),\n",
+              " Text(0.0851063829787234, 0.78125, 'x[0] <= 1.085\\ngini = 0.13\\nsamples = 215\\nvalue = [200, 15]'),\n",
+              " Text(0.07446808510638298, 0.71875, 'x[5] <= -0.132\\ngini = 0.122\\nsamples = 214\\nvalue = [200, 14]'),\n",
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+              " Text(0.925531914893617, 0.46875, 'gini = 0.0\\nsamples = 2\\nvalue = [2, 0]'),\n",
+              " Text(0.9468085106382979, 0.46875, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
+              " Text(0.9574468085106383, 0.53125, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 3]'),\n",
+              " Text(0.9787234042553191, 0.65625, 'x[7] <= 1.001\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
+              " Text(0.9680851063829787, 0.59375, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
+              " Text(0.9893617021276596, 0.59375, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
+              " Text(0.973404255319149, 0.71875, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]')]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 34
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "tree.plot_tree(clf)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "id": "QQN2-a0dorFu"
+      },
+      "outputs": [],
+      "source": [
+        "decision_train_pred = clf.predict(train_x)\n",
+        "decision_test_pred = clf.predict(test_x)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VAp47J6TorFu",
+        "outputId": "d82ff27f-694e-47c2-a42c-3f8d5265af24"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Decision Tree Training Accuracy:  100.0\n",
+            "Decision Tree Testing Accuracy:  73.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Decision Tree Training Accuracy: \", round(accuracy_score(train_y, decision_train_pred), 2)*100)\n",
+        "print(\"Decision Tree Testing Accuracy: \", round(accuracy_score(test_y, decision_test_pred), 2)*100)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PbbF0VsCorFv"
+      },
+      "source": [
+        "\n",
+        "- Random Forest"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "id": "4IUxneQporFv"
+      },
+      "outputs": [],
+      "source": [
+        "rf = RandomForestClassifier(n_estimators=500, random_state = best_random_state)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 75
+        },
+        "id": "jlC_IHV1orFv",
+        "outputId": "e756eff0-f047-4caf-93be-615c45cd2c4d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RandomForestClassifier(n_estimators=500, random_state=91)"
+            ],
+            "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>RandomForestClassifier(n_estimators=500, random_state=91)</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\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=500, random_state=91)</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ],
+      "source": [
+        "rf.fit(train_x, train_y)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "id": "vVXVMdGDorFw"
+      },
+      "outputs": [],
+      "source": [
+        "rf_train_pred = rf.predict(train_x)\n",
+        "rf_test_pred = rf.predict(test_x)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6icezn_9orFw",
+        "outputId": "55688f9f-0236-4ce5-bee1-de5497cd32b8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Random Forest Training Accuracy:  100.0\n",
+            "Random Forest Testing Accuracy:  73.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Random Forest Training Accuracy: \", round(accuracy_score(train_y, rf_train_pred), 2)*100)\n",
+        "print(\"Random Forest Testing Accuracy: \", round(accuracy_score(test_y, rf_test_pred), 2)*100)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vqg0lFigorFw"
+      },
+      "source": [
+        "- SVM"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 75
+        },
+        "id": "j7BJiK7AorFx",
+        "outputId": "44959c84-2cf1-4fb2-80ab-f84798b8557b"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SVC(kernel='linear')"
+            ],
+            "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>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator 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\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 41
+        }
+      ],
+      "source": [
+        "classifier = SVC(kernel = 'linear')\n",
+        "classifier.fit(train_x, train_y)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "U4hdRnudorFx",
+        "outputId": "425d13c6-98ba-4878-ee41-824e9502896f"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "SVM Training Accuracy:  78.66\n",
+            "SVM Testing Accuracy:  77.27\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"SVM Training Accuracy: \", round(classifier.score(train_x, train_y)*100, 2))\n",
+        "print(\"SVM Testing Accuracy: \", round(classifier.score(test_x, test_y)*100, 2))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "13FNLw_dpqqP"
+      },
+      "source": [
+        "Deep Learing\n",
+        "*simple feedforward neural network"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "id": "13we5JS8p7g8"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Dense, Dropout\n",
+        "from tensorflow.keras.regularizers import l2\n",
+        "from tensorflow.keras.utils import to_categorical\n",
+        "from tensorflow.keras.callbacks import EarlyStopping\n",
+        "from sklearn.preprocessing import StandardScaler"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {
+        "id": "W2wjWgWaqdv9"
+      },
+      "outputs": [],
+      "source": [
+        "# Example: Let's assume train_y and test_y are not one-hot encoded\n",
+        "num_classes = len(np.unique(train_y))\n",
+        "train_y = to_categorical(train_y, num_classes)\n",
+        "test_y = to_categorical(test_y, num_classes)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {
+        "id": "hrI4nhHIqBGX"
+      },
+      "outputs": [],
+      "source": [
+        "# Feature scaling\n",
+        "scaler = StandardScaler()\n",
+        "train_X = scaler.fit_transform(train_x)\n",
+        "test_X = scaler.transform(test_x)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "id": "c0VK1lgXqPaJ"
+      },
+      "outputs": [],
+      "source": [
+        "model = Sequential()\n",
+        "model.add(Dense(64, input_dim=train_x.shape[1], activation='relu'))\n",
+        "model.add(Dense(64, activation='relu'))\n",
+        "model.add(Dense(num_classes, activation='softmax'))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "metadata": {
+        "id": "dSfi7fQfqmgJ"
+      },
+      "outputs": [],
+      "source": [
+        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MBMroB0YqsZ4",
+        "outputId": "c54066b8-19fe-4678-d8a4-459388cd8d92"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/50\n",
+            "62/62 [==============================] - 2s 8ms/step - loss: 0.5957 - accuracy: 0.6840 - val_loss: 0.5412 - val_accuracy: 0.7273\n",
+            "Epoch 2/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.4757 - accuracy: 0.7720 - val_loss: 0.5210 - val_accuracy: 0.7532\n",
+            "Epoch 3/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.4538 - accuracy: 0.7850 - val_loss: 0.5201 - val_accuracy: 0.7468\n",
+            "Epoch 4/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.4388 - accuracy: 0.7915 - val_loss: 0.5247 - val_accuracy: 0.7597\n",
+            "Epoch 5/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.4307 - accuracy: 0.7866 - val_loss: 0.5279 - val_accuracy: 0.7662\n",
+            "Epoch 6/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.4207 - accuracy: 0.7964 - val_loss: 0.5310 - val_accuracy: 0.7468\n",
+            "Epoch 7/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.4150 - accuracy: 0.7980 - val_loss: 0.5400 - val_accuracy: 0.7597\n",
+            "Epoch 8/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.4071 - accuracy: 0.8078 - val_loss: 0.5323 - val_accuracy: 0.7532\n",
+            "Epoch 9/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.4025 - accuracy: 0.8029 - val_loss: 0.5421 - val_accuracy: 0.7468\n",
+            "Epoch 10/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.3974 - accuracy: 0.8078 - val_loss: 0.5525 - val_accuracy: 0.7597\n",
+            "Epoch 11/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.4050 - accuracy: 0.7948 - val_loss: 0.5457 - val_accuracy: 0.7208\n",
+            "Epoch 12/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.3864 - accuracy: 0.8257 - val_loss: 0.5597 - val_accuracy: 0.7403\n",
+            "Epoch 13/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.3785 - accuracy: 0.8127 - val_loss: 0.5829 - val_accuracy: 0.7468\n",
+            "Epoch 14/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.3723 - accuracy: 0.8290 - val_loss: 0.5896 - val_accuracy: 0.7403\n",
+            "Epoch 15/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.3726 - accuracy: 0.8192 - val_loss: 0.5749 - val_accuracy: 0.7597\n",
+            "Epoch 16/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.3626 - accuracy: 0.8208 - val_loss: 0.5774 - val_accuracy: 0.7403\n",
+            "Epoch 17/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.3535 - accuracy: 0.8306 - val_loss: 0.5938 - val_accuracy: 0.7468\n",
+            "Epoch 18/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.3567 - accuracy: 0.8339 - val_loss: 0.5932 - val_accuracy: 0.7338\n",
+            "Epoch 19/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.3440 - accuracy: 0.8371 - val_loss: 0.6072 - val_accuracy: 0.7273\n",
+            "Epoch 20/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.3373 - accuracy: 0.8534 - val_loss: 0.6110 - val_accuracy: 0.7273\n",
+            "Epoch 21/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.3321 - accuracy: 0.8485 - val_loss: 0.6200 - val_accuracy: 0.7143\n",
+            "Epoch 22/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.3202 - accuracy: 0.8648 - val_loss: 0.6396 - val_accuracy: 0.7273\n",
+            "Epoch 23/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.3208 - accuracy: 0.8518 - val_loss: 0.6470 - val_accuracy: 0.7078\n",
+            "Epoch 24/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.3131 - accuracy: 0.8762 - val_loss: 0.6404 - val_accuracy: 0.7078\n",
+            "Epoch 25/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.3087 - accuracy: 0.8697 - val_loss: 0.6449 - val_accuracy: 0.7078\n",
+            "Epoch 26/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.3016 - accuracy: 0.8664 - val_loss: 0.6817 - val_accuracy: 0.7338\n",
