Placement-Prediction.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/Niraj2003/Placement-Prediction/blob/master/Placement-Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xn7O3mQO11Kn"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sklearn\n",
        "import plotly.express as px"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fw5pQM8Nygbz",
        "outputId": "56624489-d0d7-492e-c29d-97b26f54cd9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dNteHZX311Kr"
      },
      "outputs": [],
      "source": [
        "placement = pd.read_csv(\"/content/drive/MyDrive/datasets/PlacementDataset.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sl_no gender  ssc_p    ssc_b  hsc_p    hsc_b     hsc_s  degree_p  \\\n",
              "0      1      M  67.00   Others  91.00   Others  Commerce     58.00   \n",
              "1      2      M  79.33  Central  78.33   Others   Science     77.48   \n",
              "2      3      M  65.00  Central  68.00  Central      Arts     64.00   \n",
              "3      4      M  56.00  Central  52.00  Central   Science     52.00   \n",
              "4      5      M  85.80  Central  73.60  Central  Commerce     73.30   \n",
              "5      6      M  55.00   Others  49.80   Others   Science     67.25   \n",
              "6      7      F  46.00   Others  49.20   Others  Commerce     79.00   \n",
              "7      8      M  82.00  Central  64.00  Central   Science     66.00   \n",
              "8      9      M  73.00  Central  79.00  Central  Commerce     72.00   \n",
              "9     10      M  58.00  Central  70.00  Central  Commerce     61.00   \n",
              "\n",
              "    degree_t workex  etest_p specialisation  mba_p      status    salary  \n",
              "0   Sci&Tech     No    55.00         Mkt&HR  58.80      Placed  270000.0  \n",
              "1   Sci&Tech    Yes    86.50        Mkt&Fin  66.28      Placed  200000.0  \n",
              "2  Comm&Mgmt     No    75.00        Mkt&Fin  57.80      Placed  250000.0  \n",
              "3   Sci&Tech     No    66.00         Mkt&HR  59.43  Not Placed       NaN  \n",
              "4  Comm&Mgmt     No    96.80        Mkt&Fin  55.50      Placed  425000.0  \n",
              "5   Sci&Tech    Yes    55.00        Mkt&Fin  51.58  Not Placed       NaN  \n",
              "6  Comm&Mgmt     No    74.28        Mkt&Fin  53.29  Not Placed       NaN  \n",
              "7   Sci&Tech    Yes    67.00        Mkt&Fin  62.14      Placed  252000.0  \n",
              "8  Comm&Mgmt     No    91.34        Mkt&Fin  61.29      Placed  231000.0  \n",
              "9  Comm&Mgmt     No    54.00        Mkt&Fin  52.21  Not Placed       NaN  "
            ],
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              "      <th></th>\n",
              "      <th>sl_no</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "      <td>67.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>91.00</td>\n",
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              "      <td>Commerce</td>\n",
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              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>55.00</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>58.80</td>\n",
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              "    <tr>\n",
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              "      <td>77.48</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>Yes</td>\n",
              "      <td>86.50</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>66.28</td>\n",
              "      <td>Placed</td>\n",
              "      <td>200000.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>M</td>\n",
              "      <td>65.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>68.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>Arts</td>\n",
              "      <td>64.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>75.00</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>57.80</td>\n",
              "      <td>Placed</td>\n",
              "      <td>250000.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>M</td>\n",
              "      <td>56.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>Science</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>66.00</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>59.43</td>\n",
              "      <td>Not Placed</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>M</td>\n",
              "      <td>85.80</td>\n",
              "      <td>Central</td>\n",
              "      <td>73.60</td>\n",
              "      <td>Central</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>73.30</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>96.80</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>55.50</td>\n",
              "      <td>Placed</td>\n",
              "      <td>425000.0</td>\n",
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              "      <th>5</th>\n",
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              "      <td>55.00</td>\n",
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              "      <td>Science</td>\n",
              "      <td>67.25</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>Yes</td>\n",
              "      <td>55.00</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>51.58</td>\n",
              "      <td>Not Placed</td>\n",
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              "      <td>46.00</td>\n",
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              "      <td>49.20</td>\n",
              "      <td>Others</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>79.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
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              "      <td>74.28</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>53.29</td>\n",
              "      <td>Not Placed</td>\n",
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              "      <td>Yes</td>\n",
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              "      <td>62.14</td>\n",
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              "      <td>9</td>\n",
              "      <td>M</td>\n",
              "      <td>73.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>79.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>72.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
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              "      <td>91.34</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>61.29</td>\n",
              "      <td>Placed</td>\n",
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              "      <td>70.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>61.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
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              "      <td>54.00</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>52.21</td>\n",
              "      <td>Not Placed</td>\n",
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          "metadata": {},
