AI_wbc.ipynb

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  "metadata": {
    "colab": {
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 211,
      "metadata": {
        "id": "eVLOnOk8RGgU"
      },
      "outputs": [],
      "source": [
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sn\n",
        "import sklearn\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"wbc_csv.csv\")\n",
        "df.drop(['id'],axis=1,inplace=True)\n",
        "df=df.iloc[:,0:31]"
      ],
      "metadata": {
        "id": "kCkPGTw6VUa2"
      },
      "execution_count": 212,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.shape)\n",
        "df.info()\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "skoeXOdyWj8r",
        "outputId": "db9489c2-f038-4675-f38c-aadb77c9906c"
      },
      "execution_count": 213,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(569, 31)\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 569 entries, 0 to 568\n",
            "Data columns (total 31 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   diagnosis                569 non-null    object \n",
            " 1   radius_mean              569 non-null    float64\n",
            " 2   texture_mean             569 non-null    float64\n",
            " 3   perimeter_mean           569 non-null    float64\n",
            " 4   area_mean                569 non-null    float64\n",
            " 5   smoothness_mean          569 non-null    float64\n",
            " 6   compactness_mean         569 non-null    float64\n",
            " 7   concavity_mean           569 non-null    float64\n",
            " 8   concave points_mean      569 non-null    float64\n",
            " 9   symmetry_mean            569 non-null    float64\n",
            " 10  fractal_dimension_mean   569 non-null    float64\n",
            " 11  radius_se                569 non-null    float64\n",
            " 12  texture_se               569 non-null    float64\n",
            " 13  perimeter_se             569 non-null    float64\n",
            " 14  area_se                  569 non-null    float64\n",
            " 15  smoothness_se            569 non-null    float64\n",
            " 16  compactness_se           569 non-null    float64\n",
            " 17  concavity_se             569 non-null    float64\n",
            " 18  concave points_se        569 non-null    float64\n",
            " 19  symmetry_se              569 non-null    float64\n",
            " 20  fractal_dimension_se     569 non-null    float64\n",
            " 21  radius_worst             569 non-null    float64\n",
            " 22  texture_worst            569 non-null    float64\n",
            " 23  perimeter_worst          569 non-null    float64\n",
            " 24  area_worst               569 non-null    float64\n",
            " 25  smoothness_worst         569 non-null    float64\n",
            " 26  compactness_worst        569 non-null    float64\n",
            " 27  concavity_worst          569 non-null    float64\n",
            " 28  concave points_worst     569 non-null    float64\n",
            " 29  symmetry_worst           569 non-null    float64\n",
            " 30  fractal_dimension_worst  569 non-null    float64\n",
            "dtypes: float64(30), object(1)\n",
            "memory usage: 137.9+ KB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
              "count   569.000000    569.000000      569.000000   569.000000   \n",
              "mean     14.127292     19.289649       91.969033   654.889104   \n",
              "std       3.524049      4.301036       24.298981   351.914129   \n",
              "min       6.981000      9.710000       43.790000   143.500000   \n",
              "25%      11.700000     16.170000       75.170000   420.300000   \n",
              "50%      13.370000     18.840000       86.240000   551.100000   \n",
              "75%      15.780000     21.800000      104.100000   782.700000   \n",
              "max      28.110000     39.280000      188.500000  2501.000000   \n",
              "\n",
              "       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "count       569.000000        569.000000      569.000000           569.000000   \n",
              "mean          0.096360          0.104341        0.088799             0.048919   \n",
              "std           0.014064          0.052813        0.079720             0.038803   \n",
              "min           0.052630          0.019380        0.000000             0.000000   \n",
              "25%           0.086370          0.064920        0.029560             0.020310   \n",
              "50%           0.095870          0.092630        0.061540             0.033500   \n",
              "75%           0.105300          0.130400        0.130700             0.074000   \n",
              "max           0.163400          0.345400        0.426800             0.201200   \n",
              "\n",
              "       symmetry_mean  fractal_dimension_mean  ...  radius_worst  \\\n",
              "count     569.000000              569.000000  ...    569.000000   \n",
              "mean        0.181162                0.062798  ...     16.269190   \n",
              "std         0.027414                0.007060  ...      4.833242   \n",
              "min         0.106000                0.049960  ...      7.930000   \n",
              "25%         0.161900                0.057700  ...     13.010000   \n",
              "50%         0.179200                0.061540  ...     14.970000   \n",
              "75%         0.195700                0.066120  ...     18.790000   \n",
              "max         0.304000                0.097440  ...     36.040000   \n",
              "\n",
              "       texture_worst  perimeter_worst   area_worst  smoothness_worst  \\\n",
              "count     569.000000       569.000000   569.000000        569.000000   \n",
              "mean       25.677223       107.261213   880.583128          0.132369   \n",
              "std         6.146258        33.602542   569.356993          0.022832   \n",
              "min        12.020000        50.410000   185.200000          0.071170   \n",
              "25%        21.080000        84.110000   515.300000          0.116600   \n",
              "50%        25.410000        97.660000   686.500000          0.131300   \n",
              "75%        29.720000       125.400000  1084.000000          0.146000   \n",
              "max        49.540000       251.200000  4254.000000          0.222600   \n",
              "\n",
              "       compactness_worst  concavity_worst  concave points_worst  \\\n",
              "count         569.000000       569.000000            569.000000   \n",
              "mean            0.254265         0.272188              0.114606   \n",
              "std             0.157336         0.208624              0.065732   \n",
              "min             0.027290         0.000000              0.000000   \n",
              "25%             0.147200         0.114500              0.064930   \n",
              "50%             0.211900         0.226700              0.099930   \n",
              "75%             0.339100         0.382900              0.161400   \n",
              "max             1.058000         1.252000              0.291000   \n",
              "\n",
              "       symmetry_worst  fractal_dimension_worst  \n",
              "count      569.000000               569.000000  \n",
              "mean         0.290076                 0.083946  \n",
              "std          0.061867                 0.018061  \n",
              "min          0.156500                 0.055040  \n",
              "25%          0.250400                 0.071460  \n",
              "50%          0.282200                 0.080040  \n",
              "75%          0.317900                 0.092080  \n",
              "max          0.663800                 0.207500  \n",
              "\n",
              "[8 rows x 30 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14.127292</td>\n",
              "      <td>19.289649</td>\n",
              "      <td>91.969033</td>\n",
              "      <td>654.889104</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.104341</td>\n",
              "      <td>0.088799</td>\n",
              "      <td>0.048919</td>\n",
              "      <td>0.181162</td>\n",
              "      <td>0.062798</td>\n",
              "      <td>...</td>\n",
              "      <td>16.269190</td>\n",
              "      <td>25.677223</td>\n",
              "      <td>107.261213</td>\n",
              "      <td>880.583128</td>\n",
              "      <td>0.132369</td>\n",
              "      <td>0.254265</td>\n",
              "      <td>0.272188</td>\n",
              "      <td>0.114606</td>\n",
              "      <td>0.290076</td>\n",
              "      <td>0.083946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.524049</td>\n",
              "      <td>4.301036</td>\n",
              "      <td>24.298981</td>\n",
              "      <td>351.914129</td>\n",
              "      <td>0.014064</td>\n",
              "      <td>0.052813</td>\n",
              "      <td>0.079720</td>\n",
              "      <td>0.038803</td>\n",
              "      <td>0.027414</td>\n",
              "      <td>0.007060</td>\n",
              "      <td>...</td>\n",
              "      <td>4.833242</td>\n",
              "      <td>6.146258</td>\n",
              "      <td>33.602542</td>\n",
              "      <td>569.356993</td>\n",
              "      <td>0.022832</td>\n",
              "      <td>0.157336</td>\n",
              "      <td>0.208624</td>\n",
              "      <td>0.065732</td>\n",
              "      <td>0.061867</td>\n",
