breast_cancerdetection.ipynb

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        "<a href=\"https://colab.research.google.com/github/Amishachoudhary22/AI-ML/blob/main/breast_cancerdetection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "0J5P0KqhqF-Q"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('breast-cancer.csv')\n",
        "dataset"
      ],
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        "colab": {
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          "height": 500
        },
        "id": "ncdpELCTqMUt",
        "outputId": "81d9dadc-bc20-4475-bb64-75488c761d38"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
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              "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0      842302         M        17.99         10.38          122.80     1001.0   \n",
              "1      842517         M        20.57         17.77          132.90     1326.0   \n",
              "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
              "3    84348301         M        11.42         20.38           77.58      386.1   \n",
              "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
              "..        ...       ...          ...           ...             ...        ...   \n",
              "564    926424         M        21.56         22.39          142.00     1479.0   \n",
              "565    926682         M        20.13         28.25          131.20     1261.0   \n",
              "566    926954         M        16.60         28.08          108.30      858.1   \n",
              "567    927241         M        20.60         29.33          140.10     1265.0   \n",
              "568     92751         B         7.76         24.54           47.92      181.0   \n",
              "\n",
              "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0            0.11840           0.27760         0.30010              0.14710   \n",
              "1            0.08474           0.07864         0.08690              0.07017   \n",
              "2            0.10960           0.15990         0.19740              0.12790   \n",
              "3            0.14250           0.28390         0.24140              0.10520   \n",
              "4            0.10030           0.13280         0.19800              0.10430   \n",
              "..               ...               ...             ...                  ...   \n",
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              "566          0.08455           0.10230         0.09251              0.05302   \n",
              "567          0.11780           0.27700         0.35140              0.15200   \n",
              "568          0.05263           0.04362         0.00000              0.00000   \n",
              "\n",
              "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
              "0    ...        25.380          17.33           184.60      2019.0   \n",
              "1    ...        24.990          23.41           158.80      1956.0   \n",
              "2    ...        23.570          25.53           152.50      1709.0   \n",
              "3    ...        14.910          26.50            98.87       567.7   \n",
              "4    ...        22.540          16.67           152.20      1575.0   \n",
              "..   ...           ...            ...              ...         ...   \n",
              "564  ...        25.450          26.40           166.10      2027.0   \n",
              "565  ...        23.690          38.25           155.00      1731.0   \n",
              "566  ...        18.980          34.12           126.70      1124.0   \n",
              "567  ...        25.740          39.42           184.60      1821.0   \n",
              "568  ...         9.456          30.37            59.16       268.6   \n",
              "\n",
              "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "0             0.16220            0.66560           0.7119   \n",
              "1             0.12380            0.18660           0.2416   \n",
              "2             0.14440            0.42450           0.4504   \n",
              "3             0.20980            0.86630           0.6869   \n",
              "4             0.13740            0.20500           0.4000   \n",
              "..                ...                ...              ...   \n",
              "564           0.14100            0.21130           0.4107   \n",
              "565           0.11660            0.19220           0.3215   \n",
              "566           0.11390            0.30940           0.3403   \n",
              "567           0.16500            0.86810           0.9387   \n",
              "568           0.08996            0.06444           0.0000   \n",
              "\n",
              "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
              "0                  0.2654          0.4601                  0.11890  \n",
              "1                  0.1860          0.2750                  0.08902  \n",
              "2                  0.2430          0.3613                  0.08758  \n",
              "3                  0.2575          0.6638                  0.17300  \n",
              "4                  0.1625          0.2364                  0.07678  \n",
              "..                    ...             ...                      ...  \n",
              "564                0.2216          0.2060                  0.07115  \n",
              "565                0.1628          0.2572                  0.06637  \n",
              "566                0.1418          0.2218                  0.07820  \n",
              "567                0.2650          0.4087                  0.12400  \n",
              "568                0.0000          0.2871                  0.07039  \n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
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              "      <th>0</th>\n",
              "      <td>842302</td>\n",
              "      <td>M</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
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              "      <td>17.33</td>\n",
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              "      <td>2019.0</td>\n",
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              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
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              "      <th>1</th>\n",
              "      <td>842517</td>\n",
              "      <td>M</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
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              "      <td>24.990</td>\n",
              "      <td>23.41</td>\n",
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              "      <td>1956.0</td>\n",
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              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>M</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>...</td>\n",
              "      <td>23.570</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>M</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
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              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>...</td>\n",
              "      <td>14.910</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
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              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>M</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
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              "      <td>22.540</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
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              "      <td>22.39</td>\n",
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              "      <td>0.13890</td>\n",
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              "      <td>25.450</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
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              "      <th>565</th>\n",
              "      <td>926682</td>\n",
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              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
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              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d275c45d-d450-4f8d-8d4e-b53e2fd35dbb 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-d275c45d-d450-4f8d-8d4e-b53e2fd35dbb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['diagnosis'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uoQtv5CHqMW-",
        "outputId": "78564151-62d9-4e02-ea3a-c096d29d6cb8"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['M', 'B'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#M- malignant (Dangerous) (1)\n",
        "#B- benign (Not Dangerous) (0)"
      ],
      "metadata": {
        "id": "yX9T0f77qMYy"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del dataset['id']"
      ],
      "metadata": {
        "id": "06_XFdydqMbd"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0zZxTIRqMdy",
        "outputId": "58913aed-ad2e-4f99-9d1e-7e70dc4fc1b2"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=dataset[[col for col in dataset.columns if col!='diagnosis']+['diagnosis']]\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "gFeSq8DIqMgW",
        "outputId": "76acfc75-b795-43ca-d0aa-3b374987506b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
              "0          17.99         10.38          122.80     1001.0          0.11840   \n",
              "1          20.57         17.77          132.90     1326.0          0.08474   \n",
              "2          19.69         21.25          130.00     1203.0          0.10960   \n",
              "3          11.42         20.38           77.58      386.1          0.14250   \n",
              "4          20.29         14.34          135.10     1297.0          0.10030   \n",
              "..           ...           ...             ...        ...              ...   \n",
              "564        21.56         22.39          142.00     1479.0          0.11100   \n",
              "565        20.13         28.25          131.20     1261.0          0.09780   \n",
              "566        16.60         28.08          108.30      858.1          0.08455   \n",
              "567        20.60         29.33          140.10     1265.0          0.11780   \n",
              "568         7.76         24.54           47.92      181.0          0.05263   \n",
              "\n",
              "     compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
              "0             0.27760         0.30010              0.14710         0.2419   \n",
              "1             0.07864         0.08690              0.07017         0.1812   \n",
              "2             0.15990         0.19740              0.12790         0.2069   \n",
              "3             0.28390         0.24140              0.10520         0.2597   \n",
              "4             0.13280         0.19800              0.10430         0.1809   \n",
              "..                ...             ...                  ...            ...   \n",