          "execution_count": 5
        }
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      "source": [
        "placement.head(10)"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7hI_rYjE11Kt"
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      "outputs": [],
      "source": [
        "placement_copy=placement.copy()"
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    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lBGcgfoO11Kt",
        "outputId": "6d9ab46b-5100-4b65-fd0c-f626083e11db"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(215, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
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        "placement_copy.shape"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
        "id": "3Jn4HtaE11Ku",
        "outputId": "b07a551e-336e-4be7-f771-3ab2db0188ee"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sl_no               int64\n",
              "gender             object\n",
              "ssc_p             float64\n",
              "ssc_b              object\n",
              "hsc_p             float64\n",
              "hsc_b              object\n",
              "hsc_s              object\n",
              "degree_p          float64\n",
              "degree_t           object\n",
              "workex             object\n",
              "etest_p           float64\n",
              "specialisation     object\n",
              "mba_p             float64\n",
              "status             object\n",
              "salary            float64\n",
              "dtype: object"
            ]
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          "metadata": {},
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        "placement_copy.dtypes"
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    {
      "cell_type": "markdown",
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        "id": "az1yXkS911Kv"
      },
      "source": [
        "## NAN Handling"
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      "metadata": {
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sl_no              0\n",
              "gender             0\n",
              "ssc_p              0\n",
              "ssc_b              0\n",
              "hsc_p              0\n",
              "hsc_b              0\n",
              "hsc_s              0\n",
              "degree_p           0\n",
              "degree_t           0\n",
              "workex             0\n",
              "etest_p            0\n",
              "specialisation     0\n",
              "mba_p              0\n",
              "status             0\n",
              "salary            67\n",
              "dtype: int64"
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          "metadata": {},
          "execution_count": 9
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      "source": [
        "placement_copy.isnull().sum()"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4EDgejkl11Kx"
      },
      "outputs": [],
      "source": [
        "placement_copy['salary'].fillna(value=0 , inplace = True )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "uvarvCWJ11Kx",
        "outputId": "6ef636d7-9cab-4333-ead5-cce4afb41d14"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sl_no             0\n",
              "gender            0\n",
              "ssc_p             0\n",
              "ssc_b             0\n",
              "hsc_p             0\n",
              "hsc_b             0\n",
              "hsc_s             0\n",
              "degree_p          0\n",
              "degree_t          0\n",
              "workex            0\n",
              "etest_p           0\n",
              "specialisation    0\n",
              "mba_p             0\n",
              "status            0\n",
              "salary            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "placement_copy.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g9XcBgaN11Ky"
      },
      "outputs": [],
      "source": [
        "placement_copy.drop(['sl_no','ssc_b','hsc_b'], axis = 1 , inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "JL3wKCtE11Kz",
        "outputId": "d7095b4c-4964-421d-ff59-cb68c3723961"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  gender  ssc_p  hsc_p     hsc_s  degree_p   degree_t workex  etest_p  \\\n",
              "0      M  67.00  91.00  Commerce     58.00   Sci&Tech     No     55.0   \n",
              "1      M  79.33  78.33   Science     77.48   Sci&Tech    Yes     86.5   \n",
              "2      M  65.00  68.00      Arts     64.00  Comm&Mgmt     No     75.0   \n",
              "3      M  56.00  52.00   Science     52.00   Sci&Tech     No     66.0   \n",
              "4      M  85.80  73.60  Commerce     73.30  Comm&Mgmt     No     96.8   \n",
              "\n",
              "  specialisation  mba_p      status    salary  \n",
              "0         Mkt&HR  58.80      Placed  270000.0  \n",
              "1        Mkt&Fin  66.28      Placed  200000.0  \n",
              "2        Mkt&Fin  57.80      Placed  250000.0  \n",
              "3         Mkt&HR  59.43  Not Placed       0.0  \n",
              "4        Mkt&Fin  55.50      Placed  425000.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-324cb6d2-f4be-423f-b01b-10b31170d7c9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>ssc_p</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>hsc_s</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>degree_t</th>\n",
              "      <th>workex</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>status</th>\n",
              "      <th>salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M</td>\n",
              "      <td>67.00</td>\n",
              "      <td>91.00</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>58.80</td>\n",
              "      <td>Placed</td>\n",
              "      <td>270000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M</td>\n",
              "      <td>79.33</td>\n",
              "      <td>78.33</td>\n",
              "      <td>Science</td>\n",
              "      <td>77.48</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>Yes</td>\n",
              "      <td>86.5</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>66.28</td>\n",
              "      <td>Placed</td>\n",
              "      <td>200000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <td>65.00</td>\n",
              "      <td>68.00</td>\n",
              "      <td>Arts</td>\n",
              "      <td>64.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>75.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>57.80</td>\n",
              "      <td>Placed</td>\n",
              "      <td>250000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M</td>\n",
              "      <td>56.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Science</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>59.43</td>\n",
              "      <td>Not Placed</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M</td>\n",