+            "Epoch 27/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.2962 - accuracy: 0.8762 - val_loss: 0.6971 - val_accuracy: 0.7273\n",
+            "Epoch 28/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.2981 - accuracy: 0.8664 - val_loss: 0.6788 - val_accuracy: 0.7078\n",
+            "Epoch 29/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.2851 - accuracy: 0.8746 - val_loss: 0.6976 - val_accuracy: 0.6948\n",
+            "Epoch 30/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.2750 - accuracy: 0.8860 - val_loss: 0.7293 - val_accuracy: 0.7078\n",
+            "Epoch 31/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.2686 - accuracy: 0.8876 - val_loss: 0.7208 - val_accuracy: 0.7013\n",
+            "Epoch 32/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.2639 - accuracy: 0.8876 - val_loss: 0.7449 - val_accuracy: 0.7078\n",
+            "Epoch 33/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.2555 - accuracy: 0.9039 - val_loss: 0.7383 - val_accuracy: 0.7078\n",
+            "Epoch 34/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.2496 - accuracy: 0.9023 - val_loss: 0.7726 - val_accuracy: 0.7013\n",
+            "Epoch 35/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.2442 - accuracy: 0.9137 - val_loss: 0.7973 - val_accuracy: 0.6883\n",
+            "Epoch 36/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.2488 - accuracy: 0.9007 - val_loss: 0.7848 - val_accuracy: 0.6948\n",
+            "Epoch 37/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.2414 - accuracy: 0.9072 - val_loss: 0.8017 - val_accuracy: 0.6948\n",
+            "Epoch 38/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.2313 - accuracy: 0.9055 - val_loss: 0.8272 - val_accuracy: 0.6883\n",
+            "Epoch 39/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.2235 - accuracy: 0.9218 - val_loss: 0.8343 - val_accuracy: 0.7143\n",
+            "Epoch 40/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.2213 - accuracy: 0.9235 - val_loss: 0.8287 - val_accuracy: 0.6818\n",
+            "Epoch 41/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.2214 - accuracy: 0.9153 - val_loss: 0.8593 - val_accuracy: 0.6883\n",
+            "Epoch 42/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.2112 - accuracy: 0.9251 - val_loss: 0.8627 - val_accuracy: 0.6948\n",
+            "Epoch 43/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.2081 - accuracy: 0.9300 - val_loss: 0.8830 - val_accuracy: 0.6623\n",
+            "Epoch 44/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.2052 - accuracy: 0.9137 - val_loss: 0.8844 - val_accuracy: 0.6494\n",
+            "Epoch 45/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.1966 - accuracy: 0.9300 - val_loss: 0.9286 - val_accuracy: 0.6818\n",
+            "Epoch 46/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.1934 - accuracy: 0.9300 - val_loss: 0.9353 - val_accuracy: 0.7013\n",
+            "Epoch 47/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.1858 - accuracy: 0.9414 - val_loss: 0.9280 - val_accuracy: 0.6688\n",
+            "Epoch 48/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.1847 - accuracy: 0.9349 - val_loss: 0.9669 - val_accuracy: 0.6948\n",
+            "Epoch 49/50\n",
+            "62/62 [==============================] - 0s 3ms/step - loss: 0.1748 - accuracy: 0.9365 - val_loss: 0.9919 - val_accuracy: 0.6494\n",
+            "Epoch 50/50\n",
+            "62/62 [==============================] - 0s 2ms/step - loss: 0.1766 - accuracy: 0.9300 - val_loss: 1.0252 - val_accuracy: 0.6688\n"
+          ]
+        }
+      ],
+      "source": [
+        "history = model.fit(train_X, train_y, epochs=50, batch_size=10, validation_data=(test_X, test_y))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mri-5gp8qxMR",
+        "outputId": "6d211131-6e56-4347-bb87-102b6aea1ae5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "20/20 [==============================] - 0s 2ms/step - loss: 0.1642 - accuracy: 0.9446\n",
+            "5/5 [==============================] - 0s 3ms/step - loss: 1.0252 - accuracy: 0.6688\n",
+            "Training Accuracy: 94.46%\n",
+            "Test Accuracy: 66.88%\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Evaluate the model\n",
+        "train_loss, train_accuracy = model.evaluate(train_X, train_y)\n",
+        "test_loss, test_accuracy = model.evaluate(test_X, test_y)\n",
+        "\n",
+        "print(f'Training Accuracy: {train_accuracy * 100:.2f}%')\n",
+        "print(f'Test Accuracy: {test_accuracy * 100:.2f}%')\n",
+        "#overfitting  model .........learning to much at time of training but not at the time of testing"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 50,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uT4vjPoTtApP",
+        "outputId": "39616fa1-4c64-4643-ad32-d6c0d6f7ca70"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/150\n",
+            "31/31 [==============================] - 1s 9ms/step - loss: 1.0840 - accuracy: 0.6059 - val_loss: 0.8620 - val_accuracy: 0.7078\n",
+            "Epoch 2/150\n",
+            "31/31 [==============================] - 0s 4ms/step - loss: 0.8806 - accuracy: 0.7003 - val_loss: 0.7957 - val_accuracy: 0.7468\n",
+            "Epoch 3/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.8584 - accuracy: 0.6743 - val_loss: 0.7539 - val_accuracy: 0.7792\n",
+            "Epoch 4/150\n",
+            "31/31 [==============================] - 0s 6ms/step - loss: 0.7939 - accuracy: 0.7036 - val_loss: 0.7302 - val_accuracy: 0.7727\n",
+            "Epoch 5/150\n",
+            "31/31 [==============================] - 0s 6ms/step - loss: 0.7163 - accuracy: 0.7459 - val_loss: 0.7121 - val_accuracy: 0.7792\n",
+            "Epoch 6/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.7353 - accuracy: 0.7410 - val_loss: 0.6928 - val_accuracy: 0.7987\n",
+            "Epoch 7/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.7319 - accuracy: 0.7524 - val_loss: 0.6791 - val_accuracy: 0.7857\n",
+            "Epoch 8/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.6774 - accuracy: 0.7508 - val_loss: 0.6670 - val_accuracy: 0.7922\n",
+            "Epoch 9/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.6753 - accuracy: 0.7410 - val_loss: 0.6653 - val_accuracy: 0.7922\n",
+            "Epoch 10/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.6535 - accuracy: 0.7671 - val_loss: 0.6524 - val_accuracy: 0.7857\n",
+            "Epoch 11/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.6516 - accuracy: 0.7590 - val_loss: 0.6422 - val_accuracy: 0.7792\n",
+            "Epoch 12/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.6227 - accuracy: 0.7752 - val_loss: 0.6398 - val_accuracy: 0.7727\n",
+            "Epoch 13/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.6299 - accuracy: 0.7704 - val_loss: 0.6306 - val_accuracy: 0.7792\n",
+            "Epoch 14/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.5858 - accuracy: 0.7850 - val_loss: 0.6281 - val_accuracy: 0.7727\n",
+            "Epoch 15/150\n",
+            "31/31 [==============================] - 0s 4ms/step - loss: 0.5892 - accuracy: 0.7557 - val_loss: 0.6256 - val_accuracy: 0.7727\n",
+            "Epoch 16/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.5807 - accuracy: 0.7834 - val_loss: 0.6217 - val_accuracy: 0.7727\n",
+            "Epoch 17/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.5841 - accuracy: 0.7964 - val_loss: 0.6151 - val_accuracy: 0.7857\n",
+            "Epoch 18/150\n",
+            "31/31 [==============================] - 0s 4ms/step - loss: 0.5915 - accuracy: 0.7769 - val_loss: 0.6073 - val_accuracy: 0.7792\n",
+            "Epoch 19/150\n",
+            "31/31 [==============================] - 0s 4ms/step - loss: 0.5617 - accuracy: 0.7818 - val_loss: 0.6070 - val_accuracy: 0.7662\n",
+            "Epoch 20/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.5587 - accuracy: 0.7801 - val_loss: 0.6015 - val_accuracy: 0.7468\n",
+            "Epoch 21/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.5375 - accuracy: 0.7997 - val_loss: 0.5993 - val_accuracy: 0.7662\n",
+            "Epoch 22/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.5503 - accuracy: 0.7899 - val_loss: 0.5940 - val_accuracy: 0.7792\n",
+            "Epoch 23/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.5373 - accuracy: 0.7850 - val_loss: 0.5935 - val_accuracy: 0.7468\n",
+            "Epoch 24/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.5526 - accuracy: 0.7704 - val_loss: 0.5882 - val_accuracy: 0.7532\n",
+            "Epoch 25/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.5485 - accuracy: 0.7818 - val_loss: 0.5845 - val_accuracy: 0.7532\n",
+            "Epoch 26/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.5402 - accuracy: 0.7834 - val_loss: 0.5842 - val_accuracy: 0.7597\n",
+            "Epoch 27/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.5201 - accuracy: 0.7818 - val_loss: 0.5798 - val_accuracy: 0.7597\n",
+            "Epoch 28/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.5159 - accuracy: 0.7915 - val_loss: 0.5772 - val_accuracy: 0.7662\n",
+            "Epoch 29/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.5325 - accuracy: 0.7834 - val_loss: 0.5758 - val_accuracy: 0.7532\n",
+            "Epoch 30/150\n",
+            "31/31 [==============================] - 0s 7ms/step - loss: 0.5178 - accuracy: 0.7834 - val_loss: 0.5714 - val_accuracy: 0.7532\n",
+            "Epoch 31/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.5170 - accuracy: 0.7915 - val_loss: 0.5745 - val_accuracy: 0.7662\n",
+            "Epoch 32/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.5098 - accuracy: 0.7834 - val_loss: 0.5744 - val_accuracy: 0.7532\n",
+            "Epoch 33/150\n",
+            "31/31 [==============================] - 0s 5ms/step - loss: 0.5131 - accuracy: 0.7818 - val_loss: 0.5766 - val_accuracy: 0.7532\n",
+            "Epoch 34/150\n",
+            "31/31 [==============================] - 0s 4ms/step - loss: 0.5063 - accuracy: 0.7948 - val_loss: 0.5762 - val_accuracy: 0.7597\n",
+            "Epoch 35/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.5045 - accuracy: 0.7899 - val_loss: 0.5684 - val_accuracy: 0.7532\n",
+            "Epoch 36/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.4956 - accuracy: 0.7948 - val_loss: 0.5660 - val_accuracy: 0.7468\n",
+            "Epoch 37/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.5001 - accuracy: 0.7769 - val_loss: 0.5678 - val_accuracy: 0.7468\n",
+            "Epoch 38/150\n",
+            "31/31 [==============================] - 0s 4ms/step - loss: 0.4972 - accuracy: 0.7899 - val_loss: 0.5678 - val_accuracy: 0.7403\n",