              "564           0.11590         0.24390              0.13890         0.1726   \n",
              "565           0.10340         0.14400              0.09791         0.1752   \n",
              "566           0.10230         0.09251              0.05302         0.1590   \n",
              "567           0.27700         0.35140              0.15200         0.2397   \n",
              "568           0.04362         0.00000              0.00000         0.1587   \n",
              "\n",
              "     fractal_dimension_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
              "0                   0.07871  ...          17.33           184.60      2019.0   \n",
              "1                   0.05667  ...          23.41           158.80      1956.0   \n",
              "2                   0.05999  ...          25.53           152.50      1709.0   \n",
              "3                   0.09744  ...          26.50            98.87       567.7   \n",
              "4                   0.05883  ...          16.67           152.20      1575.0   \n",
              "..                      ...  ...            ...              ...         ...   \n",
              "564                 0.05623  ...          26.40           166.10      2027.0   \n",
              "565                 0.05533  ...          38.25           155.00      1731.0   \n",
              "566                 0.05648  ...          34.12           126.70      1124.0   \n",
              "567                 0.07016  ...          39.42           184.60      1821.0   \n",
              "568                 0.05884  ...          30.37            59.16       268.6   \n",
              "\n",
              "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "0             0.16220            0.66560           0.7119   \n",
              "1             0.12380            0.18660           0.2416   \n",
              "2             0.14440            0.42450           0.4504   \n",
              "3             0.20980            0.86630           0.6869   \n",
              "4             0.13740            0.20500           0.4000   \n",
              "..                ...                ...              ...   \n",
              "564           0.14100            0.21130           0.4107   \n",
              "565           0.11660            0.19220           0.3215   \n",
              "566           0.11390            0.30940           0.3403   \n",
              "567           0.16500            0.86810           0.9387   \n",
              "568           0.08996            0.06444           0.0000   \n",
              "\n",
              "     concave points_worst  symmetry_worst  fractal_dimension_worst  diagnosis  \n",
              "0                  0.2654          0.4601                  0.11890          M  \n",
              "1                  0.1860          0.2750                  0.08902          M  \n",
              "2                  0.2430          0.3613                  0.08758          M  \n",
              "3                  0.2575          0.6638                  0.17300          M  \n",
              "4                  0.1625          0.2364                  0.07678          M  \n",
              "..                    ...             ...                      ...        ...  \n",
              "564                0.2216          0.2060                  0.07115          M  \n",
              "565                0.1628          0.2572                  0.06637          M  \n",
              "566                0.1418          0.2218                  0.07820          M  \n",
              "567                0.2650          0.4087                  0.12400          M  \n",
              "568                0.0000          0.2871                  0.07039          B  \n",
              "\n",
              "[569 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ab97b509-715d-474a-b6b0-60a06577187c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <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>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",
              "      <th>diagnosis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.30010</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.16220</td>\n",
              "      <td>0.66560</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>...</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>...</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>...</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>...</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>...</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ab97b509-715d-474a-b6b0-60a06577187c')\"\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "l1=LabelEncoder()\n",
        "data['diagnosis']=l1.fit_transform(data['diagnosis'])\n",
        "data['diagnosis'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "69WiSkvTqMiv",
        "outputId": "ee4998e8-fc12-4c71-fe9c-977d6938a8de"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "BMrnciNIqMk0",
        "outputId": "87a71095-6468-4cdb-fa42-16ec7900f34a"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
              "0          17.99         10.38          122.80     1001.0          0.11840   \n",
              "1          20.57         17.77          132.90     1326.0          0.08474   \n",
              "2          19.69         21.25          130.00     1203.0          0.10960   \n",
              "3          11.42         20.38           77.58      386.1          0.14250   \n",
              "4          20.29         14.34          135.10     1297.0          0.10030   \n",
              "..           ...           ...             ...        ...              ...   \n",
              "564        21.56         22.39          142.00     1479.0          0.11100   \n",
              "565        20.13         28.25          131.20     1261.0          0.09780   \n",
              "566        16.60         28.08          108.30      858.1          0.08455   \n",
              "567        20.60         29.33          140.10     1265.0          0.11780   \n",
              "568         7.76         24.54           47.92      181.0          0.05263   \n",
              "\n",
              "     compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
              "0             0.27760         0.30010              0.14710         0.2419   \n",
              "1             0.07864         0.08690              0.07017         0.1812   \n",
              "2             0.15990         0.19740              0.12790         0.2069   \n",
              "3             0.28390         0.24140              0.10520         0.2597   \n",
              "4             0.13280         0.19800              0.10430         0.1809   \n",
              "..                ...             ...                  ...            ...   \n",
              "564           0.11590         0.24390              0.13890         0.1726   \n",
              "565           0.10340         0.14400              0.09791         0.1752   \n",
              "566           0.10230         0.09251              0.05302         0.1590   \n",
              "567           0.27700         0.35140              0.15200         0.2397   \n",
              "568           0.04362         0.00000              0.00000         0.1587   \n",
              "\n",
              "     fractal_dimension_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
              "0                   0.07871  ...          17.33           184.60      2019.0   \n",
              "1                   0.05667  ...          23.41           158.80      1956.0   \n",
              "2                   0.05999  ...          25.53           152.50      1709.0   \n",
              "3                   0.09744  ...          26.50            98.87       567.7   \n",
              "4                   0.05883  ...          16.67           152.20      1575.0   \n",
              "..                      ...  ...            ...              ...         ...   \n",
              "564                 0.05623  ...          26.40           166.10      2027.0   \n",
              "565                 0.05533  ...          38.25           155.00      1731.0   \n",
              "566                 0.05648  ...          34.12           126.70      1124.0   \n",
              "567                 0.07016  ...          39.42           184.60      1821.0   \n",
              "568                 0.05884  ...          30.37            59.16       268.6   \n",
              "\n",
              "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "0             0.16220            0.66560           0.7119   \n",
              "1             0.12380            0.18660           0.2416   \n",
              "2             0.14440            0.42450           0.4504   \n",
              "3             0.20980            0.86630           0.6869   \n",
              "4             0.13740            0.20500           0.4000   \n",
              "..                ...                ...              ...   \n",
              "564           0.14100            0.21130           0.4107   \n",
              "565           0.11660            0.19220           0.3215   \n",
              "566           0.11390            0.30940           0.3403   \n",
              "567           0.16500            0.86810           0.9387   \n",
              "568           0.08996            0.06444           0.0000   \n",
              "\n",
              "     concave points_worst  symmetry_worst  fractal_dimension_worst  diagnosis  \n",
              "0                  0.2654          0.4601                  0.11890          1  \n",
              "1                  0.1860          0.2750                  0.08902          1  \n",
              "2                  0.2430          0.3613                  0.08758          1  \n",
              "3                  0.2575          0.6638                  0.17300          1  \n",
              "4                  0.1625          0.2364                  0.07678          1  \n",
              "..                    ...             ...                      ...        ...  \n",
              "564                0.2216          0.2060                  0.07115          1  \n",
              "565                0.1628          0.2572                  0.06637          1  \n",
              "566                0.1418          0.2218                  0.07820          1  \n",
              "567                0.2650          0.4087                  0.12400          1  \n",
              "568                0.0000          0.2871                  0.07039          0  \n",
              "\n",
              "[569 rows x 31 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>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",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.30010</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.16220</td>\n",
              "      <td>0.66560</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>22.39</td>\n",
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              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>...</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