              "      <td>85.80</td>\n",
              "      <td>73.60</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>73.30</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>96.8</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>55.50</td>\n",
              "      <td>Placed</td>\n",
              "      <td>425000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-324cb6d2-f4be-423f-b01b-10b31170d7c9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-324cb6d2-f4be-423f-b01b-10b31170d7c9 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-324cb6d2-f4be-423f-b01b-10b31170d7c9');\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": 13
        }
      ],
      "source": [
        "placement_copy.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gFKZTKKs11K0"
      },
      "source": [
        "## Outlier Handling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 870
        },
        "id": "Azpo_6tX11K0",
        "outputId": "c1d60e96-f792-442c-ece4-cd1f2db65101"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Employability Percentage')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (15,10))\n",
        "\n",
        "ax = plt.subplot(221)\n",
        "plt.boxplot(placement_copy['ssc_p'])\n",
        "ax.set_title('Secondary School Percentage')\n",
        "\n",
        "ax = plt.subplot(222)\n",
        "plt.boxplot(placement_copy['hsc_p'])\n",
        "ax.set_title('Higher secondary Percentage')\n",
        "\n",
        "ax = plt.subplot(223)\n",
        "plt.boxplot(placement_copy['degree_p'])\n",
        "ax.set_title('UG Percentage')\n",
        "\n",
        "ax = plt.subplot(224)\n",
        "plt.boxplot(placement_copy['etest_p'])\n",
        "ax.set_title('Employability Percentage')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aEbO_4O-11K1"
      },
      "outputs": [],
      "source": [
        "Q1 = placement_copy['hsc_p'].quantile(0.25)\n",
        "Q3 = placement_copy['hsc_p'].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "filter = (placement_copy['hsc_p'] >= Q1 - 1.5 * IQR) & (placement_copy['hsc_p']<= Q3+ 1.5*IQR)\n",
        "placement_filtered= placement_copy.loc[filter]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "W6kaAPtO11K1",
        "outputId": "5baa48e0-122e-4a40-be87-703026bd2555"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'whiskers': [<matplotlib.lines.Line2D at 0x7f85e169fbe0>,\n",
              "  <matplotlib.lines.Line2D at 0x7f85e169fe80>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7f85e16d0160>,\n",
              "  <matplotlib.lines.Line2D at 0x7f85e16d0370>],\n",
              " 'boxes': [<matplotlib.lines.Line2D at 0x7f85e169f940>],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7f85e16d0610>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7f85e16d08b0>],\n",
              " 'means': []}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.boxplot(placement_filtered['hsc_p'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M3-gjbtD11K2"
      },
      "source": [
        "## Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "M7G_AgWP11K2",
        "outputId": "76f3a68b-da63-4386-8aca-3610b364d71a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 6 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (15,7))\n",
        "\n",
        "plt.subplot(231)\n",
        "ax = sns.countplot(x= 'gender' , data = placement_filtered)\n",
        "\n",
        "plt.subplot(232)\n",
        "ax = sns.countplot(x= 'hsc_s' , data = placement_filtered)\n",
        "\n",
        "plt.subplot(233)\n",
        "ax = sns.countplot(x= 'degree_t' , data = placement_filtered)\n",
        "\n",
        "plt.subplot(234)\n",
        "ax = sns.countplot(x= 'specialisation' , data = placement_filtered)\n",
        "\n",
        "plt.subplot(235)\n",
        "ax = sns.countplot(x= 'workex' , data = placement_filtered)\n",
        "\n",
        "plt.subplot(236)\n",
        "ax = sns.countplot(x= 'status' , data = placement_filtered)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "rZjrXhrM11K3",
        "outputId": "cb9900f2-1d4c-410f-c518-245679a02a77"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='salary', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "placement_placed = placement_filtered[placement_filtered.salary!= 0]\n",
        "sns.distplot(placement_placed['salary'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "8TytqcZC11K3",
        "outputId": "c04482e4-3912-488b-ac24-9a572e8d10b7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "source": [
        "## Encoding"
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      "execution_count": null,
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        "id": "mkF1C4R511K4",
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            "text/plain": [
              "   gender  ssc_p  hsc_p     hsc_s  degree_p   degree_t  workex  etest_p  \\\n",
              "0       1  67.00  91.00  Commerce     58.00   Sci&Tech       0    55.00   \n",
              "1       1  79.33  78.33   Science     77.48   Sci&Tech       1    86.50   \n",
              "2       1  65.00  68.00      Arts     64.00  Comm&Mgmt       0    75.00   \n",
              "3       1  56.00  52.00   Science     52.00   Sci&Tech       0    66.00   \n",
              "4       1  85.80  73.60  Commerce     73.30  Comm&Mgmt       0    96.80   \n",
              "5       1  55.00  49.80   Science     67.25   Sci&Tech       1    55.00   \n",
              "6       0  46.00  49.20  Commerce     79.00  Comm&Mgmt       0    74.28   \n",
              "7       1  82.00  64.00   Science     66.00   Sci&Tech       1    67.00   \n",
              "8       1  73.00  79.00  Commerce     72.00  Comm&Mgmt       0    91.34   \n",
              "9       1  58.00  70.00  Commerce     61.00  Comm&Mgmt       0    54.00   \n",
              "\n",
              "   specialisation  mba_p  status    salary  \n",
              "0               1  58.80       1  270000.0  \n",
              "1               0  66.28       1  200000.0  \n",
              "2               0  57.80       1  250000.0  \n",
              "3               1  59.43       0       0.0  \n",
              "4               0  55.50       1  425000.0  \n",
              "5               0  51.58       0       0.0  \n",
              "6               0  53.29       0       0.0  \n",
              "7               0  62.14       1  252000.0  \n",
              "8               0  61.29       1  231000.0  \n",
              "9               0  52.21       0       0.0  "
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              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>ssc_p</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>hsc_s</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>degree_t</th>\n",
              "      <th>workex</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>status</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>67.00</td>\n",
              "      <td>91.00</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>0</td>\n",
              "      <td>55.00</td>\n",
              "      <td>1</td>\n",