              "      <td>0.018061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.981000</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>0.049960</td>\n",
              "      <td>...</td>\n",
              "      <td>7.930000</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>50.410000</td>\n",
              "      <td>185.200000</td>\n",
              "      <td>0.071170</td>\n",
              "      <td>0.027290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.156500</td>\n",
              "      <td>0.055040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.700000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.029560</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.057700</td>\n",
              "      <td>...</td>\n",
              "      <td>13.010000</td>\n",
              "      <td>21.080000</td>\n",
              "      <td>84.110000</td>\n",
              "      <td>515.300000</td>\n",
              "      <td>0.116600</td>\n",
              "      <td>0.147200</td>\n",
              "      <td>0.114500</td>\n",
              "      <td>0.064930</td>\n",
              "      <td>0.250400</td>\n",
              "      <td>0.071460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>13.370000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>0.092630</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.033500</td>\n",
              "      <td>0.179200</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>...</td>\n",
              "      <td>14.970000</td>\n",
              "      <td>25.410000</td>\n",
              "      <td>97.660000</td>\n",
              "      <td>686.500000</td>\n",
              "      <td>0.131300</td>\n",
              "      <td>0.211900</td>\n",
              "      <td>0.226700</td>\n",
              "      <td>0.099930</td>\n",
              "      <td>0.282200</td>\n",
              "      <td>0.080040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>15.780000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>104.100000</td>\n",
              "      <td>782.700000</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>0.130400</td>\n",
              "      <td>0.130700</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.195700</td>\n",
              "      <td>0.066120</td>\n",
              "      <td>...</td>\n",
              "      <td>18.790000</td>\n",
              "      <td>29.720000</td>\n",
              "      <td>125.400000</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>0.146000</td>\n",
              "      <td>0.339100</td>\n",
              "      <td>0.382900</td>\n",
              "      <td>0.161400</td>\n",
              "      <td>0.317900</td>\n",
              "      <td>0.092080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>28.110000</td>\n",
              "      <td>39.280000</td>\n",
              "      <td>188.500000</td>\n",
              "      <td>2501.000000</td>\n",
              "      <td>0.163400</td>\n",
              "      <td>0.345400</td>\n",
              "      <td>0.426800</td>\n",
              "      <td>0.201200</td>\n",
              "      <td>0.304000</td>\n",
              "      <td>0.097440</td>\n",
              "      <td>...</td>\n",
              "      <td>36.040000</td>\n",
              "      <td>49.540000</td>\n",
              "      <td>251.200000</td>\n",
              "      <td>4254.000000</td>\n",
              "      <td>0.222600</td>\n",
              "      <td>1.058000</td>\n",
              "      <td>1.252000</td>\n",
              "      <td>0.291000</td>\n",
              "      <td>0.663800</td>\n",
              "      <td>0.207500</td>\n",
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              "<p>8 rows × 30 columns</p>\n",
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              "        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-a28f1961-511c-4219-a2d1-cc3f902a74bb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a28f1961-511c-4219-a2d1-cc3f902a74bb')\"\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-a28f1961-511c-4219-a2d1-cc3f902a74bb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 213
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jZxqbsrFq-6I",
        "outputId": "5ac64245-c115-4477-8ecf-1886b7a9d536"
      },
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "diagnosis                  0\n",
              "radius_mean                0\n",
              "texture_mean               0\n",
              "perimeter_mean             0\n",
              "area_mean                  0\n",
              "smoothness_mean            0\n",
              "compactness_mean           0\n",
              "concavity_mean             0\n",
              "concave points_mean        0\n",
              "symmetry_mean              0\n",
              "fractal_dimension_mean     0\n",
              "radius_se                  0\n",
              "texture_se                 0\n",
              "perimeter_se               0\n",
              "area_se                    0\n",
              "smoothness_se              0\n",
              "compactness_se             0\n",
              "concavity_se               0\n",
              "concave points_se          0\n",
              "symmetry_se                0\n",
              "fractal_dimension_se       0\n",
              "radius_worst               0\n",
              "texture_worst              0\n",
              "perimeter_worst            0\n",
              "area_worst                 0\n",
              "smoothness_worst           0\n",
              "compactness_worst          0\n",
              "concavity_worst            0\n",
              "concave points_worst       0\n",
              "symmetry_worst             0\n",
              "fractal_dimension_worst    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# One Hot Encoding"
      ],
      "metadata": {
        "id": "1X-SBhJBwrs5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "df['diagnosis']=df['diagnosis'].astype('category')\n",
        "df['diagnosistics']=df['diagnosis'].cat.codes\n",
        "enc=OneHotEncoder()\n",
        "enc_df=pd.DataFrame(enc.fit_transform(df[['diagnosistics']]).toarray())\n"
      ],
      "metadata": {
        "id": "wZ3ZcqR7rFs8"
      },
      "execution_count": 215,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y=df['diagnosis']\n",
        "x=df.drop(labels=['diagnosis','diagnosistics'], axis=1)\n"
      ],
      "metadata": {
        "id": "xM5p92yqTjet"
      },
      "execution_count": 216,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "scaler = preprocessing.MinMaxScaler()\n",
        "scaled_df = scaler.fit_transform(x)\n",
        "scaled_df = pd.DataFrame(scaled_df)\n",
        "scaled_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "eJI1z5FpS47i",
        "outputId": "87071c3f-830b-4b7c-f897-4d1bc585976f"
      },
      "execution_count": 217,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           0         1         2         3         4         5         6   \\\n",
              "0    0.521037  0.022658  0.545989  0.363733  0.593753  0.792037  0.703140   \n",
              "1    0.643144  0.272574  0.615783  0.501591  0.289880  0.181768  0.203608   \n",
              "2    0.601496  0.390260  0.595743  0.449417  0.514309  0.431017  0.462512   \n",
              "3    0.210090  0.360839  0.233501  0.102906  0.811321  0.811361  0.565604   \n",
              "4    0.629893  0.156578  0.630986  0.489290  0.430351  0.347893  0.463918   \n",
              "..        ...       ...       ...       ...       ...       ...       ...   \n",
              "564  0.690000  0.428813  0.678668  0.566490  0.526948  0.296055  0.571462   \n",
              "565  0.622320  0.626987  0.604036  0.474019  0.407782  0.257714  0.337395   \n",
              "566  0.455251  0.621238  0.445788  0.303118  0.288165  0.254340  0.216753   \n",
              "567  0.644564  0.663510  0.665538  0.475716  0.588336  0.790197  0.823336   \n",
              "568  0.036869  0.501522  0.028540  0.015907  0.000000  0.074351  0.000000   \n",
              "\n",
              "           7         8         9   ...        20        21        22  \\\n",
              "0    0.731113  0.686364  0.605518  ...  0.620776  0.141525  0.668310   \n",
              "1    0.348757  0.379798  0.141323  ...  0.606901  0.303571  0.539818   \n",
              "2    0.635686  0.509596  0.211247  ...  0.556386  0.360075  0.508442   \n",
              "3    0.522863  0.776263  1.000000  ...  0.248310  0.385928  0.241347   \n",
              "4    0.518390  0.378283  0.186816  ...  0.519744  0.123934  0.506948   \n",
              "..        ...       ...       ...  ...       ...       ...       ...   \n",
              "564  0.690358  0.336364  0.132056  ...  0.623266  0.383262  0.576174   \n",
              "565  0.486630  0.349495  0.113100  ...  0.560655  0.699094  0.520892   \n",
              "566  0.263519  0.267677  0.137321  ...  0.393099  0.589019  0.379949   \n",
              "567  0.755467  0.675253  0.425442  ...  0.633582  0.730277  0.668310   \n",
              "568  0.000000  0.266162  0.187026  ...  0.054287  0.489072  0.043578   \n",
              "\n",
              "           23        24        25        26        27        28        29  \n",
              "0    0.450698  0.601136  0.619292  0.568610  0.912027  0.598462  0.418864  \n",
              "1    0.435214  0.347553  0.154563  0.192971  0.639175  0.233590  0.222878  \n",
              "2    0.374508  0.483590  0.385375  0.359744  0.835052  0.403706  0.213433  \n",
              "3    0.094008  0.915472  0.814012  0.548642  0.884880  1.000000  0.773711  \n",
              "4    0.341575  0.437364  0.172415  0.319489  0.558419  0.157500  0.142595  \n",