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              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>...</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
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              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
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              "    <tr>\n",
              "      <th>566</th>\n",
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              "      <td>28.08</td>\n",
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              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>...</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
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          "metadata": {},
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      "source": [
        "data['diagnosis'].value_counts()"
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      "execution_count": 11,
      "outputs": [
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          "data": {
            "text/plain": [
              "0    357\n",
              "1    212\n",
              "Name: diagnosis, dtype: int64"
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          "metadata": {},
          "execution_count": 11
        }
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        "y=data.iloc[:,-1].values"
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      "metadata": {
        "id": "bNI_EsFpqMpz"
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      "execution_count": 12,
      "outputs": []
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    {
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              "  <matplotlib.lines.Line2D at 0x7f60dca982b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dcaa3a90>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dca3b2b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dca43a90>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dca5b2b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dca67a90>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dc9fc2b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dca09a90>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dca1f2b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dca29a90>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7f60dcbc4580>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dcbd1d90>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dcbe75b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dcb72d90>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dcb895b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dcb94d90>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dcbab5b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dcb37d90>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dcb4c5b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dcb57d90>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dcaed5b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dcafad90>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dcb115b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dcb1bd90>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dcab15b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dcabdd90>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dcad55b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dcae0d90>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dca745b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dca82d90>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dca985b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dcaa3d90>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dca3b5b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dca43d90>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dca5b5b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dca67d90>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dc9fc5b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dca09d90>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dca1f5b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60dca29d90>],\n",
              " 'means': []}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "_O1gsCrirdch",
        "outputId": "121736e2-4613-4f86-9d81-d6ec769d0525"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f60dc911250>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "s=SMOTE()\n",
        "x_data,y_data=s.fit_resample(x,y)"
      ],
      "metadata": {
        "id": "BTsrEuKErdfK"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "print(Counter(y_data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tPrGQCu0rv2_",
        "outputId": "11376a91-78b5-48d0-ed61-df9b21e54482"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Counter({1: 357, 0: 357})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.kdeplot(data['radius_mean'])\n",
        "sns.kdeplot(data['texture_mean'])\n",
        "sns.kdeplot(data['perimeter_mean'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "ROzGVDNZrwSR",
        "outputId": "9d26c407-9c02-4d2c-f5a5-f403392517da"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f60d8d3ca90>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "ssd=StandardScaler()\n",
        "x_scaled=ssd.fit_transform(x_data)\n",
        "x_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BUVVNSBWrwUm",
        "outputId": "465f5ff4-bffa-4b43-b236-7fd48636d003"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.85949452, -2.21563472,  1.02297184, ...,  2.02792297,\n",
              "         2.5821384 ,  1.75035258],\n",
              "       [ 1.56103617, -0.48616326,  1.42127195, ...,  0.84736116,\n",
              "        -0.35941042,  0.16963571],\n",
              "       [ 1.32175065,  0.32825632,  1.30690855, ...,  1.69486775,\n",
              "         1.01204103,  0.09345659],\n",
              "       ...,\n",
              "       [ 0.86647424, -0.66419307,  0.8138014 , ...,  0.36291609,\n",
              "        -0.23786886, -0.19906118],\n",
              "       [-0.4206265 ,  0.49682644, -0.3864964 , ...,  0.65023879,\n",
              "         1.10828734,  1.04577086],\n",
              "       [ 0.14243138,  1.07864261,  0.22370163, ...,  1.05612646,\n",
              "         1.6287846 ,  0.72012956]])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.kdeplot(x_scaled[2])\n",
        "#sns.kdeplot(x_scaled[3])\n",
        "sns.kdeplot(x_scaled[4])\n",
        "sns.kdeplot(x_scaled[5])\n",
        "sns.kdeplot(x_scaled[6])\n",
        "sns.kdeplot(x_scaled[7])\n",
        "sns.kdeplot(x_scaled[8])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "lH1YQJiir_gi",
        "outputId": "507eaf17-f126-4cab-f93d-1044597fb7ea"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f60d6cbaa90>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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QPKCSyL2eO4NBFJB7xt/zGl47241CiL1CiM+EEC0uBBdC/FIIsV0Isb24uNgdfVV6GGtBAfUHD1JpiHWu83cDn3Fjqd2+A2l1rdhbWF8jp06YzskzpB0pxkOnYXxCsDu62aZIfy8CfPQqb6Bc9ATyV0CclHIosBp4t6VGUsrFUsrRUsrRoaFd/+1J6X5MqalY9EaqLfpOnyJqZBg3HkdNDbX797vUPjTWSJ353CRy2pFixsUH4e3RNUtKzySEIDnCyKGTapqot3NnMMgHzvymH93wWhMpZamUsvFfxpvAKDf2R+lFzGvXUd1/LND5+YJGPuOc96/ZstWl9k1J5BOnP3jzymvIPGW+KFNEjZIj/DhSZMLRgTMXlJ7HncFgGzBACBEvhPAAFgArzmwghOhzxl+vBVyr/qUobbCbq6nZsoXqARPQaAUhMb5ueY4uMBDPpCSqt2x2qX1IlC+as5LIjQffu7tkdVuSI4zUWOzklrt25oLSM7ktGEgpbcD9wEqcH/KfSikzhBBPCyGubWi2UAiRIYTYAywE7nJXf5Teozo9HWm1UukdRWhs5202a4nPuLHU7tyFw9J+fR+dh5bAyOZJ5NTDxUQFeNMv1D0ByxVJEc4Ri5oq6t3cmjOQUn4rpUyUUvaTUj7T8NoTUsoVDX/+g5QyRUo5TEo5TUp5yJ39UXoH89q14B9IaRlExLtniqiRYfx4ZH09dXv2uNQ+LPZ0ErnOaic9s4RpyaEIIdzaz7YkhhsRAg6p5aW92sVOICtKp5J2O+a0NByT5mKzOjp9s9nZfEaPBo2G6s2uLTEN62ukrtqKqayOLcfKqLHYmZ58/qevdQaDp47YIB8OF6kVRb2ZCgZKj1K7ezf2igpq+48BTlcMdRetnx9egwZR4+J+g9BYZ3+Kc0ysPViEl17DhH5dv6T0bEnhakVRb6eCgdKjmNauBZ2OSq8+eBn0GIM7d7NZS3zGjaVmzx4ctbXttg2ONjiTyMerWHPoFJf1D8HLjTkNVyX38eN4STV1VvvF7opykahgoPQo5nWpGMaOoaSwjrC+xi6ZizeMHw9WK7W7drXbVqfXEhRl4ERmBXnltVxxkaeIGiVHGHFIOFpkvthdUS4SFQyUHqP+2DEs2dl4T5lGWX41obGdc35Be3xGjgSdzuW8QWiMkdI8M0i4IrlrTjVrz+kVRSpv0FupYKD0GOa1awGwDJqAwyGbNnm5m8ZgwHvIEJfzBiExRqh3MCrMSIS/+6exXBEXbMBTp+Gwyhv0WioYKD2G6Yc1eA4cSFmN8wM2rK97k8dn8hk/jtr9+7Gb259m8Qp19m9ySNf1rz1ajSBRJZF7NRUMlB7BVlJC7e7dGKdPpzjHhJevHt9Azy57vmHceLDbqdm+vd22GbW1SCQJ+q7rnytUjaLeTQUDpUcwrVsHUmKcfgWnckyExXZN8riR94jhCA8PalzIG6zNLKFSB9oK16qddpWkCCMl5npKzC2fxqb0bCoYKD2Cec1a9JGRaBMGUFZQ3WX5gkYaT0+8R45s93yDepud1EOn0Ad7UpJ3aX0LT45wTlupvEHvpIKB0u05qqup3rgR3+nTKS2oRjokYbFdPx9vGD+O+oMHsZWXt9omPbMEU72N+AGBmMvqqTNfOqOD5D6qRlFvpoKB0u2Z09ORFgvG6Vc0lYfu6pEBgM+4cQDUbN3Wapvv9p3E6KVj1DDn/oLiS2h0EOLrSYivB4cK1fLS3kgFA6XbM69Zi8bfH59RoziVY8Lb2LXJ40begwej8fGhppWS1la7g9UHi5gxMJw+8c6RS0nOpbXJKynCyJFTl1aflK6hgoHSrTksFkxr12KcNg2h11N8oorQLk4eNxJ6Pd5jRre6+WxLdhkVNVZmD47A29cD30BPinMvnZEBOCuYHlUH3fRKKhgo3Vr1hg04TCb85s7BZrFTVljTpfsLzmYYNx5LdjbWolPnvPd9RiE+HtqmU81CYoyUXGLBICncedBNXnn7dZaUnkUFA6Vbq/r2OzT+/hjGj6ckz4x0yC4rQ9ESw/jGvEHz0YHN7uD7/SeZlhTWVJguNMaX8qIarPWXTnG4xIayFIeLLq0gpbifCgZKt+Woq8O8di3GmTMQHh6U5Dnnut11zKUrPJOT0fj7U725ed4gPauUErOFa4dHNr0WEmMECaX5l84cfWK4MxgcUcGg11HBQOm2zD/+iKOmBr85cwAoyTXh6aPDGHTx6v0IjQbD2LHnbD77clc+fl66ZmcdN45gzjwG82Lz9dQRHeit9hr0QioYKN1W1XffoQ0KwtCwpLMkz0xItO9FPUISnHWKrPn5WPLyAKi12FmZcZK5Q/rgqTt9doFvoCeeBt0ll0ROCjeqYNALqWCgdEuOmhrMqWkYr5yJ0OlwOCSl+WaCoy/eFFEjw4QJANQ0TBX9cLCIaoud64ZHNWsnhCA0xkhJ7qUzTQTOvEFWsRmLzXGxu6J0IRUMlG7JnJqKrK3Fb85cACpP1WCzOAiJvnjJ40Ye8fHowsIwp6cD8OXufCL8vBgbH3RO29AYI6UFZuz2S+eDNynciM0hOV5afbG7onQhFQyUbqli+XJ0ERH4jB4FcEkkjxsJITBcdhnV6Rs5VV5N6uFirhseiVZz7vRVSKwvDpukvPDS+eBtTCKrqaLeRXexO6AoHWUtOkX1hnSC774boXXOwZfkmdFoBEERBqSU5B/KIGf/XmqqKvH09iYyaSBxw0ai1em7pI++UyZT+fnnrPl8LTaHhptGx7TYLjSmMYlsviRGNQD9wgxoNUKtKOplVDBQup2qr1aAw4H/vOuaXivJNRPYx0DxiUx+eOs1irIzQQi8fI1YampwfPkZvoFBTLz5DgZPm+n2JLNh4kTQain8YR2jp8ynf1jLIxb/MB90HhqKc00MpE/HH2S3QclhKM0CaQdjJEQMBg/DeffdU6clPsSgRga9jFuDgRBiNvBvQAu8KaV8tpV2NwKfAWOklO2fDqL0WlJKKr5YjvfIkXjGxze9XppnwsvnMB89sQJDQBAzf/kASRMuw9PHgM1iIWf/HrYsX8qqRf/h6JZ05i58GC+D+6aUtH5+2JNT6HdsH9H/91Cr7TQaQUj0eexELj8OG1+BjM+hpvSsh3vCoOvgsocgfFDHO48zb7C/oPK8rlW6J7cFAyGEFngVmAnkAduEECuklAfOamcEHgRcO0BW6dVqd+/GkpVFxNNPNb1WU2WhsmgzpbWpJIwcw5z7/g8v39Mf9DoPDxJGjiF++Ch2r/qG1Pfe5KPHH+amP/0V36Bgt/V1V5+BjM74hKjItqemQmN8ObT5JNIhES3kFZqpN0Pq32Hzf0GjheSrIXE2hCaBVg/lJyBrDez9FPYvg4n3wxWPO9/rgMRwI9/uL6TGYsPHQ00g9AbuTCCPBTKllNlSSgvwMXBdC+3+AjwH1LmxL0oPUf7hR2gMBvzmXtX02t4f0rDVphI9aAwTfnU3e0wZrMlZw46iHVTUVTS1ExoNI2Zfw/zH/oKptIRP//IY1RWtnz1wISprrHxENACOzZvabBsSa8Rab6eyuJ16QIV7YNFk2PQKjLgDHtwDN70Nw26ByOEQngLJc+GqF2Dhbhh+G6T/G96/Hmor2r73WZIifJESMlUF017DncEgCsg94+95Da81EUKMBGKklN+0dSMhxC+FENuFENuLi4s7v6dKt2ArLcX0/ff4z5uH1tc5J15+soDNy15HaMNZlniAmV/M4herfsFv1v2Gu76/i8mfTObmr27mfwf/R7XVuWInJmUoN/zhSUwlxSx//i9Y6zv/e8jSHbkcMEQgwyIw/fBDm22bkshtTRXt+wzenAm2erjrW7j2P+AX2Xp7QzBc9wrMex1yNsMHN3QoIKgVRb3PRVtaKoTQAC8C/9deWynlYinlaCnl6NDQ0PaaKz1UxdLPkFYrgbfdCoDDYeezf/0Fq8OONXAKej8dvxv9O9668i0+vvpjFs1YxMIRC9EIDc9ufZY5y+bwwYEPsDlsRCenMHfh7ziZdZTVb7yKlJ1XstnhkLy/+QSj44IImjWT6vR0HNWtLx0N6mNAoxGt5w02vgLL/h9EjYJ71kPcJNc7M/xWuPk9KNwLn9wBdtdOVusbbMBTp1ErinoRdwaDfODM9XTRDa81MgKDgVQhxHFgPLBCCDHajX1Suilps1H+ySf4TBiPZ79+SCl5490nqTqWiyNgPFH9+vK/q/7HT1N+ytg+Y0kJTmFi1ETuHno3H1/9MR/O/ZDkoGSe2/Ycd3x7ByeqTjBgzAQmzr+Ng+vXkZG2ptP6mna0mBOlNdw5MQ7jzBnI+nrM6ze02l6r1xAUZaC4pZ3IG/4Fqx6