              "      <td>58.80</td>\n",
              "      <td>1</td>\n",
              "      <td>270000.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>79.33</td>\n",
              "      <td>78.33</td>\n",
              "      <td>Science</td>\n",
              "      <td>77.48</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>1</td>\n",
              "      <td>86.50</td>\n",
              "      <td>0</td>\n",
              "      <td>66.28</td>\n",
              "      <td>1</td>\n",
              "      <td>200000.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>65.00</td>\n",
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              "      <td>64.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>0</td>\n",
              "      <td>75.00</td>\n",
              "      <td>0</td>\n",
              "      <td>57.80</td>\n",
              "      <td>1</td>\n",
              "      <td>250000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>56.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Science</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>0</td>\n",
              "      <td>66.00</td>\n",
              "      <td>1</td>\n",
              "      <td>59.43</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>85.80</td>\n",
              "      <td>73.60</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>73.30</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>0</td>\n",
              "      <td>96.80</td>\n",
              "      <td>0</td>\n",
              "      <td>55.50</td>\n",
              "      <td>1</td>\n",
              "      <td>425000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>55.00</td>\n",
              "      <td>49.80</td>\n",
              "      <td>Science</td>\n",
              "      <td>67.25</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>1</td>\n",
              "      <td>55.00</td>\n",
              "      <td>0</td>\n",
              "      <td>51.58</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>46.00</td>\n",
              "      <td>49.20</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>79.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>0</td>\n",
              "      <td>74.28</td>\n",
              "      <td>0</td>\n",
              "      <td>53.29</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>82.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>Science</td>\n",
              "      <td>66.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>1</td>\n",
              "      <td>67.00</td>\n",
              "      <td>0</td>\n",
              "      <td>62.14</td>\n",
              "      <td>1</td>\n",
              "      <td>252000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>73.00</td>\n",
              "      <td>79.00</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>72.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>0</td>\n",
              "      <td>91.34</td>\n",
              "      <td>0</td>\n",
              "      <td>61.29</td>\n",
              "      <td>1</td>\n",
              "      <td>231000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>58.00</td>\n",
              "      <td>70.00</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>61.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>0</td>\n",
              "      <td>54.00</td>\n",
              "      <td>0</td>\n",
              "      <td>52.21</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b6879cea-0f64-40df-8bb7-d9b2adcaff88')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-b6879cea-0f64-40df-8bb7-d9b2adcaff88 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-b6879cea-0f64-40df-8bb7-d9b2adcaff88');\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": 20
        }
      ],
      "source": [
        "#Label Encoding\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "object_cols= ['gender','workex','specialisation','status']\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "for col in object_cols:\n",
        "    placement_filtered[col]= label_encoder.fit_transform(placement_filtered[col])\n",
        "\n",
        "placement_filtered.head(10)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8o5FkGEq11K5",
        "outputId": "943b10b9-fce3-4e54-e951-6a0e816ab0a4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   gender  ssc_p  hsc_p  degree_p  workex  etest_p  specialisation  mba_p  \\\n",
              "0       1  67.00  91.00     58.00       0     55.0               1  58.80   \n",
              "1       1  79.33  78.33     77.48       1     86.5               0  66.28   \n",
              "2       1  65.00  68.00     64.00       0     75.0               0  57.80   \n",
              "3       1  56.00  52.00     52.00       0     66.0               1  59.43   \n",
              "4       1  85.80  73.60     73.30       0     96.8               0  55.50   \n",
              "\n",
              "   status  dummy_Arts  dummy_Commerce  dummy_Science  dummy_Comm&Mgmt  \\\n",
              "0       1           0               1              0                0   \n",
              "1       1           0               0              1                0   \n",
              "2       1           1               0              0                1   \n",
              "3       0           0               0              1                0   \n",
              "4       1           0               1              0                1   \n",
              "\n",
              "   dummy_Others  dummy_Sci&Tech  \n",
              "0             0               1  \n",
              "1             0               1  \n",
              "2             0               0  \n",
              "3             0               1  \n",
              "4             0               0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1d94e5e0-aedc-4dce-9df0-ccda4c60da78\">\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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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>ssc_p</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>workex</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>status</th>\n",
              "      <th>dummy_Arts</th>\n",
              "      <th>dummy_Commerce</th>\n",
              "      <th>dummy_Science</th>\n",
              "      <th>dummy_Comm&amp;Mgmt</th>\n",
              "      <th>dummy_Others</th>\n",
              "      <th>dummy_Sci&amp;Tech</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>67.00</td>\n",
              "      <td>91.00</td>\n",
              "      <td>58.00</td>\n",
              "      <td>0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>1</td>\n",
              "      <td>58.80</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>79.33</td>\n",
              "      <td>78.33</td>\n",
              "      <td>77.48</td>\n",
              "      <td>1</td>\n",
              "      <td>86.5</td>\n",
              "      <td>0</td>\n",
              "      <td>66.28</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>65.00</td>\n",