              "..        ...       ...       ...       ...       ...       ...       ...  \n",
              "564  0.452664  0.461137  0.178527  0.328035  0.761512  0.097575  0.105667  \n",
              "565  0.379915  0.300007  0.159997  0.256789  0.559450  0.198502  0.074315  \n",
              "566  0.230731  0.282177  0.273705  0.271805  0.487285  0.128721  0.151909  \n",
              "567  0.402035  0.619626  0.815758  0.749760  0.910653  0.497142  0.452315  \n",
              "568  0.020497  0.124084  0.036043  0.000000  0.000000  0.257441  0.100682  \n",
              "\n",
              "[569 rows x 30 columns]"
            ],
            "text/html": [
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              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
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              "      <th>7</th>\n",
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              "      <td>0.605518</td>\n",
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              "      <td>0.620776</td>\n",
              "      <td>0.141525</td>\n",
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              "      <td>0.619292</td>\n",
              "      <td>0.568610</td>\n",
              "      <td>0.912027</td>\n",
              "      <td>0.598462</td>\n",
              "      <td>0.418864</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.643144</td>\n",
              "      <td>0.272574</td>\n",
              "      <td>0.615783</td>\n",
              "      <td>0.501591</td>\n",
              "      <td>0.289880</td>\n",
              "      <td>0.181768</td>\n",
              "      <td>0.203608</td>\n",
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              "      <td>0.141323</td>\n",
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              "      <td>0.435214</td>\n",
              "      <td>0.347553</td>\n",
              "      <td>0.154563</td>\n",
              "      <td>0.192971</td>\n",
              "      <td>0.639175</td>\n",
              "      <td>0.233590</td>\n",
              "      <td>0.222878</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>0.390260</td>\n",
              "      <td>0.595743</td>\n",
              "      <td>0.449417</td>\n",
              "      <td>0.514309</td>\n",
              "      <td>0.431017</td>\n",
              "      <td>0.462512</td>\n",
              "      <td>0.635686</td>\n",
              "      <td>0.509596</td>\n",
              "      <td>0.211247</td>\n",
              "      <td>...</td>\n",
              "      <td>0.556386</td>\n",
              "      <td>0.360075</td>\n",
              "      <td>0.508442</td>\n",
              "      <td>0.374508</td>\n",
              "      <td>0.483590</td>\n",
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              "      <td>0.359744</td>\n",
              "      <td>0.835052</td>\n",
              "      <td>0.403706</td>\n",
              "      <td>0.213433</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.210090</td>\n",
              "      <td>0.360839</td>\n",
              "      <td>0.233501</td>\n",
              "      <td>0.102906</td>\n",
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              "      <td>0.811361</td>\n",
              "      <td>0.565604</td>\n",
              "      <td>0.522863</td>\n",
              "      <td>0.776263</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.248310</td>\n",
              "      <td>0.385928</td>\n",
              "      <td>0.241347</td>\n",
              "      <td>0.094008</td>\n",
              "      <td>0.915472</td>\n",
              "      <td>0.814012</td>\n",
              "      <td>0.548642</td>\n",
              "      <td>0.884880</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.773711</td>\n",
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              "    <tr>\n",
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              "      <td>0.156578</td>\n",
              "      <td>0.630986</td>\n",
              "      <td>0.489290</td>\n",
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              "      <td>0.186816</td>\n",
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              "      <td>0.519744</td>\n",
              "      <td>0.123934</td>\n",
              "      <td>0.506948</td>\n",
              "      <td>0.341575</td>\n",
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              "      <td>0.172415</td>\n",
              "      <td>0.319489</td>\n",
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              "      <td>0.157500</td>\n",
              "      <td>0.142595</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
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              "      <td>0.690358</td>\n",
              "      <td>0.336364</td>\n",
              "      <td>0.132056</td>\n",
              "      <td>...</td>\n",
              "      <td>0.623266</td>\n",
              "      <td>0.383262</td>\n",
              "      <td>0.576174</td>\n",
              "      <td>0.452664</td>\n",
              "      <td>0.461137</td>\n",
              "      <td>0.178527</td>\n",
              "      <td>0.328035</td>\n",
              "      <td>0.761512</td>\n",
              "      <td>0.097575</td>\n",
              "      <td>0.105667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>0.622320</td>\n",
              "      <td>0.626987</td>\n",
              "      <td>0.604036</td>\n",
              "      <td>0.474019</td>\n",
              "      <td>0.407782</td>\n",
              "      <td>0.257714</td>\n",
              "      <td>0.337395</td>\n",
              "      <td>0.486630</td>\n",
              "      <td>0.349495</td>\n",
              "      <td>0.113100</td>\n",
              "      <td>...</td>\n",
              "      <td>0.560655</td>\n",
              "      <td>0.699094</td>\n",
              "      <td>0.520892</td>\n",
              "      <td>0.379915</td>\n",
              "      <td>0.300007</td>\n",
              "      <td>0.159997</td>\n",
              "      <td>0.256789</td>\n",
              "      <td>0.559450</td>\n",
              "      <td>0.198502</td>\n",
              "      <td>0.074315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>0.455251</td>\n",
              "      <td>0.621238</td>\n",
              "      <td>0.445788</td>\n",
              "      <td>0.303118</td>\n",
              "      <td>0.288165</td>\n",
              "      <td>0.254340</td>\n",
              "      <td>0.216753</td>\n",
              "      <td>0.263519</td>\n",
              "      <td>0.267677</td>\n",
              "      <td>0.137321</td>\n",
              "      <td>...</td>\n",
              "      <td>0.393099</td>\n",
              "      <td>0.589019</td>\n",
              "      <td>0.379949</td>\n",
              "      <td>0.230731</td>\n",
              "      <td>0.282177</td>\n",
              "      <td>0.273705</td>\n",
              "      <td>0.271805</td>\n",
              "      <td>0.487285</td>\n",
              "      <td>0.128721</td>\n",
              "      <td>0.151909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>0.644564</td>\n",
              "      <td>0.663510</td>\n",
              "      <td>0.665538</td>\n",
              "      <td>0.475716</td>\n",
              "      <td>0.588336</td>\n",
              "      <td>0.790197</td>\n",
              "      <td>0.823336</td>\n",
              "      <td>0.755467</td>\n",
              "      <td>0.675253</td>\n",
              "      <td>0.425442</td>\n",
              "      <td>...</td>\n",
              "      <td>0.633582</td>\n",
              "      <td>0.730277</td>\n",
              "      <td>0.668310</td>\n",
              "      <td>0.402035</td>\n",
              "      <td>0.619626</td>\n",
              "      <td>0.815758</td>\n",
              "      <td>0.749760</td>\n",
              "      <td>0.910653</td>\n",
              "      <td>0.497142</td>\n",
              "      <td>0.452315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>0.036869</td>\n",
              "      <td>0.501522</td>\n",
              "      <td>0.028540</td>\n",
              "      <td>0.015907</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.074351</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.266162</td>\n",
              "      <td>0.187026</td>\n",
              "      <td>...</td>\n",
              "      <td>0.054287</td>\n",
              "      <td>0.489072</td>\n",
              "      <td>0.043578</td>\n",
              "      <td>0.020497</td>\n",
              "      <td>0.124084</td>\n",
              "      <td>0.036043</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.257441</td>\n",
              "      <td>0.100682</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 30 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-e163118c-d5de-42f4-9a16-7d73f1cf01db')\"\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-e163118c-d5de-42f4-9a16-7d73f1cf01db 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-e163118c-d5de-42f4-9a16-7d73f1cf01db');\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-410086df-ea5f-4ec3-a747-abe8e855684d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-410086df-ea5f-4ec3-a747-abe8e855684d')\"\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-410086df-ea5f-4ec3-a747-abe8e855684d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 217
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Variance Threshold & CHI sq"
      ],
      "metadata": {
        "id": "T6dbvXWpwxnx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import VarianceThreshold\n",