DQfPgzuXOb/wdlTzXOUo4vh6+f9SlS7QawYBwX3UEZi/izmCwDRgghIgXQngAC4AVjW9KKSullCFSyjgpZRywGbhWrSZSWmJOTcVWWEjgbbcB8MrGlyj9YTt1UT74MoYB/WPbvH5I6BAWzVzE81OfJ9+cz4KvF7AmZw3jbriZmEFDWLPkv5Tm57Z5D1e9t/E4oUZPZqdE4DNqFNrAQEyrV7d5TePZBs1GKOtfhB/+DINvhBvfAt0FnN42bAFMfAC2vQkHVrTfHudUkRoZ9B5uCwZSShtwP7ASOAh8KqXMEEI8LYS41l3PVXqm8g8/RNenD8Zp03hn/zvsWf4lHnYt82/5M0hBcFT7y0SFEMyOm80nV39CX7++/Gbdb3h59yvMvv+36D08+eZfz2GzXtgB9UeLTKw7XMzt42Lx0GkQWi2+06/AnJaGw2Jp9brQGF9qTVaqKxrabHsL1jwFg+fD9YtB2wkreqb/GSJHwFcLoaqw3eZJ4UaKquqpqGm930rP4VIwEEJ8LoS4qmGe32VSym+llIlSyn5SymcaXntCSnnOVxMp5eVqVKC0pHZ/BtUbNxF4662sP7mRxev/TXKekWHT52C3BQIdK0MR6RvJe3PeY37ifN7c9yZ/P/AiM+59gOKc42z54pML6uuiH7Px0mu4c0Jc02t+s2bhMJsxp6W1el1IQxK5JNcEmWvg24dhwJVw/aLOCQTgXF56w5tgrYXvH2m3eeNBN0eK1Iqi3sDVD/fXgNuAo0KIZ4UQSW7sk6I0U7p4MRqjkfprp/LIj48wOTcWnVbP+BtvoSTPjN5Ti3+Id4fu6aH14InxT7BwxEK+zv6af1d+QOKkyWxdvpTinOPn1c/Cylq+3J3PgjGxBBk8ml43TJiANjSEyuVftnptSLQvCCg+eAyW3gWhyTB/SecFgqYH9Ycpv4MDX8KRVW02TVannvUqLgUDKeUPUsrbgZHAceAHIcRGIcTPhBBdU+xF6ZXqs7IwrV5NwG238tjuv+FVKwg/Jhk2Yw7GoBBKck0ER/m2v1mrBUII7h56N4+Pf5z1eev5MvYAHj4+rF70Mg5Hx4+iXLLhGA4J/++y+GavC50O/2uuxZyWhq2srMVrPbx0+Ad7ULwtHXRecNsn4OmmWkUTH4SQROfowNb6FFCEnxdGLx1HVBK5V3B52kcIEQzcBfwC2IWzzMRIoO3MmKJcgNLFbyC8vFg73psdRTu4o24aSBh11TyklJTmmS+4UunNSTfz3JTn2Fa1m6yRGgozD7P7+687dI/KWisfbsnh6qF9iAnyOed9/3nXgc1G1det3NdaR6hjNyW1feDWjyGg5cJ2nULnAVf+FcqyYcfbrTYTQqiDbnoRV3MGXwDrAR/gGinltVLKT6SUDwAXv2aw0iNZ8vKp/PprPK6/in9lv83ksInU7shiwNgJ+IWGYSqtw1Jnd06xXKA58XN45rJn+MFnH+ZoTzZ8+kGHdid/sPkE1RY790zp1+L7XomJeKWkUPHF8nP3NEgJK+4n1LIdkz2UusChF/KruGbAlRA/BVKfhfrWP+wTI4wcLjJ16j4M5dLk6sjgDSnlICnl36WUhQBCCE8AKaXaF6C4Relbb4JGwztDy7BLOzdZJ1NfbWbkXGdVk8YTwjqr9PPVCVfz9GVPs7JfNpb6WlI/eMul6+qsdt5OP87UxFAGRfq12i7gppuoP3iQ2u1nrZNI+wfsW0rImPFAOzuRO4sQMONJqC1zrlxqRVK4kcpaK6dM9e7vk3JRuRoM/trCa8o4DHIAACAASURBVG0XXFGUC2A9dYrKZZ9jnz2ZLyp/5OcpP+fYug2EJwwgMmkgAMV5JoSAoKjzL9d8tnn95/HbGY+xL76SQ+tTyTm0r91rPth8ghJzPfdN699mO/9516ENDKT0rSWnX9z3GaT+DYbdSuicnwFQktNFq3eiRkHCNNj0qnOFUQtUWYreo81gIISIEEKMAryFECOEECMbfi7HOWWkKG5R9s67SJuNRYOLCPUOZZp9GOUFeYy66rqmswhK88wEhPug99C2c7eOmZ84n1kL7qHay8aHLz+JtY0ka43FxutpWVzWP6TFYy3PpPHyIvD22zGnplKflQU5W2D5ryF2Ilzzb7z9PDEEeHbNyKDRlN9B9SnY+X6Lbyc1LS9VwaCna29kMAv4J85SEi8CLzT8/Bb4o3u7pvRW9ooKyj/+mLrLR5HKYX49/NccSV2Hj38AieNP1+UpyTV3Sr6gJbcNvYPwuZPQl9Tz9zcW4pAtn1H8/qYTlJgtPDRzgEv3DbztVoSnJ6Wv/Rs+vs1ZbO6WD5p2F4fGnsfZBhei7ySIGe+sbtpC0AsyeBBq9FQjg16gzWAgpXxXSjkNuEtKOe2Mn2ullJ93UR+VXqbsg/8ha2p4d6SZKN8oZoZdTvbObQycPK3p2Mq6aiumsrqmzVru8Mub/4SmbzDa9BP8PfXpc5Ko1fU2Fv2YzZTEUEb1bXtU0EgXFETgTddT+c1q6ortcPvSZvWGQmJ8qSiqwVrf8aWt50UI5+igKg/2trzhLincqPYa9ALtTRPd0fDHOCHEb8/+6YL+Kb2M3VxN2fvvY500gjX6o/ws5WdkbUzHYbeTMnV6U7vSfOe8erCbRgbgXFp5x/1P4WXTceKbdby086VmAeHdTccpq7bw0AzXRgUA2G2EhO9A6+ngZNZgZHDzPENojBEpT/9+XaL/DOgzzDk6aGHVUGONIodDrSjqydqbJmrMzPnirDJ69o+idKqKTz7BUVnJp2PthHqHMm/APPanrSE8oT+hsXFN7U6vJHLvyubQ2DhGzr6GxFwjX6b/j8V7FwNgqrOy+MdspiWFMiI20LWbSQnfP4I2P42wO6+i9kDWObuSG/dMFOd04TdxIWD8fVB6FLJTz3k7KcKXOquD3PKaruuT0uXa3OsupVzU8N+n2mqnKJ3BUV9P6Ttv4xg1mC+9DvC7lN9RmZtP8fFsrvjZPc3aluSb8TbqMfhfQCVPF028+XYObUxjTpYPr/i9grfOm8qTE6iosfLQzETXb7TpFWfV0IkL8Z/xFBU7iij661/xHj6s6TxnY5AXnj66rs0bgPPM5JV/cPav37Rmb525oqhvcOet3FIuLa5uOvuHEMJPCKEXQqwRQhSfMYWkKJ2icvmX2ItLWDFJj7+nPzcl3sSBH9eg0epInjS1WVvnzuOuGZx6+hiYfNvP0J+s4br6cTy//XkW7/6QGQPDGBod4NpN9n0Gq/7k/NCd8RRCoyHqn88j9Hpy770XW0kJ4JyaCo01tny2gTvpvWDknXD4W6jMa/ZWYzBQK4p6Nlf3GVwppawCrsZZm6g/8LC7OqX0PtJup2zJEsTAAXzos5fbk2/HU3hwYH0q/UaPxdt4ejOX3e6gtMBMiAtlqztLypQr6DMgiagddcRpRiCDlzEq5ZhrFx/7Eb6417ly5/rFoHH+s9NHRhL939ewnSrm+O23U5+ZCTgrmJYWmLHbW17B5DajfuacytrxTrOXDZ46YoK81UE3PZyrwaBxOukqYKmUstJN/VF6KdOaNVhOnGDr9Ch0Wj03Jd1EbsZeaqsqGXjZ5c3aVpyswWGTbk0en01oNEz/+a+oqaokdGM0/iKZ1w88w3fHvmv7wqIM+Ph2CO4HC/7n/AZ+Bp8RI4hd8hYOk5ljN86n6PnnCQoSOGyS8sLWj8p0i8C+kDgbdrx7zjLTJHXQTY/najD4WghxCBgFrBFChAKdf4q40itJKSl96y100VEsCtjNzL4zCfEO4cjmDei9vIkf3rziSUleQ/L4AgvUdVR4Qn9IHMfAsn08O/ARRoSN4NH1j7Iiq5WTwypy4YP54OELdywD75YTzT4jRhD/xRcYZ11J2ZK3qXnkXgCOf7qa+uzsrq0LNOYXzk1oB5v/TonhRrKLq7HYuni0onQZV0tYPwpMBEZLKa1ANXCdOzum9B61O3ZQt2cvOVePoMpu5tbkW7HbbBzdtpl+o8ai8/Bo1r4kz4xWpyEwvGs3wVfUWPjQPhip9+LkymW8esWrjIkYw582/IllR5Y1b1xVCO9eA5ZquOMz8I9u89768DCi/vEPElZ8SdT1M9A6LOSv3UX23KvInHo5BY88QvXWre4PDP2ugMA42Plus5eTIozYHJJjJV08WlG6TEdOLksGbhFC3AnMB650T5eU3qb0rSVoAwN5IzqTpMAkhocOJzdjL3WmKpImTD63fZ6JoEgDGq07j/A+11sbjlFq1zNs3gJy9u8lb8dOXrniFSZFTeLJTU/y0aGPnA3Np+C9a6G62DkiCE9x+RmeAwYQ8YdHCB0QimXcHCKefgqf0aMxp6aRc+dPOb5gAbUZGW76DXHmM4bf7sxzVOQ0vdy0okhNFfVYrq4meh9nWYrLgDENP6paqXLBLHl5mFNTqbvmcjKqM1mQvAAhBEc2b8DD25u4YSObtZdSUpLnvjIUramssfJO+nHmDI5g5g3zCIvvx7p3FkOdjX9P+zfTYqbxty1/492dr8J71zlX5Ny+FGLGnNfzwuP9KC2y4HfDfKJefIH+aalEPPlnbAWFHF9wK+VLl3byb3iGYQuc/93zcdNL/UJ90WmEOuimB3P1q9VoYJKU8tdSygcafha6s2NK71DxySeg0fB5igmj3sjc+LnOKaKtm+g3atw5U0Q1VRZqTdYuTR4DvJV+DFO9jYXTB6DRaLnynoXUVFWS9sFbeGg9eOHyF7gy8jL+ue913rAWIRd8BH0nnvfzIuL9sdscTfkRjZcXgQsWkPD1VxjGj+fk409Q9sH/OuvXay4g1nnWwe7/Ne1I9tBpiA8xqBVFPZirwWA/EOHOjii9j8NioeKzZXhNuYyvqjZydb+r8dH7OKeIzCYSx192zjWNH46hXZg8rqy18nb6MWalhDOwj3OJa3h8P0ZfcwP7163mxL7d6MtzeG7fj1xVY+E/AQb+Wbat1eJ2rgiPdz6n6FjzhXtaf39iXnsV3+nTKXrmGUzr1p3/L9aW4bdD+XHIOV2pPjFCrSjqyVwNBiHAASHESiHEisYfd3ZM6flMq1ZjLy/nwJQYLA4L8/rPA+Dolo3ovc6dIgKaduYGd+Eeg7fTj2Gqc44KzjRh/q0E9olk9X9fwLJ4FjpbLX+77mNuS76N9w68x2MbHsPqsJ7XM30DPTH4e3Ayu+qc94ReT9Q/n8dr4EAKHv0D1qKi83pGmwZe41wFtfv06CMp3EhOWQ01FlvnP0+56FwNBk8C84C/cbqM9Qtu6pPSS5R//BH62Fj+57ufxMBEBgYNREpJ9s6txA8bec4UETh3HjtLNui7pI9VdVaWbDjGzEHhpET6N3tP7+HJlVeOoLK0jNSCaPj5SjSRI3h07KMsHLGQr7O/5oG1D1Bj7XhNHyEE4Qn+54wMGmm8vYl68QWkxcLJp/9yXr9bmzwMMGgeZCx3rojidBL5aFEX745WuoSrS0vTcO481jf8eRuws73rhBCzhRCHhRCZQohHW3j/XiHEPiHEbiHEBiHEoA72X+mm6o4coXb7DhzXTmdf2X6u6+c8tObU8WzM5WXEj2w58VqSZ+7S/QXvpB+nqs7Gg2eNCnA4IO0fRG99lLHxDvad8uVoVjHg/CC/e+jdPDnhSTYVbOLuVXdTUVfR4WeHx/lRVVJHranlw3U84uIIufdezGvWUL15c4fv367ht4HFDAe/Bk4fdKNWFPVMrq4muhv4DFjU8FIUsLyda7TAq8AcYBBwawsf9h9KKYdIKYcD/8B5gI7SC1Qs/Qyh17MyxYZO6Lgq4SoAju3cBkIQP3zUOdfYLHYqimq6LHlsqrPy1oZjzBgYxuCoM0YFNWXw0QJY9wwMvYWJf/6U8IQBrFr8MqaykqZmNybeyIuXv8ihskPc+f2dFJoLO/T8iITGvMG5U0WNgu76KfrISIr+/izS3slnIMROAL9o2O/cQxEb5IOXXqMOuumhXJ0mug+YBFQBSCmPAmHtXDMWyJRSZkspLcDHnLVRraHeUSMDoAqm9wLSaqXq668xXDGNz4tXMzl6MsHezgNesnduo0+/RAwB5+7WLS2oRkoIje6aAnXvbjxOZa2VB6efUZk0bwcsngpZa+GqF+D6RWi9jcx94HfYrBa+/tc/sNtO5wmmx05n0cxFlNSU8JPvfkJWRZbLzw+N9UNoBCezW6/+ovH0JOz3D1N/+DCVK746r9+z9ZtrYPD1kLUGasrQagQDwlQSuadyNRjUN3ygAyCE0NH+B3cUkHvG3/MaXmtGCHGfECIL58igxeWqQohfCiG2CyG2FxcXu9hl5VJlXr8Be3k5+ZMHUFJbwnX9nd8RaiorKMw6QvzIlrewNCWPu2BkYK638eaGY1yRHMaQaH+w22Dd3+Gtmc7llj//3lm6oeE85qDIKGbd+yAFhw+wZsnrzXYKj44Yzduz38Yu7dz53Z3sPrXbpT7oPbWERPu2GQwAjLNm4TlgAGVLlnT+DuXBN4LDBgedgSYpwsjBQhUMeiJXg0GaEOKPgLcQYiawFOiUryFSylellP2AR4A/tdJmsZRytJRydGhoaGc8VrmIKr/8Em1QEF8EHSfQM5ApUVMAOLZ7B0hJwsixLV5XmmdG76XFL9irxfc703ubjlNRY3XmCk4dhCWzIO1Z54fjvRsg+tyAlTxxCmPn3cS+NSvZveqbZu8lBSXx/pz3CfAM4O5Vd/Nj3o8u9SOyfwAnj1Vht7a+TFUIQdDPf0790aNUb0jv0O/Zrj7DISihaapoUB8/Ssz1nKpSpcl6GleDwaNAMbAPuAf4llY+uM+QD8Sc8ffohtda8zHOFUtKD2avrMS8bh0+c2axrvBHZvadiV7rXBmUvWMrvoFBhMUltHhtSb5z57HQCLf2sbrexhs/ZjNzgB/DjvwHXr8MyrJg/hK48Q3wbv0Mg8tu+QkJI8ew7p3FZG5rntSNNkbz3pz3iPePZ+HahXyV1f73qcgBAditDk61c/KZ/1Vz0YWGUvb2Etd+SVcJAYPnw/H1YCoiJdKZx8gobD2PoXRPrq4mcuBMGP9aSjlfSvmGbH88ug0YIISIF0J4AAuAZnsThBBnLtG4CjjqeteV7si0ejXSYuHouD7U2mqZHT8bALvNyvG9O4kfOQYhzv2wl46GMhRdsL/g/c0nSKnbycsV98H6F2DIzXD/dueooB1Co+GqhQ8TntCfr156luxd25q9H+wdzJJZSxgdPpo/bvgj72a828qdnPr0dyauC46Wt/1cDw8Cf/ITqjduov5oJ/8zGnwjSAccWM7AhmBwoEAFg56mzWAgnJ4UQpQAh4HDDaecPdHejaWUNuB+YCVwEPhUSpkhhHhaCHFtQ7P7hRAZQojdwG+Bn17Qb6Nc8qpWrkIfE8NX+gOEeIcwMsy5sSz/0AEstbWtThFVldZhrbO7PV9Qe/Iwievu5QOPv+Ol18GdK+D6/4IhxOV7eHj7cOMfniYkpi8rXvgbx3fvaPa+r4cvr814jZl9Z/LP7f/kxR0vtjrX7230ILCPgYKj7R8hEnDjDaDTUfFFmwv9Oi4sGcJSYP8y/Lz0xAb5kFGgjjTpadobGTyEcxXRGCllkJQyCBgHTBJCPNTezaWU30opE6WU/aSUzzS89oSUckXDnx+UUqZIKYdLKadJKd1YjlG52OyVlVRv2oTXjGn8mL+eK/teiVajBSB751a0ej19Bw9r8dqSPOc0iduOuqwpg+8exXPRRMbJPeSP+C38aiMkTG3/2hZ4+foy/09/IahPFJ8/9xS7V33b7H0PrQfPT3meW5Ju4e39b/PExiewOVre2Rs5IIDCrAocjrYH47rgYHynTqVyxQqkrZN3CQ++AXK3QEUOKZF+ZKiRQY/TXjD4CXCrlLLpfD8pZTZwB3CnOzum9DymtevAZuPgsADq7fVNU0TgXFIakzIUvVfLyeGSPDNCQHBkJx/IbquHja/Af4Yjty5iOZfz+8h3ibruz+ecStZR3kY/bnnqOeKGjWTNW6/xw5uvYbOeXnaq1Wh5bNxj/GrYr1ieuZyH1j1Ene3cxGzkAH+sdXZK89rf+Rtw/TzsJSWYN2y4oL6fY/ANzv9mfEFKpB8nSmuoqju/UhvKpam9YKCXUpac/aKUshjomnoASo9hWrkSXWQfVugyCPcJZ1iocxRQVpBPeWEBCa3sOgbnSqKAcB90HtrO6YyUzlILr46FVY9B1Gg+H/sJv639OXfNGtc5zwA8fQzM+/3jjL7mBvas/pb3fv8AuQf2Nb0vhODXw3/NY+MeIy0vjXtW30OVpfm37sj+zoR1wdH2dzH7Tp2KNiiIys6eKgpKgMiRsH9ZU1mOQ2qJaY/SXjBoeR98++8pSjN2k4nq9HQ8p1/OhsJ0ZsXNQiOc//sda0iyJoxoPRiU5HbiGQZ5O2DJbFj6U9D7wB3LqL1lKX/foWFiv2DGxAV1znMaaDRapt7xc278w1M4bFY+feoPfPOf5yk6dnoD2oLkBfxj6j/YW7KXX6z8RbPyFb6BXviFepN3uO0kMjiL2PldfRXmtWuxmzr5w3rIfCjcwxBv514flTfoWdoLBsOEEFUt/JiAIV3RQaVnMKemIq1WDgz1x+awMTvuzCmirQRHx+IfFt7itfU1VkxldReePK4qgM/vgTevgLJsuObfcM966D+DD7fmUGKuP7cGUSeKGz6Kn/7zVcbOu4msHVv54NEH+fSpP7B71bdUnipidtxs/j3t32RVZPHzVT+npPb0oDxmYBD5h8uxt3AGsd1mo7qinNL8XEpyjmMdNQKTBk59922zaakLlnI9IAg+9hUhvh4qb9DD6Np6U0rZSWNypbczrVqFLiyM5Z4HiPKNYnDIYADqa2rIO5jBqKuvb/Xa4lznXPl5J4+ttbDxZdjwknM37WUPweT/A0/n/eqsdl5Py2J8QhDjEoLP7xku0nt6MfnWnzLm2hvZt2Yle374jjVvvQaAMSSUoMhofut3NWsOp/Hn/T/jxsT5eEkdlSfLqSkv5PNnNyBEHXVmE7UmE3VmE5baFqqiJsfCsvdh2fsYAgIJiIgkJCaWyKRBRCen4BfaXjWZFvhFQux4xIEvGRR5hQoGPUybwUBROoOjpgbzj+vxvv4atpz8mjtT7mzaS3Bi704cdjsJI1o/RbW4YcNVWGwHg4GUkPE5rHoCqvJg4LVw5V+cB76f4aOtORSb6vnPghEdu/8F8DL4MubaGxl9zQ2UF+ZzbNcOTmYdobywgIqsAlJqjCAlu/Y4j7fUeXhgt2opOuZDYHgQ3n7+BEVG42U04u3rh5fRiJfBF41WhxBQuvQzqrZvw+9X91BVUU55YT4HN6SxZ/V3AITE9CVpwmSSJk0hMCLS9Y4PmgffP8LkYWU8l+mg3mbHU6e+M/YEKhgoble9eTOyvp4DKb7Yas6eItqGl8GXyMSBrV5fnGPCN9ATb+O55xu0qiIHvn4IMn+AiCFwwyKIO/fktMZRwdj4ICb0c++ooCVCCIIiowmKjG72upSS3QU7Wbj2Aby9fHlz1mtsX1yMzeLg5j+2f65ytfAg55uVRAWE4bfAufDP4bBTknOC3Ix9HN2aTvqnH5D+6QfEDx/FyKvm0XfI8BY3/DUz6FpnMLBs4BnHBI4WmZtXdFW6LVfLUSjKeTOnpqExGPjCcJi+fn1JDkoGQDocZO/aTtzwUWi0rX+7LM4xEerqqMBhh83/hVfHw4lNMOcf8Mu0FgMBwKfbcymqquc3bswVnA8hBCOiRrF47lvU2mu56/u78O0nKM4xUVPV/toNn9Gj0AYFYVq1quk1jUZLWFwCo666jgVP/YNfvvYOE2+6naJjWSx75nE+fOy35GbsbfvGfpEQO4GEotWA2onck6hgoLiVlBJzWhq6caPZUrKDWXGzmr59nsw6Sm1VZZtLSi11NipO1bgWDEoy4a0r4ftHnYfR37cFxt0DmpYDTb3Nzn9TsxgTF3hRRgWuGBg8kCWzluCQDl4r+QcAuQfL2r1OaLX4XjEN8/oNrW5AMwaHMGH+rdz96ttcec9CzBXlfPr0H/niuaeoPNXGUZqD5uFRdojBHifViqIeRAUDxa3qDx3CVlREZoo/Duk4ZxWREBriWjjIplFJrhkkbQcDKWH3h7BoirOg3I1vwe1LISCm9WuAT7fnUVhZx4PTE9ufHrmIBgQO4O3Zb2PyL6ZOb2bvjmyXrvOdPAWHyUTt7rZLZuv0eoZccSU//9ciJt92F7kH9vPu7+5j57df4nC0cGDOIGc1mTuMu1QSuQdRwUBxK3NaGgArQnPp59+PAYGnp2Oydm4jMmkg3r6tf9A3Jo9bDQZ1VfD53bD8VxA5Au5Nd66Hb+fDvd5m57/rMhnVN5BJ/S/NUcGZ4v3jeWf2O5wMyyQ/o5K9hfvavcYwcQLodJjTXCuXrffwZOx187nrhVeJHpjCunffYOnTjzU7vQ1omiq63L6RA4VV2Nspk6F0DyoYKG5lXpeKdlAS62v3Myt+VtPrprISio9ntzlFBM5gYPD3wODvee6bhXth0WTY/zlM+xP8dAX4n3N+Uos+25FHQWUdD04fcEmPCs4U4xfDbXOvRm/35C9LX2r3kByt0YjPyJGYf3QtGDTyCwnj+kefZPavH6IoO5P3f7/QedbEmQbNI6Iuiz7WHLKK2y+ToVz6VDBQ3MZWVkbt3r3kDg5DIptNER3buR2g3WBwqrXk8YEVzgNn7Fb42bcw9eFWcwNns9gcvLYuixGxAUwe4Ho10kvB0OH98fDRMqB8JPesvocdRTvabO87ZTL1hw9jPXmyQ88RQpAydTq3//0lDIFBfP73P7Nl+dLT1VUbpormarawJ7f9MhnKpU8FA8VtqtevByn5PqqYpMAk4v3jm97L2rkVv9BwgqNjW73eWm+n4mR182AgJaQ9D5/+BMIGwd3rIHZ8h/q1bGce+RW1LOxGo4JGWq2GfsPD6Fs+mAjvSH71w6/YUril1faGKc5T5Mzr15/X84KjYrjtmRdInjSVDR+9y/evveTc1ewXiYwZzzW6rezNU0nknkAFA8VtzGlpiOBAVnoebVah1GqpJ2f/HhJGjm7zw7gkz4w8M3lsrYVlv4B1f3UeOHPXN2BsuYRFa6x2B6+uy2RYtD+XJ3bPI1T7jQzDVufg6ZgXiPKN4r4197Exf2OLbT0HDEDXpw/VHZwqOpPew5O5D/yOiTffzoEf17Lsmcepr6lGpFxPosih9Hj7+Qvl0qeCgeIW0m6nOn0jpwZHIYVgVtzpfEFexj5s9fWtHmTT6HTy2A/qKuH9G2D/ZzD9Cbhh8XmVmP58Zx555bU8OKP7jQoaRScH4umj4+TuGpbMWkK8fzz3r72/xXOVhRD4Tp5MdfpGpOX8a0sKIZhw