              "      <td>68.00</td>\n",
              "      <td>64.00</td>\n",
              "      <td>0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0</td>\n",
              "      <td>57.80</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>56.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>52.00</td>\n",
              "      <td>0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>1</td>\n",
              "      <td>59.43</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>85.80</td>\n",
              "      <td>73.60</td>\n",
              "      <td>73.30</td>\n",
              "      <td>0</td>\n",
              "      <td>96.8</td>\n",
              "      <td>0</td>\n",
              "      <td>55.50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1d94e5e0-aedc-4dce-9df0-ccda4c60da78')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-1d94e5e0-aedc-4dce-9df0-ccda4c60da78 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-1d94e5e0-aedc-4dce-9df0-ccda4c60da78');\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": 21
        }
      ],
      "source": [
        "# One Hot Encoding\n",
        "dummy_hsc_s = pd.get_dummies(placement_filtered['hsc_s'], prefix = 'dummy')\n",
        "dummy_degree_t = pd.get_dummies(placement_filtered['degree_t'], prefix = 'dummy')\n",
        "\n",
        "placement_coded = pd.concat([placement_filtered , dummy_hsc_s , dummy_degree_t],axis = 1)\n",
        "placement_coded.drop(['hsc_s','degree_t','salary'],axis = 1 , inplace = True)\n",
        "placement_coded.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hescIM2w11K5"
      },
      "source": [
        "## Splitting Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZUUuFpJU11K6"
      },
      "outputs": [],
      "source": [
        "X = placement_coded.drop(['status'],axis=1)\n",
        "y = placement_coded.status"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O21UcA5I11K6"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X , y , train_size = 0.8 , random_state = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kl2Ga3vf11K6"
      },
      "source": [
        "## Classification Models"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Logistic Regression Classifier"
      ],
      "metadata": {
        "id": "3OcFT2kBAwH0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_RIqysaC11K6",
        "outputId": "0af7f0b2-000a-4183-90c6-43fccbe6afa7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8333333333333334\n"
          ]
        }
      ],
      "source": [
        "#Logistic Regression -\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import metrics\n",
        "\n",
        "logreg = LogisticRegression()\n",
        "\n",
        "logreg.fit(X_train , y_train)\n",
        "\n",
        "y_pred = logreg.predict(X_test)\n",
        "\n",
        "print(logreg.score(X_test , y_test))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install yellowbrick"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gmJUcv3taCsR",
        "outputId": "f77f61f3-e0f8-4379-ceae-9b1db0694b79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: yellowbrick in /usr/local/lib/python3.10/dist-packages (1.5)\n",
            "Requirement already satisfied: cycler>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from yellowbrick) (0.11.0)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from yellowbrick) (1.22.4)\n",
            "Requirement already satisfied: scikit-learn>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from yellowbrick) (1.2.2)\n",
            "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from yellowbrick) (3.7.1)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from yellowbrick) (1.10.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (1.0.7)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (3.0.9)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (4.39.3)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (8.4.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (23.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.0.0,>=2.0.2->yellowbrick) (1.4.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.0->yellowbrick) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.0->yellowbrick) (3.1.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib!=3.0.0,>=2.0.2->yellowbrick) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from yellowbrick.classifier import ClassificationReport\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "conf_mat = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Plot the confusion matrix as a heatmap\n",
        "plt.figure(figsize=(4,3))\n",
        "sns.heatmap(conf_mat, annot=True, cmap='Blues', fmt='g', cbar=False)\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n",
        "\n",
        "# Calculate the classification report, which includes precision, recall, and f1 score\n",
        "report = classification_report(y_test, y_pred, output_dict=True)\n",
        "\n",
        "# Plot the classification report\n",
        "class_names = ['negative', 'positive']\n",
        "\n",
        "visualizer = ClassificationReport(logreg, classes=class_names, support=True, cmap='YlGn',figsize=(4, 3), fig_scale=0.5)\n",
        "visualizer.fit(X_train, y_train)\n",
        "visualizer.score(X_test, y_test)\n",
        "visualizer.show()"
      ],
      "metadata": {
        "id": "W0AWhnA2Rzw7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 882
        },
        "outputId": "b4349211-87a7-4690-9d67-34e0be68280e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'LogisticRegression Classification Report'}>"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Logistic Regression Classifier with Hyperparameter"
      ],
      "metadata": {
        "id": "yetFVm2NqYPN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from scipy.stats import loguniform\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, random_state=1)\n",
        "\n",
        "param_dist = {\n",
        "    'penalty': ['l1', 'l2', 'elasticnet', 'none'],\n",
        "    'C': loguniform(1e-4, 1e4),\n",
        "    'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],\n",
        "    'l1_ratio': [0.0, 0.25, 0.5, 0.75, 1.0]\n",
        "}\n",
        "\n",
        "logreg = LogisticRegression()\n",
        "\n",
        "search = RandomizedSearchCV(\n",
        "    logreg,\n",
        "    param_distributions=param_dist,\n",
        "    n_iter=100,\n",
        "    cv=5,\n",
        "    random_state=1,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "search.fit(X_train, y_train)\n",
        "\n",
        "print(\"Best hyperparameters:\", search.best_params_)\n",
        "print(\"Best score:\", search.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_UQ3hC2gqX3Y",
        "outputId": "c2be269b-b6cf-4e4e-f8f1-48c1697ba2b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best hyperparameters: {'C': 1.3827762586749435, 'l1_ratio': 0.75, 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "Best score: 0.8787878787878789\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Decision Tree Classifier"