        "var_thres=VarianceThreshold(threshold=0)\n",
        "var_thres.fit(scaled_df)\n",
        "x.columns[var_thres.get_support()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HS2lPqB8tWnv",
        "outputId": "5988c337-b900-4e03-d773-36e50a9d1c6b"
      },
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean',\n",
              "       'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
              "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
              "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
              "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
              "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
              "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
              "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
              "       'symmetry_worst', 'fractal_dimension_worst'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Variance Threshold\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(scaled_df,y,test_size=0.3,stratify=y,random_state=50)\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)\n",
        "X_train.shape, X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gsvK1S_St2wy",
        "outputId": "b7fe2366-1681-41e6-9cb5-154f36d82792"
      },
      "execution_count": 316,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((398, 30), (171, 30))"
            ]
          },
          "metadata": {},
          "execution_count": 316
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "var_thres=VarianceThreshold(threshold=0)\n",
        "var_thres.fit(X_train)\n",
        "sum(var_thres.get_support())\n",
        "# Finding non constant features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xXslC0B2vm9B",
        "outputId": "c71a0b4d-9ba5-4beb-e06e-e4b55a7b84c7"
      },
      "execution_count": 220,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {},
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# constant_columns = [column for column in X_train.columns if column not in X_train.columns[var_thres.get_support()]]\n",
        "# print(len(constant_columns))\n"
      ],
      "metadata": {
        "id": "2x-SPT8kv-DU"
      },
      "execution_count": 221,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Regression"
      ],
      "metadata": {
        "id": "5RCk3P_hzNOm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "from sklearn import metrics\n",
        "from sklearn import linear_model\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "\n"
      ],
      "metadata": {
        "id": "cL1C7ui3zLBW"
      },
      "execution_count": 222,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train-test split for 'mean' features\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    df[['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean',\n",
        "        'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
        "        'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean']],\n",
        "    df.diagnosistics, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "2Wgr4o_XhHlx"
      },
      "execution_count": 317,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Mean Regression\n",
        "reg = linear_model.LinearRegression()\n",
        "reg.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "zP7Z396qzac_",
        "outputId": "fc3eafcf-51d9-42be-dd3d-09456438830a"
      },
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 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-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 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-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 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-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 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-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming X_test has the same features as X_train but possibly in a different order\n",
        "X_test_reordered = X_test[X_train.columns]\n",
        "\n",
        "# Predictions for 'mean' features\n",
        "y_pred_binary_mean = (reg.predict(X_test_reordered) >= 0.5).astype(int)\n"
      ],
      "metadata": {
        "id": "v0FoienxhKay"
      },
      "execution_count": 225,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reg.predict([[13,21.82,87.5,519.8,0.1273,0.1932,0.1859,0.09353,0.235,0.07389]])\n",
        "#if 0.5> B and if 0.5<  M\n",
        "#10th row"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y9gAt_L1EXBI",
        "outputId": "cafcb4ab-f06c-4bec-cd36-3e26c81dcb44"
      },
      "execution_count": 226,
      "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 LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.77062805])"
            ]
          },
          "metadata": {},
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm_mean = confusion_matrix(y_test, y_pred_binary_mean)\n",
        "accuracy_mean = accuracy_score(y_test, y_pred_binary_mean)\n",
        "print(\"Confusion Matrix (Mean Features):\")\n",
        "print(cm_mean)\n",
        "print(\"Accuracy (Mean Features):\", accuracy_mean)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ArM0rsXjjtKA",
        "outputId": "805d9b80-d7b9-4b8f-d8e1-d216e94952f2"
      },
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix (Mean Features):\n",
            "[[67  4]\n",
            " [ 4 39]]\n",
            "Accuracy (Mean Features): 0.9298245614035088\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    df[['radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
        "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
        "       'fractal_dimension_se']],\n",
        "    df.diagnosistics, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "mk13FzaDjVsP"
      },
      "execution_count": 228,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reg.fit(X_train, y_train)\n",
        "y_pred_binary_se = (reg.predict(X_test) >= 0.5).astype(int)\n"
      ],
      "metadata": {
        "id": "lDEz1h75C6dn"
      },
      "execution_count": 229,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reg.predict([[0.8068,0.9017,5.455,102.6,0.006048,0.01882,0.02741,0.0113,0.01468,0.002801]])\n",
        "#if 0.5> B and if 0.5<  M\n",
        "#26th row"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aPumYF0gEXj-",
        "outputId": "02658e0b-05c5-4703-efe3-44f219d61c21"
      },
      "execution_count": 230,
      "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 LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.77433124])"
            ]
          },
          "metadata": {},
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm_se = confusion_matrix(y_test, y_pred_binary_se)\n",
        "accuracy_se = accuracy_score(y_test, y_pred_binary_se)\n",
        "print(\"\\nConfusion Matrix (SE Features):\")\n",
        "print(cm_se)\n",
        "print(\"Accuracy (SE Features):\", accuracy_se)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HaWsbEmdjyPE",
        "outputId": "7af150fe-8e98-48a4-c12a-942a28b6f0a4"
      },
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Confusion Matrix (SE Features):\n",
            "[[68  3]\n",
            " [14 29]]\n",
            "Accuracy (SE Features): 0.8508771929824561\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train-test split for 'worst' features\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    df[['radius_worst', 'texture_worst',\n",
        "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
        "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
        "       'symmetry_worst', 'fractal_dimension_worst']],\n",
        "    df.diagnosistics, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "R43i-8aAD-UW"
      },
      "execution_count": 232,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Linear Regression for 'worst' features\n",
        "reg.fit(X_train, y_train)\n",
        "y_pred_binary_worst = (reg.predict(X_test) >= 0.5).astype(int)\n"
      ],
      "metadata": {
        "id": "5vm83bk8jmlV"
      },
      "execution_count": 233,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reg.predict([[14.67,23.19,96.08,656.7,0.1089,0.1582,0.105,0.08586,0.2346,0.08025]])\n",
        "#if 0.5> B and if 0.5<  M\n",
        "#53rd row"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s098k7TwEYBq",
        "outputId": "995e73cf-db2e-40b1-c146-a970e6efc08c"
      },
      "execution_count": 234,
      "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 LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.14219081])"
            ]
          },
          "metadata": {},
          "execution_count": 234
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm_worst = confusion_matrix(y_test, y_pred_binary_worst)\n",
        "accuracy_worst = accuracy_score(y_test, y_pred_binary_worst)\n",
        "print(\"\\nConfusion Matrix (Worst Features):\")\n",
        "print(cm_worst)\n",
        "print(\"Accuracy (Worst Features):\", accuracy_worst)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jI3QcwoyhrFY",
        "outputId": "5c90eeb6-eece-4bfb-e27b-943b06d2883c"
      },
      "execution_count": 235,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Confusion Matrix (Worst Features):\n",
            "[[70  1]\n",
            " [ 3 40]]\n",
            "Accuracy (Worst Features): 0.9649122807017544\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Logistic Regression\n",
        "\n",
        "  "
      ],
      "metadata": {
        "id": "zkc3mKJ8Mj5y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score,confusion_matrix\n",
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "nxqLHDSJP7By"
      },
      "execution_count": 236,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZekcqJyeMmU5",
        "outputId": "8cd38340-2ae7-482b-e288-f30328850425"