463MXfgwBUcO8ulTf6Qm+nIAEkvXYGmhZpLSvahgoLhF3YGD2CsrWR9VRUpwCjHG08s8s3ZuQ+fpScygtmsdFudU4W3UY9BXwbvXQN5W57LRyf/X7mqhlljtDl5em8nQaH+mJZ1HbZ5LhFanIXl8H7J3F+NpMfDmlW+SGJjIg+seZE3OmnPa+06dgqOmhpqdOy/42QMnTWXew49TVpDHx/98iVzfUcwSmzl0Ui0x7e5UMFDcojo9HYDvQvKbJY6llBzbtY2+Q4aj82i7vERxjpnQSD3inblQfBgWfORcNnqePtvhHBU8NOPS3lfgipQpkTjskgPpBfh7+vPGlW8wKHgQv0v9HSuPr2zW1jB+POh0VHfSgTfxI0Yz/7G/UF1RxvdHQoh1nOTYobZXNimXPhUMFLeo3rgRc99QKg3Np4hKc09QVXyq3VVENoudskIzocVfgOkk/OQLSLzyvPtjsTl4ZW0mw2MCuDype+YKzhQYYSAqKZCM9fk4HBKjh5HFMxczNHQov//x93x6+NOmthqDAe/hw6jetLnTnh+VPIgbHn2K2jo7S08MQbv/8067t3JxqGCgdDpHTQ01u3axq6+DYaHD6OPbp+m9rJ3Og2zi26hSClCccRjpgDD9UfjpV87yEhdg6Y5c8itq+U03zhWcbcjUKMxl9ZzYXwqAQW/gvzP+y+Soyfxl8194edfLTUtBDeMnUHfgAPaKzlsGGpU8iOsf+TPlVh9O7jpErVmdfNadqWCgdLqa7dvBaiU1srzZFBE4TzULi+uHMaiN9f0VORR97qzxH/6TJyFy+AX1p95m59W1mYyIDWBqN11B1JK4YSH4Bnqya9WJpg99H70P/5r2L24YcAOL9y7mzxv/jNVhxTBhPEhJ9ZatndqHmJSh9BudhLlex9I/P9zy2QpKt6CCgdLpqtPTcei1HI7WcGXc6amdWlMVBYcPkTCqjSmiqgJ49xqKqqPw9dNg6D/0gvvz6XbnbuOekCs4k1arYeSsvhRmVpJ38PSRmDqNjicnPMm9w+7li8wveHDtgzgG9kfj40P15k2d3o/QGT/jmqiDlOTn8dVLz2JvpTCecmlTwUDpdOb0jWT19WRI9CjCfE6v2jm+ewdSOlrPF5iKnKuGqksp0o8nvP+F7w6ut9l5raEGUXfbbeyKQZMi8Q3yJH1ZJg776eWdQgjuG34fj49/nI0FG/nJ6rsQIwZT04l5g6Y+JCVTZghhfGwFx/fs5Ic3Xz29U1npNlQwUDqVtegUlsxMNsfUnTNFlL1rOz7+AUQktHB2QF0VfHADVBVSM+9TTJWS8Hi/C+7PJ9tyKeyBo4JGWr2GSTcOoDTfzL7U/HPevznpZl6f+TrFtcV8aNiP5fhxd4UPUAAAIABJREFUrIWFrd5PSonVYu/Qh7m/t57tPlOZ6LOP8XOuZP+61Wxe9vF5/T7KxePWYCCEmC2EOCyEyBRCPNrC+78VQhwQQuwVQqwRQvR1Z38U96ve6NwJuz9Bw4y+M5ped9jtHNu9nfjhoxGas/63s1mc5SWKD8Et71NkdQaLCw0GdVY7r67LZExc96hMer76jQyl7+BgNn2RRWn+uUXjxvcZz0dXfUTRIOco7etP/ob9jNLUFUU1bPvmGEv/vo3XH0hl8cI0Xn8glc+e286etblYLS2UsT5LRfwc57P61pIydTobl/6P/ak/dNJvqHQFtwUDIYQWeBWYAwwCbhVCDPr/7Z13eJRV9vg/d2Yy6b2TAkkInUDooTdpUixgVywguuLq+nXddfmprGVdwbXiuvaKIipiQAHpvQRCCyRAGkkgvSeTmUy5vz8mAqFlkkwMCe/neeZJ5r33Pe9580zeM/ece865aNpBYICUMgb4AVjUUvoo/DFU79pJpauagN6D8HM+75Y5eyIZQ3X1pfECKWHVE5C+Baa9A53HkZ9RgVAJ27ubXYFl+7LIrzC021XB7wghGHtfd7QuGn757xGqyw2XzAlzD+P1B75H7+5IwfaNzP1tLkcOprHqnUMsfWEP+1ZloFKriBkdypCbIokZE4bFLNmx/BTfvLCH7JSrN9TpHNWFBEsXTEk/ccPD8wnv3Zf1Hy4hJ+VYS922gp1pyZXBICBVSpkupawFlgEzLpwgpdwspfx9+8EeIBSFNou0WCjfuYNDHS1Mipxcbyz9YAIqtYaOvS9qOr/lVTj8DYx+FmLvBiA/swLfEFcctE1vtK43mnlvS+v1Nv6jcfHQcuOfYqiprGXF4gOXXSG4OrrhN3w8UTXDiVg/lu0fnCYns4hB0yOY/eowbn2mP8NmRtN/UieG3dqZ2/4xkJv/rx8Ojmri3z7E0S05V7x+v47e/GoejGNxMuqyTKY9+Xc8AwJY9carVBQVtOStK9iJljQGIUD2Be9z6o5diYeANZcbEEI8LITYL4TYX1hYaEcVFeyJ4eRJREkZxyLVjA8fX28sPTGB0O49cXRxOX8w8UvY+hrE3gOj/gaAxSLJz6wgMKJ5TdaX7s2isLL9rwouJLCTBzP+EovRYGb5vxLY/FUy6YcKyU0rJ/VAAZuXpvCbeRInwu8gXNuB9L47+V/vp/lUu4gKh+LLyuwQ7cWsZwfSqbcf25adJGnr5Q1CpJ8rO7XDrG+OrcTJzY0Zf30OU20tP7/+CkaDvqVuW8FOXBMBZCHEPcAAYPHlxqWUH0opB0gpB/j7t5994u2NqroSFJohA/By8jp3vCwvl+KcrPqF6VI3wKonIWocTH3rXK2h0rxqjHozQc2IF1QZTPx3cypDIq+PVcGFBEV4cufzg+k+NJiT+/JZ87+jrFh8gHUfJXFqXz5BkR70OfIeU7ufZfG8BfxtyDMcyD/A9JXTeffgu+iMl+YJODiqmTyvF516+7J12Umyj1/qMhJCENqxM0nq7nB8JWAtfz3l8acpyExn3f/eUXYYXeO0pDE4A1zYhDa07lg9hBDjgQXAdCnlpc5OhTZD3pZ1ZPvB6Nhb6h1P3W/dzth54GDrgdzDsHw2BPaA274AtcO5ufkZ1oJnzQkef7ojg+LqWp6Z1K3JMtoyzu5aRt/djQf/M4Jbn+nP1Mf7MOvZATz0nxHc+OQgglwr0e3dg0qouKv7XcTfFM/Y8LF8eORDpv00jeUnllNrrl/hVKVWMWFuL7yDXFn/+XF0FZdWQO3f0Zsf9QMgPwmKTgEQ1X8Qw2+/lxO7tpEQ/+Mfcv8KTaMljUECEC2EiBBCaIE7gPgLJwghYoEPsBoCxbHYhrEYDHDoOMcjHRgbPrbeWNr+vfiFd8IzIAjKsmHpbeDkBXd9D471g8T5mRVonTV4BbjQFEqqa/lwWzoTegTSL9y7yffTHnDQqgmK9KRjT18COnqg1lj/3V2HDEG3dy+yLjksyDWIRSMX8eXkLwlyC+KlPS9x40838l3Kd/VWCg5aNRPn9KS2xsTGz48jL+p9PCjChzXmutXfsZXnj980iy5DhrPj2y/JPq70PrhWaTFjIKU0AfOBdUAysFxKeUwI8aIQYnrdtMWAG/C9EOKQECL+CuIUrnEqE/ahNpoRg/ri6uB67riuopwzKcfpPGAw1JTB0pnWJjX3/AAewZfIyc+oILCTO0LVND//+1tS0dWaeHpi1ybfS3vHdWgclqoq9Mfq7/SJDYjl68lf88H4DwhwCeDlvS8z/ofxvJ7wOtmV1vCfb4gbw2d2Jut4Ccm76+crxIR6UqrxJ8s15pyrCKwupImP/BmvoGB+eXsR1WWlKFx7tGjMQEr5q5Syi5QySkr5St2x56WU8XW/j5dSBkop+9a9pl9dosK1Sur6HzCpoM+Eu+sdzzi4HyktRMX2g+/ugeI0uONrCOh+iYxavYmSs9VNDh6fLavhi92nuaVfKF0Cm7cttT3jMtjqrrtcFVMhBENDhvL15K/5fNLnDO0wlKXJS7lxxY08tvExNmVtosvwAIIiPdmzMg2DznjuXEeNmn7h3qyRg+tcRannxrTOLkx76lkMOh2/vrsYi6Xh3AWFP5ZrIoCs0Pap2bWX1HANw6Lqu4hSE/bg5uNL4JE3IHM73PRfiBh5WRn5GRVIiyQoqmnG4O0Np0DCk+Mvk+GscA6Njw+O3bpRvfvKdYqEEPQP7M/ro15n7a1rmddnHseLj/PE5ieY8MMEsvrso6bKyL7VGfXOGxThw+eldfWkjv9Ub8w/vBPjHnqUrKQj7P7+G7vfl0LzUIyBQrOpzMvBN7scY//uOFwQDDbWGsg8kkhUoArxe7vKmNuuKOdsahlCQHBk441BakEV3x/I5u4h4YR6Ny3ecD3hGhdHTWIilpqaBucGugbyWN/HWD9zPe+OfZcY/xi+yP+AYwE7Obw5i+V7fj4XWxgc6UOu9KXMr1+9uMHv9Bo9nl5jbmDPiu/IOHTA7vel0HQUY6DQbA7++jkAUTfcWu941tHDmAwGOldtgv4PwPCnrionN7UM31A3tM6aRuvwxvoTODuoeWxM50afez3iGjcEaTQ2qhWmRqVhdNho3hn7DhtmbWDA1HDMKhO7Vp1kzPIxvLDrBdTOp9GqBQkuoy5xFf3O2AcfwT+8E78u+Q8VRUre0LWCYgwUmk3x1k1UuajoO7z+ltK0jT+gVZkIjRkEU16/at9is8lCfnoFHTp7XXHOlTiSU8avR/OYMyISPzfHRp9/PeLSvz9oNOj2NK2KqZ+zHw8OnM3A8Z2JLunPFK+bWZOxhjnr78e985u8UVtFkUoFxy7tgOagdWTaU89iMRn55e1FWMxK/OBaQDEGCs2itKaUwGO5VMR0Qq057yKS2ftJO3yYTn4Sze2fgfrq3/YLsyoxGS0EN9IYSCl5bW0KPq5a5oyIaNI9XI+ca4W5q3n9Dfrd0BEHrZrBZ6ay5bYtvDj0RbwcvchxXscN4aE8fWopp0pOXnKed3AIN8ydz9mTyez+8dtm6aBgHxRjoNAstm9fincVdBg75fzB0kxyP74fncmBzjP+BI5uDco5m2ptxxjcuXHxgs0nCtiZWszjYzvj7uTQ8AkK57BHK0wnNwf6jAsjLbGAmkILN0ffzEuD3qc67SkmO8ewQ23i1lUz+evWv5JZnlnv3G7DRtFz1Hj2rlhOzvGkZt6NQnNRjIFCs8jeuAqAqBvqXES6Evh6JqklLqhUKiLixtkkJze1HE9/Z1w9bXfzGM0WXvklmUg/V+4ZolQ/byyuQ+Ps0gozZmwoGgcVhzdacxH6d/JGK4Nw1/6ZtbnFzHGJYGvOVm6Ov5m3DrxVL5Ft7IPz8AoK4pclrys9lFsZxRgoNJnsymx8jmRTHeqDNjgYjHpYdheyNItU2Y3QHr1xcm14VSAtkty0MoKjG+ci+nZfFmmF1Tw7pTsOauWj3Fice/e2SytMZzctXeOCObE3D11FLY4aNYMifNmQXoNX9CT+nHGUX2fEMyViCp8kfcK0ldPYlrMNAK2TMzf++Rl0ZWWs/+BdpX5RK6L8Byk0mdXJK+iRLfEZORYsZlgxF7L2UDTsX5QWltBlyHCb5BSdqcJQbSKkEcagvMbIm+tPEhfpy/juAQ2foHAJwsEB54ED0DUzbgDQd5y1/8HvZa5HdPYjrbCa4qibQVeM35mDvDL8Fb6a/BWejp48tvExXtz9IjqjjsDIzgy/8z5O7dvF0Y3rmq2LQtNQjIFCk7BIC8kbf0RrgoBR42Ht3yE5Hia9yskiR4RQET0oziZZOSnW8gSh3WyvJfTe5lTKaowsuLH7dVOiuiVwjYuj9vTpq7bCtAWvQBc69fYjaesZjLVmRnSxNjbaZOwNLn5w2NoGs29AX5bduIwHej7ADyd/YNaqWZwoOcGAG2+iY0wsm7/4iOKcrGbfl0LjUYyBQpPYl7eP0OQipEaNi2kf7PsQhj6OHPwIJ3bvIKxnb1w8bfumn5NSilegC27eTjbNzyrW8fnOTGb2C6VXSPP6HlzvuMZZDfblSlM0ltgbwtBXGzmxJ4+uge74uzuyLb0cet0KJ9ZYa1MBWrWWpwY8xScTP0Fv0nPvmnv5LWs9kx97CgcnJ355exGm2kuroiq0LIoxUGgSK1NX0jdThUu3MFTbX4ZeM2H8ixRlZVKae4aucSNskmM2WTibWkZoV9tXBf9em4xaJZRidHbAMToatY/PVUtT2EpwZy8COrpbA8kShnf2Y2dqEZbet4PZcEnOwcCggSybuoyu3l15euvTfJL+FRMffYLCrEy2ffNZs/VRaByKMVBoNJW1lSQk/UZYgRl3TRJ0GmGtOaRScWL3DoRKRWcbXUT5mRWYDGabXUQ7ThXx69E8Hh0dRaCHbSsJhSsjVCpchwyhes/uZgdvhRD0GR9GWb6O08eKGRHtR0l1LUlEQmAvSPzqknP8Xfz5dOKn3Bp9Kx8d/YhPqlfSd9JUDq5ZRfrBhGbpo9A4FGOg0GjWZq6le6q1D5Fr92C4YyloHJFScnLPdsJ6xuDiYZv7JielFASE2LAyqDVZeD4+iY6+Ljw8MrJZ96BwHpe4IZgLi6hNvbR0RGOJig3AxUPL0S1nGNXFHyFgY0ohxN4LZxMh79J+Bg5qB16Ie4HHYx9nVfoqfgw+hG94R9a9/7ZS7voPRDEGCo1mxfFvGJZuQe0Mjk+sACfrg7/wdAaluWfpauMuIoCclBL8w9xxcm04YezjHemkF1azcHpPnBzUTdZfoT6ucUMB7OIqUmtU9BwZQtaxYtTVZvqFe7MxJd9aoFDteNnVAVhXFQ/HPMwLcS+ws2A3O2JLMeiqWfv+W0iLpdl6KTSMYgwUGsWxzI0cLz1Fr3SJ26ixCK/Qc2PJO7agUqttdhHV6k3kZ1TYFC84U1bDuxtTmdAjkDFdla2k9kQbGoK2Y0eqtu+wi7yeIzqgUguObs1hXPcAks5UkGd0ge7T4Mgya3OjKzCzy0wWj1zMftNxTvd3IPPQARLXrLKLXgpXRzEGCrZTVcDy9X+hd7ZEYxC4TTrfi8hiMZOyYwsRsQMa5SKymCXhvRpuWv/y6uNIJM9P69Fk9RWujOuokej27rWppHWDsjwdieoXQMquXEZH1m0xTSmAfveBvhySV1/1/AmdJrBo5CI2+5ykPMyBbd98RkFmerP1Urg6ijFQsI2aUsq/uolfNRZuKQgFjQbX4cPODWclHaGqtIQeI8bYLPL00SIcnNQEN9DMZuvJQtYk5fH42GilV0EL4TZyFLK2luq9e+0iL2ZMKLV6M+b0KkK9ndmYnG/daODdCRK/aPD8CZ0m8Nqo11jX7TQGrYXV7yzCaNDbRTeFy6MYA4WGMVTB0lmsMpxBrxJ0T7XgMnAAarfzpSaOb9uEo6srkf0G2SRSSsnppGLCu/uca9R+OfRGMwvjjxHp56pUJW1BXAYOQDg7U71tm13kBUZ44B/uztGtZxjfLYAdqUXUmKQ1kJy5HQovrWR6MRM7TWTBmIVs6pVL6Zkctnz5sV10U7g8ijFQuDrGGmu9oTMHWB4cxSi6QmYO7mPOrwBq9TWc2reLrkNGoNFqbRJblFNFdXktHXtf3UX01oZTZBRV89JNvXDUKEHjlkLl6IhrXBxVW7fZpT6QEILeo0Mpza1miLsbBpOFrScLod9sUGutSYo2MD1qOndO/BNHI8s5smEtp/btarZuCpdHMQYKV8ZYA9/eCRnbSBj3DBn6QmYVW7d0uo0efW7aqb27MBkMdB/ZGBdRMQDhPa9sDI7mlPPR9nRuHxDGsM5+TbsHBZtxGzkS45kz1Kbbxz8fPTAAJzcHzCcq8HHV8svRXHDzt2YkH/rGGj+wgft73k+PGTdS5GFg1X8XU1lSZBf9FOqjGAOFy1O3IiB9C8x4j69qz+Ll6EX4kQK0kZFow8PPTT2+bROeAYGEdLU9uHs6qRj/cPcrlqw2mi088+MRfF21/OPG7s29GwUbcBtpzRqv2mofV5HGQU2P4R04faSIG6P82Zicj95ohkEPg7HaahBsQAjB04OfQUztSW2tgS8XP6tsN20BFGOgcClGPSy7G9I2w4wlZEQOZUv2Fu4JvxX9gUTcxow+N7UsL5espMP0GDnO5oJxuopa8jPKr+oi+mBrGsm5Fbx8Uy88nZWmNX8EDh064BgdTZWd4gYAvUaGABCjV6OrNbPlRAGE9IPQgVZXkY0PdZVQ8c+piymO80afnsuKb962m44KVlrUGAghJgkhTgghUoUQf7/M+EghRKIQwiSEmNmSuijYSF1PAtI2wfR3IfYevjz+JY5qR6YWhYLRiPsFLqIjm9YhVCp6j5tg8yXSDxUiJXTud/l8geTcCt7ZmMqNvYOZ0DOouXek0AjcRo9Cd+AA5qoqu8hz93Eioq8/5cdKCXDRsvpIXXXUwY9ASTqkbrBZloPagecefp/CMEH66g0cPLzVLjoqWGkxYyCEUAPvAZOBHsCdQoiL/QhZwP2AbetFhZbFqIfv7oa0jVZD0O9eimuKiU+NZ3rUdNiyG7W3N86xsQCYTUaSNq8nqv8g3H1s9+mnJRbgFeiCTwfXS8b0RjN/+e4QHs4OvDijp91uTcE23EaNAqOR6u3b7Saz9+hQDNUmpvt4sSmlgJpaM3SfDm6BsPf9RsnycPRg7tOLMThJVr+7mKLyfLvpeb3TkiuDQUCqlDJdSlkLLANmXDhBSpkppTwCKA7A1qa2Gr693fpNrc4QAHyb8i1Gi5F7Im+jassW3MePR2isze1P7dtNTUU5fcZPtvkyNZW1nDlZRlSs/2XdSm+sP0lKXiWLZvbG1832FpgK9sE5Nha1ry+V69fbTWZIFy98OrjSocCEzmDmt+N5oNHC4HnWFejZQ42SFxXUjbg5D+FcKXlr8WMYzUa76Xo905LGIATIvuB9Tt2xRiOEeFgIsV8Isb+wsNAuyilcQE0ZfHUzZGyDm963ZooCNaYavjvxHaPDRuOfdAaLTof7xInnTjuyfg2eAYF0jIm1+VIZh4uQFknUZVxEu9OK+Wh7OncPDmdst8Dm35dCoxFqNe7jxlG1ZSsWg8E+Muu2mdYU1NDH2ZkfDli7oTFwDjh6wI43Gi1z3PBb8Rkdi/cpPa998ZTSLtMOtIkAspTyQynlACnlAH9//9ZWp31RVQhfTIUziTDrc+h717mh5SeWU2Yo44FeD1Cxbh1qT09cB1uTyorPZJN9/Cgx4ycjVLZ/jNISC/Dwc8IvrH5v5HKdkf9bfohOvq4sUHYPtSruN9yARaejeqf99vR3GRSI1lnDOI0zO1KLyC2vsRY4HDQXjsdD0alGy5w9dyEi1AvNhjQ+3brEbrper7SkMTgDhF3wPrTumMK1QvkZ+GwyFKXCXcugx3kvns6o49OkTxkSPIQ+Xj2p2rQZt/HjEA7WnT0H16xCrdHQa/R4my+nq6glJ6WUqH4B9VxEUkqe/uEwhVUG3rq9Ly5ajf3uUaHRuA4ehMrd3a6uIq2Thu5Dg9Gc1eNihhWJdY+CwY+Cxgl2vNVomWqNhjn/eBOV1oHML1ez4aTSP7k5tKQxSACihRARQggtcAcQ34LXU2gMJenw6SSoyod7V0Dn+g/1b1O+pURfwmN9H6N6+3YsVVV41LmIdOVlHNuygR4jx9rc2hLg5L48LBZJ1yH1dwh9siOD9cfzeXZyd/qE2S5PoWUQWi1uY0ZTuWkT0o7tJ3uNCkFKyRQXd344kGN17bj5Q//Z1mqmZdkNC7kID19/bvnL/8NT50D8/xaTXJxsN32vN1rMGEgpTcB8YB2QDCyXUh4TQrwohJgOIIQYKITIAWYBHwghjrWUPgoXkHvEaghqq2B2PHQcWm+4qraKz459xrCQYfQN6Ev5z/GofX1xHWqdd3DdL5iMtfSferPNl5RSkrI7l4CO7vh2OO8iSswq5d9rUpjYM5AHhnWyy+0pNB+PSZOxlJdTtXOn3WR6BbjQsacvkeWS04XVJGTWNa4Z+rj1ZxNiBwCd+wyk3y230vGME4s/fIICXYGdNL6+aNGYgZTyVyllFylllJTylbpjz0sp4+t+T5BShkopXaWUvlJKZS9hS5O2CT6bAioHeGANdLg0+PtNyjeUG8qZ33c+5vJyqjZvxuPGKQiNBqNez6F1q4kaMATfkLDLXODyFGVXUXymmm5xweeOlVTXMn9pIsFeTiya2cfmpDWFlsdt+DDUXl5UrLJvL4HeY0Kx1JjpgwNf7s60HvQMhf4PwIEvrC7LJjBm5mwCeveg22EH/v7dn9AZdXbT+XqhTQSQFezE4WWwdBZ4hcOc9RDQ7ZIpZfoyPj/2OaNDR9PLrxcVa9chjUY8p1l7FyRtWY++qpKB025p1KVTduei0giiB1p3CZnMFuZ/k0hRdS3v3dVPyTK+xhBaLe6TJ1G5cZPdEtAAwrv74BngzGjhzNqkPPLK68pSj3rGGjvY9FLT9FWpmPnkczh7edJxSxULfnsGi1R2rDcGxRhcD0gJO96En+ZBeBw8uAY8Olx26pJDS6g2VvN4P+vSvTw+Hm1kJE69emI2Gdm/eiXBXboR0s32OkSmWjMn9+UTEeN/rr3lv35NYVdaMa/c1IuYUCVOcC3iOW060mCgcr3tWcINIVSCmDGhaMqMBBkFS/eetg64BcDQ+XB8JeQcaJJsZzd3Zv31n7gZtWhWp/BmQtPcTtcrijFo71jMsOYZ2LAQes2Ee34817P4Yk6UnOD7k99zW5fb6OLdBUNGBjUHDuA5YwZCCI5u/I2KwnyG3HJ7o1Q4mZCPvtpIr1HWNJMfD+Tw6c4M7h/aiVkDbHc1KfyxOMf2xSE0lPL4n+0qt/uwDji7OzBF5cK3+7IwmMzWgbj54OIHG16wfoFpAkFR0Ux+9C8ElThxcvkqfjz5ox01b98oxqA9Y6i0Fpzb9yEM/TPc8hFoLp/VK6XktYTXcNe6Mz92PgBly74DjQavW27GqNezZ8UyQrr1IKLvAJtVkFJyZFM2viFuhHTxIjGrlGd/OkpcpK+ST3CNI4TA8+ab0O3eQ21Wlt3kOmjV9BkXhme5GXWZkZ8PnrUOOHlY3UWZ2+HEmibL7zFiDANvmkmXbHd++PZN9uXus5Pm7RvFGLRXSk/DJxPg1G8w5XWY8BJcJTls/en1JOQlML/vfDwdPbHo9ZStXIn7+PFo/P1JXLuK6rJSht85u1GB3jMnyyg+U03M2FCySnTM+WI/QR5OLLkrFge18vG71vGaORPUasqWL7er3N6jQnF01jBBOPPfLamYzHX+/f4PgH8362q2trrJ8kfcfh+dBgykf7IXr373V9LK0uykeftF+W9sj5zeDR+NgYozVrfQoLlXnV5ZW8mihEVEe0czs4u1eGzF2rVYysvxvuMO9FVVJMT/QGS/gYR2a9yGryObsnFyc8C/pw/3f5aAlJLPHxio1B1qIzgEBuI2ZjRlK37CYsecA