+            "Epoch 39/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.4899 - accuracy: 0.7883 - val_loss: 0.5687 - val_accuracy: 0.7597\n",
+            "Epoch 40/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.4917 - accuracy: 0.7899 - val_loss: 0.5698 - val_accuracy: 0.7403\n",
+            "Epoch 41/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.4898 - accuracy: 0.7997 - val_loss: 0.5672 - val_accuracy: 0.7403\n",
+            "Epoch 42/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.5017 - accuracy: 0.7980 - val_loss: 0.5620 - val_accuracy: 0.7662\n",
+            "Epoch 43/150\n",
+            "31/31 [==============================] - 0s 4ms/step - loss: 0.4894 - accuracy: 0.7948 - val_loss: 0.5616 - val_accuracy: 0.7597\n",
+            "Epoch 44/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.4963 - accuracy: 0.8013 - val_loss: 0.5604 - val_accuracy: 0.7532\n",
+            "Epoch 45/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.4638 - accuracy: 0.7997 - val_loss: 0.5626 - val_accuracy: 0.7532\n",
+            "Epoch 46/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.4854 - accuracy: 0.7704 - val_loss: 0.5621 - val_accuracy: 0.7532\n",
+            "Epoch 47/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.4879 - accuracy: 0.7883 - val_loss: 0.5623 - val_accuracy: 0.7468\n",
+            "Epoch 48/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.4725 - accuracy: 0.8013 - val_loss: 0.5620 - val_accuracy: 0.7468\n",
+            "Epoch 49/150\n",
+            "31/31 [==============================] - 0s 4ms/step - loss: 0.4738 - accuracy: 0.8111 - val_loss: 0.5628 - val_accuracy: 0.7532\n",
+            "Epoch 50/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.4897 - accuracy: 0.7736 - val_loss: 0.5572 - val_accuracy: 0.7597\n",
+            "Epoch 51/150\n",
+            "31/31 [==============================] - 0s 4ms/step - loss: 0.4908 - accuracy: 0.7801 - val_loss: 0.5572 - val_accuracy: 0.7727\n",
+            "Epoch 52/150\n",
+            "31/31 [==============================] - 0s 4ms/step - loss: 0.4699 - accuracy: 0.7915 - val_loss: 0.5539 - val_accuracy: 0.7532\n",
+            "Epoch 53/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.4871 - accuracy: 0.7948 - val_loss: 0.5541 - val_accuracy: 0.7662\n",
+            "Epoch 54/150\n",
+            "31/31 [==============================] - 0s 4ms/step - loss: 0.4666 - accuracy: 0.7850 - val_loss: 0.5537 - val_accuracy: 0.7597\n",
+            "Epoch 55/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.4791 - accuracy: 0.7834 - val_loss: 0.5527 - val_accuracy: 0.7597\n",
+            "Epoch 56/150\n",
+            "31/31 [==============================] - 0s 4ms/step - loss: 0.4765 - accuracy: 0.7915 - val_loss: 0.5524 - val_accuracy: 0.7468\n",
+            "Epoch 57/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.4660 - accuracy: 0.7948 - val_loss: 0.5603 - val_accuracy: 0.7532\n",
+            "Epoch 58/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.4650 - accuracy: 0.7883 - val_loss: 0.5573 - val_accuracy: 0.7532\n",
+            "Epoch 59/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.4770 - accuracy: 0.7997 - val_loss: 0.5564 - val_accuracy: 0.7468\n",
+            "Epoch 60/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.4784 - accuracy: 0.7883 - val_loss: 0.5575 - val_accuracy: 0.7662\n",
+            "Epoch 61/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.4630 - accuracy: 0.7964 - val_loss: 0.5611 - val_accuracy: 0.7662\n",
+            "Epoch 62/150\n",
+            "31/31 [==============================] - 0s 4ms/step - loss: 0.4619 - accuracy: 0.7915 - val_loss: 0.5620 - val_accuracy: 0.7662\n",
+            "Epoch 63/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.4612 - accuracy: 0.7687 - val_loss: 0.5654 - val_accuracy: 0.7532\n",
+            "Epoch 64/150\n",
+            "31/31 [==============================] - 0s 3ms/step - loss: 0.4596 - accuracy: 0.7883 - val_loss: 0.5673 - val_accuracy: 0.7532\n",
+            "Epoch 65/150\n",
+            "31/31 [==============================] - 0s 4ms/step - loss: 0.4608 - accuracy: 0.8013 - val_loss: 0.5633 - val_accuracy: 0.7532\n",
+            "Epoch 66/150\n",
+            "31/31 [==============================] - 0s 4ms/step - loss: 0.4632 - accuracy: 0.7915 - val_loss: 0.5598 - val_accuracy: 0.7532\n"
+          ]
+        }
+      ],
+      "source": [
+        "#using regularization , dropout , earlystoppinng for overfitting\n",
+        "# Build the model with regularization and dropout\n",
+        "model = Sequential()\n",
+        "model.add(Dense(64, input_dim=train_X.shape[1], activation='relu', kernel_regularizer=l2(0.001)))\n",
+        "model.add(Dropout(0.5))\n",
+        "model.add(Dense(64, activation='relu', kernel_regularizer=l2(0.005)))\n",
+        "model.add(Dropout(0.5))\n",
+        "model.add(Dense(num_classes, activation='softmax'))\n",
+        "\n",
+        "# Compile the model\n",
+        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
+        "\n",
+        "# Use EarlyStopping to prevent overfitting\n",
+        "early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
+        "\n",
+        "# Train the model\n",
+        "history = model.fit(train_X, train_y, epochs=150, batch_size=20, validation_data=(test_X, test_y), callbacks=[early_stopping])\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 51,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dP0h9PDAsrbk",
+        "outputId": "8fa616c2-bb2f-4b7f-ac46-baead112545e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "20/20 [==============================] - 0s 2ms/step - loss: 0.4426 - accuracy: 0.8029\n",
+            "5/5 [==============================] - 0s 3ms/step - loss: 0.5524 - accuracy: 0.7468\n",
+            "Training Accuracy: 80.29%\n",
+            "Test Accuracy: 74.68%\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Evaluate the model\n",
+        "train_loss, train_accuracy = model.evaluate(train_X, train_y)\n",
+        "test_loss, test_accuracy = model.evaluate(test_X, test_y)\n",
+        "\n",
+        "print(f'Training Accuracy: {train_accuracy * 100:.2f}%')\n",
+        "print(f'Test Accuracy: {test_accuracy * 100:.2f}%')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "metadata": {
+        "id": "3vnVSEv5w7yQ"
+      },
+      "outputs": [],
+      "source": [
+        "#saving to pkl file\n",
+        "pkl.dump(model, open('deeplearning.pkl', 'wb'))\n",
+        "# nb"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 53,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_zNnNjwVorFy",
+        "outputId": "a53d6e1e-cc4c-4139-ae6e-a3d56cb9d1d7"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([ 10.   , 139.   ,  80.   ,   0.   ,   0.   ,  27.1  ,   1.441,\n",
+              "        57.   ])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 53
+        }
+      ],
+      "source": [
+        "input_data = (10,139,80,0,0,27.1,1.441,57)\n",
+        "data = np.array(input_data)\n",
+        "data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 54,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gKucl9g3orFy",
+        "outputId": "1053ec1b-8127-4b8a-ebf9-b017a6035350"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
+      "source": [
+        "std_data = scalar.transform(data.reshape(1, -1))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 55,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Q98ySHLOorFz",
+        "outputId": "2891824e-4941-4ba6-e8b2-5f2afdc02718"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 55
+        }
+      ],
+      "source": [
+        "prediction = rf.predict(std_data)\n",
+        "prediction"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 56,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0XbltysMorFz",
+        "outputId": "89f7149e-535a-4bf5-a491-bd82d56b6301"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The patient does not have diabetes\n"
+          ]
+        }
+      ],
+      "source": [
+        "if prediction == 1:\n",
+        "    print(\"The patient has diabetes\")\n",
+        "else:\n",
+        "    print(\"The patient does not have diabetes\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 57,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HNMqeVbjorF0",
+        "outputId": "af17b1bc-0a84-474c-a58a-ac6279bd2826"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
+              "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 57
+        }
+      ],
+      "source": [
+        "df.columns"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 58,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "cRBV717PorF0",
+        "outputId": "919759f9-1fa0-4e9d-896c-08ba569f6ed1"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 0, 'SkinThickness')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 58
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x2500 with 8 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig, ax = plt.subplots(4, 2, figsize=(20, 25))\n",
+        "plt.style.use('ggplot')\n",
+        "\n",
+        "sns.histplot(x = df['Age'], hue = df['Outcome'], palette=\"viridis\",kde = True, ax = ax[0, 0])\n",
+        "ax[0,0].set_xlabel('Age', fontsize = 15)\n",
+        "\n",
+        "sns.histplot(x = df['BMI'], hue = df['Outcome'], palette=\"viridis\",kde = True, ax = ax[0, 1])\n",
+        "ax[0,1].set_xlabel('BMI', fontsize = 15)\n",
+        "\n",
+        "\n",
+        "sns.histplot(x = df['BloodPressure'], hue = df['Outcome'], palette=\"dark\",kde = True, ax = ax[1, 0])\n",
+        "ax[1,0].set_xlabel('BloodPressure', fontsize = 15)\n",
+        "\n",
+        "sns.histplot(x = df['DiabetesPedigreeFunction'], hue = df['Outcome'], palette=\"dark\",kde = True, ax = ax[1, 1])\n",
+        "ax[1,1].set_xlabel('DiabetesPedigreeFunction', fontsize = 15)\n",
+        "\n",
+        "\n",
+        "sns.histplot(x = df['Glucose'], hue = df['Outcome'], palette=\"flare\",kde = True, ax = ax[2, 0])\n",
+        "ax[2,0].set_xlabel('Glucose', fontsize = 15)\n",
+        "\n",
+        "sns.histplot(x = df['Insulin'], hue = df['Outcome'], palette=\"flare\",kde = True, ax = ax[2, 1])\n",
+        "ax[2,1].set_xlabel('Insulin', fontsize = 15)\n",
+        "\n",
+        "\n",
+        "sns.histplot(x = df['Pregnancies'], hue = df['Outcome'], palette=\"viridis\",kde = True, ax = ax[3, 0])\n",
+        "ax[3,0].set_xlabel('Pregnancies', fontsize = 15)\n",
+        "\n",
+        "sns.histplot(x = df['SkinThickness'], hue = df['Outcome'], palette=\"viridis\",kde = True, ax = ax[3, 1])\n",