      },
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B    357\n",
              "M    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(scaled_df,y,test_size=0.3,stratify=y,random_state=50)\n"
      ],
      "metadata": {
        "id": "skbz9ftLktp7"
      },
      "execution_count": 238,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#logistic regression on heart.csv\n",
        "model=LogisticRegression(max_iter=600)\n",
        "model.fit(X_train,y_train)\n",
        "test_data_prediction=model.predict(X_test)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "9X5PrHejPrBT"
      },
      "execution_count": 239,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy=accuracy_score(y_test,test_data_prediction)\n",
        "print(\" Accuracy of =\",model,'=',accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6uxjxqQykfSd",
        "outputId": "29f3acb2-afdc-4f21-f728-3f253f08e776"
      },
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Accuracy of = LogisticRegression(max_iter=600) = 0.9590643274853801\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_data=(13.8,15.79,90.43,584.1,0.1007,0.128,0.07789,0.05069,0.1662,0.06566,0.2787,0.6205,1.957,23.35,0.004717,0.02065,0.01759,0.009206,0.0122,0.00313,16.57,20.86,110.3,812.4,0.1411,0.3542,0.2779,0.1383,0.2589,0.103)\n",
        "input_data_array = np.asarray(input_data)\n",
        "reshaped = input_data_array.reshape(1, -1)"
      ],
      "metadata": {
        "id": "C5MlRG3JkiCU"
      },
      "execution_count": 241,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrix\n",
        "cm = confusion_matrix(y_test, test_data_prediction)\n",
        "print(\"Confusion Matrix:\\n\", cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0H2AUWAkkj-C",
        "outputId": "4fc274e3-0c52-4b1e-8d78-aff338286971"
      },
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            " [[107   0]\n",
            " [  7  57]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sn\n",
        "plt.figure(figsize=(12,7))\n",
        "sn.heatmap(cm, annot= True)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "id": "8c5i9SYQtQaQ",
        "outputId": "ffe74b82-1bf5-4cd3-cf5d-97cdbebba2dd"
      },
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(120.72222222222221, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 243
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# KNN"
      ],
      "metadata": {
        "id": "YncEdz4PTKyR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "HUodAph5TMXR"
      },
      "execution_count": 244,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "math.sqrt(len(y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xapwat5YTYnN",
        "outputId": "de7b11ed-0663-49f3-ebef-ef5fbc1fc798"
      },
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13.076696830622021"
            ]
          },
          "metadata": {},
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = KNeighborsClassifier(n_neighbors=11, p=2, metric='euclidean')\n",
        "# Define the model: Init K-NN"
      ],
      "metadata": {
        "id": "Jx3hZxmaTeRm"
      },
      "execution_count": 246,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(scaled_df,y,test_size=0.3,stratify=y,random_state=55)\n"
      ],
      "metadata": {
        "id": "X0a02TGzlhLV"
      },
      "execution_count": 247,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier.fit(X_train, y_train)\n",
        "# Predict the test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DTTMVZmPTvC7",
        "outputId": "463944d7-0819-478a-9aa7-4ca4177bd872"
      },
      "execution_count": 248,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['B', 'B', 'B', 'B', 'M', 'M', 'B', 'M', 'B', 'M', 'B', 'M', 'M',\n",
              "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'B',\n",
              "       'B', 'B', 'M', 'B', 'B', 'B', 'M', 'B', 'B', 'M', 'B', 'B', 'B',\n",
              "       'M', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'M', 'M', 'M', 'B', 'M',\n",
              "       'M', 'B', 'B', 'M', 'M', 'M', 'M', 'B', 'B', 'B', 'M', 'M', 'M',\n",
              "       'B', 'B', 'B', 'M', 'B', 'M', 'M', 'B', 'B', 'M', 'M', 'B', 'M',\n",
              "       'M', 'B', 'B', 'B', 'M', 'B', 'M', 'B', 'B', 'M', 'B', 'B', 'B',\n",
              "       'M', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'M', 'B', 'B', 'B', 'B',\n",
              "       'B', 'M', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'M', 'B',\n",
              "       'B', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'M', 'B', 'B', 'M', 'B',\n",
              "       'B', 'M', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'M',\n",
              "       'B', 'M', 'M', 'B', 'B', 'M', 'B', 'M', 'B', 'M', 'B', 'B', 'B',\n",
              "       'M', 'B', 'B', 'B', 'M', 'B', 'M', 'B', 'B', 'M', 'M', 'B', 'B',\n",
              "       'M', 'B'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 248
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate Model\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print (cm)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ueTghAnLT0Gh",
        "outputId": "62ea27a6-ba03-4c61-8564-19daacd19663"
      },
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[107   0]\n",
            " [  4  60]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#sc = StandardScaler()\n",
        "#y_train = sc.fit_transform(y_train)\n",
        "#y_test = sc.transform(y_test)"
      ],
      "metadata": {
        "id": "s8-wcpg8WucN"
      },
      "execution_count": 250,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(accuracy_score(y_test, y_pred))\n",
        "#print(f1_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yIiSM4XQUFJr",
        "outputId": "e6aa88ab-02eb-4c74-d973-bf4fc9400173"
      },
      "execution_count": 251,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9766081871345029\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.figure(figsize=(12,7))\n",
        "sn.heatmap(cm, annot= True)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "id": "yR9ExDR6tKfL",
        "outputId": "dedc0743-9911-4dc2-f6db-729b2e1ab6b0"
      },
      "execution_count": 252,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(120.72222222222221, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 252
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SVM"
      ],
      "metadata": {
        "id": "PKN4zdi6V5Cw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC"
      ],
      "metadata": {
        "id": "zjYbLd-eVQlI"
      },
      "execution_count": 253,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(scaled_df,y,test_size=0.25,stratify=y,random_state=50)\n"
      ],
      "metadata": {
        "id": "rMq72cOPmTcy"
      },
      "execution_count": 254,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf_linear = SVC(kernel='linear')\n",
        "clf_linear.fit(X_train, y_train)\n",
        "#Check for SVM accuracy\n",
        "y_pred = clf_linear.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wvt7S23HWGBd",
        "outputId": "c36bbe9b-0c1d-4274-9fc5-30ee042beede"
      },
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9790209790209791\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, y_pred )\n",
        "print(\"Confusion Matrix:\\n\", cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q1vx6cvLlZ_E",
        "outputId": "abef8811-ec0a-4129-a701-41345c57a878"
      },
      "execution_count": 256,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            " [[90  0]\n",
            " [ 3 50]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.figure(figsize=(12,7))\n",
        "sn.heatmap(cm, annot= True)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "id": "y66RjaeEtFDB",
        "outputId": "a6a072d0-4454-4351-d798-9f1864c469e2"
      },
      "execution_count": 257,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(120.72222222222221, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 257
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DecisionTree\n",
        "\n"
      ],
      "metadata": {
        "id": "kylLay3ZV4bP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "model = tree.DecisionTreeClassifier()"
      ],
      "metadata": {
        "id": "iY2OTBTB-87c"
      },
      "execution_count": 258,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(scaled_df,y,test_size=0.15,stratify=y,random_state=50)\n"
      ],
      "metadata": {
        "id": "uuilDyd2oxuP"
      },
      "execution_count": 259,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "qODN_VxB-9B8",
        "outputId": "9cc226a9-cd60-4763-a83a-738274a28cca"
      },
      "execution_count": 260,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 260
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model.score(inputs_n,target)"
      ],
      "metadata": {
        "id": "u6SQSDtUAQnQ"
      },
      "execution_count": 262,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_accuracy = model.score(X_train, y_train)\n",
        "print(f\"Training Accuracy: {train_accuracy}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_gsihO0Zo5Da",
        "outputId": "97d4a1a6-a5a4-4194-f477-95cc5315661e"
      },
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([[2,1,0,2,1,0,2,1,0,2,1,0,2,1,0,2,1,0,2,1,0,2,1,0,2,1,0,2,1,0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XAK-Vv49BqgY",
        "outputId": "648232b0-a8cc-469a-e3e2-f76f87b3c164"
      },
      "execution_count": 264,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['M'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 264
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([[1,1,5,2,1,6,3,1,8,2,1,7,4,1,8,2,1,8,2,7,3,2,1,4,2,1,5,2,1,3]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L_-4G8vjB33-",