62zht5jQwmqlOjya85XM9VoYeqbUJ4NW/7dZPlCpWL643/DOzSMwQluPL18HmerztpJ+/aJYgzaGwe/hi+mgbM3zNkEUQ13H1uUsIjCmkIWxi1Eo9IgpaR06TdoIyJwGTyIPSuWYaiuZtjt9zZKleIzVWQcKaLb8GAe+TaRM2U1fDx7AJH+bg2frHDN4H37HZhLSqj8zX4ZyQB9x4fj6KphknRmyeZULJa6OEHHoRB7D+x+D/Kbnnrk4OTEbc++hLu7NwN2aHlixcMU1xTbSfv2h2IM2gtmE6xbAD8/Bp2Gw5wN4Ne5wdO2Zm9lZepKHuz1IDH+MQDo9iWgP3oUn9n3UXg6g8Q18cSMm0RAp8hGqZSwOgOto5pvS0tJzCrl7dv70r+jT5NuT6H1cB02FIfwcEq+/NKuBeEcnTX0n9iJwGqJ/qyO1Udzzw/e8BI4e8GqJ62bIJqIu68fdzz3Ku4aN3ptMvPn+HlU1lbaQfv2h2IM2gOV+fDlDNi9BAbNg7t/sK4MGqDcUM7C3QuJ9o7m0T6Pnjte/PHHqP388Jgxgw0fvYeTmzvD75rdKJUKsytJO1hIcYgjq08UsGBKdyb3Dm74RIVrDqFS4fvA/eiPHEG3L8GusnuPDsHVy5FJZicWrUmxtsUEcPGBia9Czr4mZyb/jm9IGLf942U8zE5ErK/kiV//hN6kt4P27QvFGLR1MnfCByPgbCLc/CFMWQTqhgu9SSlZuGshZfoyXhn2Clq1FgB9cjLV27fjc++9JO3YTG7qCUbf+xDObu6NUmvfqgykRvBZYTHzRkXy0PCIJt2ewrWB5803o/b1pfjjj+0qV6NVM2haBN41Eo+CWj7bmXl+MOY263boza9C1t5mXSeocxdu+esL+Ogc8V+TxxNrH6PGVNM85dsZijFoq1gssPNta3zA0R3mbIQ+tu///yTpEzZkbeDJ/k/S3ff8Fs+i/76PytUV9YRxbP8p3pOJAAASnUlEQVTmC8J6xtB9RMNxhwvJTi4h80gROzS13D6sI3+f1E0pNdHGUTk54XPffVRv347++HG7yu4eF0xAJw8mGB35cGMqRVV1fRSEgKlvWMtV/DjH2nejGXSM6cu0J/9OQIUT3vFZPP7ro0rZigtQjEFbpCIXvr4F1j8P3afC3M0QaHtG8Pac7byT+A6TIyZzX4/7zh3XHTxI5fr1eD1wP2s+fR8pLUx4+PFGPcjNJgvxnyZRqrIQNiSQhdN6KoagneB95x2oPD0peLPx5aavhlAJRt3ZBQeTpF+l4KXVFxgbJ0+Y+SlUnrXGwyzNKzERPWgoM55agH+VM/6rzzL/FyWG8DuKMWhrJK+C94dC1h7rFrxZX1iTdWwkszyTv23/G128u/DPof8896CWUlKw+HXU/n6c8HAi92QKN8ydj1eQ7X5+KSXvvp8IlSZ0PT349219UKkUQ9BeUHt44DdvHtXbt1O9y36NbwACOnrQc3gHYvUa9u3PZf3xC3obhw6AG16ElNWw8Z/NvlbngUO4+a/P4V/tTMiqAub9MJv8aqWXsmIM2gr6Coh/HL67x1po7pHtMOBB61LaRs5WnWXu+rlohIa3xryFs8b53Fjlut+oSUzEMPMWEn79md5jJ9Bt2CibZUsp+deyo3CsnBp/LS89OhC1YgjaHd733I1DSAj5i19HNvNb+sUMvaUz7j6O3GRw4vkfj1Kuu6C38ZA/wYCHYOdbsP+zZl8rMnYgsxa8jK/ZnR5ra3j023tJLW1axdT2gmIM2gIpv8B7gyHxKxj+FDy0HvyiGyWiUFfI3N/mUm2s5oMbPiDUPfTcmLm8nLxXXqamR1e2JO7CL6wjY+5/2GbZtSYLzyw/TO32AtRaFfOeGqAYgnaKSqvF/y9/wZCcTOk339pVttZZw7jZPXAzQq8iC8/8ePj8VlYhYPIiiJ4Av/wfnFjb7OuF9ejNPS/9B28XHwZvduBvnzzEluwtzZbbVlGMwbVMxVnrSmDZXdatdnM2wvgXrFmajaBAV8Dc3+ZSWFPI++PfrxcwBshfvJjKykr2ejujdXXllr8vxMHRySbZ5TVG7v9sH0W7CgiwqJg6pxfu3radq9A28bhxCq7Dh1PwxhvU5uTYVXZoV2/6jAujr0FD1sEiPtiWfn5QrbHGD4J6W/8vkpvfa8EvrCP3/ettAsMjGZrgzmfvP8eSxCXXZflrxRhcixj1sPMd62rg1HoYvxAe3gKh/Rst6kTJCe7+9W5yq3N5b9x79PHvU2+8cuNG8leuJLFPNGZp4dZn/4m7r59NsrNLdMx8fxe6lHIGGDT0Hh1KRIx/o3VUaFsIIQh+0Rpvyl3w/5DmpieFXY64W6LoEO3JFL2Wz1efYNvJwvODju5w38/WpkzLZ8OR5tdMcvfx454X36DHmPHEpHly+qOVzP9pznUXR1CMwbWExQJHvoclA2H9cxA2GB7dBcP/AurGN3/ZcWYHs9fOxiItfDH5CwYGDaw3bsjI4OT/W8CeHhHUILnpmefxC+tok+x1x/K48Z3taAoN3KBzIKyHD8NmNZzxrNA+cOjQgcBn/45u714K33nXrrLVahUT5/bG3cORmTVOPP35AQ6cLj0/wdkL7v3JWrZixcOw7fVm7zLSODgw+ZEnmfjokwRXuRH2Yx5/fu92VqettmvW9bWMaGs3OmDAALl///7WVsO+SAmpG2DTy5B7yLoMnvAyRI5ukrhacy1LDi7h82Of09WnK0vGLiHQNbDeHHNZGYn33cMerQW1pye3LniJwMiGH+a1Jguvrknms52ZjPZ0Z/BZC94BztzydH+0zg0nuym0H6SU5D3/PGXf/0DIW2/hMWmiXeUX5VSx4j+JlBlN/ORt5KNHhtAr5IJeHMYa63bTpB+h6xS46X2roWgmZfl5rHz7XxSnpXPGrwY5Lpqnxi8gzKNt994QQhyQUl6x/rxiDFoTswmOrbAmj+UngUcojHsOet921XLTVyOlJIV/7PgHp0pPMavLLJ4e8DQuDi715pjKy9k4ZzbHhBE3T29mvfga3sEhDcpOzCrlHyuOkpJXycORQXgfqcA70JXpT/TFxaNxcQyF9oGltpas+2ajP3aM0P++h9uIEXaVn59Zwco3D1JqNrHKw8i/7u/PqC4XuCKltPbrWPcP8AiBaW/bVJyxISxmMwfXrWbrss8wGY2kRFTRZ+p0HhowDzdt2yy0qBiDa5HKfDjyHez7CMqzrPXbhz1hTb1vZHD4dwp1hSw5tISVqSvxdvTmxWEvMjJ05KXzjh1l7T8XUCAsRER2YcqCF3Fyu/qHu0JvZPHaE3y99zTB7k48HuBPyf4iAiM8mDq/z7lWlgrXJ+bycrIeeBBDaiohb72J+9ixdpWfn1HBqvcOU6kzEu9ay70zunL/0E71c1iy9sLKR6Ak3fplauIr1laazaSqtITfvnif9N27MWosnIo2MHDSDO7udz8eWtvze64FFGNwrWCqhZNr4dBSa1BYmq39iIc9AdETm7wSyK3K5duUb1l2YhlGi5E7u93JvJh5eDrWb21p0FWz43/vcnjPdlRSMnTURAY+9uerZgfrjWa+2n2a/25JpbzGyIO9Q+mabaQgo4Iewzsw4rZoNFp1k/RWaF+Yy8rImjMX/bFj+D0+H79HHkE08TN9OSqKali15DCleTr2Oxqx9PLktdv6EOx5PlcGo95a1G7Hm6BygIEPQtzj4B54ZcE2Ung6g7VfvU/B0eMY1RZOh9cSPiqO2+LuJ8orqtny/wgUY9CaVBdZH/wn10LaJjBUgHsw9LkD+twF/l2aJNZoNrI7dzcrU1eyKWsTEsmEjhN4PPZxwj3C680tL8jnQPwKkjauxWg2Ea43M/a5F/EdOOiK8gsrDSzde5qvdp+muLqW8Z18mergytkDRTg6axh+WzRdBwc1SXeF9otFryfvhRco/zke59hYghYuxKlr0z7jl6NWb2L3ijSStp2hTCXZ42pi9PiOzBvdGTfHC+JVRamw9TVI+gHUWug9E/reA+FDGpWkeTkKMtPZ9NOX5Ozbj7BAoacBQzdvYoeOZ2LvGZfE5q4lFGPwR1KeA9l7ITsBsvfA2UOABLcg6DIRuk+DyDE2VRW9mKKaIvbl7mPX2V1szt5MRW0FHloPbo2+lTu63UEHtw7n5laVFJO6fy8ntm8m52QyQkqCyqroEzuYbs89j/oybqEKvZHfjuUTf/gsO1OLMJsl0zv4MFTlRGlKGVJC96HBxN0UhZOb4hZSuDxSSsp//pmCf7+GubISjylT8J3zEE5du9rtGtkpJWxedpLKPB2FKgvJrpJeccHcObwjnQMuqK5bnGbNWD76IxirwScSuk2F6BsgbEiTXbIA1WWlHNj8Kwc2/YKloAKAEvdaDOFuhPTuzaC+44gNHXBJvK41aVVjIISYBLwNqIGPpZT/vmjcEfgS6A8UA7dLKTOvJrPVjYFRD5W51paSRSeh8AQUpkBBClTlWedonCGkH0SMhC6TICjGZjeQ0WzkbPVZsiuzSStL43jxcY4XHyezIhMAd607o0NHMyliEnHBcVgMtRRlZ1GUlUlBZhpZhw9SVmjdH+1qMBJcWknXzt3p+MQTOMdYm9dIKTlTVkNKbiUJp0vYk17CsZxyPE3Qw8mJAc7OuJaYMFQZcXBS03VwELE3hOPh53wltRUU6mEqLaX4gw8pXb4cqdPh2KM7HhMm4DJoMM69eiK0zdtwYLFITiXks3tNBtV5NZiQnNZYqPLWENXDl9heAfSP8La2VzVUQXK8NU6XuRMsRnBwseYqhPSH4D7g2xl8o6x5DI2kOCeL/bvXkZywA1NWMUKCRFLuZkTvr8U1JIiQsM506tidiPAehHmF46j+49u+tpoxEEKogZPADUAOkADcKaU8fsGcPwExUspHhBB3ADdLKa9ah7nJxsBksDbYNhnApAdzrfWnqe6n2WB90BsqQV9e/1VdaM0GrjwLuova5jm4gn9X66tDLIQNosY3inKTDoPZgN6kx2A2YDAbqDHVYDAb0Bl1lBnKKDOUUaovPfczrzqPPF1evezHQJdAuvt2p69/XwKP1+KsU1FdXER59mmqysvQ1ZwvwauxWPCuqsG3qgZftTP0H0ZB3ARynAIoKdVTWm6gvNJASakeUWvBw6LCC0GgWoNbLQiz9bPg6KIhrIcPETF+RPT1x0GJCyg0EVNpKRWrVlEevwp9UhIAwskJx6gotBERaDt1wiE4CI9p01A1wUBIKcnPrODIrrOkHixEVpkAMCIpVksMjirU7hrcvBzx9HLE01USbj5JmOEAwbq9OBUdQ1guqIHkFgjencDV3xqAdg0AVz+rkXB0B60bOLqBX1frz4sw6HSkHUvk8JHt5KWfwnimGHXN+aQ8i5DoHM2YnAW4OqJyc8LB3QUnV3dcXNxxdfXA2dkNR2cXnF3ccHFxxzs4BGetC05qJ7wcvZq82mhNYxAHLJRSTqx7/yyAlPLVC+asq5uzWwihAfIAf3kVpZpsDHa8CRsWNu4crZu1hK6Lj3Xbmnuw9adHB+vLt7O11vpFfsilyUv5976Gm3lrhAYvJy+8HK2vQNdAwtzDCHULJcw9jE6enfBxOt8m8oNH7sNQU4O7jw/ixCmcjCbcDEY8tU44uXqzzeBBqlcYx306cto9CIRgdI2GgYbLu3VUDio8fJ3w8HXCM8CFgI7uBIR74BXkolQbVbA7ptJSdPv3U7N/P4bUNAwZ6ZjO5oIQdDtyGOHQPPejlJLKEj05qWUkJxWRl1NJbVktGr0F1UVPlGMOJn51NZLywhicKjKgONXqVipOg7LT1i+AVQVQU3L5iz2wxpr0ZoNONZUVnD6dQkZGEvlnMqkoLqSmvBxzVQ0qnQkHvURw5f+3ryZmYVZbb2DB4AXc0e0Om/8mF9KaxmAmMElKOafu/b3AYCnl/AvmJNXNyal7n1Y3p+giWQ8Dv1dO6wqcaIZqfkBRg7OuLRSd/zjaot5tUWdom3q3ZZ07SimvWC+mTaSMSik/BD60hywhxP6rWcdrEUXnP462qHdb1Bnapt7tWeeWrE10Brgwfzu07thl59S5iTyxBpIVFBQUFP5AWtIYJADRQogIIYQWuAOIv2hOPDC77veZwKarxQsUFBQUFFqGFnMTSSlNQoj5wDqsW0s/lVIeE0K8COyXUsYDnwBfCSFSgRKsBqOlsYu76Q9G0fmPoy3q3RZ1hrapd7vVuc0lnSkoKCgo2B+ln4GCgoKCgmIMFBQUFBSuQ2MghHhJCHFECHFICPGbEKJDw2e1PkKIxUKIlDrdfxJCNL+LRwsjhJglhDgmhLAIIa7p7XhCiElCiBNCiFQhxN9bWx9bEEJ8KoQoqMvXaRMIIcKEEJuFEMfrPhtPtLZOtiCEcBJC7BNCHK7T+5+trZOtCCHUQoiDQojVV5t33RkDYLGUMkZK2RdYDTzf2grZyHqgl5QyBmuZj2dbWR9bSAJuAba1tiJXo650ynvAZKAHcKcQokframUTnwOTWluJRmIC/k9K2QMYAjzWRv7WBmCslLIP0BeYJIQY0so62coTQHJDk647YyClrLjgrSvQJiLoUsrfpJSmurd7sOZtXNNIKZOllM3JFv+jGASkSinTpZS1wDJgRivr1CBSym1Yd+G1GaSUuVLKxLrfK7E+pBpus9fKSCtVdW8d6l7X/LNDCBEK3Ah83NDc684YAAghXhFCZAN303ZWBhfyILCmtZVoR4QA2Re8z6ENPKDaOkKITkAssLd1NbGNOnfLIaAAWC+lbAt6vwU8A1gamtgujYEQYoMQIukyrxkAUsoFUsowYCkw/+rS/jga0rtuzgKsS+2lrafpeWzRWUHhYoQQbsCPwJMXrdavWaSU5jr3cigwSAjRq7V1uhpCiKlAgZTygC3z20RtosYipRxv49SlwK/ACy2ojs00pLcQ4n5gKjDuWsnUbsTf+lrGltIpCnZCCOGA1RAslVKuaG19GouUskwIsRlrvOZaDt4PA6YLIaYAToCHEOJrKeU9l5vcLlcGV0MIEX3B2xlASmvp0hjqGgU9A0yXUuoamq/QKGwpnaJgB4S16fYnQLKU8o3W1sdWhBD+v+/gE0I4Y+3Tck0/O6SUz0opQ6WUnbB+pjddyRDAdWgMgH/XuTGOABOwRtrbAksAd2B93bbY/7W2Qg0hhLhZCJEDxAG/1PWvuOaoC8z/XjolGVgupTzWulo1jBDiW2A30FUIkSOEeKi1dbKBYcC9wNi6z/Ghum+u1zrBwOa650YC1pjBVbdqtjWUchQKCgoKCtflykBBQUFB4SIUY6CgoKCgoBgDBQUFBQXFGCgoKCgooBgDBQUFBQUUY6CgoKCggGIMFBQUFBSA/w+sNE28HAT9GgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.boxplot(x_scaled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "T1YZPRDrr_kY",
        "outputId": "b65b6f47-7512-42c9-ad6d-e15f5d7a92c1"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'whiskers': [<matplotlib.lines.Line2D at 0x7f60d6c52b50>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6c52eb0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6bf23a0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6bf2700>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6bfdbb0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6bfdf10>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6c143d0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6c14730>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6ba0bb0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6ba0f10>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6bb53d0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6bb5730>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6bc2bb0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6bc2f10>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6b583d0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6b58730>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6b64bb0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6b64f10>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6b7b3d0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6b7b730>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6b86bb0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6b86f10>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6b1d3d0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6b1d730>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6b2abb0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6b2af10>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6b413d0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6b41730>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6b4dbe0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6b4df40>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6ae34c0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f60d6ae37f0>,\n",
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              " 'means': []}"
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          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x_scaled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "wUpLjcfisIWb",
        "outputId": "f2d7facd-0156-46fe-86f0-77bd2eb365c7"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f60d6927be0>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x_scaled,y_data,test_size=0.2,random_state=15)"
      ],
      "metadata": {
        "id": "m9UyUJkVsIZB"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "l1=LogisticRegression()\n",
        "l1.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vr5XlD8gsIcV",
        "outputId": "7eeca66d-e9cd-457a-9b8d-20d40abe8eab"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=l1.predict(x_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l7uhccnNsXlP",
        "outputId": "c8833b40-dcf4-4225-efe8-92dbc9842c70"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1,\n",
              "       0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0,\n",
              "       0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0,\n",
              "       0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0,\n",
              "       1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1,\n",
              "       0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
              "       1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "A1=accuracy_score(y_test,y_pred)*100\n",
        "A1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cb96jdLQsXye",
        "outputId": "7d326a94-164b-4a7f-9809-8a318ce0ff9e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "97.9020979020979"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn=KNeighborsClassifier(n_neighbors=5,metric='minkowski',p=1)\n",