+        "ax[3,1].set_xlabel('SkinThickness', fontsize = 15)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 59,
+      "metadata": {
+        "id": "2kYatJCforF1"
+      },
+      "outputs": [],
+      "source": [
+        "train_x, test_x, train_y, test_y = train_test_split(X, Y, test_size = 0.2, stratify=y, random_state = 2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 60,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KwdpuB3YorF1",
+        "outputId": "178b3792-a809-4392-bbfd-2f1fd59ef894"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[87, 13],\n",
+              "       [28, 26]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 60
+        }
+      ],
+      "source": [
+        "cf_matrix = confusion_matrix(test_y, rf_test_pred)\n",
+        "cf_matrix\n",
+        "\n",
+        "# Order: TN, FP, FN, TP"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 61,
+      "metadata": {
+        "id": "uW5twhYVorF2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6f13b413-4555-4335-84c0-3854727f9d85"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "87 13 28 26\n"
+          ]
+        }
+      ],
+      "source": [
+        "tn, fp, fn, tp = cf_matrix.ravel()\n",
+        "print(tn, fp, fn, tp)\n",
+        "\n",
+        "# Ravel function is used to extract the confusion matrix values"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 62,
+      "metadata": {
+        "id": "Tjq3Wy1PorF3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "outputId": "ad8dd637-720c-4675-ef09-aae1e929eda1"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 62
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "sns.heatmap(cf_matrix, annot=True)\n",
+        "\n",
+        "# Annot displays labels"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 64,
+      "metadata": {
+        "id": "WLpdafmsorF3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6d7e59ea-c47c-4f22-f3a8-f35150d168ac"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(0.7115942028985507, 0.6757407407407408, 0.6842210552638159, None)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 64
+        }
+      ],
+      "source": [
+        "precision_recall_fscore_support(test_y, rf_test_pred, average = 'macro')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 65,
+      "metadata": {
+        "id": "sGgj1JjworF5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "012f9524-a346-46c1-edfc-0b21c0cfd0c1"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": 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PPvlkpQvhTCYTL7/8cq1jmzNnDnl5edxwww1VWnWfeuopgoKCatw3MjKy2v7Ib7/9NlDeOnPq/rfccgvdu3ev9FjqWz9nMnXq1Co3d4zcYDab+e9//1upnjp27Ej//v3ZuXMnRUVFruXvvPMOAG+88UaV5zJQ7WupLubMmUNOTg7jx493tbBV+M9//kNcXByLFy/m4MGDVfZ95plnKvXb1+l03HrrrUDlbjfuUPHYz/T+UqG6157BYMDf37/Oxx49ejSXXnppnfe76aabqrRKT5kyhcDAQL799lssFkudyzwbhw8fZtGiRcTGxvLII49UWtevXz+uu+46cnJymD17dpV9r732WoYMGVJp2W233QbUva7j4+OZPHlypWWjR49m4MCB7N27lxUrVriW33nnncA/rboVvv32W4qKipg4cWKl11FNKroAnNqtoToV25zcDc7d5s6dS1paGldccQXXX399lfUnv67P5jX69NNPV3rfCAoK4oorrqCkpIQ777yzUr9lo9HI+PHjsVqtlbqtfPnll+Tl5TF16lQ6duxYqfzOnTszadIkNm3aRHJyct1PRBMm3RuaOfWUr9tPZ8eOHbz22mv8+eefpKenU1ZWVmn9kSNHquyj0+no169fleUVX+ed3Ndv1apVQHl/t+oS6MzMTBwOBykpKbXqZlBR3oEDB6q92KZiSKWdO3cyYsQIOnbsSPfu3fnuu+84cOAAo0eP5sILL6R379617jJwsjlz5jBnzhyg/MM8Li6OG264gccee4zg4GB27dpFTk4OCQkJvPDCC9WW4ePjU+mNbOPGjQBVkkSACy+8sFYfJPDPeb/wwgurrPPz86N79+41jlfarVu3Sv/EVFi1ahV6vZ4ffviBH374ocp6q9VKVlYWx48fJzQ0tM71U1sVz2l3S0hIqNQ1o0LFh21ubi5+fn4ArF69GkVR6pV01UbF82Dw4MFV1ul0Oi666CLS0tLYtGlTlQtTe/fuXWWfkx+DO9X2/WXgwIFER0fzyiuvsHHjRkaMGEH//v3p3r17rZ/Tp+rTp0+99qvutRUYGEj37t1Zvnw5O3fuPKcjgVS8VgcMGIBer6+yfvDgwXz99dds2rSJf/3rX5XWubOuBwwYgEZTtZ1s0KBBLF++nE2bNrnO3WWXXUabNm346quvePXVV10jZXz88cfodLpqu8I1BatXrwbKH9+ZuPs12rJlS4BqP/sqEuTDhw+7llW8v27ZsqXa99eUlBSg/P311KTYm0nS24yVlZWRk5MDcMZ+aqtXr2bw4MHY7XZXq2tAQAAajYbNmzczZ86caltAwsLCqv3QioqKAqh08czx48cBeO21104by8ktaqdTUV51CVh15Wm1Wv744w+ee+45Zs2axaOPPgqAv78/N998My+//LIrqamNzz///LSDzFfEt2fPHqZOnXrG+OCf8xUZGVllO51OV+s+eqcr53TL4Z+6O9Xx48ex2+2nfSxQ/nhCQ0PrXD+eVlPrd8VwXCePe52Xl0dwcHC1LZfuUFF/NfVzrFhe3cWD1T2O6h6DO1S0up3p/SUgIIDVq1fz7LPP8ssvv7Bw4UKg/P1j8uTJPPXUU9UmfKdT0/P0TGp67lf3nnUuNJa6rst50Wg03H777Tz22GN8//333HrrrWzYsIGNGzcyZswYVwJ3JlFRUaSmpnLo0KEzjmpx6NAhgFqXXR8V57i6b29OdTb1Vt1oLRX1drp1NpvNtazi/bWiX3RNGsv767ki3RuasZUrV2K324mMjDzjRTMvvPACpaWlLFq0iAULFvDmm2/y3HPPMWXKFM4///wa98vOzq72zbXiSuiTX8AVf+fn56Oqao236lpiqlNR3pw5c05b3slf1QcHB/O///2PQ4cOuS6gS0pK4t1333V9ZecuFfGNHTv2tPGlpqZW2ScjI6NKeXa7nezs7Fodu6LFsrpyTrccam61CwwMJDg4+LSPRVVVWrduXemx1KV+3EGj0WC326tdV9MIE3UVFBREbm5ujVdUn62Kc1fdiALwz9fCnh7qbOnSpQCnfY+o0KpVK6ZNm0ZmZibbt2/n7bffJjQ0lOeee47nnnuuzseu7wQDNT33q3vPOhfPpcZS13U5LwATJkzAaDS6ujhU/L799ttrfcyKb6IqRjupSW5urmsUi/79+7uWV7RMV1dH9amfin8iqvtW81SerreKcrds2XLa99fqLjb0ZpL0NlNOp5MXX3wRoNq+Safau3cvISEh1V5lXDHES3Xsdjt///13leUVX52f3Heub9++AJX6hp1JRStydYl1fco7WXx8PP/+979Zvnw5fn5+rq4K7pKUlERQUBCrV6+u9B/66VRcaVvdOV+5cmWtW28qzvvKlSurrCsqKmLz5s21Kudkffv2JTc3lx07dtR6e6h//dRXcHAwGRkZ1Z7zU4dfqq++ffuiqiq//fbbGbc93XO4JhX1V10XFLvd7jqnnrwye9euXfzwww8oilKr95gKiqLQqVMn7rnnHhYvXgyU94+vUJ/zVRfVvbby8/PZvHkzJpOpUn/K+jyXNBpNveq6opHiVBX/WDR0Xa9cubLaIRSrey+H8tb9cePGsWbNGv766y++++472rRpw7Bhw2p9zIpuEJ9++ulp/xF//fXXsVgsDB06tNJoCBXXYVS0Ap+spvqpzWfKmYbpBM+/Rj31/trYSdLbDGVmZnLttdeybNkyYmNjeeKJJ864T1xcHDk5OWzdurXS8mnTprm+iqzJ448/XqnrQ05OjqsPa8UFNAB33303er2eBx54wNXf6GRWq7XKC7jiQrzqLgYYPXo07dq147333mP+/PnVxrZq1SrX7HOpqamu8UBPlpubi8VicftX1TqdjnvuuYf09HTuvffealsF09PTK11oUNFd4sUXX3R1TYHyriqPP/54rY89evRoAgMD+eabb9iyZUuldS+88EK9WkEeeOABoHx4oeouJikuLnb1iauIoS714y59+vTBbrdXGV93+vTp/PXXX245xj333AOUX7BSXavQyctO9xyuyZgxYwgJCeG7776rdE4B3nzzTVJTUxk6dKjHJppZvnw5l156KVarlTvvvPO0Q+BB+fUC1SU1FctOnj2tPuerLr766qtK1xpA+YVs+fn5XHfddZX6s9fnuRQaGlptElaTVq1acckll5CWlsabb75Zad2aNWv49ttvCQ4OZuzYsbUusz727NnD+++/X2nZnDlzWL58OfHx8VUu1oJ/LmgbP348RUVFTJo0qdp+wTW56KKLuOmmm8jJyWHUqFGV+qxW+PDDD3n11Vfx8/PjrbfeqrSuol/3qV/xb9u2rcq2FU73/Lr88suJi4vjl19+4bvvvquy/uT4PP0avfXWWwkKCmLq1KnVXrTodDprvG7Dm0mfXi9X0YHd6XS6piFeuXIlVquVPn368M0339SqH+j999/PwoULufDCC7nmmmsIDAxk/fr1rFy5knHjxjFr1qxq92vRogUWi4XOnTtzxRVXYLPZmDVrFunp6UyePLnStJFJSUl89tlnTJgwgU6dOnHppZeSmJiIzWbj4MGDrFixgvDwcHbt2uXaZ8iQIfzwww9ceeWVjBgxAh8fH1q3bs1NN92EXq9n9uzZDB8+nJEjR9KvXz+6d++O2Wzm0KFDrFu3jv3795Oeno7ZbGbLli1ceeWVnHfeeXTo0IGWLVuSlZXFnDlzsNlsrj6+7vT000+zZcsWPvzwQ+bOncvgwYOJjo4mMzOTPXv28Ndff/Hiiy+6LjTo378/99xzD++88w6dO3dm3LhxrnF6g4ODaz1VbUBAAO+99x433XQT/fr1qzRO75YtWxg4cCDLly+v0wfUkCFDeOWVV3j88cdJSEhgxIgRtGnThqKiIg4cOMDy5cu58MILXa2fda0fd7nnnnv4/PPPufPOO1myZAkxMTFs3ryZVatWMWrUKH799dezPsawYcN46qmneOGFF+jQoYNrnN6MjAxWrlxJ3759XSNKtG/fnujoaGbMmIFer6d169YoisJNN93k6gpyKj8/Pz777DOuvvpqBg4cyNVXX01sbCwbNmxg0aJFREVFVblyviFs3rzZ9R5jsVjIyMhgzZo1JCcno9FoePDBB/m///u/M5azePFiHn74YS644AISExOJiIjg8OHDzJkzB41Gw8MPP+zatj7nqy4uu+wy+vfv73pNrFy5kpUrVxIXF8crr7xSadv6PJeGDBnCjBkzuPzyy+nZsyd6vZ6LLrrotFPofvjhh/Tv35+HH36YRYsW0bt3b9c4vRqNhs8//7xeI1zUxaWXXsp//vMfFixYQLdu3Vzj9JpMJj777LNq3yv69+9Pt27d2LJlC3q9ngkTJtT5uB9//DF2u53vvvuO9u3bc9lll5GQkEBxcTFLly5l+/bthIaG8uOPP1a5IGv06NEkJCTw3XffcfjwYc4//3wOHjzInDlzGD16NDNnzqxyvNN9phgMBn744QeGDRvG9ddfz0cffUTfvn0pKytj586dLFmyxNUa7+nXaGhoKLNmzXJNYz5kyBA6deqEoigcOnSIVatWcfz48SoXpHu9BhgGTTQCnBhXseJmMBjU0NBQtWfPnurEiRPVBQsWVBpX8WQ1jVM4d+5c9fzzz1f9/PzUwMBA9ZJLLlGXL19e7TiiqvrPGJZ5eXnq5MmT1ZYtW6oGg0FNSkpS33rrLdXpdFZ7/K1bt6o333yzGhsbqxoMBjU4OFjt1KmTetttt6lLliyptK3dblcff/xxtU2bNq4xh08dAzMjI0N99NFH1U6dOqk+Pj6qr6+vGh8fr1511VXqV199pdpsNlVVy8dYfPzxx9V+/fqpkZGRqsFgUKOjo9VLL71UnT9/fi3PfM3j9NbE6XSqX375pTp48GA1ODhY1ev1asuWLdX+/furL774onrw4MEq27/zzjtqUlKSajAY1BYtWqiTJ09W8/Lyqh03tKb6UVVVnT9/vnrBBReoPj4+alBQkHrFFVeoO3fudI2zefJ4uzWNIXqqFStWqFdffbXaokULVa/Xq2FhYWq3bt3UBx54QF23bl2V7WtbP2dS8VyvjRUrVqgDBgxQfXx8VH9/f3XEiBHqli1b6jW26unGAp03b546fPhwNTg4WDUYDGqrVq3UMWPGVHker127Vh08eLAaEBCgKopSKYbT1d/atWvVMWPGqGFhYaper1djYmLUO+64Qz1y5Eid4jzT2Lenqojp5JvZbFajo6PVIUOGqM8884y6Z8+eavet7nmUnJysPvDAA2qvXr3UsLAw1WAwqK1bt1avuuoq9a+//qr2cdfnfFWorj5PrvvPP/9c7datm2oymdSwsDD1lltuUY8ePVptWXV9LmVkZKjXXXedGhERoWo0mkrn/XSvscOHD6t33HGHGhsbq+r1ejU0NFQdPXq0unbt2irbnukcnO75fKqTnxt///23OmTIENXf31/18/NTL7nkkmqPf7I333xTBdRx48bV6ng1WbhwoTpu3Dg1OjpaNRgMakBAgNqzZ0/12WefrTRm+akOHjyoXnPNNWpwcLBqMpnU3r17qz/++GONz/nafKYcOHBAvfPOO9W4uDhVr9erISEhap8+fdQXX3yxyvHd9Rqt6fmkqqev79TUVPWuu+5S4+PjVaPRqPr7+6vt27dXb7zxRvWnn36q8bx5K0VVG2h8HyFEk+RwOGjbti1Wq7XaqTKFEKK2brnlFr744gt+//33KmMGC3GuSZ9eIZqpvLy8Kv1lVVXlhRde4ODBgw3eR1AI4d0OHTrEjBkz6NChQ7Xj1QpxrkmfXiGaqdWrVzN+/HiGDRtGXFwcRUVFrF69ms2bNxMTE1PtgOZCCHEm3377LSkpKcyYMQOLxcLzzz9f7yHkhHAn6d4gRDOVmprKU089xV9//UVWVhZ2u51WrVoxatQonnjiidNOUCGEEDUZNGgQf/75JzExMTzwwAPcf//9ng5JCECSXiGEEEII0QxIn14hhBBCCOH1JOkVQgghhBBeT5JeIYQQQgjh9STpFUIIIYQQXk+GLDuN3Nxc15SC1QkPDycrK+scRiTOJalf7yd17N2kfr2b1K/3O1Md63Q6goODa12eJL2nYbfbsdls1a6rGHPQbrcjA2B4H6lf7yd17N2kfr2b1K/3a4g6lu4NQgghhBDC60nSK4QQQgghvJ4kvUIIIYQQwutJ0iuEEEIIIbyeXMgmhBBCCOEBFosFi8Xi6TAarbKyMgCMRqNbymtUSW9ycjK//PILqamp5Obm8tBDD9GnTx/XelVVmTlzJkuWLKG4uJikpCQmTpxIixYtXNsUFRXx2WefsWHDBhRF4fzzz+fWW2/FZDJ54iEJIYQQQlRRXFyMoij4+/u7RioQlel0OgoKCiguLsbX1/esy2tU3RssFgtxcXH8+9//rnb9nDlzWLBgAZMmTeKll17CaDTy4osvYrVaXdu8/fbbHDp0iKeeeorHHnuMnTt38tFHH52rhyCEEEIIcUZ2ux2z2SwJ72koioLZbD7tnAl10ahaenv06EGPHj2qXaeqKvPnz+fKK6/kvPPOA+Duu+9m0qRJrFu3jv79+3P48GE2b97Myy+/TLt27QCYMGECL7/8MjfddBMhISHVlm2z2SqNx6soCj4+Pq6/q1OxXJ6s3knq1/tJHXs3qV/v5g3125RjP9cURXHL+WpUSe/pZGZmkpeXR9euXV3LzGYz8fHxpKSk0L9/f1JSUvD19XUlvABdunRBURT27t1bqavEyX766SdmzZrlut+mTRteffVVwsPDzxhXVFTUWTwq0dhJ/Xo/qWPvJvXr3Zpy/ZaWlqLX6z0dRqOn1+sxGAyVurLWV5NJevPy8gAIDAystDwwMNC1Li8vj4CAgErrtVotfn5+rm2qM3bsWEaNGuW6X/HfRFZWVo1N6oqiEBUVxbFjx2Q2GC8k9ev9pI69m9Svd/OG+rVarTXO+irK6fV6bDYbVquV9PT0Kut1Ol2tGihd27szuKZKr9fX+N/WmV5Mqqo22RecODOpX+8ndezdpH69m9Rv8+GOem5UF7KdTlBQEAD5+fmVlufn57vWBQUFUVBQUGm9w+GgqKjItY2oO5tdoajE6ZabzS59mIQQQojaOHLkCA8++CA9e/YkLi6OPn368Mwzz5CTk1PrMg4dOkR0dDTbt29vwEibhibT0hsREUFQUBDbtm0jLi4OgJKSEvbu3cuwYcMASExMpLi4mP3799O2bVsAtm/fjqqqxMfHeyr0Js9idfDRNxvdUtbtN/REr2sy/2sJIYQQHnHgwAGuuOIK2rZty3vvvUdsbCy7d+/mhRde4I8//mDu3LkEBwd7OswmpVFlH2VlZaSlpZGWlgaUX7yWlpZGdnY2iqIwYsQIZs+ezfr16zl48CDvvvsuwcHBrtEcWrVqRffu3fnoo4/Yu3cvu3bt4rPPPqNfv341jtwghBBCCNHYPPnkk+j1er799lsuuOACoqOjGTx4MDNmzODYsWO8+uqrAERHR/Pbb79V2rdDhw58//33APTt2xeA4cOHEx0dzbhx41zbzZgxg4svvpg2bdrQo0cPnnzySde6I0eOcOutt5KQkED79u25/fbbycrKcq1/4403uOSSS5gxYwbnnXceCQkJPP744zgcDt5//326d+9O165deeuttyrFlp+fz0MPPUSXLl1o3749V199NTt27HDvyatBo2rp3bdvH1OnTnXd//LLLwEYOHAgd911F6NHj8ZisfDRRx9RUlJCUlISTzzxBAaDwbXPvffey7Rp03juuedck1NMmDDhnD8WIYQQQoj6yM3NZdmyZTz66KOuIVQrREREcOWVVzJ37lxefvnlM5Y1b948Ro4cyYwZM2jfvr3rGqYvvviC5557jscff5yLL76YwsJC1q1bB4DT6eTWW2/F19eXH3/8EbvdzpNPPsmdd95ZabSrAwcO8Mcff/DNN9+QlpbG7bffzsGDB2nbti2zZs1iw4YNPPjggwwYMICePXsCcPvtt2Mymfj666/x9/fn66+/Zvz48axYsaLBW64bVdLbqVMnZs6cWeN6RVEYP34848ePr3EbPz8/7rvvvoYITwghhBCiwaWmpqKqKgkJCdWuj4+PJy8vj+PHj5+xrNDQUACCg4OJiIhwLX/77be57bbbmDhxomtZ9+7dAVi5ciW7du1i1apVREdHA/DWW29x8cUXs3nzZtd2TqeT//73v/j5+ZGYmEi/fv3Yt28fX331FRqNhvj4eN577z3+/vtvevbsydq1a9m8eTNbtmxxTS38zDPPsHDhQubNm8eNN95Y53NVF40q6RVCCCGEEOUaamSK7Oxsjh07xoUXXljt+j179tCyZUtXwgvl100FBgayZ88eV9IbExODn5+fa5uwsDA0Gg0azT+9Z8PDw8nOzgYgOTmZ4uJiOnfuXOl4ZWVlHDhwwF0Pr0aS9AohhBBCNCJxcXEoisKePXu47LLLqqzfu3cvQUFBhIaGoihKleT4TOP/mkwmt8Sp01VOIxVFqTIErKIoOJ1OAIqLi4mIiKjURaLCqfMwNIRGdSGbEEIIIURzFxISwkUXXcQXX3xBaWlppXWZmZnMnj2byy+/HEVRCA0NJSMjw7V+//79lfapSEIrEk8o7woaExPDypUrqz1+QkICR48e5ciRI65lKSkp5Ofnk5iYWO/H1aVLF7KystDpdLRp06bS7VwMOCBJrxBCCCFEI/PCCy9gtVq54YYbWL16NUeOHGHp0qVcd911REVF8eijjwLQv39/pk+fzvbt29myZQuPPfZYpdbWsLAwTCYTS5cuJSsryzWfwYMPPsjHH3/MtGnT2L9/P9u2beOzzz4DYMCAASQlJXHPPfewbds2Nm3axH333ccFF1xAt27d6v2YBgwYQK9evZgwYQLLly/n0KFDrFu3jldeeYUtW7acxdmqHUl6hRBCCCEambZt27JgwQJiY2O544476N+/P4888gj9+vXjl19+cY108Mwzz9CyZUvGjh3LXXfdxR133FFpxAedTsfzzz/P119/Tc+ePV0jWl1zzTVMmTKFL774gsGDB3PzzTeTmpoKlHdJ+PzzzwkMDOTKK6/k2muvJTY2lg8++OCsHpOiKHz11Vf07dvXNarD5MmTOXLkCGFhYWdVdq2Or8r8fTXKysqqsV+Moii0aNGC9PR0r58CsajE6dbJKfzMjf9/reZUv82V1LF3k/r1bt5QvwUFBQQEBHg6jEZNr9djs9lqPFd6vZ7w8PBal9f4sw8hhBBCCCHOkiS9QgghhBDC60nSK4QQQgghvJ4kvUIIIYQQwutJ0iuEEEIIIbyeJL1CCCGEEMLrSdIrhBBCCCG8niS9QgghhBDC60nSK4QQQgghvJ7O0wEIIYQQQojasdkVLFbHOTmW0aBFr2uaM95VR5JeIYQQQogmwmJ18NE3G8/JsW6/oSd6Xd07BUyfPp0PPviArKwsOnbsyPPPP0+PHj0aIMK6ke4NQgghhBDCLebMmcPUqVN58MEH+e233+jYsSM33HAD2dnZng5Nkl4hhBBCCOEen3zyCddffz3jx48nMTGRV155BR8fH2bMmOHp0KR7gzdyd38f1Xu68wghhBCigVitVrZu3crdd9/tWqbRaLjwwgvZsGGDByMrJ0mvF3J3f5+J13m+H44QQgghGrecnBwcDgdhYWGVloeHh7Nv3z4PRfUP6d4ghBBCCCG8niS9QgghhBDirIWEhKDVaqtctJaVlUV4eLiHovqHJL1CCCGEEOKsGQwGunbtysqVK13LnE4nK1eupFevXh6MrJz06RVCCCGEEG4xadIkHnjgAbp27UqPHj345JNPKC0tZfz48Z4OTZJeIYQQQoimwmjQcvsNPc/ZsaBuQziNHj2anJwcXn/9dbKysujUqRNff/11o+jeIEmvEEIIIUQTodep9ZolrX7qN2bprbfeyq233urmWM6e9OkVQgghhBBeT5JeIYQQQgjh9STpFUIIIYQQXk+SXiGEEEII4fUk6RVCCCGEEF5Pkl4hhBBCCOH1JOkVQgghhBBeT5JeIYQQQgjh9STpFUIIIYQQXk9mZBNCCCGEaCK0ThuqzXpOjqXoDTg0+nNyrHNBkl4hhBBCiCZCtVk59Otn5+RYMaMmgLFuSe/q1av54IMP2LZtGxkZGUybNo1LL720gSKsG+neIIQQQggh3KKkpISOHTvy4osvejqUKqSlVwghhBBCuMXgwYMZPHiwp8OolrT0CiGEEEIIrydJrxBCCCGE8HqS9AohhBBCCK8nSa8QQgghhPB6kvQKIYQQQgivJ6M3CCGEEEIItyguLiY1NdV1/+DBg2zfvp3g4GCio6M9GJkkvUIIIYQQTYaiN5RPGnGOjlVXW7Zs4eqrr3bdnzp1KgBXX301b775prtCqxdJeoUQQgghmgiHRl/nWdLOpX79+nHkyBFPh1GtJpX0Op1OZs6cyYoVK8jLyyMkJISBAwdy1VVXoSgKAKqqMnPmTJYsWUJxcTFJSUlMnDiRFi1aeDh6IYQQQgjhKU3qQraff/6ZxYsX8+9//5v//e9/3HDDDfzyyy8sWLDAtc2cOXNYsGABkyZN4qWXXsJoNPLiiy9itVo9GLkQQgghhPCkJpX0pqSk0Lt3b3r27ElERAR9+/ala9eu7N27Fyhv5Z0/fz5XXnkl5513Hq1bt+buu+8mNzeXdevWeTh6IYQQQgjhKU2qe0NiYiJLlizh6NGjtGzZkrS0NHbv3s2//vUvADIzM8nLy6Nr166ufcxmM/Hx8aSkpNC/f/9qy7XZbNhsNtd9RVHw8fFx/V2diuU1rfcoxfXDLSxWO04naDRuKFNppOfsFI26foVbSB17N6lf7yb12/y4o66bVNI7ZswYSktLeeCBB9BoNDidTq699loGDBgAQF5eHgCBgYGV9gsMDHStq85PP/3ErFmzXPfbtGnDq6++Snh4+BljioqKqvsDaWCWg5n4+/uddTnZOSUs+SuV/366AQCjQUuAv5GB57emY2J4vZ6ABr2BFi0izjq2c6Ux1q9wL6lj7yb1692acv2WlZWh1zfeC9IaC71ej9FodMu1WU0q6V21ahUrV67k3nvvJSYmhrS0NKZPn05wcDCDBg2qd7ljx45l1KhRrvsVyVxWVhZ2u73afRRFISoqimPHjqGqar2P3RCsNgeFhUVntf/y1YfZnpLNyQ/NYnWQdbyEWfN3Erf1CEP6xxLob6xj2VbS09PrHdu50pjrV7iH1LF3k/r1bt5QvxaLBb1ej0bTpHqanlN6vR6LxYLFYqk2d9DpdLVqoHRt787gGtrXX3/N6NGjXd0UYmNjycrK4ueff2bQoEEEBQUBkJ+fT3BwsGu//Px84uLiaixXr9fX+N/WmV5Mqqo2vhec6vpRZ06nyq9L9pN2uACAdrGBvP7UYBYu34vF6mDXvlzWbj5G2uECvpydzNUjEogK961TbI3ufJ1Go6xf4VZSx95N6te7NeX6NZvNFBYW4u/vL4lvDZxOJ4WFhfj6+rqlnptU0muxWKo8MTQajetEREREEBQUxLZt21xJbklJCXv37mXYsGHnOtwmR1VVlq46RNrhAnRahdHD2tE6OoD27UJYsVaP2UfPBT1b0L5tMAv/PEB6ZjE/LdzHdVe0Jyigbi2+QgghRHOm0+nw9fWlqKj+38x6O5PJhK+vLzqde9LVJpX09urVi9mzZxMWFkarVq1IS0vj119/5eKLLwbKv+4YMWIEs2fPpkWLFkRERDBjxgyCg4M577zzPBx947dxeyZbdmYDcNnFbWgdHVDtdiFBJq66NJ6Z81LIPF7K7N/2ct0V7fExNamnkxBCCOFROp2OgIDqP2ubO0VRaNGiBenp6W5rzW9SWcqECRP4/vvv+fTTT8nPzyckJIRLLrmEcePGubYZPXo0FouFjz76iJKSEpKSknjiiScwGOo+lV5zkp5ZzPI15TOoXNQnmoS4oNNubzBoGTM8nhm/7CavwMKcxfu4ZmSie0Z4EEIIIYRwsyaV9Pr4+HDLLbdwyy231LiNoiiMHz+e8ePHn7vAmjhVVVm++jAASe2C6dWldqMr+Jn1jB3ejhlzUziaUcymHZn06hLZkKEKIYQQQtSL9JwW7EnL42hmMTqdhgF9ous0FFlosA8X9YkG4K8N6eQXWhoqTCGEEEKIepOkt5mzO5ysWFveraF3lwj8feveDaRz+1Cio/yw250s+etQk72SVgghhBDeS5LeZm5Lchb5hVZ8fXT07lq/rgmKonDJhbFoNQpphwvYvT/XzVEKIYQQQpwdSXqbMavNwerNxwDo17slBr223mWFBJk4v0f5zDjL1xzBZne6JUYhhBBCCHeQpLcZ27k3B4vFQVCAkU4JoWddXu+ukQT4GSgusbF1Z5YbIhRCCCGEcA9JepspVVXZklyemHbvGO6WocZ0Wo2rtXftlgysNsdZlymEEEII4Q6S9DZTRzKKyc4tQ6dV6JgQ4rZyOyaEEhRgpLTMzuYd0torhBBCiMZBkt5mqqKVNyk+BJPRfcM1azUKF/RsAcC6rRmUWexuK1sIIYQQor4k6W2Gikts7EnLA6B7h3C3l9++bTChQSYsVgcbt2e6vXwhhBBCiLqSpLcZ2rY7G6dTpUWELxFhZreXr9Eo9D3R2rs5OUtGchBCCCGEx0nS28yoqsr23ccB6NYhrMGOkxAXRKC/gTKLg+Q9xxvsOEIIIYQQtSFJbzOTnlVCQZEVvV5DQpvgBjuORqPQo1MEABu3Z8osbUIIIYTwKEl6m5k9J2ZLaxcbiF7XsNXfuX0oRoOW3HwL+w/mN+ixhBBCCCFOR5LeZkRVVVJSy5PexLYN18pbwaDX0iWpvAvFhm1yQZsQQgghPEeS3mYkPbOYwmIbBr2GuOiAc3LMHp3C0Shw+FgRGdkl5+SYQgghhBCnkqS3GUlJzQOgbWwgugbu2lDB39fgalXetENae4UQQgjhGZL0NhMnd21ofw66Npys+4kL2nbvzyWvoOycHlsIIYQQAiTpbTbSM4spOtG1ofU56tpQoUW4mfBQHxwOlZ9+23NOjy2EEEIIAZL0Nhu7K0ZtaB10zro2VFAUhW4nZn6bMXenDF8mhBBCiHNOkt5mQFVV15BhCW2CPBJDUrtgDHoNaYcLWLn+qEdiEEIIIUTzJUlvM5BXYCG/0IpWoxDb0t8jMRj0WjrEhwDw5Y/JHolBCCGEEM2XJL3NQOqhAgCio/ww6LUei6Oii8PCP9M4llXssTiEEEII0fxI0tsMpB0uT3rjWp3bC9hOFRbiQ68ukTgcKjPnpXg0FiGEEEI0L5L0ejmb3cmh9EIA4mI8m/QCXD2iPQDfz90tF7QJIYQQ4pyRpNfLHU4vxOFQ8ffVExpk8nQ4DB/YBl+znrTDBazZfMzT4QghhBCimZCk18tV9OeNiwlEURQPRwNmHz2jL2kHwIy5uz0cjRBCCCGaC0l6vVxFf942Hu7Pe7Lxl5d3cfh1yX4Ki6wejkYIIYQQzYEkvV4sN7+MvAILGgWPDVVWnV6dI0iIC6K0zM4vv+/zdDhCCCGEaAYk6fViFa28LaP8MBg8N1TZqRRF4doTrb3SxUEIIYQQ54IkvV7s4NETozY0oq4NFa66LAGtVmHj9kxSTkyRLIQQQgjRUCTp9VKqqnL4WBEAMS0aT9eGCuGhZob2jwXg+1+ltVcIIYQQDUuSXi+VlVOKxeJAr9cQGWb2dDjVqujiMGv+Hmx2p4ejEUIIIYQ3k6TXSx1OL2/ljY70Q6Px/FBl1RncL5aIUB+yc0tZ8tdBT4cjhBBCCC8mSa+XqpiFLaaFn4cjqZlOp2HciEQAZszd5eFohBBCCOHNJOn1Qg6H09XS26oR9uc92fhR5V0c/vj7EBnZJR6ORgghhBDeSpJeL7R7fw4WqwNDI+7PWyE+LojeXSNxOFR+XJDi6XCEEEII4aUk6fVCazenAxAd1Xj7856s4oK2737ZjaqqHo5GCCGEEN5Ikl4vtPpE0tvYuzZUuHxIW8w+OvYfzGf91gxPhyOEEEIILyRJr5dxOJys33IMaNwXsZ3Mz9fA5UPaAjJDmxBCCCEahiS9XiZ5Tw6FxVYMeg0RoY27P+/Jrr08CYBfft9HcYnNw9EIIYQQwttI0utlVm06CjSd/rwVzusWSZuYQEpK7fy6ZL+nwxFCCCGEl5Gk18tU9ImNjmwaXRsqKIriuqBNujgIIYQQwt0k6fUiqqq6kt4Wkb4ejqbuxo1IQKNRWLvlGPsO5nk6HCGEEEJ4EUl6vcjh9CIyskvQaRWiwpte0hsV7svFF8QAMHOujNkrhBBCCPeRpNeLrNtaPmpDx4Qw9LqmWbUVXRx+mJ+C3e70cDRCCCGE8BZNMzMS1aro2tCjc6SHI6m/oRfGEhJkIiO7hGWrD3s6HCGEEEJ4CUl6vci6E0lvzyac9Br0Wq66LAGA7+fu8nA0QgghhPAWkvR6icIiK7v25QDQo1OEh6M5OxVdHBatOMDx3FIPRyOEEEIIb6DzdAB1lZOTw9dff83mzZuxWCxERUUxefJk2rVrB5SPYDBz5kyWLFlCcXExSUlJTJw4kRYtWng48oa1aUcmTqdKTAt/IsOa3kVsJ0tqF0L3juFsTs7ix9/2cNt1XT0dkhBCCCGauCbV0ltUVMTTTz+NTqfjiSee4H//+x//+te/8PX9J8mbM2cOCxYsYNKkSbz00ksYjUZefPFFrFarByNveBUXsfXu2nS7NpxsfMWYvb/sRlVVD0cjhBBCiKauSSW9c+bMITQ0lMmTJxMfH09ERATdunUjKioKKG/lnT9/PldeeSXnnXcerVu35u677yY3N5d169Z5OPqGVXERm7ckvaMvaYfJqGX3/ly27MzydDhCCCGEaOKaVPeG9evX061bN/773/+SnJxMSEgIw4YNY+jQoQBkZmaSl5dH167/fB1uNpuJj48nJSWF/v37V1uuzWbDZrO57iuKgo+Pj+vv6lQsr2n9ueRwONm4IxOAPt1agAInfriRm8pTanfOggJMjLi4DbN/28t3v+ymR6dzm8w3pvoVDUPq2LtJ/Xo3qV/v1xB13KSS3szMTBYvXszIkSMZO3Ys+/bt4/PPP0en0zFo0CDy8vIACAwMrLRfYGCga111fvrpJ2bNmuW636ZNG1599VXCw8PPGFNFK7MnbUk+RlGxDT9fAxdf2JFDR7Lx93ffNMQaReO28gx6Ay1a1O5Cu7tv7c/s3/by86J9fPDyGPx8jW6JoS4aQ/2KhiV17N2kfr2b1K/3c2cdN6mk1+l00q5dO66//nqgPDk9ePAgixcvZtCgQfUud+zYsYwaNcp1v+K/iqysLOx2e7X7KIpCVFQUx44d83if00XLkgHo3jGczMwMrDYHhYVFbivfqTrdVp7VZiU9Pb1W27aPM9I2JpD9h/L58Mu/uGFMB7fEUBuNqX5Fw5A69m5Sv95N6tf71aaOdTpdrRooXdvXN5ipU6dy5ZVX0qVLl2rXb9++nR9//JFnn322voeoIjg4mFatWlVa1qpVK9asWQNAUFAQAPn5+QQHB7u2yc/PJy4ursZy9Xo9er2+2nVnejGpqurxF9zG7eVdG7p3DC+PRYUTP9zITeWpZz6nJ7thTBLPv7OGr3/ayfWjk9wTQx00hvoVDUvq2LtJ/Xo3qV/v5846rveFbMnJyeTn59e4vqCggOTk5PoWX6327dtz9OjRSsuOHj3qyvIjIiIICgpi27ZtrvUlJSXs3buXxMREt8bSmGw60Z+3ZxMfn7c6V49MxKDXsGVnFtt2ZXs6HCGEEEI0UQ02esOxY8dcF4O5y8iRI9mzZw+zZ8/m2LFjrFy5kiVLljB8+HCgvCl8xIgRzJ49m/Xr13Pw4EHeffddgoODOe+889waS2NRWGQlJTUXgB6dvS/pDQ324bJBbQD4+uedHo5GCCGEEE1Vnbo3LFu2jOXLl7vuz549myVLllTZrqSkhAMHDtCjR4+zj/Ak8fHxPPTQQ3z77bf8+OOPREREcPPNNzNgwADXNqNHj8ZisfDRRx9RUlJCUlISTzzxBAaDwa2xNBZbdmWhqhAd5UdEqNnT4TSIG8Z0YM7iffy0cC/P3NsXX3P1XVGEEEIIIWpSp6TXarVSUFDgul9aWlplKAlFUTAajVxyySWMGzfOPVGepFevXvTq1avG9YqiMH78eMaPH+/2YzdGm3eUj2Hb1KcePp1+vVrQNjaQ/Qfz+WnhXm4ce+4uaBNCCCGEd6hT0jts2DCGDRsGwF133cWtt95K7969GyQwUTsV/Xm9OelVFIUbx3bgubdW88WPydwwJknGZhRCCCFEndS7T+97770nCa+Hqarq1RexneyakYmYjFqS9xxn3ZYMT4cjhBBCiCbmrMfpLS0tJSsri+Li4mqHlOjYsePZHkLU4GhmMRnZJWi1Cl2SwjwdToMKDjQxdng83/2ym+mzdtCnuwxILoQQQojaq3fSW1BQwGeffcaaNWtwOp01bvf999/X9xDiDDadGJ83qV0IPqYmNc9IvdwyrhPf/bKbeX/sJyP7AiLDvPPCPSGEEEK4X70zpY8//pgNGzZw2WWXkZSUhJ+f+6a9FbWzOdn7+/OerHP7MM7rGsm6rRl88/NOHpxY8wWNQgghhBAnq3fSu2XLFkaOHMmNN97oznhEHWw6MXKDt/fnPdkt4zqxbmsGX/+0k3tu6YFe12BDTQshhBDCi9Q7YzAajXWa71i4l93uZMtO7x+u7FQjBrchPMSHjOwS5v+x39PhCCGEEKKJqHfSO2DAANauXevOWEQd7D2QR2mZHV+znvi4IE+Hc84Y9Fr+dVX5xZEff7dN5lwXQgghRK3UO+nt27cvRUVFvPjii6xZs4a9e/eyf//+KjfRMCpaebsmhaHRNK8xa/91ZUeMBi2bk7NYv1WGLxNCCCHEmdW7T+8zzzzj+nvr1q01biejNzSMra6kt/l1MQkL8eGqyxL4ds4uPv52K+d1k+HLhBBCCHF69U5677zzTnfGIepoy85soLyltzmaeG1nvp2ziwXL00g7XEBcqwBPhySEEEKIRqzeSe+gQYPcGIaoC5vdyc69xwHo2qH5tfQCtG8bwsUXxLB01SE++347z/2nn6dDEkIIIUQjJuM9NUEp+3MpszgI8DM06xbO267rAsB3c3eRV2DxcDRCCCGEaMzq3dL7/vvvn3EbRVGkG0QD2LqrvD9v5/bN7yK2kw3oE02H+BB27s3hix93cN+tPT0dkhBCCCEaqXonvTt27KiyzOl0kpeXh9PpJCAgAKPReFbBiept3VXen7dbh+bZn7eCoijc/a/u3PXMH3w6YzuTru2C2Ufv6bCEEEII0QjVO+l97733ql1ut9v5/fffmTdvHk8//XS9AxM1c43c0Ez7855s1JC2/N9H6zhwpJBv5+xi4rVdPB2SEEIIIRoht/fp1el0XHrppXTr1o1p06a5u/hmz2pzkLyn/CK2bpL0otNpmHxTdwA+/GYrVpvDswEJIYQQolFqsAvZWrduzc6dOxuq+GZr975crDYnQQFGYlv6ezqcRuHqkYlEhplJzyxm9oI9ng5HCCGEEI1QgyW9W7dulT69DaDiIrYuSWEoSvO9iO1kRoOW267vCsC7X27B4XB6OCIhhBBCNDb17tM7a9asapcXFxezc+dOUlNTGT16dL0DE9WrmH64WzOcie10bhrbgXembyL1UD4/LdzLuBGJng5JCCGEEI1IvZPeH374odrlvr6+REZGMmnSJIYMGVLvwJobm13BYj1zf9TNO8qT3sS2oRSVVN+iqapuDc2ttFpNjXHXh9GgRa9T8TXrmXxTN156by3/m7aRMcPi0elkGGohhBBClKt30vv999+7M45mz2J18NE3G0+7jd3hZOe+HAC27DxG2uHcarebeF0Pt8fnLlabg0+/2+S28m6/oSf6E8ntLeM68dG3W0k7XMCs+Slce0WS244jhBBCiKZNmsKakOycUpxOFZNRS4CfwdPhNDrlrb3dAXjzs00ykoMQQgghXOrd0lshOTmZjRs3kpVV/rV7eHg4PXv2pGPHjmcdnKgsI7sEgMhwX7mIrQY3X9WRj77ZwqH0Qr6fu5ubrpTnoRBCCCHOIum12+28+eabrFu3DgCz2QxASUkJc+fOpU+fPtx3333odGedV4sTMrLKk96oMLOHI2m8fEw67r65B8/892/e/GwT40Yk4mOS56AQQgjR3J3VhWzr1q3j8ssvZ9SoUQQFBQGQn5/P3LlzmTt3LrNmzeLaa691V6zNXsbx8qQ3QpLe07phTBIffbuVI8eK+HTGNu65pfH2cRZCCCHEuVHvPr0rV65k4MCB3Hjjja6EFyAwMJAbb7yRiy66iBUrVrgjRgHY7E6O55QC0tJ7JiajjkfvOA+Ad7/YzPHcUg9HJIQQQghPq3fSm5eXR3x8fI3rExISyMvLq2/x4hTZOaU4VTCbdPj56j0dTqM3dng8nduHUlRi463P3DdahBBCCCGapnonvSEhISQnJ9e4Pjk5mZCQkPoWL05RcRFbRJhZLmKrBY1G4al7+gLwxY87SD2U7+GIhBBCCOFJ9U56Bw4cyKpVq/j44485evQoTqcTp9PJ0aNH+eSTT1i1ahWDBg1yY6jN2z8jN0jXhtoacF40F18Qg92h8tK7azwdjhBCCCE8qN4Xsl155ZVkZGSwZMkSlixZgkZTnj87neWzbQ0cOJCxY8e6J0pBRlYxIP156+qpe87nz7WHmb8sjRXrjjDgvGhPhySEEEIID6h30qvRaLjrrrsYNWoUmzZtqjROb48ePWjdurXbgmzubHYnx/PKAIiUpLdOktqFcPNVHfls5g6eeeNvFn19lWsGNyGEEEI0H3VKeq1WK9OnTycmJobLLrsMgNatW1dJcOfPn8/ixYu55ZZbZJxeN8g6XoKqgq+PDj9fmYmtrv4zqTc/L9pHSmou03/YwaTrung6JCGEEEKcY3Vq8vr9999Zvnw5PXv2PO12PXv2ZOnSpfzxxx9nFZwo5+rPK6289RIUYOSxO8uHMHvjk/VknRjvWAghhBDNR52S3lWrVnH++ecTGRl52u2ioqLo27cvf/3111kFJ8qdPP2wqJ9rL29P16QwCottPPf2ak+HI4QQQohzrE5J78GDB0lKSqrVtu3bt+fAgQP1CkpUVjH9sLT01p9Wq+GlRy5EUWD2b3v5c81hT4ckhBBCiHOoTkmv3W6vdR9dnU6HzWarV1DiH1abg5x8uYjNHXp0imDC1Z0BeOzVFZSW2T0ckRBCCCHOlTolvSEhIRw8eLBW2x48eFAmp3CDrOOlqCr4mfX4mmUmtrP1yB29aRHhy4Ejhfxv2gZPhyOEEEKIc6ROSW+XLl34888/yc8//exW+fn5/Pnnn3TpIlfJny2ZlMK9/HwNvPTIhQB8+M1Wtu/O9nBEQgghhDgX6pT0jh49GpvNxnPPPceePXuq3WbPnj0899xz2Gw2rrjiCrcE2ZzJyA3uN2xAa0YNaYvDoXL/c8uwWB2eDkkIIYQQDaxOg+hGRkbywAMP8NZbb/HUU08RGRlJbGwsJpOJsrIyDh06xLFjxzAajdx3331ERUU1VNzNRsVMbJL0utdLD/dn1caj7Nybw38/3cDjk/t4OiQhhBBCNKA6T03Vs2dPXnvtNYYOHYrNZmPdunWsWLGCdevWYbFYGDJkCK+99hq9e/duiHibFYvVQU6+BZCk191Cg334v8cvAuD9r7awYVuGhyMSQgghREOq13RpERERTJo0CYDS0lJKS0vx8fHBx8fHrcE1d5knJlHw9zNg9pGL2Nzt0oFxXHVZAj8u2MP9zy1j4ZdXynkWQgghvNRZzxEsyW7Dyazozxsq57cmWq2GohJnvfd/9M6+rFx3hP0H83n6jVW88dRFboxOCCGEEI3FWSe9ouEck5nYzshqc/Dpd5vOqowBfaKZNX8PM+bu4qLzoxl9STs3RSeEEEKIxqLOfXrFuSMzsZ0bsS39Ob97+UWXj778JwePFng4IiGEEEK4myS9jZTF6iCvQC5iO1cu6NmCHp0iKCy2MfmpP7DaZBgzIYQQwptI0ttIVYzPG+BnwMckvVAamkaj8PpTFxPob2DTjkymvrnK0yEJIYQQwo2adNL7888/c8011zB9+nTXMqvVyqeffsqECRO46aabeP3118nLy/NYjPUlM7Gde62i/HlrysUAfP7DDr75aauHIxJCCCGEuzTZpHfv3r0sXryY1q1bV1r+xRdfsGHDBh588EGmTp1Kbm4ub7zxhoeirD+ZlMIzLrmwNfdP6AnApEd+IXnPcQ9HJIQQQgh3aJJJb1lZGe+88w633347vr7/jGxQUlLCH3/8wc0330znzp1p27YtkydPZvfu3aSkpHgw4ro7duIitigZueGce3BiTwb1bUVpmZ1/P7KInPwyT4ckhBBCiLPUJDuLfvrpp/To0YOuXbsye/Zs1/L9+/fjcDjo0qWLa1l0dDRhYWGkpKSQmJhYbXk2mw2bzea6ryiKa+xhRVGq3adieU3r60xx/aCk1EZBkRWAyDBf1/J6FOhG7iyvkcamlNenTqflveeHMurfP5N6MI/bHlvMd++MxKDXuuc4olFw+2tYNCpSv95N6tf7NUQdN7mk96+//iI1NZWXX365yrq8vDx0Ol2l1l+AwMDA0/br/emnn5g1a5brfps2bXj11VcJDw8/YzxRUVG1D/40LAcz8ff3A+BYdvlX6qHBPoSFBta5LI2icZXlDgqgU90zmoECbo3NnY9Vr9NhsZV/+WE2m/nwxUu56vYfWbUxncde/ZuXHhlY6xefj0lPZESwW+ISDctdr2HROEn9ejepX+/nzjpuUklvdnY206dP56mnnsJgMLit3LFjxzJq1CjX/YrEJisrC7vdXu0+iqIQFRXFsWPHUFX1rGOw2hwUFhYBkHqwPOmNCPVxLasLp+qs136nk7tzg5tK6uXW2Nz5WMssNj79bjMAigJ+fn5cMqA1Py3cy4xfkknPKKBXl8halXX7jT1wOqRbRGPm7tewaFykfr2b1K/3q00d63S6WjVQurZ3V3Dnwv79+8nPz+fRRx91LXM6nezcuZPffvuNJ598ErvdTnFxcaXW3vz8fIKCgmosV6/Xo9frq113pheTqqruecGprh+nXMRW37Ld+yagurU8d79BuT82VS3/x6dNTCAX9Ynmz7VHWL7mMP6+ehLb1qIFVz3zc0c0Dm57DYtGSerXu0n9ej931nGTSnq7dOnC66+/XmnZBx98QMuWLRk9ejRhYWFotVq2bdtG3759ATh69CjZ2dk19udtbFRVdU0/HCXDlTUKvbpEkF9oYcvObBYsS8Pso6NVC39PhyWEEEKIOmhSSa+Pjw+xsbGVlhmNRvz9/V3LBw8ezJdffomfnx9ms5nPPvuMxMTEJpP0FhbbKCm1o1EgPFSS3sZAURQuviCGohIb+w7kM2fxfsZfnkhYsI+nQxNCCCFELTWppLc2br75ZhRF4Y033sBut9OtWzcmTpzo6bBq7diJrg2hIT7odU1yRDmvpNEojLi4DbPm7yE9s5gfF+xl/KhEggKMng5NCCGEELXQ5JPeKVOmVLpvMBiYOHFik0p0T5ZRMT6vTErR6Oh1GsYMa8fMeSkczy3jxwV7GD8qET9f911UKYQQQoiGIU2Jjcwx1/TDMilFY+Rj0nHVZQkEBhjJL7Ty44K9lJTazryjEEIIITxKkt5GRFVV18gNchFb4+Vn1jPusnj8zHqO55Xxw/w9FJdI4iuEEEI0ZpL0NiK5+RasNidarUKoXCTVqAX6Gxk3IgFfs57juWX8MC+FIkl8hRBCiEZLkt5GpOIitohQM1qNTK3Y2IUEmbhmZAJ+vnpy8i3M/DWFwmKrp8MSQgghRDUk6W1EMmR83iYnONDENSMT8fczkFdQnvgWFEniK4QQQjQ2kvQ2IscqRm6Qi9ialKAAI+NHJhDobyC/0MrMX1M4fKzQ02EJIYQQ4iSS9DYSNruTzOPS0ttUBfgbuWZk+bi9BUVWbrj3V1L253o6LCGEEEKcIElvI7EnNQeHQ8Vo0MqEB02Uv5+Bq0cmEBJo5FhWMWNv/4V1W495OiwhhBBCIElvo7FtVzYAkWFmFEUuYmuq/H0NjL+8Pd06hJNXYOHau+exaMUBT4clhBBCNHuS9DYS23ZnARApXRuaPB+TjulvjGBwvxjKLA4mPrqI737Z5emwhBBCiGZNkt5GYtuu8qRXph/2DmYfPZ+9NpxrRibicKg89OKfvPX5RlRV9XRoQgghRLMkSW8jUFpmZ09q+UVPMnKD99DrNPz36YHcfXN3AP7vw/U88dpf2O1OzwYmhBBCNEOS9DYC21OycThVzD46/Hz1ng5HuJGiKDw+uQ/PPdgPRYEvf0zm348uoqRUZm8TQgghziVJehuBLcn/dG2Qi9i807/Hd+ajl4ZiMmr5feVBrrpjrmuIOiGEEEI0PEl6G4EtOysuYmseXRsUu819N08/mDoYObgt3783ipAgE1t3ZXP5v392dWsRQgghRMPSeToAAZsrWnqbycgN+Smb3FhaHzeW1fB6d4nkl09Hc+P9C0g7XMDoSXP49NVh9OvV0tOhCSGEEF5NWno9LL/Qwv6D+YBcxNZctIkJZO60MfTuGkl+oZXr753P7N/2eDosIYQQwqtJ0uthW09MShEd5YePSRrem4uQIBMz3hnJyMFtsNmd3PPsUhnSTAghhGhAkvR6WMf4EN59bjB339zT06GIc8zHpOPDF4dyxw1dgfIhzR55eQU2GdJMCCGEcDtJej0sNNiHscPjufLSRE+HIjxAo1F4+t6+vPhQfzQahW/n7OKW//xGUbHV06EJIYQQXkWSXiEagVuu7sS0Vy/BZNSybPVhxt4+l/TMYk+HJYQQQngNSXpFw5H+qXUy7KI4fvzwcsKCfUjec5zL//0zO/fmeDosIYQQwivIlVPirOicFsKthwi0Z+Bvz8HPkYvBWYpWtaPFgV3RY1VMWDU+FOpCyF1sJNR6iFx9C5yKPP1O1b1jBHM/G81ND/zG3rQ8xt42h49fuYSL+rTydGhCCCFEkyZZhxdSKJ8AoqFoVRvRZbtpWZZCqO0oGmq+8Eqn2tCpNszOQoLsmWTN2kU/wIGObEM0mYY4jpoSsGq8a4xirVZDUUn9LkgLCfLjm7dGcffTv7Nu6zFuun8BLz82kCuGxrslNqNBi14nrfBCCCGaF0l6vVRDTABhchTRpnQzsaU7MKgW19oibTDZhlYUakMo1IVg0ZhxKHocig6d04pBLcPkLMbfnk3vRH+yt6zC5Cwm0nqASOsBOhWtINMQyyFTRzKMbVGVpt/rxmpz8Ol3Z1cHF/SKoqDYwu59uTz84jLmLdlLz84RZx3b7Tf0RK9r+udYCCGEqAtJesUZOcuKSSxaTXzJRrTYASjWBHDApwvHjO0o1gXVuK9VY6bkxN/HjO244o7b+e6pD/F3HCfCcoCWlj0E2TOJsqYRZU2jRONPmk9XDvp0wqYxNfyDa8R0Wg0jBsXh56Nnw/ZMlq0+jNXm4PzuUShKU5qAWQghhPA8SXrFaUWV7SX1mZG0LymfKjlH34I95t5kGlpDfVtkFYVCXRiFujD2+fbCz55Dq7JdxJZux+wspGPxXySUrCXNpxv7zT2wanzc+IiaFkVRuOj8aC7o1Yp3v9jI3xvSsVgdXNQnWhJfIYQQog4k6RXV0jktdC76k5iynTgob9nd6Xch6cZ24OZkq0gXwi6/fqT49iG6LIW2JZsIcBwnoWQ9bUq3sN+nO3vNvXBoDG49blOhKAqT/9WD7bvLW3s3bMvEanMypF8MGo0kvkIIIURtSNIrqgi0ZdA7fz5mZyEqCqGXTmTeBn2Dj7bgVHQc8unIIVMHoqz7SSxeS6A9i8SSdbQu3c5u3/M56NPZK/r81kfPzhHo9RoWrzjItl3ZWG0OLh0Yh1YSXyGEEOKMmmf2IGoUXbaL/rmzMDsLKdYE8FfQVYSNvf/cDi+mKBwztuPP4GtZFziSIm0QRrWUrkXLGJjzDZGW/c12DOAu7cMYObgNGgV278tl/h+pOJzN81wIIYQQdSEtvaKcqpJU/DcJJRsAOGaIY1PAcOwao+diOpH8ZhjiaF26ncTiNfg7cumT/yvZ+mh2+A3wXGwe1L5tMHqdhrm/72dPWh4LlqYy4uI20tVBCCGEOA1p6RUoqpPuhYtdCe8ec2/WBY7ybMJ7ElXRkmbuxh+hN7PH3AsHWsJsR7godwYZX09Bby3wdIjnXNvYQC4f2haNRiElNY8Fy9JwSouvEEIIUSNJeps5jWqnV8ECYsp24URhk/8l7PLrV/+RGRqQXWNkl19//gj9F4eNiShA/oofOG/tQ0QfXojitHs6xHOqbWwglw8pb+HdvT+X35ZL4iuEEELUpPFlNuKc0ah2zsv/lRaWfTjQsD5wBId9Ong6rDMq0/qzKfBS/gq6CmNMEnp7CfF7v6LX+icJytnu6fDOqXatgxh1oo/vrn25LFpxQBJfIYQQohqS9DZTiuqgV/4CIqwHsaNjbdAVZBjbeTqsOskxRBP7xExSEidg0/nhW3KEbltfodP2/2EqzfR0eOdMfFwQIwa3QVEgeU8Oi1ceRG2mF/oJIYQQNZGktzlSnfQoWESUNRUHWtYGXUG2IdbTUdWLotGS3nIwa89/g8PRw1DREJa9gfPWPkrc/h/QOMo8HeI5kdgmmBEXlye+O1KOs3TVYUl8hRBCiJNI0tvcqCpdC5cSbdmDEw3rA0dy3NDK01GdNbvel30J/2L9eS+RG9wZjWqj9cE59FnzMBEZfzeLIc7atw3m0oFxAGxOzmLVxnTPBiSEEEI0IpL0NjMJJetoXbYDFYWNAcPJNMZ5OiS3KvFtxdauj7K90/2UmsIxWnPpsPN9um96Hr/CVE+H1+A6xIcwuF8MAKs3HWPj9ubTzUMIIYQ4HUl6m5Hosl0kFa8GYJvfQNJNCR6OqIEoCsfDe7PuvFdJbXM1Do2RwIIUem54hoTd09Bb8jwdYYPq3jGcfr1aALBs9WG2pxz3cERCCCGE50nS20yEWg/TveB3APaae3LA3NXDETU8VWvgYOvRrO3zf2RE9ENBpWX6Us5f8x/a7P8ena3Y0yE2mPO7R9GrcwQAi1ccYE9qrocjEkIIITxLkt5mwMeRT+/8+WhwctQYz07f/p4O6ZyymkLZ1XEym7o/TYF/O7ROC7EH59JnzQPEHPjFKy92UxSFi86PplNiKKoK85emceBI85vEQwghhKggSa+X0zqtnJc/D4NaRp4ugk0Bw0BpntPVFgS1Z1PPKWzv/ADF5lbo7SW0TZ3J+av/Q8vDi3BavSv5VRSFSy6MJSEuCIdT5ZfF+0nP9N7WbSGEEOJ0JOn1ZqpK98LfCbRnU6Yxsy5wJE5F5+moPEtROB7Wi/XnvcTOpDsoNYVjsOWTsPdL0p64hNgDc7yq24NGo3DZxXHEtvTHZnfy08K97DsgXR2EEEI0P5L0erH4kg20tOwtH5osYARlWn9Ph9R4KBoyoy5kXZ/XSEm4lVJTOI7CHNqk/sD5q++j7d5v0Jd6xwVgOq2GKy5pS1S4mTKLgwkP/8aRY0WeDksIIYQ4pyTp9VKh1sMkFa8CYJv/IHINLT0cUeOkanSkRw9hbZ/Xifz3qxT5xqBzlBFzeD6dltxG+50fEJCf0uTH+TXotYwdHk9IkIljWcVcd+88jueWejosIYQQ4pyRpNcL2fOz6FnwGwoqB00dOGjq5OmQGj+NloA+o9jQ+yW2dXmYvMAkNKqdqIyV9Nj0HL3WP0HLI4vR2ks8HWm9+Zh0XHVZPC0j/dh3IJ8b719AUbHV02EJIYQQ54Qkvd7G6SD94/9gcpZQoA1lu/+gZnvhWr0oCjmh3djS42l2Xfga6VEDcWgM+BUfImHPF1zw9z0k7v6UgLzdoDo9HW2d+fsamPZ/lxISZGLrrmwmPLKIMovd02EJIYQQDU6SXi8Td2A2pXs3YFP0rA8cgUPRezqkJkMBFLvtxM3KcW0ke+JvYU2f/7K37fUUm1uidVpokb6MHpuf5/zVD9Bm77f45e1DsVlP2tdGY/43o21sEF+/eRm+Zj1/rT/KPc/8gcPR9BJ4IYQQoi6a+aX83iUwbxexB34BYKv/EIp1wR6OqOnJT9kEgIKCwajHarGhonKcMHb6XkWI4SixZclEWfZishwn9vB8Yg/Pp1AbwhFTIunGdhRpQ4A+nn0gZ9CtQzifvTaMm+5fwPxlaTz26kr+7/EBKPKtgBBCCC/VpJLen376ibVr13LkyBEMBgOJiYnceOONtGz5z0VaVquVL7/8kr///hubzUa3bt2YOHEiQUFBngv8HNDaikna+QEKKgEXjObovtaeDsn7KAo5hmhyDNFo1IuJtKQRbdlNhCUVf0cOScWrSSpeTbE2kKxZhQTkxVAQmABK4/xC5cLe0bz3/BBuf+J3vp2zi5BAE4/f1biTdSGEEKK+mlTSm5yczPDhw2nXrh0Oh4PvvvuOF154gf/+97+YTCYAvvjiCzZu3MiDDz6I2Wxm2rRpvPHGGzz//PMejr4BqSqJez7HZDlOqSmC+GufhBe/9nRU54xit53zYzoVHemmeNJN8eicFlpY9tLCso8w6yF8HfnkLp5OD8CqDyAnpBs5IV3JDemMXV+/YeMqul64g8I/I1GMuLgN//f4AB568U/e/XIzwUEm7rjB+6eoFkII0fw0qaT3ySefrHT/rrvuYuLEiezfv5+OHTtSUlLCH3/8wX333Ufnzp0BmDx5Mg888AApKSkkJiZWW67NZsNm+yehUBQFHx8f19/VqVjutq+DFdePOovIWElE5mpUNOzseBfdTL4obu5V6s7y3B1bQcpmN5XU55/YKkJUQFFPH69DY+KwT2cO+3RG67QSYT3AoA5acjYswWArICpjBVEZK1BRKAxoR05IV3JCulHo37ZOrcBue5xqb5STJim5fnQHcvIsvPTeGp5/ezUhQSbGj2rvnmM1Ym5/DYtGRerXu0n9er+GqOMmlfSeqqSkfPgoPz8/APbv34/D4aBLly6ubaKjowkLCztt0vvTTz8xa9Ys1/02bdrw6quvEh4efsYYoqKizuYhuFgOZuLv71fn/QzFx0jc+wUA6e2vg+ju5cuN7r2AzZ3lNaXYDIa6lq3nuE9HWky4leVhu/DLSSYwcyMBmRvxKTxAQMFeAgr2Epc2G7ven4KIHhSEl9/sptP3wXbX41QUDS1atKi07IVHo7Datbz+0d889OJy2sRGMXp4kluO19i56zUsGiepX+8m9ev93FnHTTbpdTqdTJ8+nfbt2xMbGwtAXl4eOp0OX1/fStsGBgaSl5dXY1ljx45l1KhRrvsV/1VkZWVht1c/nJOiKERFRXHs2DFUN0xcYLU5KCys4yxZTgc9Nr+G1l5KfmB79kRdCifKsFrc+5W/O8trErEp5Qmv1WqDelZvYXEZhca2pMe0hZhxGMqOE5K7jZCcLQTnbEdnKyTkyJ+EHPmzfHu/OHJPtAIXBMSjaiq/PN31OFXVSXp6epXlD0zozKGjx/l+7m7GT/6Bz18bzqALYtxyzMbI3a9h0bhI/Xo3qV/vV5s61ul0tWqgdG3vruDOtWnTpnHo0CGee+65sy5Lr9ej11ffinamF5Oqqu55wamuH7XW+sBPBBTsxa41s7PDnSfG41VPFOfeNwF3ltcUYnN1aVDPpvzK+1lNIRxrMZBjLQaC00FA4T5Cjm8hJGcr/kWp+Bel4V+URuzBX7BrfcgN7szx0B7khHavFJs71PSc/b/HBlBQaGHBsjRufXghn782nIF9W7ntuI2R217DolGS+vVuUr/ez5113CST3mnTprFx40amTp1KaGioa3lQUBB2u53i4uJKrb35+fleN3pDQH4KrQ/8DEBK4q1YTGGeDUjUjUZLQWAiBYGJpLW9Gr01n+CcbYTkbCUkZyt6exHh2esIz16HisLBY92IL/Yhw9iGQm1og004otNpeP+FIdzxxO8s/PMAEx4pT3wvOt+7E18hhBDer0klvaqq8tlnn7F27VqmTJlCREREpfVt27ZFq9Wybds2+vbtC8DRo0fJzs6usT9vU6S1l9Ah+X0UVI5FXkhW5AWeDkmcJZshkMyoC8mMuhBUJ/6FqYQc30zo8Y34Fx2gbP9mOgAdildRovEnw9iGDEMbsg2tUBWtW2Mx6LV8+NJQbn/8dxatOFDe4vv6cC7qI4mvEEKIpqtJJb3Tpk1j5cqVPPLII/j4+Lj66ZrNZgwGA2azmcGDB/Pll1/i5+eH2Wzms88+IzEx0auS3oSU6Zgs2ZSaItib8C9PhyPcTdFQGNCOwoB2HGhzFYay44xul8O2H74gzHoIs7OQNqVbaVO6Fati5JixLenGBLIMMW5LgA16LR+9PJTbHl/M4hUHufUhSXyFEEI0bU0q6V20aBEAU6ZMqbR88uTJDBo0CICbb74ZRVF44403sNvtrskpvEVExl9EZv6NioZdHe7EoTN7OiTRwKymUIIuGsraxbloVRth1kNEWlKJtKZicpYQW7aT2LKdWBUjGca2HDUmkGWIRT3LSTEMei0fvXQJtz2+mN9XSuIrhBCiaWtSSe/MmTPPuI3BYGDixIlelehWMJVmkpDyOQAH4saWz/YlmhWHoifD2JYMY1tQnYTajp6YGGMvJmcJMWU7iSnbSZnGzFFjIodMSRTowuvdB9ho0PLxy5cw6bHFLPnrILf8ZyEfvDCE4QPj3PvAhBBCiAbWOOdHFVU5HSTt/ACdo4z8gEQOxF7h6YiEpykajhtasd1/EItDJ/B30JWk+nTFopgwOUtoW7qZgbkzGJjzDe2K12N01HFIvBOMBi2fvHIJlw6Mw2J1MOnxxcycl+LmByOEEEI0rCbV0tuctT44h8CCPdi1PuXDk2nce/GScL9zOj3yiQT4uKEVO/wGEG49SKuyXURZ9hPgyKFj8d8kFa8i09CGwq3tMZ8/CKUOzyGjQctHLw3l4Zf+ZOa8FB54bhk5uaXcfkNXmRFJCCFEkyBJbxMQkJ9C67SfANiTeCsWn9oPxCw8Jz9lkxtL61PrLVVFS6axDZnGNuicFlpa9tCqbBehtqNEWfdz+N27yPyuBWEDryJs0FUYQlucuVDKhzN746mBBAYY+eS7bTz/zhoOHyti6gMXoNXKl0ZCCCEaN0l6GzmtvYSknR+goJIR2Z/MyH6eDkk0IXaNkYM+nTno0xk/ew6xpdtJ1KRiPZ7O0dnvcvSn9wnqNYTIS2/CL+m8M7baajQKz97Xl6hwX55/ezWf/7CDw8cKef/5IZh93Du9tBBCCOFOkvQ2EgpqtV+HJ+z+HJ+yLEqN4extc8O5/cpceJUiXQjJ/hcx6vnp5K9bTN6KHyjZvZa89YvJW78YY6v2BA+9iYA+I9Hojact68axnQkL8eXhF5exeMVBRk/6hc9fG0arFv7n6NEIIYQQdSNJb2OhOqt8HR5dtpvIglU4UdhgHkTu/l21LKz2X4WL5seBnu/2toAW92IOOEz04UVEZqzEcng3x6Y/xcFv/o/0lhdztOVQrMbg05Z15aXtWLzyIMl7jnPZLT/xySuX0LdH7bpLCCGEEOeSdMRrpHwcBXQpXArAHt8+5OolkRDuV+Lbij3tJ7D6grfZ3/ZayoyhGGwFtD4wh/NX309S8nv4F+yrcf+WkX7M+mAMXdqHkZNXxvi7fuXj77a6bZ50IYQQwl0k6W2EFNVBz/zf0KtWcvQt2GM+z9MhCS+j2G2Vbg7FyOGWw1nb+1WSkyaTH5CARnUQmbmKnhufpdumFwjJ2ohis1bZNzrSl58+voIxw9phd6hMfXM1tz22mIIiq6cfphBCCOEi3RsaofbFawixH8OmGNgYMPysZ9YS4lSnG1kiHx37TJcRqO1Fm9LNRJelEJS/i6D8XeTrwthr7kW6MeGf56XaGx+TnnefG8x5XaOY8uYq5i9LY8eeH3nr2Ys5r1vUOXpUQgghRM0km2pkwqyHiC9ZD8AW/yGUagM8HJForvL1EWwOGMYfoTez36c7dnQE2rPpVbCQi49/SVzJFrTqPxdWKorCLVd34udPriCmhT8HjhRy5R1zefm9tVisDg8+EiGEEEKS3kbF4CyhR8FCFOCAqTPpJplmWHheqdafHf4X8XvYrezyPR+LYsLXWUCXouUMyZ7O8QWfkJ+VS1GJk6ISJ/FxYfz08VjGDk/A6VR598vNXHbzT6zfluXapqabzS4TXQghhGgY0r2hkVCdTroXLMbkLKFAG8IO/wGeDkmISmwaH/b4ns9+c09iSpNpV7IJs7OAzJ/e5ugvH5PecjCHW13mGvGhTYw/Vwxty+KVB9m9P4ext/1Mv14t6N0lEo2m+uT29ht6otfJ/+JCCCHcT5LeRiLn96+ItB7AgZaNgZfhUGSgf9E4ORQ9aeZuHPDpQkvLHvoGpMLhFGIOzSf68CKORQ3gUOwoynwiiY8LomWkL4tXHmTfgXxWrjvKnrQ8hvaPJTLM7OmHIoQQohmRJpVGoHj/NjJ/ehOAHf4XUagL9WxAQtSCqmg4YmpP66dms73j/SdGfLDTMn0pfdY8RIft7+CXtw9fPYweFMOlF7bCoNeQkVXCt3N28cfKA1hLyiqPBoEMdSaEEKJhSEuvhzlKitj/7n/AYeeosR0HTJ09HZIQdaIoCqnZTlJNlxGi6Up8yXoirQeIyF5DRPYaMgxx7DX3ppWhJeO6wZo0DfuyNWzedZzd+7LpG+ekbZiKogBqb0Dr6YckhBDCC0nS62F5m5ZiyTiIPrQlW5UhlH/yC9E05RiiWWuIJsCWRXzJelpa9hJpTSPSmsZxfUv2mntjTmhNYoTK3/s15JcpLN2jZXemkwvaOD0dvhBCCC8mSa+Hhfa/HK2PLzadP7bP13k6HCHcokAfzsbAy9htz6NdyQZale0k1HaU0PxfToz125uobvFsPapl8xENR/M1zN6soP/f3zx253mEh0p/XyGEEO4lfXobgaCegzG36+7pMIRwu2JdEFsDhvBH6C3s8+mBXdGfGOv3Ny7J+4rRYdu4upuVuBAnKgoz5u7mwnHf8870TZSW2T0dvhBCCC8iSa8QosGVaf1I9h/A76HlY/1aFRO+jny6Ff7B2OLp3Ba7nis6ltG5fRhFJTZe+WAdA8fP5KeFe3E65eI2IYQQZ0+6NwghzhmbxuQa6ze2dDvtSjbi4yymU9FKEpR1tBj1b/4aOYA3v07myLEi7n7mDz76dhuPTT6f3l3qPp2x0aBFr5OkWQghhCS9QggPcCh6Us09SPPpSquyXcSXbMDPkcfxue+ToPmUxxIu4qeofvy1s4Rtu7K44d5fiW8dSP/eLQkN9qn1cWSyCyGEEBUk6RVCeIyqaDnk04lDpg60sOyjf9ABLAeTicv4nQf4nTGdevFN3iDWH1DZeyCffQfz6ZgQygU9WxDgZ/B0+EIIIZoQaQIRQnieoiHdlEDsEzPZ3O0JssN6oaLQpmgDT+ne4P/if6VzpAVVhR0px/l85g6WrT5MSanN05ELIYRoIqSlVwjRaCiKQn5wR/KDO2IqzaTlkcW0SF9GoiOFF/1TSDbG8VnBcPbk+bJxeybbdmfTq3MEvbpEYjTIpBZCCCFqJkmvEKJRKvOJYH/8DRyIu5LIYyuIPrKIjqTxWuhHbDa35vO8oRwoCWT1pmNs2ZlNn26RdOsQjk768AohhKiGJL1CiEZFsVfusuBER3rUxaRHDiQ4dxvR6UvonrudN32msao4ga9yBpBeFsTyNUfYsC2D87qE0zUhBJ1Og4KM3CCEEKKcJL1CiEYlP2VTzeuANP3F+IT2IrY0mZ7aHfT1/ZwlBZ34Prcv2SUBLF2TztqNR+jaSuWWa7vja679aA9CCCG8l3wPKIRockq1Aez268uS0FvZEDSSruHFfNB6OneE/06orpBim4ZVqVqGXPM1n369QWZ3E0IIIS29QoimS1U0ZBjbkmFsi8FZQox/Cq8E/8qG3HB+yD2fzAJ/nn1nA//9aDXXX+THpNuGENm6lafDFkII4QGS9AohvIJVYybV3J1Uc3fMQXk8GLWNo8VmfjjYhkxrIB/8buGzJXMYEXOMCZe3osOgQaiRkZ4OWwghxDkiSa8QwuuU6IJI9e/Dw29O5J7jqcz6Zimf/1HMvkJ/fjoYw5z3nPT74iMub/k4fXvHEtCpLwGdzscYFYeiKJ4OXwghRAOQpFcI4bUURcGvdXtufaI9tzyusmTJdt6btpa1+2FlURIrU5KIScvm0kU/c7H/SwSFBeMX3w1zu674tuuCb5tOaH38PP0whBBCuIEkvUKIZkFRFIYO7cLQoV3YvjubL2Zt5+eF+zhkCeOT7MF8eXwAFx3fxcVHd9Bh7SI0CqAomFq2wxzXEZ/otphatsMU3Q5jRAwand7TD0kIIUQdSNIrhGh2OrcP4/WnLub9l6/kvekr+PLHZHbvz2VxQRcWF3Qh3MfGgMB99NNtIP7wXsqO7K20v6LVY4yMxRgZgyG0JYawFhhCW2AMa4khtAW6gFA0eoOHHp0QQojqSNIrhGi2AgNM3Hp1Z26+qiNrNx9jxtzd/LY8jawimF2axGySiAnTMzjBSu+AI8Tbk7Ef24/TUkLZ0X2UHd1XY9laHz90ASHoAkLQ+4eg9Q9C6+OP1scXvcmMxmhCa/JF6+N34uZbfjOV3xRN7UeU1Oj0OE+Z1KO+FL0Bh0ZasYUQ3keSXiFEs6coCuf3aMH5PVrwitXBslWH+HnRXhatOMChbBtfZCt8QSv8fdtw4XnRDOjiz/mtSgl1ZmHJPoo1Ox3r8aNYj6djy8lAddhxlBbhKC3CknGwfjHp9Ch6IxrXbwOKvvym0RtQdCd+6w1E9BtFzra/0Rh90Bp9UM6i60XMqAlglKRXCOF9JOkVQoiTGA1ahg+MY/jAOIpLbCxecYDf/zrIstWHyM23sGBZGguWlW8b1yqAPt16cF63y+gzIor2rQMBcJQUYM8/jq0wF3vBcewFOdiL8l2JsLMoj6K0nah2K06b9Z/fNiuoTgBUuw3VbsNZi5jzN/xR6b6i06Mx+py4mdGazGjN/pVuGpOvjFQhhGhWJOkVQoga+Jr1jBkez5jh8TgcTrbuymbZqkMsXX2YTTsySTtcQNrhAmbOSwEgONBI766RdEsKp3NSGF3aJxHV3rdKuRpLMYd+/azKclVVwemokgirNitOuxXVZsFpt6HaLCcts6Lz8aUs4xAOSyk4Hah2Gw67DUdxQc0PTqMp71ZhDkBr9kfnF4jOP5jSYwcwtEpE0crHgxDCu8i7mhBC1IJWq6FHpwh6dIrggYm9yCuwsGFbBuu2HmPdlgw2J2eSm29h8YqDLF7xT5eGiFAfOrcPo0v7MLoklf+OCa6+hVVRFNDq0Gp1gLnWscWMuIVD86ejqmp567ClBKelFKelFEdZCc6yYhwlRThKCnCUFOIoLQKnE0dxQZXEOHf1AhSdHmNUHD4t22KKboepZVt8ouMxRbdFo5ML9IQQTZMkvUIIUQ9BAUaG9I9lSP9YAKw2B9t3H2fDtgy27c5m++5s9qTlkXm8lD/+PsQffx9y7RscYKBdkJ72kU7aR6gkRqq0DlHR1v7atWopiuLq84tfUI3bqU4njrIiHMWF5UlwSQH2wjzshbk4SwtxWi2UHd5D2eE9lcvX6fFplYBP6yTMsUmY4zpijk1Ca5axjIUQjZ8kvUIIr6XR6SgurnlUg717D6Gqtek1Czq9Hrvt9CMktG8TTPs2wa77pWV2du/PYXvKcZL3HGfHnuPsSc0lt8DK+gIN6w/+k+WaDBoSYgNIalN+69AmkIRYf0xG7Rljs2h8avUYKigaDTpzADpzQJV1MaP+jSXrMKXpqZQeTaUsPZXS9DRKj+zDUVpESVoyJWnJHD9pH2N4NObYJHxbJ2GOS8K3dQf0/uXnQUaDEEI0FpL0CiG8ls3m5I2nP6l2nYKCwajHarGhop6xrP88f3uNZdVGENA/AvqGwcibr+Kt12dzvFghu1ghpxjKrE627c1j2968k2JUCfKBEF+VUF+VMD8IMauYTskhH3l+Ur3jqsLpIHPNQtddXXgr/MNb4delP46SQux52djys7DlZ2PLy8ZZWoQl6wiWrCPkblji2k9r9kcfFE7YBZfhE98D3zad0PkHV3dEIYQ4JyTpFUKIc0irgU6JYbSPVOFEsu1UobAMsosVjhcrHC+G40UKZXaF3FLILVXYl/1PGX7G8iS4/AZHM4rwUaEhB2NQFAWdbwA63wBM0W1dy52WsvIEOD8LW24WtrwsHEV5J7pNFHL4x/dc2xrCo/GN64S5bWfMbTqVJ8Kn6YYhhBDuJEmvEEJ4mEaBQB8I9FFpF1aeCKsqlNjKk9/jxZxIhhUKLQpFJ24Hcsr3X3z9DwSYDMSHqyREOEkIV0mIUGkbpmJs4Hd5jdGEMaIVxohWrmVOmwVbXja23EwMvgEUpSVjyTiINesI1qwj5K5b5NrWGBZd3iUiriO+rTvgG9cBnW/VbhfQvLpK2OwKFqvDLWUZDVr0ujN/myGEt5OkVwghGiFFAV8D+IaoxIZARauwxQ45JyXB2cUKhRYNBWWw8ZDCxkP/9BPWKiqxISrx4eUXysWFlv+ODVbxacBBGDR6I8bwaIzh0a6RJZxWy4nW4ExseeWtwo7ifCzZR7BkHyF3/UldI3wD0AeFow+OKP8dFI7GYGpWE2dYrA4++majW8q6/Yae6HVneZWkEF5Akl4hhGhCjDpoEQgtAv/pHnH/0zezecanpGQp7MlU2JOpYU+WQn6pQupxhdTjVcuJ9FdpFaQSFagS5a8SGaDSIlCl7EAB1jLwM5a3QLuLxmDEGN4KY/hJLcJWS3kCnJd5omtEpmsYNUdxAWVH/pnmWesbgOVQCub47pjbdMIc17HGFmEhhKiOJL1CCNHEmcxGul0+lm4nLVNVlcwcC7sPFLD3YCFpR4tIO1pM6pEi8gptZBQqZBQqcOiUwmbOA4xoFLW8y4VJJdAMAcbyrhIGXXnibdSVX1CnUcpTb1U9+dhgd5bfdLs3UVoUj93uxGZXsTvKf9vsTuwOJ3ZHXPnfFeusdmxWKzarDZvVUb6NE+yqFvsWDWBDp2zAoKxFr1Ux6jUYDToMJgMmswmj2YyP2YjZpMPXbMRk0uBj1GH20eNj0uFj0mE2lf8d4G8gwPfEOh8dZlP532YfHXqdptoZ61RVxWpzYrM5sNqcWG2OEzcnVquDMouDkjIbpWV2SkrtlJXZKSmzUVJqdy07eX1pWcVyGyUn/rbbnThVKCmxlv9bc+LcarUKWq0GnU6D7sTfep0Gk1GL0ajDZNRiMmgxGXWYfXT4mvX4mfU4ndK1QQiQpFcIIZo8m93JW/+dc9ptQoHQEOgVAmU2yCvlRN/gf34XWxSsioHiEhtOVSG3BHJLFMg5i+A27KrnjpoTt6qsqgarqgcnYANKABxA8Ymb+ygKaDQKmhMJsM1euyHuGpNPZ2wnPNRMVLiZyDBfIsPMRISZaRHhW34LL//t5ysTjwjv5rVJ72+//cbcuXPJy8ujdevWTJgwgfj4eE+HJYQQHmfSQ5QeXE2IJw3Z9sjzN5I570PySyG/TCGvBPJKy5Niix0sdgWrveLv8pEnoDw5PLldVKcFnQb82/dm1dINaBT+uWn++Vt7yn2NolbZpuI2+dEb+WbODhwOJ1iKMBQdQ1echbYkG11pLprSXFRrGRZVR5lTj0XVV/pdpuopVX0oUcyUYkIx+pavs2sotUGpxYndUblVVFXB4VBx1DCsnVarYNBrMeg16HVaTCYtZlN5a7GrZflEK3N5S/JJrc0+/6w3u7bVodNpKC1T+XH+zn9OLuBwOHE4VOwnWskdDhWr3YnFYqfM4qDMYqfsRGtzaamNopLyFma7QyU9s5j0zGIgq8bnhb+vnqgTCXD5zY+ocDMtIvxcy4IDjdW2gAvRFHhl0vv333/z5ZdfMmnSJBISEpg3bx4vvvgib775JoGBgZ4OTwghGjWDDsL9Idy/alJcV0HDe1C6Z71b4ooI88XPfOJCNn8jhIW61jlP3LT2EoJLjmIuPkqQPRtNwRFMZYfwKc1Cby864zFsqgabPgD8w9D4haL1C0Ux+6OY/FBMfhj9/DH5l/82+gdi8AtAY/LF5GvG4GNEYzChaM7+orGiEifhobWfiromDqfKuBEdOJZZRObxEtctI6uEzOxijmUVcyy7mKJiG4XFNgqL89iTlldjeQa9hhbhPkSFmogMMRLiryfIX0eQn55gPy1BfloCfbUE+2kJ9NXga1DQoKI6HaCW/1adTnA6yieG0WhxopSvd6rly1S1fFtVBdUJqvPE3xXLnSiAPTCAvJzcE/s4QeWk/Z3lXW5q2L/6Y5zY3uksP4bDhuqwu+7jdJbHrlb/Wz1p3yq/UcqnCXc9dvWfc1DdPtVs53Sq2BwqNrsGm0PF6lDKn69OLTa02Jwa7OiwOTXYVK3rZle12Jzak17F//yHqignZnJERVFAr1HQaUHvuinodaDXKeU3jYJBr6DTajDoFYx6BZNeg9GgoNNq0RgMBHS/mKBeg8/6udsQvDLp/fXXXxkyZAgXX3wxAJMmTWLjxo0sXbqUMWPGeDY4IYQQDcahM1MYEE9hQAIl/n4UFhZRkbRr7SWYyrIxlWVhKs2id2vYs3U3BmseBms+BmseeqcVvT0PcvMgd2+V8m0nboWniaF8KmgTGsOJJNhgQtFoUTQaFK0ONJoT97VwYjkaLYr2xDLAbnfS6VBueW5Sno2hoJ70N+UJGqBU/KY8mVNU54llThRVpWivAVteCSGqk1DVSQf+2UYxOyHWSZldR47Nhxy7Dzk2X47bzeTY/Thu8+W4w4/jdj/yHb5YbU4OHC3mwNHadyMxKjZMGismxYZJY/vnt8aGUbGjUxxoFSc6HOgUZ/nfigMtTnQVfytOdDhRlBPnAdBUnIcTCdvJyyvOUXmjtHpiWfk2J+1VflNxrXWevE51nfXK+6iVlzkp79juRMGharCjxa5qcKgnfqPBrmqwq1rX3xXr7GhxqJrKCeqpCeuJ33bOPDujJ2lwolcc/OuCnUyRpPfcsNvt7N+/v1Jyq9Fo6NKlCykpKdXuY7PZsJ00vaiiKPj4+KDT1Xx6Kr7e0ev15f+JnSWjUaF1m5ZnXU55WXq3leXu8ppSbDq9DrvN7vG43F2exPaPutRxU36cdSvLgG94tFvKqijPnectOiqoVtsqCpjNPgT6aU+6yC4I+CeWmEsTWf/byZ8LKlqHBZ2tCL29sPy3rRCtowytvQydo6z8b0cpWkcZOnv53xqnFY1alzF1VXDay29V1wCgBdqFVHlUVO5EUvskyMfX94zbxFS6V3ri9k93CLuqId/hQ77DTJ7dTIHDhxLVSLHDeOK3gRLVRInDQJHTiE09fYphPXFr7rQnbqY67lfeFUhFC2g0KtoTXYO0J7oJaTUqGij/raj/TF5zSsoS2TIMR34WKmB3Ktid4Dhx++e+gl1VTvwGu0PBpirYnVW/1WjbIwS9/uyHFqxNnnW6PK3aMlV3ZGyNSE5ODnfccQcvvPACiYmJruVff/01ycnJvPTSS1X2mTlzJrNmzXLd79+/P/fdd985iVcIIYQQQjQ8Ga0aGDt2LNOnT3fdJk2aVKnltzqlpaU8+uijlJaWnqMoxbkk9ev9pI69m9Svd5P69X4NUcde170hICAAjUZDXl5epeV5eXkEBQVVu49er69zU7yqqqSmprqla4NofKR+vZ/UsXeT+vVuUr/eryHq2OtaenU6HW3btmX79u2uZU6nk+3bt1fq7iCEEEIIIZoPr2vpBRg1ahTvvfcebdu2JT4+nvnz52OxWBg0aJCnQxNCCCGEEB7glUlvv379KCgoYObMmeTl5REXF8cTTzxRY/eG+tDr9YwbN84tVyiKxkfq1/tJHXs3qV/vJvXr/Rqijr1u9AYhhBBCCCFO5XV9eoUQQgghhDiVJL1CCCGEEMLrSdIrhBBCCCG8niS9QgghhBDC63nl6A3u8ttvvzF37lzy8vJo3bo1EyZMID4+vsbtV61axffff09WVhZRUVHccMMN9OzZ8xxGLOqiLvW7bNky3n///UrL9Ho933zzzbkIVdRRcnIyv/zyC6mpqeTm5vLQQw/Rp0+f0+6zY8cOvvzySw4dOkRoaChXXXWVDHPYiNW1jnfs2MHUqVOrLP/444/dOrKPOHs//fQTa9eu5ciRIxgMBhITE7nxxhtp2bLlafeTz+Cmoz517I7PYUl6a/D333/z5ZdfMmnSJBISEpg3bx4vvvgib775JoGBgVW23717N2+99RbXX389PXv2ZOXKlbz22mu8+uqrxMbGeuARiNOpa/0C+Pj48NZbb53jSEV9WCwW4uLiGDx4MK+//voZt8/MzOSVV17hkksu4Z577mH79u18+OGHBAUF0b1794YPWNRZXeu4wptvvonZbHbdDwgIaIjwxFlITk5m+PDhtGvXDofDwXfffccLL7zAf//7X0wmU7X7yGdw01KfOoaz/xyWpLcGv/76K0OGDOHiiy8GYNKkSWzcuJGlS5cyZsyYKtvPnz+f7t27c8UVVwBw7bXXsm3bNn777Tduu+22cxm6qIW61i+AoijSItRE9OjRgx49etR6+0WLFhEREcG//vUvAFq1asWuXbuYN2+eJL2NVF3ruEJgYCC+vr4NEJFwlyeffLLS/bvuuouJEyeyf/9+OnbsWO0+8hnctNSnjuHsP4cl6a2G3W5n//79lZIfjUZDly5dSElJqXaflJQURo0aVWlZt27dWLduXUOGKuqhPvULUFZWxuTJk1FVlTZt2nDdddcRExNzDiIWDW3Pnj106dKl0rJu3boxffp0zwQkGswjjzyCzWYjJiaGq6++mqSkJE+HJM6gpKQEAD8/vxq3kc/gpq02dQxn/zksF7JVo6CgAKfTWeW/iaCgIPLy8qrdJy8vr8rX4oGBgTVuLzynPvXbsmVL7rzzTh555BHuuecenE4nTz31FMePH2/4gEWDq+n1W1paitVq9VBUwp2Cg4OZNGkS//nPf/jPf/5DaGgoU6dOZf/+/Z4OTZyG0+lk+vTptG/f/rTdFOQzuOmqbR2743NYWnqFqIXExEQSExMr3X/ggQdYvHgx1157rQcjE0LURsuWLStdJNO+fXsyMjKYN28e99xzjwcjE6czbdo0Dh06xHPPPefpUEQDqW0du+NzWFp6qxEQEIBGo6nyH2JeXl6NfUmCgoLIz8+vtCw/P1/6gDZC9anfU+l0Otq0acOxY8fcH6A452p6/fr4+GAwGDwUlWho8fHx8hpuxKZNm8bGjRt59tlnCQ0NPe228hncNNWljk9Vn89hSXqrodPpaNu2Ldu3b3ctczqdbN++vdJ/GSdLTExk27ZtlZZt3bqVhISEBo1V1F196vdUTqeTgwcPEhwc3FBhinMoISGh2tdvbZ8PomlKS0uT13AjpKoq06ZNY+3atTzzzDNERESccR/5DG5a6lPHp6rP57AkvTUYNWoUS5YsYdmyZRw+fJhPP/0Ui8XiGrfz3Xff5dtvv3VtP2LECLZs2cLcuXM5cuQIM2fOZN++fVx66aUeegTidOpav7NmzWLLli1kZGSwf/9+3n77bbKyshgyZIiHHoE4nbKyMtLS0khLSwPKhyRLS0sjOzsbgG+//ZZ3333Xtf2wYcPIzMzk66+/5siRIyxcuJBVq1YxcuRIT4QvaqGudTxv3jzWrVvHsWPHOHjwINOnT2f79u0MHz7cE+GL05g2bRorVqzgvvvuw8fHh7y8PPLy8ir1r5fP4KatPnXsjs9h6dNbg379+lFQUMDMmTPJy8sjLi6OJ554wvVVSXZ2NoqiuLZv37499957LzNmzOC7776jRYsWPPzwwzI+YCNV1/otKirio48+Ii8vD19fX9q2bcsLL7xAq1atPPQIxOns27ev0kQEX375JQADBw7krrvuIjc315UcAURERPDYY4/xxRdfMH/+fEJDQ7njjjtkuLJGrK51bLfb+fLLL8nJycFoNNK6dWuefvppOnfufM5jF6e3aNEiAKZMmVJp+eTJk10NE/IZ3LTVp47d8TmsqKqqnnX0QgghhBBCNGLSvUEIIYQQQng9SXqFEEIIIYTXk6RXCCGEEEJ4PUl6hRBCCCGE15OkVwghhBBCeD1JeoUQQgghhNeTpFcIIYQQQng9SXqFEEIIIYTXkxnZhPBC11xzTaX7Wq0Ws9lMUFAQbdu2pVevXpx33nlotdoa9w8PD+e99947qzhmzpzJrFmzKs2y442WLVvG+++/X2mZVqslMDCQpKQkrrjiCtq2bdtgx9+xYwdTp051zUZWwRvPf8VjOp1nn32WTp06naOI3G/KlCkkJyfz7rvvEhER4elwhPAakvQK4cUGDhwIgKqqlJSUkJ6ezp9//sny5cuJiori3nvvJT4+3sNRnr2KpHPcuHFVEv5zKTIykqSkJADKysr+v707j4rqPB84/h0GASGAjGBmGNkFJiQgJirGLS4VaRvTFGM8sTVppe1pNI1JTUtt01NNjvUYY614rGkWm+pJaNRGbcBCFFJMDAOiISyyDZsgiwQRRgSGZX5/cOb+GGeGLS4JvJ9z/Ofe933ve9+Zwee+97nvpaKigs8//5ysrCw2bdrEzJkz71rfxho/Pz/8/f2t7jO9Tvyb6lZdVAqCMDIi6BWEMWzgrJ9JQ0MDiYmJZGZmsnXrVl599VWL4GH37t02Z4EF2zQajdmY9/T08Pbbb5Oens5bb71FZGQk9vZ37s9uTEwM8+bNw8PD444d806ZNWvWXb3AuZ2ee+45urq6UCgUd7srgjCmiJxeQRhnlEolL774IkuWLKGrq4v9+/dblFGr1SiVyrvQu7HF3t6en/zkJzg5OdHS0oJOp7ujx3dzc0OtVuPs7HxHjyt8PZ6enqjV6jt6gSQI44H4RQnCOPX0009z9uxZKisrKS4ulm7Lg/Xbr0ajkbNnz5KTk0NlZSVXr15FJpOhVqtZtGgRy5Ytw87O9nV0WVkZhw8fpqysDKPRyLRp01i9ejUhISFWy9fW1nLixAkKCgpobW3FxcWF+++/n5UrV+Lj4yOVM+U/Ahw9etQs3/PmXNbhtmly4cIFkpOTqa2tRa/X4+rqypQpU4iIiGDVqlVDDzLg5OSESqWisrKSr776Stre29tLWloaGRkZ1NbW0tPTg7e3N4sWLSImJsbqTHtNTQ2JiYkUFRXR29uLv78/sbGxTJgwweqxB8vpvXLlComJieTl5WEwGFCr1axYsYLg4GCee+45wsLC2LJli9W2pk6dytGjRyktLeX69eu89tpr0t2CsrIyPvroI4qLi9Hr9bi7uxMZGckTTzyBp6en1X7m5uby3//+F51OR0dHBwqFglmzZhEbG4urq+uwxtmaDRs20NTUxOHDhy322cqD3rdvHxkZGfzpT39CJpNx5MgRysvLkclkaDQa1q5dy9SpU22eR2pqKjqdjvb2dtzd3fHz82PRokXMmTPHLPe7qanJbKZ64HgPltNbW1vLhx9+SEFBAXq9Hjc3N8LDw4mNjcXb29vmOT7zzDMkJiZy7tw5rl+/jkql4vvf/z5LliwZ1dgKwreRCHoFYZxydnZmxowZaLVaCgoKzIJea7q7u0lISMDV1ZWpU6cSEBCAXq+ntLSUd955B51OZzWdAqCkpIQ333wTpVJJZGQkjY2N5OfnU1RURHx8PNOnTzcrn52dzZ49e+ju7sbf35/g4GCam5vJzMzk/PnzbN68mbCwMAAiIyPp7e2lpKTEIs9z4Gz1SNoESElJ4cCBA9jZ2REaGkpYWBh6vZ7Lly9z5MiRYQe90J/fC0jBqcFgYPv27RQWFnLPPfcQHBzMhAkT0Ol0/POf/6SwsJCXXnrJ7CKivLycrVu30tnZiY+PDz4+PjQ0NLB9+3aWLVs27L5Af4rLyy+/TFtbG0qlkvDwcFpaWkhISOC73/3uoHUvXrzI3//+d7y9vYmIiKClpQWZTAZAamoqBw4cAGDatGloNBrq6+tJT08nJyeHLVu2WASM7733HidOnMDe3p6goCA8PDyorq4mOTmZnJwcXn311buSo3v+/HlOnjxJUFAQM2bMoKqqii+++AKdTseuXbss+nTw4EGSkpKQyWSEhITg6elJS0sLJSUlNDc3M2fOHJRKJY888ggZGRk4OjoyZ84cqb5arR6yT/n5+ezYsQODwUBAQABhYWHU1dVx5swZsrOz2bx5M/fdd59Fvfb2dv7whz/Q2dmJRqNBr9dTVFTEG2+8gdFoZOnSpV97vATh20AEvYIwjvn7+6PVarl8+fKQZeVyOS+99BIPPvig2W3XtrY2tm/fTkZGBosXLzYLHE3S0tKIjY1l9erVUoD08ccf8/bbb/O3v/2NvXv34uDgAPTPQO7duxe5XE58fDwRERFSO7m5uezYsYO9e/eyd+9e7O3tefzxx5k0aRIlJSU28zxH2ibAf/7zH2QyGdu2bSMoKEgqbzQapZnl4aitraWxsRHof/gK+gOkwsJC5s6dyy9+8Qsp/aCjo4O//vWv5OTkcPr0aaKjo6Vj7tu3j87OTouH9VJTU3nnnXeG3R+At956i7a2NpYtW0ZcXJwUXJvGYjD/+9//+NGPfsQPfvADs+2lpaX84x//wMPDg9/+9rdmq1Wkp6fzxhtvsH//frZt2yZtz8zM5MSJE/j4+PCb3/xGukgxGo0cOXKEo0eP8u677/LCCy+M6PxuheTkZDZt2sTs2bMB6OvrY/fu3WRlZZGamsrq1aulsmfOnCEpKQkPDw82b95sduFlMBgoLi4G+nO+NRoNGRkZuLm52bxItKazs5OEhAQMBgPr1q0jJiZG2peUlMTBgwdJSEhgz5490m/JJCcnh7lz57Jhwwbpwis7O5vXX3+df//73yLoFcYNkdMrCOOY6dZxe3v7kGXlcjmzZ8+2yDN0c3PjqaeeAvr/c7XGy8uLVatWSQEvQHR0NMHBwbS0tKDVaqXtJ0+epKurizVr1pgFp9A/qxsdHU1zczMXLlwY3kmOss22tjacnZ3NAl4AmUw2rOWwOjs7yc/P5/XXX6evr4/w8HCUSiWtra2kpaUxefJk1q9fb5ZvO3HiRJ599lns7e05deqUtL2wsJDa2lruvfdennjiCbPjLF++nODg4GGPRUNDA/n5+bi4uLB27Vqz2eTIyEgefvjhQev7+vry2GOPWWw/fvw4fX19/PznP7dYnm3JkiXMnDmTsrIyKisrpe0ffvghABs3bjSblZfJZKxatUq6KGtra7M43tGjR3nyySct/t2qFRHmzZsnBbwAdnZ2/PCHPwSgqKjIrOyxY8cAeOaZZyweCnVwcLD4zo1GZmYmra2thISEmAW8AI8++iiBgYE0NzeTlZVlUXfixInExcWZpcHMnj0bHx8fvvrqK65cufK1+ycI3wZiplcQxjGj0QhgFowOpaqqii+//JKmpiYMBgNGo5GOjg4A6uvrrdaJioqymqM6b948ysrKKC4uZuHChQB8+eWXAGYBx0D33XeflP9pq8zNRtNmYGAgxcXF7N+/n0cffdRqzu/NMjIyyMjIsNgeFBTEr371K6A/gO3t7SUyMtJiRg76l9tSqVRcunQJg8GAg4ODNFMYFRVlNW/aNI7DYWpr+vTpODk5WeyfO3cun332mc36Dz74oMX3pa+vj4KCAhwdHS1SVUw0Gg05OTnodDoCAgJobW2luroalUqFr6+vRXmZTEZoaChVVVVUVFQQGRlptt/WkmVDpekMl7XzUKlUALS0tEjbrl69yuXLl3FxcRnyguHrMAXa8+fPt7p/wYIFVFRUUFRUxIIFC8z2BQYGWs2NVqlU1NTUcO3aNbEesDAuiKBXEMYxvV4PgIuLy5Ble3p62LdvH2fPnrVZxpS7ejNbDzB5eXkB/YGDSVNTEwC//OUvB+2Ptdk/W0bTZlxcHDt37uSTTz7hk08+wd3dnbCwMKKiopgzZ47V4HPgOr1yuZxJkyah0WiIiIiQypv6kpaWRlpa2qD9uX79OgqFQhof03jdzNZ2a65duwbY/kxsbR9sv16vlz77NWvWDFrf9J0zjUN9ff2QS4+Z6gx0u5csmzx5ssW2iRMnAv2/BZPm5mYApkyZMqKLx5EyBdq2glNrvyUTa+cC/38+3d3dt6KLgvCNJ4JeQRjHTLeabT2NPlBSUhJnz57F19eXH//4xwQEBODi4oK9vT11dXW88MIL0szx12Fqw/RiDVtGckt/NG36+fnxl7/8hdzcXL744gsuXrxIZmYmmZmZhISEsGXLFotUj5vX6R2sL/7+/lKOry3fxCWrrM1Om87JycmJqKioQeubvmt9fX1A/8y2rdlhk5EE9cM11Hf1dgawt8Ng/f22nYsg3C7fvL+ogiDcETdu3JBu+z/wwANDls/Ozgb68y9vvtU/VE7gwKW6BjLN9g1chF+hUNDY2MjTTz/9tZarGmi0bTo4ODB79mwp5aGmpoaEhARKS0tJS0tj+fLlo+oL9AfI69atG1Yd08slTON1M1vbrTGtOmDrMzHNXI6Eq6srEyZMQCaTsX79+mEFWabZR1dX1xE90DUSpouGzs5Oi1QOW+c/UqbzuHLlCkaj8bYFmMP9DogXWgiCbeJBNkEYpw4ePEhXVxdBQUE218odyPSwm7VbpZ9//vmgdbOysqSZPWv1BuZhmh76MQXZw2EKbqwdY7RtWuPj4yMFujU1NaNq44EHHsDOzo7z58+b3SYfjGkZqqHGcThCQ0OB/jxna+komZmZw27LRC6Xc//999PR0UF+fv6w6kyePBm1Wk1tbS11dXUjPuZwmAJ8a+3n5eXdkmMoFArUajXt7e3DHju5XE5vb++IjmP6DthKL/r000/NygmCYEkEvYIwzjQ2NrJ7927S09NxdHTk2WefHVY900M8H3/8sdl2rVbLmTNnBq3b1NTEkSNHzLadPn2a0tJS3N3dzW6Jr1ixAgcHBw4dOmT1SfTu7m60Wq3ZjKRpFsxW8DTSNru6ujh58qTFqhZ9fX3k5uYCtvMkh6JQKFi8eDFNTU3s2bNHyrEdqKGhwWxFi7CwMNRqNY2NjdKKByanTp2itLR02MdXqVSEh4fT3t7Oe++9ZxZE5+XlDZqzPZjY2FhkMhn79++nsLDQYn9nZyfp6ekYDAZp28qVKzEajezatYuqqiqLOnq9ntOnT4+qP4C0fJ5pZQmTzz77bNTnac3jjz8O9F9IVldXm+0zGAwWAbZCoaC1tXVYq6aYPPzww7i7u1NcXGwxJidPnqS8vByFQjFkeokgjGcivUEQxjDT8k1Go5EbN25QX19PXV0dRqMRlUrF888/b/XJeWsee+wxcnNzef/999FqtahUKhoaGigvL2fFihV89NFHNusuXbqU48ePk52dja+vr1RPLpezYcMGHB0dpbJKpZKNGzeSkJDArl27UCqVqNVqHB0daWlpoaKigq6uLl577TUp8AwODsbd3R2tVsuWLVuYMmUKdnZ2LF68mNDQ0BG32dPTw7vvvsuhQ4cIDAzEy8uLnp4eysvLaW5uxsvLi+985zuj/lx++tOf0tTURFZWFrm5ufj7++Pp6UlXVxe1tbU0NDQwc+ZM6eUFdnZ2rF+/nldeeYXDhw+j1Wqll1NUVFQQHR1tcTEymJ/97Gf88Y9/JDU1lby8PAIDA2lpaaGoqIjly5eTkpIy4nxijUZDXFwcBw4cYOvWrfj4+KBSqbC3t6epqYmqqiq6u7uJioqS8oLnz59PTU0Nx44dIz4+Hn9/f+69916g/+KsuroaJyenUY/18uXLOXXqFFqtlhdffBE/Pz/q6+upqanhe9/7HsnJyaNq92aPPPII5eXlpKSkEB8fT0hICJMnT+batWtUVVXh6enJzp07pfIPPfSQWVkHBwe8vb2tLgVn4uTkxPPPP8+OHTt48803OX36NCqVirq6OiorK3FycmLjxo1Wc64FQegngl5BGMNMy2fJ5XImTpyIh4cHCxcuZNasWcycOXPQ1wbfLCwsjFdeeYV//etfVFZWUl9fj6+vL5s2bSIgIGDQoDc0NJTFixfzwQcfcOHCBYxGI+Hh4Tz55JPS7faBZs2axc6dO0lKSiI/P5+8vDzs7e3x8PDgoYceIioqyuzhOwcHB373u9+RmJiITqejqKgIo9GIRqOR2h9Jm05OTsTFxZGfn091dTWXLl3C3t4eT09Pli5dSkxMDPfcc8+wx+5mDg4O/P73v+fTTz8lIyODqqoqdDodbm5ueHl5sXDhQubOnWtWJzg4mG3btkmvIW5sbMTPz4/4+HgcHR1HFPSqVCr+/Oc/S68hPnfuHGq1mg0bNjBlyhRSUlJGdX7R0dGEhISQnJzMxYsXuXDhAo6OjigUCubPn09UVJTZusQATz31FNOnTyclJYWSkhIuXbqEs7MzCoWC6Ohos7eWjdSkSZPYunUrhw4doqioiKtXrxIYGMjLL7+MTCa7ZUEvwLp164iIiCA1NZXy8nJ0Oh3u7u6EhoZavOrXtMLFuXPnyMzMpLe3l7CwsEGDXoDw8HC2b98uvYa4uroaNzc3FixYwMqVKy1eQywIgjmZ8VY8bi0IgiCMCcePH+f9999nzZo10m17QRCEsUDk9AqCIIwzBoOB2tpai+0FBQUcO3YMuVzOvHnz7kLPBEEQbh+R3iAIgjDO3Lhxg1//+td4e3ujVCpxcHCgvr5eeghr7dq1t2VtXEEQhLtJpDcIgiCMMwaDgQ8++IC8vDyam5vp6OjA2dmZadOmERMTw4wZM+52FwVBEG45EfQKgiAIgiAIY57I6RUEQRAEQRDGPBH0CoIgCIIgCGOeCHoFQRAEQRCEMU8EvYIgCIIgCMKYJ4JeQRAEQRAEYcwTQa8gCIIgCIIw5omgVxAEQRAEQRjzRNArCIIgCIIgjHn/B5Yux2av2J9GAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "plt.style.use('ggplot')\n",
+        "\n",
+        "# Plot for Age\n",
+        "plt.figure(figsize=(8, 4))\n",
+        "sns.histplot(x=df['Age'], hue=df['Outcome'], palette=\"viridis\", kde=True)\n",
+        "plt.xlabel('Age', fontsize=15)\n",
+        "plt.title('Age Distribution by Outcome')\n",
+        "plt.show()\n",
+        "\n",
+        "# Plot for BMI\n",
+        "plt.figure(figsize=(8, 4))\n",
+        "sns.histplot(x=df['BMI'], hue=df['Outcome'], palette=\"viridis\", kde=True)\n",
+        "plt.xlabel('BMI', fontsize=15)\n",
+        "plt.title('BMI Distribution by Outcome')\n",
+        "plt.show()\n",
+        "\n",
+        "# Plot for BloodPressure\n",
+        "plt.figure(figsize=(8, 4))\n",
+        "sns.histplot(x=df['BloodPressure'], hue=df['Outcome'], palette=\"dark\", kde=True)\n",
+        "plt.xlabel('BloodPressure', fontsize=15)\n",
+        "plt.title('Blood Pressure Distribution by Outcome')\n",
+        "plt.show()\n",
+        "\n",
+        "# Plot for DiabetesPedigreeFunction\n",
+        "plt.figure(figsize=(8,4))\n",
+        "sns.histplot(x=df['DiabetesPedigreeFunction'], hue=df['Outcome'], palette=\"dark\", kde=True)\n",
+        "plt.xlabel('DiabetesPedigreeFunction', fontsize=15)\n",
+        "plt.title('Diabetes Pedigree Function Distribution by Outcome')\n",
+        "plt.show()\n",
+        "\n",
+        "# Plot for Glucose\n",
+        "plt.figure(figsize=(8,4))\n",
+        "sns.histplot(x=df['Glucose'], hue=df['Outcome'], palette=\"flare\", kde=True)\n",
+        "plt.xlabel('Glucose', fontsize=15)\n",
+        "plt.title('Glucose Distribution by Outcome')\n",
+        "plt.show()\n",
+        "\n",
+        "# Plot for Insulin\n",
+        "plt.figure(figsize=(8,4))\n",
+        "sns.histplot(x=df['Insulin'], hue=df['Outcome'], palette=\"flare\", kde=True)\n",
+        "plt.xlabel('Insulin', fontsize=15)\n",
+        "plt.title('Insulin Distribution by Outcome')\n",
+        "plt.show()\n",
+        "\n",
+        "# Plot for Pregnancies\n",
+        "plt.figure(figsize=(8,4))\n",
+        "sns.histplot(x=df['Pregnancies'], hue=df['Outcome'], palette=\"viridis\", kde=True)\n",
+        "plt.xlabel('Pregnancies', fontsize=15)\n",
+        "plt.title('Pregnancies Distribution by Outcome')\n",
+        "plt.show()\n",
+        "\n",
+        "# Plot for SkinThickness\n",
+        "plt.figure(figsize=(8,4))\n",
+        "sns.histplot(x=df['SkinThickness'], hue=df['Outcome'], palette=\"viridis\", kde=True)\n",
+        "plt.xlabel('SkinThickness', fontsize=15)\n",
+        "plt.title('Skin Thickness Distribution by Outcome')\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 65,
+      "metadata": {
+        "id": "FXlTreHXorF5"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/Diabetes prediction (Machine Learning Algorithms)/deeplearning.pkl b/Diabetes prediction (Machine Learning Algorithms)/deeplearning.pkl
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diff --git a/Diabetes prediction (Machine Learning Algorithms)/diabetes.csv b/Diabetes prediction (Machine Learning Algorithms)/diabetes.csv
new file mode 100644
index 000000000..9e6a36214
--- /dev/null
+++ b/Diabetes prediction (Machine Learning Algorithms)/diabetes.csv	
@@ -0,0 +1,769 @@
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diff --git a/Diabetes prediction (Machine Learning Algorithms)/lr .pkl b/Diabetes prediction (Machine Learning Algorithms)/lr .pkl
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diff --git a/Diabetes prediction (Machine Learning Algorithms)/requirements.txt b/Diabetes prediction (Machine Learning Algorithms)/requirements.txt
new file mode 100644
index 000000000..4102b9034
--- /dev/null
+++ b/Diabetes prediction (Machine Learning Algorithms)/requirements.txt	
@@ -0,0 +1,27 @@
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diff --git a/Diabetes prediction (Machine Learning Algorithms)/scaler .pkl b/Diabetes prediction (Machine Learning Algorithms)/scaler .pkl
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