        "outputId": "e4c53cbd-f3bc-4c6d-d7ac-676ace2cfe84"
      },
      "execution_count": 265,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['M'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 265
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "test_accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Test Accuracy: {test_accuracy}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-791B-DpCaAq",
        "outputId": "720560af-f2b0-41d1-f358-88c04ccdab40"
      },
      "execution_count": 266,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy: 0.9534883720930233\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, y_pred )\n",
        "print(\"Confusion Matrix:\\n\", cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vBiqKDZUpW7p",
        "outputId": "f7125923-453e-4c0a-b715-b0ba8c07a429"
      },
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            " [[54  0]\n",
            " [ 4 28]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import matplotlib.pyplot as plt\n",
        "# import seaborn as sn\n",
        "plt.figure(figsize=(12,7))\n",
        "sn.heatmap(cm, annot= True)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "id": "shO_thm-s1Tq",
        "outputId": "db9bd025-20b1-46ec-d0dc-d058febd5c2b"
      },
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(120.72222222222221, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 268
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 2 Axes>"
            ],
            "image/png": 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LwggAAEAiCSMAAEAKu6QCAABAAgkjAABAGgkjAAAA5JMwAgAApLFLKgAAAOSTMAIAAKSwSyoAAAAkkDACAACksYYRAAAA8mkYAQAASGRKKgAAQAqb3gAAAEACCSMAAEAam94AAABAPgkjAABAiqyEEQAAAPJJGAEAANJIGAEAACCfhBEAACCFNYwAAACQQMIIAACQRsIIAAAA+SSMAAAAKaxhBAAAgAQSRgAAgBQSRgAAAEggYQQAAEghYQQAAIAEEkYAAIA02aJCV1BQEkYAAAASaRgBAABIZEoqAABACpveAAAAQAIJIwAAQIpsxqY3AAAAkEfCCAAAkMIaRgAAAEggYQQAAEiRzVrDCAAAAHkkjAAAACmsYQQAAIAEEkYAAIAUzmEEAACABBJGAACAFNlsoSsoLAkjAAAAiSSMAAAAKaxhBAAAgAQSRgAAgBQSRgAAAEigYQQAAOhkrrvuujjggAOif//+0b9//6ioqIhf/epXzZ9v3bo1Zs6cGYMGDYq+ffvG1KlTo76+vtXv0TACAACkyGbb72qN3XffPa644oqora2NJ554IiZOnBgnnnhi/OlPf4qIiDlz5sQvf/nLuP322+PBBx+M119/PaZMmdLqn78om935ThbZtv6lQpcAQBvoPfzwQpcAQBt4p/EvhS7hA3t53Cfb7V0jn7r/Q33/wIED4zvf+U6cdNJJMWTIkFi6dGmcdNJJERHx3HPPxdixY2PFihVx6KGHtviZNr0BAABI0Z6b3jQ0NERDQ0POWElJSZSUlGz3+5qamuL222+PLVu2REVFRdTW1sa2bduisrKy+Z4xY8bEiBEjWt0wmpIKAADQAVRXV0dpaWnOVV1dnXr/008/HX379o2SkpI466yz4s4774x999036urqori4OAYMGJBzf1lZWdTV1bWqJgkjAABAimy2/RLGuXPnRlVVVc7Y9tLF0aNHx6pVq2Ljxo1xxx13xLRp0+LBBx9s05o0jAAAAB1AS6af/r3i4uIYNWpURESMHz8+Hn/88bjqqqvilFNOicbGxtiwYUNOylhfXx/l5eWtqsmUVAAAgBTZTPtdH1Ymk4mGhoYYP3589OzZM2pqapo/W716daxZsyYqKipa9UwJIwAAQCczd+7cOO6442LEiBHx17/+NZYuXRoPPPBA3HfffVFaWhpnnHFGVFVVxcCBA6N///4xe/bsqKioaNWGNxEaRgAAgFSZdlzD2Brr1q2LL37xi7F27dooLS2NAw44IO6777745CffPQZk4cKF0a1bt5g6dWo0NDTEpEmT4tprr231e5zDCECH5RxGgJ1DZz6H8c9jj223d+3z7L3t9q6WkjACAACkaM9dUjsim94AAACQSMIIAACQIpuRMAIAAEAeCSMAAECKnW+L0NaRMAIAAJBIwggAAJCiq69h/MANY2NjY6xbty4ymUzO+IgRIz50UQAAABReqxvG559/Pk4//fT4/e9/nzOezWajqKgompqa2qw4AACAQsp08XMYW90wTp8+PXr06BHLly+PYcOGRVFR1/4FAgAA7Kxa3TCuWrUqamtrY8yYMTuiHgAAADqIVjeM++67b6xfv35H1AIAANChZLv4lNQWHauxadOm5utb3/pWXHjhhfHAAw/Em2++mfPZpk2bdnS9AAAAtJMWJYwDBgzIWauYzWbj6KOPzrnHpjcAAMDOJpstdAWF1aKG8be//e2OrgMAAIAOpkUN45FHHtn8z2vWrIk99tgjb3fUbDYbr776attWBwAAUEBd/ViNFq1h/HsjR46MN954I2/8rbfeipEjR7ZJUQAAABReq3dJfW+t4j/avHlz9OrVq02KAgAA6Ai6+i6pLW4Yq6qqIiKiqKgoLr744thll12aP2tqaopHH300DjzwwDYvEAAAgMJoccP4hz/8ISLeTRiffvrpKC4ubv6suLg4xo0bF+eff37bVwgAAFAgdkltofd2Sp0xY0ZcddVV0b9//x1WFAAAAIXX6jWMixcv3hF1AAAAdDhdfZfUVjeMEydO3O7nv/nNbz5wMQAAAHQcrW4Yx40bl/P1tm3bYtWqVfHMM8/EtGnT2qywD2PkPicUugQA2sCTww8qdAkAdHF2SW2lhQsXJo7Pnz8/Nm/e/KELAgAAoGPo1lYP+vznPx8333xzWz0OAACg4DLZona7OqI2axhXrFgRvXr1aqvHAQAAUGCtnpI6ZcqUnK+z2WysXbs2nnjiibj44ovbrDAAAIBC6+LHMLa+YSwtLc35ulu3bjF69Oi45JJL4phjjmmzwgAAACisVjWMTU1NMWPGjNh///1j11133VE1AQAA0AG0ag1j9+7d45hjjokNGzbsoHIAAAA6DpvetNJ+++0XL7300o6oBQAAgA6k1Q3jZZddFueff34sX7481q5dG5s2bcq5AAAAdhbZbFG7XR1Ri9cwXnLJJXHeeefFpz71qYiIOOGEE6Ko6H9/qGw2G0VFRdHU1NT2VQIAANDuWtwwLliwIM4666z47W9/uyPrAQAA6DAyhS6gwFrcMGaz755AcuSRR+6wYgAAAOg4WnWsxt9PQQUAANjZZaNr90Ctahj32Wef920a33rrrQ9VEAAAAB1DqxrGBQsWRGlp6Y6qBQAAoEPJZAtdQWG1qmE89dRTY+jQoTuqFgAAADqQFjeM1i8CAABdTaaLr2Hs1tIb39slFQAAgK6hxQljJtPVTyABAAC6mq6+S2qLE0YAAAC6llZtegMAANCVdPV5lhJGAAAAEkkYAQAAUljDCAAAAAkkjAAAACmsYQQAAIAEGkYAAAASmZIKAACQwpRUAAAASCBhBAAASOFYDQAAAEggYQQAAEiR6doBo4QRAACAZBJGAACAFBlrGAEAACCfhBEAACBFttAFFJiEEQAAgEQSRgAAgBSZQhdQYBJGAAAAEkkYAQAAUmSK7JIKAAAAeSSMAAAAKeySCgAAAAkkjAAAACnskgoAAAAJNIwAAAAkMiUVAAAgRaZrn6ohYQQAACCZhBEAACBFJrp2xChhBAAAIJGEEQAAIEW20AUUmIQRAACARBJGAACAFHZJBQAAgAQSRgAAgBSZQhdQYBJGAAAAEkkYAQAAUtglFQAAABJIGAEAAFLYJRUAAAASSBgBAABS2CUVAAAAEkgYAQAAUkgYAQAAIIGEEQAAIEXWLqkAAAB0FtXV1XHIIYdEv379YujQoTF58uRYvXp1zj1bt26NmTNnxqBBg6Jv374xderUqK+vb/W7NIwAAACdyIMPPhgzZ86MlStXxv333x/btm2LY445JrZs2dJ8z5w5c+KXv/xl3H777fHggw/G66+/HlOmTGn1u0xJBQAASNERN7259957c75esmRJDB06NGpra+OII46IjRs3xk033RRLly6NiRMnRkTE4sWLY+zYsbFy5co49NBDW/wuCSMAAEAH0NDQEJs2bcq5Ghoa3vf7Nm7cGBERAwcOjIiI2tra2LZtW1RWVjbfM2bMmBgxYkSsWLGiVTVpGAEAAFJk2vGqrq6O0tLSnKu6unr79WUyce6558bHP/7x2G+//SIioq6uLoqLi2PAgAE595aVlUVdXV2rfn5TUgEAADqAuXPnRlVVVc5YSUnJdr9n5syZ8cwzz8TDDz+8Q2rSMAIAAKTItuO7SkpK3rdB/HuzZs2K5cuXx0MPPRS7775783h5eXk0NjbGhg0bclLG+vr6KC8vb1VNpqQCAAB0ItlsNmbNmhV33nln/OY3v4mRI0fmfD5+/Pjo2bNn1NTUNI+tXr061qxZExUVFa16l4QRAAAgRaao0BXkmzlzZixdujR+8YtfRL9+/ZrXJZaWlkbv3r2jtLQ0zjjjjKiqqoqBAwdG//79Y/bs2VFRUdGqHVIjNIwAAACdynXXXRcREZ/4xCdyxhcvXhzTp0+PiIiFCxdGt27dYurUqdHQ0BCTJk2Ka6+9ttXv0jACAACk6IjnMGaz77+yslevXrFo0aJYtGjRh3qXNYwAAAAkkjACAACk6IgJY3uSMAIAAJBIwggAAJCiPc9h7IgkjAAAACSSMAIAAKToiOcwticJIwAAAIkkjAAAACnskgoAAAAJNIwAAAAkMiUVAAAghWM1AAAAIIGEEQAAIEWmi2eMEkYAAAASSRgBAABSOFYDAAAAEkgYAQAAUnTtFYwSRgAAAFJIGAEAAFJYwwgAAAAJJIwAAAApMkWFrqCwJIwAAAAkkjACAACkyHTxfVIljAAAACSSMAIAAKTo2vmihBEAAIAUEkYAAIAUzmEEAACABBJGAACAFHZJBQAAgAQaRgAAABKZkgoAAJCia09IlTACAACQQsIIAACQwrEaAAAAkEDCCAAAkMKxGgAAAJBAwggAAJCia+eLEkYAAABSSBgBAABS2CUVAAAAEkgYAQAAUmS7+CpGCSMAAACJJIwAAAAprGEEAACABBJGAACAFBlrGAEAACCfhBEAACBF184XJYwAAACk0DACAACQyJRUAACAFDa9AQAAgAQaRujEZp5zRrz21jMx//KvFboUAFIMOfukGPWL78dHnrk19n3iP2LP//v1KNl7t5x7egwZEHt8vyrGPv6j2O+/bo9/Xn5l9D/2XwpUMfD3Mu14dUQaRuikxn10vzht+snxX8+sLnQpAGxH3wn7xZv/cXe88JkL4qUvXBxFPbrHyB9dEkW9S5rv2eN7VVGy927xypcujT9PmhUb7/197Lnowuj1kb0LWDmAhhE6pV369I4f3HBFXHju/Ni4YVOhywFgO16eNj/+546aaHh+TWx99pV49fwro3j3obHL/qOa79ll/JhYf8vy+NtTz0fjq/Wx7prbomnTlthlv1HbeTLQHrLt+J+OSMMIndA3v/3vUXP/Q/HwgysLXQoArdS9X5+IiHhnw1+bx96ufS4G/J/Do3tp34iioig9/vDoVlIcm1c+XagyASLCLqnQ6Zww5bjYf9zY+PTRpxa6FABaq6gohn/jzNjy+H9Fw5/XNA//96xvxZ7XXBgfeeo/I7vtncj8rSFe+bfLo/G/1xawWCCi464tbC8dOmF89dVX4/TTT9/uPQ0NDbFp06acK5vt6v+1srMatlt5LLj8opj95YuioaGx0OUA0Eq7XXpW9Bo9ItbM/nbOeHnVadG9f5946V+/Hs+fMCfeuGnZu2sYR+9ZoEoB3tWhG8a33norbrnllu3eU11dHaWlpTnXX7eub6cKoX0dMG7fGDJ0UPzqgdvilXWr4pV1q6LisEPi9C+fFq+sWxXdunXo/0kDdGnDF/xb9Jt4SLx46tdjW92bzePFI8pj8PTj49ULro7Nv/9jbH32lVh31U/j7T++EIO++OkCVgxEWMNY0Cmpd91113Y/f+mll973GXPnzo2qqqqcsbF7Hvqh6oKO6uGHVsbRH5+cM/a9H1wWLz7/clx79U2RyUjXATqi4Qv+LUonVcSLp86Nba/V53zW7b3dUv/xb3gmE1FU1E4VAiQraMM4efLkKCoqimw2vZsuep8/lCUlJVFSUpIzVlQkZWHntGXz27H62Rdyxv729t/if/5nQ944AB3D8Eu/ErueeES8cuY3I7Plb9FjyICIiGja9HZkGxpj64uvRcPLr8dul8+MtZffHE3/89fof8yh0fewA+OV0y8pbPGANYyFfPmwYcPi5z//eWQymcTrySefLGR5AAAf2uAvfCq69+8b/3Rrdez7+H80XwOOP/zdG95pipdnzI+mtzbFXjdeHPvc+4PYderEePW8K+OvD9QWtnigyytowjh+/Piora2NE088MfHz90sfgYiTT5hR6BIA2I4/7nX8+97T+Mra+O+vVLdDNUBrZbp4P1LQhvGCCy6ILVu2pH4+atSo+O1vf9uOFQEAAPCegjaMhx9++HY/79OnTxx55JHtVA0AAECurp0vdvBjNQAAACicgiaMAAAAHVmmi2eMEkYAAAASSRgBAABSZCWMAAAAkE/DCAAAQCJTUgEAAFJkCl1AgUkYAQAASCRhBAAASOFYDQAAAEggYQQAAEjhWA0AAABIIGEEAABIYZdUAAAASCBhBAAASJHNWsMIAAAAeSSMAAAAKZzDCAAAAAkkjAAAACnskgoAAAAJJIwAAAApstYwAgAAQD4JIwAAQAq7pAIAAEACDSMAAEAn89BDD8Xxxx8fw4cPj6Kioli2bFnO59lsNr7xjW/EsGHDonfv3lFZWRnPP/98q9+jYQQAAEiRzWbb7WqNLVu2xLhx42LRokWJn3/729+Oq6++Oq6//vp49NFHo0+fPjFp0qTYunVrq95jDSMAAEAnc9xxx8Vxxx2X+Fk2m40rr7wy/v3f/z1OPPHEiIj40Y9+FGVlZbFs2bI49dRTW/weCSMAAECKTDteDQ0NsWnTppyroaGh1TW//PLLUVdXF5WVlc1jpaWlMWHChFixYkWrnqVhBAAA6ACqq6ujtLQ056qurm71c+rq6iIioqysLGe8rKys+bOWMiUVAAAgRbYdj9WYO3duVFVV5YyVlJS02/uTaBgBAAA6gJKSkjZpEMvLyyMior6+PoYNG9Y8Xl9fHwceeGCrnmVKKgAAQIpMZNvtaisjR46M8vLyqKmpaR7btGlTPProo1FRUdGqZ0kYAQAAOpnNmzfHCy+80Pz1yy+/HKtWrYqBAwfGiBEj4txzz43LLrss/vmf/zlGjhwZF198cQwfPjwmT57cqvdoGAEAAFK09nzE9vLEE0/EUUcd1fz1e2sfp02bFkuWLIkLL7wwtmzZEl/+8pdjw4YNcdhhh8W9994bvXr1atV7irId9TfwIew+cL9ClwBAG7in/8hClwBAGzjglV8WuoQP7Ojdj2m3d9W89v+327taSsIIAACQoi3XFnZGNr0BAAAgkYQRAAAgRXuew9gRSRgBAABIJGEEAABIkdn59ghtFQkjAAAAiSSMAAAAKbp2vihhBAAAIIWGEQAAgESmpAIAAKTIdPFJqRJGAAAAEkkYAQAAUkgYAQAAIIGEEQAAIEU2K2EEAACAPBJGAACAFNYwAgAAQAIJIwAAQIqshBEAAADySRgBAABS2CUVAAAAEkgYAQAAUtglFQAAABJIGAEAAFJYwwgAAAAJJIwAAAAprGEEAACABBJGAACAFFkJIwAAAOTTMAIAAJDIlFQAAIAUGcdqAAAAQD4JIwAAQAqb3gAAAEACCSMAAEAKaxgBAAAggYQRAAAghTWMAAAAkEDCCAAAkMIaRgAAAEggYQQAAEhhDSMAAAAkkDACAACksIYRAAAAEkgYAQAAUljDCAAAAAkkjAAAACmy2UyhSygoCSMAAACJNIwAAAAkMiUVAAAgRcamNwAAAJBPwggAAJAim5UwAgAAQB4JIwAAQAprGAEAACCBhBEAACCFNYwAAACQQMIIAACQIiNhBAAAgHwSRgAAgBRZu6QCAABAPgkjAABACrukAgAAQAIJIwAAQIqMNYwAAACQT8IIAACQwhpGAAAASCBhBAAASJGRMAIAAEA+DSMAAACJTEkFAABIYdMbAAAASCBhBAAASJEJCSMAAADkkTACAACksIYRAAAAEkgYAQAAUmQkjAAAAJBPwggAAJAia5dUAAAAyCdhBAAASGENIwAAACSQMAIAAKRwDiMAAAAkkDACAACksEsqAAAAJJAwAgAApLCGEQAAABJoGAEAADqhRYsWxV577RW9evWKCRMmxGOPPdbm79AwAgAApMhms+12tcatt94aVVVVMW/evHjyySdj3LhxMWnSpFi3bl2b/vwaRgAAgE7m+9//fpx55pkxY8aM2HfffeP666+PXXbZJW6++eY2fY+GEQAAIEW2Ha+WamxsjNra2qisrGwe69atW1RWVsaKFSs+6I+ayC6pAAAAHUBDQ0M0NDTkjJWUlERJSUnO2Pr166OpqSnKyspyxsvKyuK5555r05p2yobxtbeeKXQJsEM1NDREdXV1zJ07N+8PCACdh7/n0PG90/iXdnvX/PnzY8GCBTlj8+bNi/nz57dbDf+oKNvVDxaBTmjTpk1RWloaGzdujP79+xe6HAA+IH/Pgb/X0oSxsbExdtlll7jjjjti8uTJzePTpk2LDRs2xC9+8Ys2q8kaRgAAgA6gpKQk+vfvn3MlzT4oLi6O8ePHR01NTfNYJpOJmpqaqKioaNOadsopqQAAADuzqqqqmDZtWhx88MHxsY99LK688srYsmVLzJgxo03fo2EEAADoZE455ZR444034hvf+EbU1dXFgQceGPfee2/eRjgfloYROqGSkpKYN2+eDRIAOjl/z4EPY9asWTFr1qwd+g6b3gAAAJDIpjcAAAAk0jACAACQSMMIAABAIg0jAAAAiTSM0AktWrQo9tprr+jVq1dMmDAhHnvssUKXBEArPPTQQ3H88cfH8OHDo6ioKJYtW1bokgASaRihk7n11lujqqoq5s2bF08++WSMGzcuJk2aFOvWrSt0aQC00JYtW2LcuHGxaNGiQpcCsF2O1YBOZsKECXHIIYfENddcExERmUwm9thjj5g9e3ZcdNFFBa4OgNYqKiqKO++8MyZPnlzoUgDySBihE2lsbIza2tqorKxsHuvWrVtUVlbGihUrClgZAAA7Iw0jdCLr16+PpqamKCsryxkvKyuLurq6AlUFAMDOSsMIAABAIg0jdCKDBw+O7t27R319fc54fX19lJeXF6gqAAB2VhpG6ESKi4tj/PjxUVNT0zyWyWSipqYmKioqClgZAAA7ox6FLgBonaqqqpg2bVocfPDB8bGPfSyuvPLK2LJlS8yYMaPQpQHQQps3b44XXnih+euXX345Vq1aFQMHDowRI0YUsDKAXI7VgE7ommuuie985ztRV1cXBx54YFx99dUxYcKEQpcFQAs98MADcdRRR+WNT5s2LZYsWdL+BQGk0DACAACQyBpGAAAAEmkYAQAASKRhBAAAIJGGEQAAgEQaRgAAABJpGAEAAEikYQQAACCRhhGADmH69OkxefLk5q8/8YlPxLnnntvudTzwwANRVFQUGzZsaPd3A0BHo2EEYLumT58eRUVFUVRUFMXFxTFq1Ki45JJL4p133tmh7/35z38el156aYvu1eQBwI7Ro9AFANDxHXvssbF48eJoaGiIe+65J2bOnBk9e/aMuXPn5tzX2NgYxcXFbfLOgQMHtslzAIAPTsIIwPsqKSmJ8vLy2HPPPeMrX/lKVFZWxl133dU8jfSb3/xmDB8+PEaPHh0REa+++mp89rOfjQEDBsTAgQPjxBNPjFdeeaX5eU1NTVFVVRUDBgyIQYMGxYUXXhjZbDbnnf84JbWhoSG+9rWvxR577BElJSUxatSouOmmm+KVV16Jo446KiIidt111ygqKorp06dHREQmk4nq6uoYOXJk9O7dO8aNGxd33HFHznvuueee2GeffaJ3795x1FFH5dQJAF2dhhGAVuvdu3c0NjZGRERNTU2sXr067r///li+fHls27YtJk2aFP369Yvf/e538cgjj0Tfvn3j2GOPbf6e733ve7FkyZK4+eab4+GHH4633nor7rzzzu2+84tf/GL853/+Z1x99dXx7LPPxg033BB9+/aNPfbYI372s59FRMTq1atj7dq1cdVVV0VERHV1dfzoRz+K66+/Pv70pz/FnDlz4vOf/3w8+OCDEfFuYztlypQ4/vjjY9WqVfGlL30pLrrooh31awOATseUVABaLJvNRk1NTdx3330xe/bseOONN6JPnz5x4403Nk9F/fGPfxyZTCZuvPHGKCoqioiIxYsXx4ABA+KBBx6IY445Jq688sqYO3duTJkyJSIirr/++rjvvvtS3/vnP/85brvttrj//vujsrIyIiL23nvv5s/fm746dOjQGDBgQES8m0hefvnl8etf/zoqKiqav+fhhx+OG264IY488si47rrr4p/+6Z/ie9/7XkREjB49Op5++un41re+1Ya/NQDovDSMALyv5cuXR9++fWPbtm2RyWTiX//1X2P+/Pkxc+bM2H///XPWLT711FPxwgsvRL9+/XKesXXr1njxxRdj48aNsXbt2pgwYULzZz169IiDDz44b1rqe1atWhXdu3ePI488ssU1v/DCC/H222/HJz/5yZzxxsbG+OhHPxoREc8++2xOHRHR3FwCABpGAFrgqKOOiuuuuy6Ki4tj+PDh0aPH//7fR58+fXLu3bx5c4wfPz5+8pOf5D1nyJAhH+j9vXv3bvX3bN68OSIi7r777thtt91yPispKflAdQBAV6NhBOB99enTJ0aNGtWiew866KC49dZbY+jQodG/f//Ee4YNGxaPPvpoHHHEERER8c4770RtbW0cdNBBiffvv//+kclk4sEHH2yekvr33ks4m5qamsf23XffKCkpiTVr1qQmk2PHjo277rorZ2zlypXv/0MCQBdh0xsA2tRpp50WgwcPjhNPPDF+97vfxcsvvxwPPPBAfPWrX43XXnstIiLOOeecuOKKK2LZsmXx3HPPxdlnn73dMxT32muvmDZtWpx++umxbNmy5mfedtttERGx5557RlFRUSxfvjzeeOON2Lx5c/Tr1y/OP//8mDNnTtxyyy3x4osvxpNPPhk/+MEP4pZbbomIiLPOOiuef/75uOCCC2L16tWxdOnSWLJkyY7+FQFAp6FhBKBN7bLLLvHQQw/FiBEjYsqUKTF27Ng444wzYuvWrc2J43nnnRdf+MIXYtq0aVFRURH9+vWLz3zmM9t97nXXXRcnnXRSnH322TFmzJg488wzY8uWLRERsdtuu8WCBQvioosuirKyspg1a1ZERFx66aVx8cUXR3V1dYwdOzaOPfbYuPvuu2PkyJERETFixIj42c9+FsuWLYtx48bF9ddfH5dffvkO/O0AQOdSlE3bYQAAAIAuTcIIAABAIg0jAAAAiTSMAAAAJNIwAgAAkEjDCAAAQCINIwAAAIk0jAAAACTSMAIAAJBIwwgAAEAiDSMAAACJNIwAAAAk0jACAACQ6P8BmhjVos8qsSYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# RandomForest"
      ],
      "metadata": {
        "id": "O9y5bnOQB187"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "model = RandomForestClassifier(n_estimators=20)\n"
      ],
      "metadata": {
        "id": "sdmRpWKMCP2S"
      },
      "execution_count": 269,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(scaled_df,y,test_size=0.25,stratify=y,random_state=42)\n"
      ],
      "metadata": {
        "id": "ORyVl2LFpLth"
      },
      "execution_count": 270,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train)\n",
        "model.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "npNIWExtCS8t",
        "outputId": "e0aede69-a3c1-4c97-fdbc-d0294b143b70"
      },
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.951048951048951"
            ]
          },
          "metadata": {},
          "execution_count": 271
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "test_accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Test Accuracy: {test_accuracy}\")"
      ],
      "metadata": {
        "id": "1PlPf7MACU51",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f49898a-2c49-488a-a7a1-5311b95ce628"
      },
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy: 0.951048951048951\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conf_matrix = confusion_matrix(y_test, y_pred )\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BZuhOQrFplTg",
        "outputId": "5925e35c-ae84-457b-d7ea-a89227e8c64c"
      },
      "execution_count": 273,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            " [[90  0]\n",
            " [ 7 46]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.figure(figsize=(12,7))\n",
        "sn.heatmap(cm, annot= True)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "id": "woHnmodJCceO",
        "outputId": "c4a28897-5bd2-4e9a-8514-1c83dfdf44c4"
      },
      "execution_count": 274,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(120.72222222222221, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 274
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#ANN Artificial Neural Networks"
      ],
      "metadata": {
        "id": "0s_2G3ElqqQc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "9UnCSiKiqoLk"
      },
      "execution_count": 309,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(scaled_df, y, test_size=0.15, stratify=y, random_state=26)\n"
      ],
      "metadata": {
        "id": "EvOaArXbq-95"
      },
      "execution_count": 331,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "OFMC4irLt7yF"
      },
      "execution_count": 333,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Initialising ANN\n",
        "ann = tf.keras.models.Sequential()\n"
      ],
      "metadata": {
        "id": "f7jFVamSrDV2"
      },
      "execution_count": 321,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Adding  Hidden Layer\n",
        "ann.add(tf.keras.layers.Dense(units=6, activation=\"relu\"))\n",
        "ann.add(tf.keras.layers.Dense(units=6, activation=\"relu\"))\n",
        "\n",
        "#Adding Output Layer\n",
        "ann.add(tf.keras.layers.Dense(units=1,activation=\"sigmoid\"))\n"
      ],
      "metadata": {
        "id": "Pl-R21h5rHDu"
      },
      "execution_count": 322,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=['accuracy'])\n"
      ],
      "metadata": {
        "id": "wi4OPZDIrV4a"
      },
      "execution_count": 323,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Fitting ANN\n",
        "ann.fit(X_train, y_train, batch_size=32, epochs=100, verbose=1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nOeLBlu5rZxz",
        "outputId": "6d8cafd2-d043-49d4-9ba1-69f57e041092"
      },
      "execution_count": 324,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "15/15 [==============================] - 1s 2ms/step - loss: 0.8389 - accuracy: 0.3868\n",
            "Epoch 2/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.7472 - accuracy: 0.4022\n",
            "Epoch 3/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.6776 - accuracy: 0.4088\n",
            "Epoch 4/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.6252 - accuracy: 0.4396\n",
            "Epoch 5/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.5870 - accuracy: 0.4769\n",
            "Epoch 6/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.5565 - accuracy: 0.5560\n",
            "Epoch 7/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.5316 - accuracy: 0.6396\n",
            "Epoch 8/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.5108 - accuracy: 0.7319\n",
            "Epoch 9/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.4919 - accuracy: 0.8132\n",
            "Epoch 10/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.4746 - accuracy: 0.8637\n",
            "Epoch 11/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.4589 - accuracy: 0.8945\n",
            "Epoch 12/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.4437 - accuracy: 0.9121\n",
            "Epoch 13/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.4295 - accuracy: 0.9253\n",
            "Epoch 14/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.4150 - accuracy: 0.9275\n",
            "Epoch 15/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.4011 - accuracy: 0.9297\n",
            "Epoch 16/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.3872 - accuracy: 0.9341\n",
            "Epoch 17/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.3725 - accuracy: 0.9363\n",
            "Epoch 18/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.3574 - accuracy: 0.9341\n",
            "Epoch 19/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.3412 - accuracy: 0.9319\n",
            "Epoch 20/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.3238 - accuracy: 0.9297\n",
            "Epoch 21/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.3065 - accuracy: 0.9297\n",
            "Epoch 22/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2887 - accuracy: 0.9297\n",
            "Epoch 23/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2707 - accuracy: 0.9297\n",
            "Epoch 24/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2539 - accuracy: 0.9297\n",
            "Epoch 25/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2367 - accuracy: 0.9297\n",
            "Epoch 26/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2223 - accuracy: 0.9297\n",
            "Epoch 27/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2099 - accuracy: 0.9297\n",
            "Epoch 28/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1999 - accuracy: 0.9297\n",
            "Epoch 29/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1907 - accuracy: 0.9297\n",
            "Epoch 30/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1831 - accuracy: 0.9297\n",
            "Epoch 31/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1772 - accuracy: 0.9341\n",
            "Epoch 32/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1721 - accuracy: 0.9363\n",