        "knn.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-AFRyeThsX1E",
        "outputId": "5dda0501-cf4f-423c-f7b9-ce39e68961fb"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(p=1)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn_pred=knn.predict(x_test)\n",
        "knn_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "53oW8tRNsX39",
        "outputId": "afc728af-88f0-4001-8d27-230ec6e754f3"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1,\n",
              "       0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0,\n",
              "       0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0,\n",
              "       0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0,\n",
              "       1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1,\n",
              "       0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
              "       1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "A2=accuracy_score(y_test,knn_pred)*100\n",
        "A2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vo-IpdTPsX6v",
        "outputId": "a28ac2d2-6366-4867-d831-c5615aee4770"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96.5034965034965"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rfc=RandomForestClassifier()\n",
        "rfc.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_vzTRF-dtNAU",
        "outputId": "e63041c1-de20-48e4-d171-ecdb9ee4a895"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rfc_pred=rfc.predict(x_test)\n",
        "rfc_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y4ai2gxetNCz",
        "outputId": "05dcb17e-1090-46b1-a903-05684066bfcc"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1,\n",
              "       0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0,\n",
              "       0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0,\n",
              "       0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0,\n",
              "       1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
              "       0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1,\n",
              "       1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "A3=accuracy_score(y_test,rfc_pred)*100\n",
        "A3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SC1vpWTftNFO",
        "outputId": "ffef6bef-5d5f-4c8d-dbb8-ac1b11be113d"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "93.00699300699301"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "dt=DecisionTreeClassifier(criterion = \"entropy\",random_state=20)\n",
        "dt.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8lGzS6fAtNIX",
        "outputId": "dde6be54-a727-4699-a478-965261b7af51"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', random_state=20)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt_pred=dt.predict(x_test)\n",
        "dt_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Aa-tLEKtd9O",
        "outputId": "db31c67d-13de-4453-c2c8-3033aac52a9f"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1,\n",
              "       0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0,\n",
              "       0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0,\n",
              "       0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0,\n",
              "       1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1,\n",
              "       0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
              "       1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "A4=accuracy_score(y_test,dt_pred)*100\n",
        "A4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kUtbX7Zftd_O",
        "outputId": "b0e7ebfc-3a4b-4945-93cf-7c8b47bb34a9"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "93.00699300699301"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Avg_accuracy=(A1+A2+A3+A4)/4\n",
        "Avg_accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rscUzcnsteCs",
        "outputId": "6d5893a4-b328-489c-e0f0-2fea6e6d5761"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "95.1048951048951"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Counter(y_train))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xrVknR4JteFv",
        "outputId": "17850c19-78f1-4b05-e92f-0b4dba20246d"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Counter({1: 286, 0: 285})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Counter(y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dv09_yxxteIc",
        "outputId": "d5ed9397-c47e-415e-a37a-3ee52164b0d6"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Counter({0: 72, 1: 71})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "skf=StratifiedKFold(n_splits=5,shuffle=True,random_state=20)\n",
        "skf.split(x_scaled,y_data)\n",
        "print(skf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g4MXZe_6tvNd",
        "outputId": "dde9b05c-37a5-4ffb-fcd9-486376a4fddd"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "StratifiedKFold(n_splits=5, random_state=20, shuffle=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import cross_val_predict\n",
        "y_pred1=cross_val_predict(l1,x_test,y_test,cv=skf)\n",
        "score1=cross_val_score(l1,x_train,y_train,cv=skf)\n",
        "print(y_pred1)\n",
        "print(score1)\n",
        "AA1=print(score1.mean()*100)\n",
        "AA1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8W0FCNZQtvP7",
        "outputId": "4e4132b2-49ae-4353-d487-24d7b2a207cd"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 1 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 1 0 1 1 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0\n",
            " 0 0 1 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 1 0 0 0 1 1 1 0 1 0 1\n",
            " 0 1 1 1 1 1 0 1 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0\n",
            " 1 0 0 1 1 0 1 1 1 0 1 1 0 0 0 0 0 1 1 0 0 1 1 0 1 1 1 0 0 1 0 1]\n",
            "[0.97391304 0.98245614 0.98245614 0.97368421 0.98245614]\n",
            "97.89931350114416\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred2=cross_val_predict(knn,x_test,y_test,cv=skf)\n",
        "score2=cross_val_score(knn,x_train,y_train,cv=skf)\n",
        "print(y_pred2)\n",
        "print(score2)\n",
        "AA2=print(score2.mean()*100)\n",
        "AA2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NAAlsj06tvSG",
        "outputId": "8a490297-1454-46c7-e29c-2b48d3534157"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 1 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 1 1 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0\n",
            " 0 0 1 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 1 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1\n",
            " 0 1 1 1 1 1 0 1 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 1 0 1 1 0 1 0 1 1 1 1 0 1 0\n",
            " 1 0 0 1 1 0 1 1 1 0 1 1 0 0 0 0 0 1 1 0 0 1 1 0 1 1 1 0 0 1 0 1]\n",
            "[0.93913043 0.98245614 0.98245614 0.97368421 0.97368421]\n",
            "97.0282227307399\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred3=cross_val_predict(rfc,x_test,y_test,cv=skf)\n",
        "score3=cross_val_score(rfc,x_train,y_train,cv=skf)\n",
        "print(y_pred3)\n",
        "print(score3)\n",
        "AA3=print(score3.mean()*100)\n",
        "AA3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ImPpIJyitvVa",
        "outputId": "29da7027-4160-42fb-8839-567adb6b5c19"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 1 0 0 1 0 1 1 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0\n",
            " 0 0 1 1 1 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 1 1 0 1 0 0 0 0 0 1 0 1 0 1 0 1\n",
            " 0 1 1 1 1 1 0 1 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 0 1 0 1 1 1 1 1 1 0\n",
            " 1 0 0 1 1 0 1 1 1 0 1 1 0 0 0 0 0 1 1 0 0 1 1 0 1 1 1 0 0 1 0 1]\n",
            "[0.93043478 0.97368421 0.98245614 0.97368421 0.97368421]\n",
            "96.67887109077041\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred4=cross_val_predict(dt,x_test,y_test,cv=skf)\n",
        "score4=cross_val_score(dt,x_train,y_train,cv=skf)\n",
        "print(y_pred4)\n",
        "print(score4)\n",
        "AA4=print(score4.mean()*100)\n",
        "AA4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XAE-1ryEuBzq",
        "outputId": "f711543d-7263-4604-9387-b67c7ac420d9"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 1 0 1 1 1 1 0 1 0 0 0 0 0 1 0 0 1 0 1 1 0 1 0 0 0 0 0 1 0 1 1 0 1 0 0\n",
            " 1 0 1 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 1 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1\n",
            " 0 1 1 1 1 1 0 1 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 0 1 0 1 1 1 1 0 1 0\n",
            " 1 0 0 1 1 0 0 1 1 0 0 1 0 0 0 0 1 1 1 0 0 1 1 1 1 1 1 0 0 1 0 1]\n",
            "[0.89565217 0.94736842 0.95614035 0.95614035 0.95614035]\n",
            "94.22883295194508\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CV_Final_Accuracy=(97.37604881769641+97.55301296720059+97.37604881769641+94.40122044241036)/4\n",
        "CV_Final_Accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l9M3Q-iKuB2I",
        "outputId": "50fd2341-9a62-4b27-d4e3-159810511c3a"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96.67658276125094"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import VotingClassifier\n",
        "vc=VotingClassifier(estimators=[(\"Logistic Regression\",l1),(\"KNN\",knn),(\"Random Forest\",rfc),(\"Decision Tree\",dt)])\n",
        "vc.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ZhqTumBuB4x",
        "outputId": "cfa9d801-6f35-4869-c6e2-bcc2f5a4bbbf"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VotingClassifier(estimators=[('Logistic Regression', LogisticRegression()),\n",
              "                             ('KNN', KNeighborsClassifier(p=1)),\n",
              "                             ('Random Forest', RandomForestClassifier()),\n",
              "                             ('Decision Tree',\n",
              "                              DecisionTreeClassifier(criterion='entropy',\n",
              "                                                     random_state=20))])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vc_pred=vc.predict(x_test)\n",
        "vc_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PUqBV6jluB7X",
        "outputId": "e8f1e33f-7638-4f0e-e084-b2802c5539b5"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1,\n",
              "       0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0,\n",
              "       0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0,\n",
              "       0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0,\n",
              "       1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1,\n",
              "       0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
              "       1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Voting_accuracy=accuracy_score(y_test,vc_pred)\n",
        "Voting_accuracy*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JY_EcZ_puB9-",