            "Epoch 33/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.1680 - accuracy: 0.9363\n",
            "Epoch 34/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1643 - accuracy: 0.9363\n",
            "Epoch 35/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1616 - accuracy: 0.9385\n",
            "Epoch 36/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1591 - accuracy: 0.9385\n",
            "Epoch 37/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1572 - accuracy: 0.9407\n",
            "Epoch 38/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1554 - accuracy: 0.9407\n",
            "Epoch 39/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1537 - accuracy: 0.9407\n",
            "Epoch 40/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1521 - accuracy: 0.9451\n",
            "Epoch 41/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1509 - accuracy: 0.9451\n",
            "Epoch 42/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1496 - accuracy: 0.9451\n",
            "Epoch 43/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1483 - accuracy: 0.9451\n",
            "Epoch 44/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1472 - accuracy: 0.9451\n",
            "Epoch 45/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1463 - accuracy: 0.9451\n",
            "Epoch 46/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1454 - accuracy: 0.9495\n",
            "Epoch 47/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1446 - accuracy: 0.9451\n",
            "Epoch 48/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1438 - accuracy: 0.9473\n",
            "Epoch 49/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1430 - accuracy: 0.9473\n",
            "Epoch 50/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1422 - accuracy: 0.9473\n",
            "Epoch 51/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1418 - accuracy: 0.9451\n",
            "Epoch 52/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.1406 - accuracy: 0.9473\n",
            "Epoch 53/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1400 - accuracy: 0.9451\n",
            "Epoch 54/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1396 - accuracy: 0.9451\n",
            "Epoch 55/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1391 - accuracy: 0.9473\n",
            "Epoch 56/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1386 - accuracy: 0.9495\n",
            "Epoch 57/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1383 - accuracy: 0.9473\n",
            "Epoch 58/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1376 - accuracy: 0.9473\n",
            "Epoch 59/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1371 - accuracy: 0.9473\n",
            "Epoch 60/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1366 - accuracy: 0.9473\n",
            "Epoch 61/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1363 - accuracy: 0.9473\n",
            "Epoch 62/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1358 - accuracy: 0.9473\n",
            "Epoch 63/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1354 - accuracy: 0.9451\n",
            "Epoch 64/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1347 - accuracy: 0.9473\n",
            "Epoch 65/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1343 - accuracy: 0.9473\n",
            "Epoch 66/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1342 - accuracy: 0.9516\n",
            "Epoch 67/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1336 - accuracy: 0.9516\n",
            "Epoch 68/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1331 - accuracy: 0.9495\n",
            "Epoch 69/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1326 - accuracy: 0.9516\n",
            "Epoch 70/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1323 - accuracy: 0.9538\n",
            "Epoch 71/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1320 - accuracy: 0.9516\n",
            "Epoch 72/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1317 - accuracy: 0.9516\n",
            "Epoch 73/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1315 - accuracy: 0.9538\n",
            "Epoch 74/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1310 - accuracy: 0.9516\n",
            "Epoch 75/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1309 - accuracy: 0.9473\n",
            "Epoch 76/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1306 - accuracy: 0.9473\n",
            "Epoch 77/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1300 - accuracy: 0.9473\n",
            "Epoch 78/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1298 - accuracy: 0.9495\n",
            "Epoch 79/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.1296 - accuracy: 0.9495\n",
            "Epoch 80/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1291 - accuracy: 0.9495\n",
            "Epoch 81/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1289 - accuracy: 0.9516\n",
            "Epoch 82/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1286 - accuracy: 0.9516\n",
            "Epoch 83/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1282 - accuracy: 0.9495\n",
            "Epoch 84/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1278 - accuracy: 0.9495\n",
            "Epoch 85/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1275 - accuracy: 0.9516\n",
            "Epoch 86/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1272 - accuracy: 0.9538\n",
            "Epoch 87/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1267 - accuracy: 0.9538\n",
            "Epoch 88/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1270 - accuracy: 0.9560\n",
            "Epoch 89/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1259 - accuracy: 0.9560\n",
            "Epoch 90/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1255 - accuracy: 0.9560\n",
            "Epoch 91/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1252 - accuracy: 0.9560\n",
            "Epoch 92/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1250 - accuracy: 0.9582\n",
            "Epoch 93/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1246 - accuracy: 0.9560\n",
            "Epoch 94/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1243 - accuracy: 0.9582\n",
            "Epoch 95/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1240 - accuracy: 0.9560\n",
            "Epoch 96/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1235 - accuracy: 0.9516\n",
            "Epoch 97/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1231 - accuracy: 0.9538\n",
            "Epoch 98/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1226 - accuracy: 0.9538\n",
            "Epoch 99/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1222 - accuracy: 0.9516\n",
            "Epoch 100/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1216 - accuracy: 0.9516\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7d5b392b47f0>"
            ]
          },
          "metadata": {},
          "execution_count": 324
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ann.evaluate(X_test,y_test,batch_size=32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FuIYGlH5rZ3q",
        "outputId": "ad66027d-8d7d-43a3-fe31-a0b590df27e1"
      },
      "execution_count": 325,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0871 - accuracy: 0.9649\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.0871346965432167, 0.9649122953414917]"
            ]
          },
          "metadata": {},
          "execution_count": 325
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_accuracy = ann.evaluate(X_test, y_test, batch_size=32)\n",
        "print(f\"Test Loss: {test_loss}\")\n",
        "print(f\"Test Accuracy: {test_accuracy}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NEdOmlYcrkOL",
        "outputId": "93660792-9eac-4975-93cf-29e77a59e948"
      },
      "execution_count": 326,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0871 - accuracy: 0.9649\n",
            "Test Loss: 0.0871346965432167\n",
            "Test Accuracy: 0.9649122953414917\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_probs = ann.predict(X_test)\n",
        "y_pred = (y_pred_probs > 0.5).astype(int)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1tfx5jvGrpQ-",
        "outputId": "03ba782e-bd90-4719-c1c3-e13d1a328dc3"
      },
      "execution_count": 327,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 3ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Test Accuracy (using sklearn): {test_accuracy}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GWlgqJ7HsZ1K",
        "outputId": "82009911-16c5-4ffd-8f36-019ca248ca20"
      },
      "execution_count": 328,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy (using sklearn): 0.9649122807017544\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conf_matrix = confusion_matrix(y_test, y_pred )\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fVNf4laSsa5S",
        "outputId": "ff1baf48-7385-462e-c82d-09588ea30d56"
      },
      "execution_count": 329,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            " [[70  1]\n",
            " [ 3 40]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import matplotlib.pyplot as plt\n",
        "# import seaborn as sn\n",
        "plt.figure(figsize=(12,7))\n",
        "sn.heatmap(cm, annot= True)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "id": "qidy9is8suJM",
        "outputId": "3d532439-fabe-43d5-e76e-eec9811f2b7d"
      },
      "execution_count": 330,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(120.72222222222221, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 330
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}