        "outputId": "22119786-ec23-4f25-b916-b67ae96bf982"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "95.1048951048951"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ann=tf.keras.models.Sequential()\n",
        "ann"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ldsCzituY6H",
        "outputId": "76186e10-c443-426a-b8bd-216d330a48cf"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.engine.sequential.Sequential at 0x7f60d68f2850>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ann.add(tf.keras.layers.Dense(units=8,activation='relu')) #1st hidden layer i.e input layer\n",
        "ann.add(tf.keras.layers.Dense(units=8,activation='relu'))\n",
        "ann.add(tf.keras.layers.Dense(units=8,activation='relu'))\n",
        "ann.add(tf.keras.layers.Dense(units=8,activation='relu'))\n",
        "ann.add(tf.keras.layers.Dense(units=8,activation='relu'))\n",
        "ann.add(tf.keras.layers.Dense(units=8,activation='relu'))\n",
        "ann.add(tf.keras.layers.Dense(units=8,activation='relu'))\n",
        "ann.add(tf.keras.layers.Dense(units=8,activation='relu'))\n",
        "ann.add(tf.keras.layers.Dense(units=8,activation='relu'))\n",
        "ann.add(tf.keras.layers.Dense(units=8,activation='relu'))\n",
        "#Output Layer\n",
        "ann.add(tf.keras.layers.Dense(units=1,activation='sigmoid'))"
      ],
      "metadata": {
        "id": "tv57fA-kuY8f"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "xlBgKoPiuY-q"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann.fit(x_train,y_train,batch_size=30,epochs=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HbULSG4OuZBR",
        "outputId": "756d4a2f-05af-44f1-d892-705395ded98c"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "20/20 [==============================] - 4s 4ms/step - loss: 0.6925 - accuracy: 0.5289\n",
            "Epoch 2/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.6803 - accuracy: 0.6830\n",
            "Epoch 3/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.6381 - accuracy: 0.5009\n",
            "Epoch 4/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.5282 - accuracy: 0.6252\n",
            "Epoch 5/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.4289 - accuracy: 0.8984\n",
            "Epoch 6/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.3900 - accuracy: 0.9317\n",
            "Epoch 7/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.3656 - accuracy: 0.9510\n",
            "Epoch 8/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.3426 - accuracy: 0.9632\n",
            "Epoch 9/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.3246 - accuracy: 0.9580\n",
            "Epoch 10/50\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.3017 - accuracy: 0.9702\n",
            "Epoch 11/50\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.2744 - accuracy: 0.9737\n",
            "Epoch 12/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.2413 - accuracy: 0.9772\n",
            "Epoch 13/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.1971 - accuracy: 0.9790\n",
            "Epoch 14/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.1406 - accuracy: 0.9825\n",
            "Epoch 15/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0933 - accuracy: 0.9825\n",
            "Epoch 16/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0662 - accuracy: 0.9860\n",
            "Epoch 17/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0555 - accuracy: 0.9877\n",
            "Epoch 18/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0507 - accuracy: 0.9877\n",
            "Epoch 19/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0491 - accuracy: 0.9895\n",
            "Epoch 20/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0469 - accuracy: 0.9895\n",
            "Epoch 21/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0449 - accuracy: 0.9895\n",
            "Epoch 22/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0431 - accuracy: 0.9877\n",
            "Epoch 23/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0425 - accuracy: 0.9895\n",
            "Epoch 24/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0412 - accuracy: 0.9895\n",
            "Epoch 25/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0397 - accuracy: 0.9912\n",
            "Epoch 26/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0383 - accuracy: 0.9895\n",
            "Epoch 27/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0376 - accuracy: 0.9895\n",
            "Epoch 28/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0370 - accuracy: 0.9912\n",
            "Epoch 29/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0370 - accuracy: 0.9895\n",
            "Epoch 30/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0349 - accuracy: 0.9912\n",
            "Epoch 31/50\n",
            "20/20 [==============================] - 0s 2ms/step - loss: 0.0343 - accuracy: 0.9895\n",
            "Epoch 32/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0329 - accuracy: 0.9895\n",
            "Epoch 33/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0317 - accuracy: 0.9895\n",
            "Epoch 34/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0288 - accuracy: 0.9895\n",
            "Epoch 35/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0250 - accuracy: 0.9912\n",
            "Epoch 36/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0209 - accuracy: 0.9947\n",
            "Epoch 37/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0209 - accuracy: 0.9930\n",
            "Epoch 38/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0178 - accuracy: 0.9947\n",
            "Epoch 39/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0172 - accuracy: 0.9947\n",
            "Epoch 40/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0589 - accuracy: 0.9825\n",
            "Epoch 41/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0347 - accuracy: 0.9877\n",
            "Epoch 42/50\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.0325 - accuracy: 0.9895\n",
            "Epoch 43/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0283 - accuracy: 0.9912\n",
            "Epoch 44/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0249 - accuracy: 0.9912\n",
            "Epoch 45/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0236 - accuracy: 0.9912\n",
            "Epoch 46/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0211 - accuracy: 0.9912\n",
            "Epoch 47/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0248 - accuracy: 0.9895\n",
            "Epoch 48/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0215 - accuracy: 0.9912\n",
            "Epoch 49/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0208 - accuracy: 0.9912\n",
            "Epoch 50/50\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.0187 - accuracy: 0.9912\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f60d41ef370>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ann_pred=ann.predict(x_test)\n",
        "ann_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dNh1JmkQuZD4",
        "outputId": "e8cdb1a1-11c0-4f0c-e831-bc2b68c36f42"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/5 [==============================] - 0s 4ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.3442387e-04],\n",
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              "       [1.0000000e+00],\n",
              "       [1.0000000e+00],\n",
              "       [9.9999988e-01],\n",
              "       [7.2194147e-04],\n",
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              "       [1.0000000e+00],\n",
              "       [8.3534524e-06],\n",
              "       [9.9971938e-01]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "New_ann_pred=[]\n",
        "for x in ann_pred:\n",
        "    if x>=0.5:\n",
        "        New_ann_pred.append(1)\n",
        "    else:\n",
        "        New_ann_pred.append(0)\n",
        "print(New_ann_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0FQ5lVR1u132",
        "outputId": "bfb31a41-27a1-4dcb-b38b-2ea469ef8be8"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ANN_score=accuracy_score(y_test,New_ann_pred)\n",
        "ANN_score*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1kzR6pMGu16a",
        "outputId": "9b154590-3a6f-4305-aaaa-439cb5825470"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "97.2027972027972"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(np.c_[y_test,New_ann_pred],columns=[\"Actual_Values\",\"Predicted_Values\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "6U22NRaFu187",
        "outputId": "1b147453-8e75-4643-8151-aa44732e41e1"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Actual_Values  Predicted_Values\n",
              "0                0                 0\n",
              "1                1                 1\n",
              "2                1                 1\n",
              "3                0                 0\n",
              "4                0                 0\n",
              "..             ...               ...\n",
              "138              0                 0\n",
              "139              0                 0\n",
              "140              1                 1\n",
              "141              0                 0\n",
              "142              1                 1\n",
              "\n",
              "[143 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Actual_Values</th>\n",
              "      <th>Predicted_Values</th>\n",
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              "<p>143 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3b10266c-bd46-4c02-852c-1653328d4f9d')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3b10266c-bd46-4c02-852c-1653328d4f9d 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-3b10266c-bd46-4c02-852c-1653328d4f9d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(np.c_[y_test,New_ann_pred],columns=[\"Actual_Values\",\"Predicted_Values\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "QMpHx6NCu1_5",
        "outputId": "077c6aca-432c-42e0-85a3-7d04bc536963"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Actual_Values  Predicted_Values\n",
              "0                0                 0\n",
              "1                1                 1\n",
              "2                1                 1\n",
              "3                0                 0\n",
              "4                0                 0\n",
              "..             ...               ...\n",
              "138              0                 0\n",
              "139              0                 0\n",
              "140              1                 1\n",
              "141              0                 0\n",
              "142              1                 1\n",
              "\n",
              "[143 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-de5ed268-6e18-4433-bb5f-b23dbf0f822a\">\n",
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              "<p>143 rows × 2 columns</p>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-de5ed268-6e18-4433-bb5f-b23dbf0f822a 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-de5ed268-6e18-4433-bb5f-b23dbf0f822a');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    }
  ]
}