      ],
      "metadata": {
        "id": "yNCWy1t3zGjI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AzF5gUAJ11K7",
        "outputId": "5ae7f2c1-8285-4157-e434-07b28821fc9e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 0.7380952380952381\n"
          ]
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dt = DecisionTreeClassifier(criterion = 'gini' , max_depth = 3)\n",
        "\n",
        "dt = dt.fit(X_train , y_train)\n",
        "y_pred = dt.predict(X_test)\n",
        "\n",
        "print(\"Accuracy\", metrics.accuracy_score(y_test , y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from yellowbrick.classifier import ClassificationReport\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "conf_mat = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Plot the confusion matrix as a heatmap\n",
        "plt.figure(figsize=(4,3))\n",
        "sns.heatmap(conf_mat, annot=True, cmap='Blues', fmt='g', cbar=False)\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n",
        "\n",
        "# Calculate the classification report, which includes precision, recall, and f1 score\n",
        "report = classification_report(y_test, y_pred, output_dict=True)\n",
        "\n",
        "# Plot the classification report\n",
        "model = dt  # replace with your trained classifier\n",
        "class_names = dt.classes_.tolist()  # replace with your list of class names\n",
        "visualizer = ClassificationReport(model, classes=class_names, support=True, cmap='YlGn')\n",
        "visualizer.score(X_test, y_test)\n",
        "visualizer.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 882
        },
        "id": "e1oRQuELXr3o",
        "outputId": "2592c5ec-015b-4dab-e233-ed69e33ed350"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": 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IEQ0ePFj169eXs7Oz1q9fr2XLlmnbtm2aM2eOsmTJoq5du+q9994ztN9Ss+1J/noi418lhbyrV68+dT+kxMDVvXt3VaxYUZ9++qk8PT1la2trtY9Ssw+mTZum9evXa8eOHVq7dq0cHBzUtGlTjRo16m+3ISWPHj36n38rZrNZPXr0UGBgoEaOHKlSpUrJ0dFRq1evTjYP+H9Jer/+tY9JIfPxv5sn/c383Q+9TJkyacOGDZbHZ86c0eDBg9WjRw+rKTlJffm7z5Gk/v11SpOzs7MePXpkWVdK+/3x7Ur6f9IINIBXE0Ear6S9e/eqZMmS8vDw0P379yUlzhNNOlHncUlfaFmyZLGM+KWGyWRSy5Yt1bJlS4WHh+vQoUOaMWOGBg8erLVr16bYTtKX8eOe9KX6PCS107VrV7Vp0ybZ80lhJGPGjMqRI4emTJmSrE5SQKhUqZJsbW31/fffPzFI/+c//5Gbm1uKz2/fvl3lypXTxIkTLWWPX3c6iZeXl6ZNmyaz2SxfX18tXbpU/fv31+7du1WgQAF5eHho2LBhGjZsmAICArR582bNnj1bHh4ehq7akpptT63s2bOrcOHC+v777y0n8P3V9evXdebMGb355pvJgv+uXbtkY2OjL774whKsEhISkr1P/24f2Nvbq1OnTurUqZNCQ0P1/fffa8aMGYqLi9P06dOfapuS2gsLC5PZbE7xx8qlS5d04cIFffTRR3rnnXcs5Ulz8FMrKUQ+evTIahpKUiB9FiHT1tbWahpQ/vz5tXfvXn388ceqUaOGZcpJ0tSav/sckaSIiIhk+yY8PNwyxSdjxowKDAxMto6k7Up6rQG8+jjZEK+c1atX6+zZs+rTp48kqWDBgsqYMaMCAgKUP39+y788efIoLi7OMspYtGhRnThxwmpd33//vTp06JBsxDgyMlK7du2yHDZ2cXFRkyZN1KVLF50/fz7FfpUtW1a+vr6Kjo62lJnNZv32229Wh4GfJxcXFxUtWlT+/v5W+yJ//vyKiYlRlixZJEnlypWTv7+/cubMaVXHbDZbDkd7enqqWbNmWr16tS5dupSsrZs3b2r48OGWkxz/KjY21nL4PUnSSXtJo4gnT57UH3/8ISnxh0uZMmU0ZcoUxcfH69KlS7p69arlpDspcf7soEGD9Nprr+nChQuG9lFqtv1p9OjRQ7///ru++eabZM/FxMRozJgxmjZtWrL3mJS4jxwcHKyC1e7duxUVFWXZR3+3D0JDQ/Xtt99artCRKVMmtWnTRs2bN3/ie/XvFC1aVHFxcVY3LImMjFTHjh21Z88eyzkFj4/gh4WFad++fZKSjxI/adQ46QfYX/8uT548KRsbG8uc72dt9OjRioyMtDpxOLWfI1LiTYpOnz5teRwRESF/f38VKlTIsl0BAQHJrpRz8uRJFS5cOFVHxoxOqQLwYhGk8dJKSEjQ3bt3dffuXd2+fVunTp3SuHHj9PHHH6t379564403JCXOM+3Zs6fWrVunr776SlevXtX58+c1atQotWnTxvJl1qNHDwUEBGjy5MkKCAjQ0aNH9emnn8rDwyPZF5udnZ2mT5+u4cOH6/Tp0woMDNRvv/2m7du3q3Llyin2t1OnToqOjtaQIUN08eJFXb58WRMmTNCVK1csJxa+CL1799YPP/ygefPmyc/PT5cvX9a0adP09ttv69y5c5ISL60VHh6uIUOGyNfXVwEBAdq4caNatmxpdRh89OjRKlSokDp27KiVK1fKz89P/v7+lkubZc2aVZMmTUqxH+XKldOvv/6qX375RdeuXdOMGTOUkJAgW1tbnT59WsHBwTp48KD69eunffv26ebNm7py5YoWLVokJycnlS5dWtevX9cHH3ygFStW6OrVq7p586a2bNkif39/VapUydD+Se22p1arVq3UqlUrjRs3Tp988onOnDmjGzdu6NChQ+rUqZMuXbqkOXPmpDgKWa5cOYWHh2vlypW6ceOGtmzZorVr16pcuXL6888/dePGjb/dB2azWRMnTtTYsWN14cIFBQYG6pdfftGBAwee+F79Ow0aNFChQoU0fvx4+fr66sqVKxo/frwuXLigsmXLqlChQnJ3d9fatWvl7++v33//XT179rScg/Drr78qMjLSMsp76NChZHd4lBIDZ9WqVTV16lT9+OOPCggI0LfffqtFixapZcuWhn7YpEaOHDnUv39/bdu2Tf/5z38kpf5zREo8sjN9+nSdPHlSly9f1sSJExUXF6cWLVpIkt555x1lypRJgwYN0unTp+Xv76+5c+fqp59+sjqxMCVJ++w///mPzp07R6AGXnJM7cBLKzg4WK+//rqkxNFKd3d3lS1bVl9++aWlPEnv3r3l4uKitWvXavr06XJwcFClSpW0du1ay3zmqlWrasGCBZo/f742btwoDw8PNWjQIMXbFdvb22vlypWaPn263nvvPYWHhytbtmyqWbPmE29vXKhQIa1cuVKzZs3Su+++q4SEBBUvXlyLFi2ynLD0Irz11luysbHR0qVLtXjxYtnZ2al06dL68ssvLSc25c+fX6tXr9bs2bPVuXNnxcbGqkCBAhoxYoR8fHws63J3d9e6deu0evVqbd++XZ9//rlsbGyUN29ederUST4+Pk8cXRs4cKDu3r2rDz74QI6OjmrevLkmTJggZ2dnrVu3TiaTSZMmTZKtra2mTZumO3fuyNnZWcWLF9fSpUuVM2dO5cyZU5988olWrlypzz//XCaTSfnz59fYsWOfeJLo30nttj+NpBuubNiwQd27d1dUVJRy5sypmjVravbs2VaXWHtc06ZN5evrq8WLF2vu3LmqUqWK5syZo5MnT2rs2LHq2rWr9u/f/7f7YMWKFfr888/VqVMnRUVFKUeOHGrcuLE+/PBDQ9vj4OBgmePevXt3JSQkqGTJklq5cqVlXvhnn32mTz/9VC1atFD+/Pk1cOBAeXt769SpUxowYIC++OIL1a5dW+XLl9fUqVNVtGhRbdmyJVlbCxYs0PTp0zVmzBiFhobK09NTHTt2THbJuWetc+fO2rp1q8aPH6+dO3cqQ4YMqfockRKD9Pvvv6+PPvpIV65ckaenp2UbpcSR+tWrV2v69Onq1q2boqOjVahQIU2bNk0tW7b8n/1Kui71ihUr9M033+jw4cOytbV9nrsCwD9gMvNzFwCAVBk5cqQOHz5sGckG8O/G1A4AAADAAII0AAAAYABTOwAAAAADGJEGAABAunH48GFVr179iRcHSJKQkKDZs2erfv36qlSpkuXqXk+DIA0AAIB0YenSpZoyZYrVjZieZO3atdqxY4eWLFmigwcPqkCBAnr//fef6rKTBGkAAACkC46Ojtq8eXOqgvSGDRvUtWtXFS5cWK6urho0aJD8/PwsNwpLjRd+HelTp07JbDbL3t7+RTcNAACQLsTGxspkMsnb2zutu2Jx8eJFxcTEPJd1Ozg4yMvL62/rde7cOVXri4qK0uXLl63uoOrq6qr8+fPL19dX5cqVS9V6XniQNpvN3KkJAADgH3gZs1RMTIwiIiMV9OD+M11vDvcsz3R9kvTgwQOZzWbL3USTuLu7KyQkJNXreeFBOmkk+u3t/3sCOJBal8fslySZGpb4m5pA6pj3Jd5K3dT3xd2REumbeeFRSVK9zakbLQP+zudew9K6CykKenBfLWcMeabr3DZspgplyPNM15nkn/4gYY40AAAA/lUyZcokGxsbhYaGWpWHhoYqS5bUj4ATpAEAAPCv4ujoqNdee01nz561lD18+FDXr19XmTJlUr0egjQAAADSvdu3b6tx48aWa0X7+Pjoq6++kp+fn8LCwvTZZ5+pePHiKl26dKrX+cLnSAMAAADPQ1IIjouLkyTt3594HpWvr69iY2Pl7+9vubJIu3btdPfuXXXq1Enh4eGqUqWK5s+f/1TtEaQBAACQLvj6+j7xuTx58ujixYuWxyaTSQMGDNCAAQMMt8fUDgAAAMAAgjQAAABgAEEaAAAAMIAgDQAAABhAkAYAAAAMIEgDAAAABhCkAQAAAAMI0gAAAIABBGkAAADAAII0AAAAYABBGgAAADCAIA0AAAAYQJAGAAAADCBIAwAAAAYQpAEAAAADCNIAAACAAQRpAAAAwACCNAAAAGAAQRoAAAAwgCANAAAAGECQBgAAAAwgSAMAAAAGEKQBAAAAAwjSAAAAgAEEaQAAAMAAgjQAAABgAEEaAAAAMIAgDQAAABhAkAYAAAAMIEgDAAAABhCkAQAAAAMI0gAAAIABBGkAAADAAII0AAAAYABBGgAAADCAIA0AAAAYQJAGAAAADCBIAwAAAAYQpAEAAAADCNIAAACAAQRpAAAAwAC7tO4AAAAA0gk7G6lwxme/zpfUy9szAAAA4CVGkAYAAAAMIEgDAAAABhCkAQAAAAMI0gAAAIABBGkAAADAAII0AAAAYABBGgAAADCAIA0AAAAYQJAGAAAADCBIAwAAAAYQpAEAAAADCNIAAACAAQRpAAAAwACCNAAAAGAAQRoAAAAwgCANAAAAGECQBgAAAAwgSAMAAAAGEKQBAAAAAwjSAAAAgAEEaQAAAMAAgjQAAABgAEEaAAAAMIAgDQAAABhAkAYAAAAMIEgDAAAABhCkAQAAAAMI0gAAAIABBGkAAADAAII0AAAAYABBGgAAADCAIA0AAIB04+bNm+rVq5eqVKmiunXrasaMGUpISEhWLyEhQXPnzlW9evXk7e2tZs2aaffu3U/Vlt2z6jQAAACQ1vr376+SJUtq//79un//vnr37q2sWbOqW7duVvXWrVunTZs2adWqVcqfP79++uknffDBBypUqJCKFSuWqrYYkQYAAEC64OvrqwsXLmjo0KFyc3NTgQIF1LVrV23YsCFZ3bNnz6pChQoqVKiQbG1tVbduXWXKlEkXL15MdXsEaQAAAKQLZ8+eVe7cueXu7m4pK1mypPz9/RUWFmZVt06dOjp27JjOnz+vmJgY/fDDD4qMjFTlypVT3R5TOwAAAJAuhIaGKmPGjFZlSaE6JCRErq6ulvKGDRvq/PnzatmypSQpQ4YMmjZtmnLmzJnq9gjSAAAASDfMZnOq6m3btk3btm3Tpk2b5OXlpSNHjmjIkCHKmTOnypQpk6p1MLUDAAAA6YKHh4dCQ0OtykJDQ2UymeTh4WFVvmbNGr377rsqU6aMHB0dVadOHVWtWlXbt29PdXsEaQAAAKQLpUqVUmBgoIKDgy1lvr6+KlKkiFxcXKzqJiQkKD4+3qosJibmqdojSAMAACBdKFGihEqXLq2ZM2cqLCxMfn5+WrFihXx8fCRJjRs31okTJyRJ9erV0+bNm3XhwgXFxcXp559/1pEjR1S/fv1Ut8ccaQAAAKQbc+fO1bhx41SjRg25urqqXbt2at++vSTJ399fERERkqTevXsrLi5O77//voKDg5U7d25NmTJF1apVS3VbBGkAAACkGzly5NDSpUtTfO7xa0Tb29tr4MCBGjhwoOG2mNoBAAAAGECQBgAAAAwgSAMAAAAGMEcaAAAAz4S9nUmF87j+fcWnXOfLihFpAAAAwACCNAAAAGAAQRoAAAAwgDnSz0Hbcm+qe+XWypc5p4IjH2rHmQOa+eMyxSXEJ6v7TpmGmt5s+BPXVXt+B918cNvyuFvlVhpWr6f2XfhZA7d9nOIy1QuU18wWIyVJ1T5v+w+3Bi+Lge901rQeQ7TlP9/L55Oh/7Oug729Pu46UO3qvKnsmbLIL/C6pm9crpX7tlrV69G4lQa36qrCOfPq3sMQfX1wl0Yvn6O4+DhLnRoly+uT7gNV8bWSiomL1d4T/9GgRVMVGHz3uWwnXoweNZprcH0fFc6aW/fCQ/X18X0avW1hip9TkuThklGTm/VW45JVlds9mwIf3teX/9muGd+vUUxcrCTJZDJpUD0fdaveVIWz5lZ4TJQOXDyhYVvm63pwkNX66herpDVdJ0qSco5s+ly3FS+nwAOXdWP3BUUGPZKdm6M8yuZUIZ9ycnDPoFMT9in03O0Ul8tUwlPeHzV8wb0FUkaQfsZaln5DU5oM0qf7F+v7i/9RMc9C+rjJIDk7ZNCEPZ8nq7/r3I/6ye94svIPXu+oqgXKKehhYlhxd3LT9GbDVDJnUUXHpnwfeBuTjQbU6qweVVrrbliIMtg7PtuNQ5rI7OaulUM/VoXXSioyJipVyyzsP0HNqtZR91ljdf76FTWtXEtfDpqkiOhIbTy0R5LUqUFzLRn4kYYsma5tv/ygMgW9tHTQR3J1cla/eZMkSUXzFNC+T5dq40979N7s8cqaMbNm9h6uPZ8sUYX321gFbrw6OlV5U0vaj9SQb+Zq2x+HVCZ3ES3tOEqujs7qt256ists7/uZsrtl1ntrPtWVezdVpWBJLe0wSp5uHhqwcaYk6bN3Bui915ur77oZ+o/fHyqSLa8WtR+ug4MWqNjEdxUbHycbk40mvtVTQxq0V9DD+3K2d3qRm46XxPUd5+S3+jcV7lReWSvmUWTQI11cfFQRNx/Ke1JDlRpaWwlxCVbLmOPi9dvYvcpcJkca9RpIztDUjpUrV6pUqVIaNGjQs+7PK29AzU7aefZHrTj2jW48CNL+S79o9qGVaufdRJ5uWZLVj46L0b3wEKt/Lg4Z1Kbcm/rk+0WKNyd+kDQvVU/ODhnU/Ms+ehD1KMW2C2fNp5alGshn9WAdv376uW4nXpz2dZvKNYOzvPu1Usijh39bP1/2XOrasKXGrPxcO4/+KL9b1zV32xpt/GmPJnXub6k3oWM/rf9xt+Zs+UpXg25q+5EDGrdqnno1aaNcWbJLkka07al7D0PVc9Z4XbpxVb+cO6UuM0apTCEvta7JiNCrakLTHlp/8nvNObBeV+8Havvpwxq3fYl6vd5CudyzJavv5ZlfNQqX0eTdy3Xg4gldvR+oDSf2a82xvepUpbEkydbGVq2862j6vjVae2yPrt4P1P4LxzRh55cqlDW3yuQuIkkqnrOAOlV5U7Vm9tFPf/7+ArcaLwuz2ayA7eeUo3Yh5WtWQs45MyqLd24VaF1GDy7cUfi1ENm7Ocoxcwarf0GHrshka1LeZiXTehMAi6cK0qGhoerTp4+WLVsmR0dGO/8qf+bcypc5l368fNSq/JDfcdna2KpmoUqpWs/Yhu/rl6un9NOV/x+pPvjnr+ry9QgFR4Q+cbnbj+6pxbK+OhN4yVD/8XLadeyQ3hjZU3dDg1NVv2GF6rKxsdGuXw9ZlX93/LC88hZUwRx5VCRXfhXOlU+7jv1kXefYYdna2qpRxdclSY0q1tDeEz8r/rHD/ZduXNWVwAC9WanmP9wypIUi2fKqcLY82uX7i1X5d2ePyNbGVo1KVHnisvEJ1iOE0XExjz0XrwJj39aU71ZY1Un472BA7H+PXtwMvavyn3TRyesX/tF24NVlMplUeVYzvda1olW5o4ezJCkuKvmRrqi74bq29YwKd6ogWwfbF9JPIDWeKkjv3LlTERER2rZtm9zd3Z9Xn15ZhbLkkSRdDwm0Kg98eEcxcTEqlCXv366jbK5iqlukij7/aZVV+Y0HQZYvpCd5GBX2xNFqvLquBt1UQsL/fu0f55WnoKJionXr/h2r8su3rkuSiuUtJK+8BSRJfv8tSxJwN1DRMTEqlregXJyclTurp/wCA5K1cfnWdRXLW/AptwQvAy/PfJIkv3s3rMoDQm4rOjZGxXIUSLbMxdvXdODiCQ1v2FH5PXJKkrzzeqldhTe06PDWZPWTlMtTVOPe7K7tpw/r9M3LkqTQiEcKifj7IytI3+zdHGXn4mBVdu9EgGwd7eSaN1Oy+te2nlEGTzdlq5rvBfUQSJ2nmiNdu3Zt+fj4yNaWX4MpcXV0kSSFx0Qkey4sJlJu/33+f+lT3Uc/XznJqDIMy+jiokcR4cnKH0aESZLcXVxlMiVe3P5RZPL36qPIcLm7uCmjc+L79Unryp8917PsNl6QjBn++7pGpfDaR0fIPUPKn1MtFw3X5vc+1dWPtyoqNlpO9o5acGizRm37IlndqS3f16D67WRrY6MFh77RkM3Jzw8BHnfvRIBu7f9TBduVSxawo0MiFfSjn7z6VrV8dgEvi6cK0nnz/v2IKozLmymn6hetpt4bx6V1VwDAyuquE1U4W261WjJKfndvqHKBEprasp9CI8I0dvsiq7ozvl+jVUd3yTuvlz5t2VdenvnUZP7gvz2qhn+nO0eu6dzcn+X5ekHlf7tUsudvfX9Jds72yl69wIvvHPA3uGrHM/QwKnHEz9XROdlzrg4ZLM8/SUOv1xUVG63/+P/2XPqHf4fQsEdyc04+quju7CZJCgl7KBtT4qyujCnUc8vgopBHD/TgvyPYGV2S3+rV3cVNIWEcnn8VhUYkTv/K6JTCa+/orJCI5NPDmpaqoRZla+n1z3rrP35/SJL+uPGnMtg7albrD7Xg0GYFPrhnqX8//IHuhz/Q+aCrunj7mk6MWqlW3nW16bcfntNW4VV147sL+nPFCeVuVFSvdauU4ojz3aPXlKV8btnYcusLvHx4Vz5DV+4nziXNnzm3VXke9xxysHPQn/eu/c/lG3rV0JGrpxQTH/vc+oj070KAv5wcHJUnm/UloormKSBJOnfNTxcCrkiSiuSynm9YIEduOTo46Nx1P0VERer6ncBkdSSpaO4COnfN7/lsAJ6rC7cTP4eKZMtjVV4gS0452jvoXKB/smWK5ywgSTpzy/o1v3j7umxtbFU4W25lcXFX2woN5JnRw6rOmVuJ77USOZlTD2s3913SnyuOq1AHbxXtUVkmm+QhOiLwocIDHihLhTwprAFIewTpZyggNFCX711TvdeqWZW/4VVDsfFxKV4vOomjnYPK5S6ukzfOPu9uIp3bc/yw4uPj1axqHavyltXr6w+/Cwq4Gyj/oBs6f91PzarWTVYnNi5We078LEna9eshNa74uuxs///gVbnCxZXfM5d2/HrwuW8Lnj3/e7d0PvCqmpV53aq8Zdnaio2P055zR5Mtc+1+4s1U/hqGi//3xMSr9wOVwcFRG3pOUecqTazqlM3zmqTEq3UASUJ8A3Xpy2Mq0rmC8rd48uXsQs4kvvfcvZJflhF4GRCkn7E5h1aqcfGa6l65lXJlzK4GRavrg9c7auWxLQqOCFWZXF7a23u5KuSxngdW0COPbG1sdT3kVorrdXdyU1aXzMrqklm2NjZytHOwPHa0Szwxw9neyVLmZO8oG5PJ8tg1FSc64uWU2c1dnpmzyjNzVtna2sjJwdHy2MnBUZW8Suv8sp2qUbK8JCkw+K6+2LFek7sM0FtV6yhf9lwa1qa7mlWtozEr//+kr3Gr5ql1zYYa1KqL8mXPpebV6ml8h76as3W15VJ70zctk1sGFy0bPFmv5c6vSl6ltWLoxzp6/g99+8uBNNkf+OfG7Vis1t71NKi+j/J55FDzMjU1vml3zTmwXncfhahS/hI6P2G9ahQuK0na6fuz/O7e0JIOI1XPq6IKZs2ld7zralTjztp77qhuhNzRjZA7WvHLTo19s5u6VmuqQllzq65XBX3ZcZQCH9yzTOtwccwgz4we8szooQz2jrKxMVkepzTdBOmP2WzWpWXH5e6VTdlrFFR0SKTVv7jI/z8qG3HzgWwd7eSQKUMa9hh4MuZIP2N7LhzW0G+nqm+N9hpat4fuhYdq5fEtmn94jSQpg52TCmfNJxcH6w+FTBkS568+ik5+hQRJ+qL1RFXJX9byOGfG7HrDq4YkafiO6dpyep96Vm2rAbU6Wy13dOAmSdI3f+zViJ0zns1G4oXaMv5z1Slb2fI4b7acalm9viSp64zRunr7porlLSTXDP8/N3/Qoql6GBGmhf3HK5u7hy7e8Ne7Hw+xurb0N4f3qdP0kRrdrpc+7TZIt0Pva87W1Zq8dqGlztWgm6o3vJtm9h6uPxZtVWR0tHb8elCDF02T2Wx+AVuP5+GbUwfVaeVHGt24iz5t0Ve3HwVrzoENmrx7uSTJ2cFJxXIUkKtj4udUZGy03pg7QFNb9tOGnlPknsFVtx8Ga/2J/Rrz7f+faNhn3TTdfHBX45p0V55M2RX08L4OX/5DY75dpAeRiXPuhzbooIlv9bTqT9C03ZKklUd2qdtXk1/ELkAair4XroibDyRJv/TanOz5Am3KqGDbxO+7uLAY2Trbv9D+AU/DZH6Kb8PQ0FDFxib+UmzdurVKliypjz76SJLk5uYmJ6e/v9Wrr6+vJOnt7dwVEc/G5TH7JUmmhiXSuCdIL8z7zkmSTH2rpnFPkF6YFyZOmam3ufPf1ARS53OvYZKk0qVLp3FP/p+vr68CQgM14Kfpz3S9c2sNV95MOV+qbU3yVCPS/fv317FjxyyPg4KC9MMPiYfrPv30U73zzjvPtncAAADAS+qpgvTq1aufVz8AAACAVwonGwIAAAAGEKQBAAAAAwjSAAAAgAEEaQAAAMAAgjQAAABgAEEaAAAAMIAgDQAAABhAkAYAAAAMIEgDAAAABhCkAQAAAAMI0gAAAIABBGkAAADAAII0AAAAYABBGgAAADCAIA0AAAAYQJAGAAAADCBIAwAAAAYQpAEAAAADCNIAAACAAQRpAAAAwACCNAAAAGAAQRoAAAAwgCANAAAAGECQBgAAAAwgSAMAAAAGEKQBAAAAAwjSAAAAgAEEaQAAAMAAu7TuAAAAANIHO3s75fPK9czX+bJiRBoAAAAwgCANAAAAGECQBgAAAAwgSAMAAAAGEKQBAAAAAwjSAAAAgAEEaQAAAMAAgjQAAABgAEEaAAAAMIAgDQAAABhAkAYAAAAMIEgDAAAABhCkAQAAAAMI0gAAAIABBGkAAADAAII0AAAAYABBGgAAADCAIA0AAAAYQJAGAAAADCBIAwAAAAYQpAEAAAADCNIAAACAAQRpAAAAwACCNAAAANKNmzdvqlevXqpSpYrq1q2rGTNmKCEhIcW6fn5+6tSpk8qWLavatWtr5cqVT9UWQRoAAADpRv/+/eXp6an9+/drxYoV2r9/v1atWpWsXlRUlHr27KnatWvr6NGjmjdvnjZv3iw/P79Ut0WQBgAAQLrg6+urCxcuaOjQoXJzc1OBAgXUtWtXbdiwIVnd7777Tq6ururZs6cyZMigMmXKaOfOnSpcuHCq2yNIAwAAIF04e/ascufOLXd3d0tZyZIl5e/vr7CwMKu6J0+eVNGiRTVq1ChVrFhRjRs31vbt25+qPYI0AAAA0oXQ0FBlzJjRqiwpVIeEhFiVBwUF6YcfflD16tV1+PBh9e7dWyNGjNC5c+dS3R5BGgAAAOmG2WxOdb2SJUuqWbNmypAhg95++22VKVNGe/bsSXVbBGkAAACkCx4eHgoNDbUqCw0NlclkkoeHh1V5tmzZ5ObmZlWWO3du3b17N9XtEaQBAACQLpQqVUqBgYEKDg62lPn6+qpIkSJycXGxqlu4cGFdunTJagT75s2byp07d6rbI0gDAAAgXShRooRKly6tmTNnKiwsTH5+flqxYoV8fHwkSY0bN9aJEyckSc2bN1dISIgWLVqkqKgo7dy5U2fPnlXz5s1T3R5BGgAAAOnG3LlzdefOHdWoUUOdO3dWy5Yt1b59e0mSv7+/IiIiJEmenp5avHix9uzZo0qVKmnevHlasGCB8uXLl+q27J7LFgAAAABpIEeOHFq6dGmKz128eNHqceXKlfXtt98abosRaQAAAMAAgjQAAABgAEEaAAAAMIAgDQAAABhAkAYAAAAMIEgDAAAABhCkAQAAAAMI0gAAAIABBGkAAADAAII0AAAAYABBGgAAADCAIA0AAAAYQJAGAAAADLBL6w4AAAAgfXC0s1OV3Hme+TpfVoxIAwAAAAYQpAEAAAADCNIAAACAAQRpAAAAwACCNAAAAGAAQRoAAAAwgCANAAAAGECQBgAAAAwgSAMAAAAGEKQBAAAAAwjSAAAAgAEEaQAAAMAAgjQAAABgAEEaAAAAMIAgDQAAABhAkAYAAAAMIEgDAAAABhCkAQAAAAMI0gAAAIABBGkAAADAAII0AAAAYIBdWjV8ecz+tGoa6ZR537m07gLSGfPCo2ndBaQzB1p/ldZdQDrh6+ub1l2AGJEGAAAADEmzEWlTwxJp1TTSmaSR6EsP1qRxT5BeFHXvKEnacmV2GvcE6cU7hQZJ4j2FZ+c1NUjrLkCMSAMAAACGEKQBAAAAAwjSAAAAgAEEaQAAAMAAgjQAAABgAEEaAAAAMIAgDQAAABhAkAYAAAAMIEgDAAAABhCkAQAAAAMI0gAAAIABBGkAAADAAII0AAAAYABBGgAAADCAIA0AAAAYQJAGAAAADCBIAwAAAAYQpAEAAAADCNIAAACAAQRpAAAAwACCNAAAAGAAQRoAAAAwwC6tOwAAAID0wdHWRpU8sj3bdT54ecd9X96eAQAAAC8xgjQAAABgAEEaAAAAMIAgDQAAABhAkAYAAAAMIEgDAAAABhCkAQAAAAMI0gAAAIABBGkAAADAAII0AAAAYABBGgAAADCAIA0AAAAYQJAGAAAADCBIAwAAIN24efOmevXqpSpVqqhu3bqaMWOGEhIS/ucyt2/flre3t+bNm/dUbdn9k44CAAAAL5P+/furZMmS2r9/v+7fv6/evXsra9as6tat2xOXmTJlimxtbZ+6LUakAQAAkC74+vrqwoULGjp0qNzc3FSgQAF17dpVGzZseOIyhw4d0uXLl1WnTp2nbo8gDQAAgHTh7Nmzyp07t9zd3S1lJUuWlL+/v8LCwpLVj4qK0qRJkzRhwgTZ2T39RA2CNAAAANKF0NBQZcyY0aosKVSHhIQkq79gwQKVK1dOVatWNdQec6QBAACQbpjN5lTVu3z5sjZt2qQdO3YYbosRaQAAAKQLHh4eCg0NtSoLDQ2VyWSSh4eHpcxsNmvixInq37+/smXLZrg9RqQBAACQLpQqVUqBgYEKDg62BGdfX18VKVJELi4ulnq3bt3S8ePH9eeff2ru3LmSpIiICNnY2OjAgQPaunVrqtojSAMAACBdKFGihEqXLq2ZM2dq1KhRun37tlasWKHu3btLkho3bqwpU6bI29tbhw4dslr2008/VY4cOdSzZ89Ut0eQBgAAQLoxd+5cjRs3TjVq1JCrq6vatWun9u3bS5L8/f0VEREhW1tb5ciRw2q5DBkyyNXV9ammehCkAQAAkG7kyJFDS5cuTfG5ixcvPnG5qVOnPnVbnGwIAAAAGECQBgAAAAwgSAMAAAAGEKQBAAAAAwjSAAAAgAEEaQAAAMAAgjQAAABgAEEaAAAAMIAgDQAAABhAkAYAAAAMIEgDAAAABhCkAQAAAAMI0gAAAIABBGkAAADAAII0AAAAYABBGgAAADCAIA0AAAAYQJAGAAAADCBIAwAAAAYQpAEAAAADCNIAAACAAQRpAAAAwAC7tO4AAAAA0gcHG1uVypLtma4z7JHtM13fs8SINAAAAGAAQRoAAAAwgCANAAAAGECQBgAAAAwgSAMAAAAGEKQBAAAAAwjSAAAAgAEEaQAAAMAAgjQAAABgAEEaAAAAMIAgDQAAABhAkAYAAAAMIEgDAAAABhCkAQAAAAMI0gAAAIABBGkAAADAAII0AAAAYABBGgAAADCAIA0AAAAYQJAGAAAADCBIAwAAAAYQpAEAAAADCNIAAACAAQRpAAAAwACCNAAAAGAAQRoAAAAwgCANAAAAGGCX1h1Irwa+01nTegzRlv98L59Phv7Pug729vq460C1q/OmsmfKIr/A65q+cblW7ttqVa9H41Ya3KqrCufMq3sPQ/T1wV0avXyO4uLjLHVqlCyvT7oPVMXXSiomLlZ7T/xHgxZNVWDw3eeynXgxNn31o1Ys+E7X/e8ocxY3vdW6mgaPbyN7+5T/hCPCo7Tws2+1b/sJBd0KVs48WdSy3evqMaCJ1TIhwY80few6/bD7N8XGxatshcIaOcVHxUrnlyR1avqxjv3nQoptVK5RTKt3jXn2G4sXYv+Go9qx/JCCrt9Txswuer1ZeXUY2lR29rZ/u+yj0HANbDRd9o52WvTTOKvnftj0q3avOqyga/fkmslF5V4vqvZDmsg9q5tVvStnb2jWgNUKvHpX685Nk4Oj/TPdPrz8diw/pDXTd6pKw9IaPLezpXzesHX6ccvxFJdZcXySMnq4vqguAn+LIP2MZXZz18qhH6vCayUVGROVqmUW9p+gZlXrqPussTp//YqaVq6lLwdNUkR0pDYe2iNJ6tSguZYM/EhDlkzXtl9+UJmCXlo66CO5Ojmr37xJkqSieQpo36dLtfGnPXpv9nhlzZhZM3sP155PlqjC+22sAjdeHdvW/axxHy7XyCk+qt+0gi6eDdC4D5cpIjxKH83qluIyg3t8oT9OXNZHs7qpWOl8OnLorCYN/UqREdEaNK6NJCkuLl493pkhBwdbLd82Qg4Odpo2bp26tJiqH/6YJVe3DJq35kPFxli/b2Ki4+TTeJKq1Sn53Lcdz8ePW45r0ZhN6jK6uSq/UUrXLgRq4eiNioqIUe/Jrf92+RWTv9Wj0HB5eLpblW9f9qNWT92hTiOaqVKDkgq6dk8LR2/SzSt3NHn9BzKZTJKk71b/rK+m7lDWXJmfy/bh5fYoNFzzh6/XlTMBT/wB5eVdQMMWdk1W7pbZ5Tn3Dng6hqZ2bNq0SU2aNFGpUqVUs2ZNTZs2TbGxsc+6b6+k9nWbyjWDs7z7tVLIo4d/Wz9f9lzq2rClxqz8XDuP/ii/W9c1d9sabfxpjyZ17m+pN6FjP63/cbfmbPlKV4NuavuRAxq3ap56NWmjXFmyS5JGtO2pew9D1XPWeF26cVW/nDulLjNGqUwhL7Wu2fC5bTOer/nTtqppq6rq+v6bylsguxo0raAPx7TWxpUHdftWcLL6fhdv6uCeUxo+yUcNm1dSvoKeerdrPTV+u7K+/nK/pd7OzUd0+cINzVv9oUqVK6iiJfJq2sLeGje9sxLiEyRJmTK7KptnJqt/29b/LDs7W/Xo3+SF7QM8Wxvn7VONt8qpWffa8sybRZXfKCWfQY21f/0R3Q8K/Z/Lnjp0Xkf2/KHaLStalZvNZn279KBqv11RzXvWUc4C2eRdu7ja9H9D50/469qFW5KkyPBobfh8r4Yv7KbX3yr3nLYQL7PD239TVHi0Pts+RC7uGVKsY+dgq8zZMib7l/RjDHhZPHWQ3rZtm8aNG6e2bdvqu+++04QJE7Rt2zZNmTLlefTvlbPr2CG9MbKn7oYmDzgpaVihumxsbLTr10NW5d8dPyyvvAVVMEceFcmVX4Vz5dOuYz9Z1zl2WLa2tmpU8XVJUqOKNbT3xM+KT4i31Ll046quBAbozUo1/+GWIS1c9QtSwNU7qt2wrFV5rQZllJBg1uEffJMtU/C1nPr54jw1bVXVqtwzZ2ZFRsYoISExJO/bflxVahZXNs9MljpZs7vrrdbVlDFTyqM+twLuacnsHRo2qZ0cnRz+4dYhLdzyv6vb1++rQt0SVuXedYorIcGs3w9ffOKykWFRWjR2s979sJGy5spk9ZzJZNKcPcPVbVxLq/KkUeuo8BhJkr2DnaZ/O0jetYr9843BK6lC3RIa/1WfZNN9gFfRUwfp+fPnq2nTpuratavy5s2rBg0a6MMPP9TGjRt1+/bt59HHV8rVoJuWoJIaXnkKKiomWrfu37Eqv3zruiSpWN5C8spbQJLk99+yJAF3AxUdE6NieQvKxclZubN6yi8wIFkbl29dV7G8BZ9yS/Ay8P8zUJKUr6CnVXnOPFlk72CnK3/eSraMjY2NsnlmsjpkGhcXr5++/0NlKhSSjU3in/0F3+sqWCSnls3bpUYVh6lKob7q1fYzBVy9k2ydSRbP3qF8BbOrcYvKz2LzkAZu+Se+vjnyZbEqz5ozk+wcbHXT78mv/5oZu5Qxs4ua9aiT4vNumVzk4mY9wnjih7NycnZQXq8ckiQ7e1tlz+3xD7YArzrPvFlka8u1DpA+PNU7+erVqwoICFDt2rWtymvVqqWEhAQdPnz4mXbu3yCji4seRYQnK38YESZJcndxVUbnxBMrHkVGJKv3KDJc7i5uyuicOIL4pHW5u/DL/1UU9ihSkuTi6mRVbjKZ5OLqpLCHkalaz8yJG3TlUqAGj2trKbt394H2fHtM505f02dL++qzpX118/o9+TSapPCw5PP7794O1davD+u9gW9xePUVFvnf19bJJfl7KoOLkyIepfyeOnf8ivZvPKq+n7ZNdQg6/sNZfb/+qN7p2yBZwAb+lwfBYZo39Gv1q/uxulUap096fin/czfTultAMk8VpP39/SVJ+fLlsyrPmTOn7O3tdeXKlWfXMwD/mNls1tQxX2vFgj2aMLOLKlb3sjwXFxsve3s7TVvYW6W9C6lm/TKatex93b39QJtWHUy2rvXLD8gto7OavFM12XNI32KiY7Vw1Aa91a22CpXMk6plftn9uz77YKVqtiivd/rWf849RHri7OakhHizilcupJGLe2jAzA4KC43Q6DZzdfPKk4+YAGnhqa7aERaWOErq4mI9f9JkMsnFxcXyPFIvNOyR3JyTz0d1d04cQQ4JeygbU+LvnYwp1HPL4KKQRw/04L8j2Bldkl8WyN3FTSFhf3/iI14+Gd2dJf3/yHQSs9ms8LCoJ85llqTY2DiN7LdEe789rumLe6t52xpWz7tldFbx0vllZ/f/lzvzKplXGd2ddeHM9b+uTnu3H1PthmWt6uPV4/zfkeHIvxx1MJvNigyPkst/33OP2/D5XtnY2ejdDxulqo3dqw5rxZRtatShhrqPb8kRDDyVHuPftnqcr2gOFS2XX71en6Rtiw/o/Wnt0qhnQHJc/i6NXQjwl5ODo/Jky6Ebd4Ms5UXzFJAknbvmJzvbxOBSJFc+HT3/h6VOgRy55ejgoHPX/RQRFanrdwJVJJf10QJJKpq7gH44dfT5bgiei0JFc0mSrl25Le/Kr1nKb1y7q9iYOBXxyp3icmazWSP6LNaP+37X0k1DVa128kvVFSiSQw9Ckv/4TTCb5fqXw/DXrtzWn+dvasCoVv9kc/ASyF04cb594LV78ipfwFJ+50aw4mLilbeIZ7Jlftn1u+7dClGH0qMsZeYEs8xms9oUHao2/d9Q2/6JIXvv179o+eRt6jisqVr2rvd8Nwb/Gi4ZMyhbrsy6f/tBWncFsPJUUzsyZswoSclGns1ms8LDwy3PI/X2HD+s+Ph4Natax6q8ZfX6+sPvggLuBso/6IbOX/dTs6p1k9WJjYvVnhM/S5J2/XpIjSu+Ljvb//99VK5wceX3zKUdvyY/VI+XX94C2VWoaC4d3HPKqvyHXSdlZ2ermg1Kp7jcgmlb9cPu37R007AUQ7Qk1XqjrE4dv6y7t0MtZRfOXFfYw0h5lbL+QXbk0FlJUvmqRf/B1uBlkCNfFuUunF0nDpy1Kj/2/RnZ2tmkeDWNcSt7aebOoZq5Y4jlX8P21eThmVEzdwxRI5/qkiTfX/7UlxO+UZfRzQnRMCQ2Ok6LxmzUkT1/WJU/Cg3X7ev3lKtAtjTqGZCypwrShQoVkiRdu3bNqvzGjRuKjY1VkSJFnl3PXlGZ3dzlmTmrPDNnla2tjZwcHC2PnRwcVcmrtM4v26kaJctLkgKD7+qLHes1ucsAvVW1jvJlz6VhbbqrWdU6GrPyc8t6x62ap9Y1G2pQqy7Klz2Xmlerp/Ed+mrO1tWWS+1N37RMbhlctGzwZL2WO78qeZXWiqEf6+j5P/TtLwfSZH/gn/twTCvt/fa4Vsz/Tjev39P+XSe1YPo2de7bSFmyuev0ST81rjRcJ44kXrYs8MZ9LZq5XR17vaF8BbPr7u1Qq38x/73BSof3GihzFjd90PFzXThzXb/9ekkj+y5R7nxZ1eJd62kgVy7dkrOLo7Jmd0/WP7x6fAa9qaPfndb2ZT/qzs1gHfv+jDbN26emXWvJPaub/vzjmvq/MVXnTySe95KrYHbl88pp9c89i6ts7WwT/5/VTWazWV9+tEVe5Qvo9WbeCrn70OpfZHi0JCk6KsZSlnRJvNB7jxRy96EehSY/WRrpz6PQcMt7ICHerJjouP9/bE7Qw5BwLRy1UT9uPaGg6/d14aS/ZvRbKRtbGzXp8npadx+w8lRTO/LmzatChQrp4MGDatmypaX8hx9+kJ2dnWrW5FrFW8Z/rjpl///SYHmz5VTL6okn2nSdMVpXb99UsbyF5Jrh/+chDlo0VQ8jwrSw/3hlc/fQxRv+evfjIVbXlv7m8D51mj5So9v10qfdBul26H3N2bpak9cutNS5GnRT9YZ308zew/XHoq2KjI7Wjl8PavCiaTKbzS9g6/E8NG5RWdMX99biWTs0c9JGZc3uri59G6nf8JaSpMiIGPn/GaiI/waVoz+dU2xsvL78fJe+/HxXsvV9tWO0qtQsrkyZXbVm1xh9OnqtfBpPliTVqFNK4z8bmuxuYw9CwuWaMfncWbyaqr1ZVgNmttc3C/dr7We7lCmLm97qVkutP3hDkhQdGatbV+4o6r/vqdS4eytENy4nXgK1Z9WJyZ5vO6Ch3v2wsf6z83ctGLHe6rm+tRLvQ1CySmFN+vp9g1uFV8WMfit19lc/y+P7QaE6vv+MJOn9ae304cwO2rxgvzbN26f7gaFycLJXsYoFNWVDf+UqmD2tug2kyGR+yoS1Z88eDRw4UCNGjFDDhg11/vx5jRo1Sq1bt9aIESP+dnlf38QbSJQZ8q6xHgN/Yd53TpJ06cGaNO4J0oui7h0lSVuuzE7jniC9eKfQIEm8p/DsvBbeQJJUunTKU/zSgq+vr2ITwuRawO/vKz+FsKuFZW/j+lJta5KnPtmwcePGmj59uhYvXqyZM2cqa9as6tKli/r16/c8+gcAAAC8lAxdtaN58+Zq3rz5s+4LAAAA8MrgHp0AAACAAQRpAAAAwACCNAAAAGAAQRoAAAAwgCANAAAAGGDoqh0AAADAX9nZ2Cqf67O9cc4lG9tnur5niRFpAAAAwACCNAAAAGAAQRoAAAAwgCANAAAAGECQBgAAQLpx8+ZN9erVS1WqVFHdunU1Y8YMJSQkpFh33bp1atSokby9vdWiRQvt37//qdoiSAMAACDd6N+/vzw9PbV//36tWLFC+/fv16pVq5LV27t3r2bOnKlPPvlEx44dU8eOHTVw4EAFBASkui2CNAAAANIFX19fXbhwQUOHDpWbm5sKFCigrl27asOGDcnqRkVFafDgwapQoYLs7e3Vpk0bubi46Pfff091e1xHGgAAAOnC2bNnlTt3brm7u1vKSpYsKX9/f4WFhcnV1dVS3qJFC6tlHz58qPDwcHl6eqa6PUakAQAAkC6EhoYqY8aMVmVJoTokJOSJy5nNZo0dO1Zly5ZV5cqVU90eI9IAAABIN8xm81PVj42N1ciRI3X58mV99dVXT7UsQRoAAADpgoeHh0JDQ63KQkNDZTKZ5OHhkax+VFSU+vXrp8jISK1du1aZM2d+qvaY2gEAAIB0oVSpUgoMDFRwcLClzNfXV0WKFJGLi4tVXbPZrEGDBsnOzk4rV6586hAtEaQBAACQTpQoUUKlS5fWzJkzFRYWJj8/P61YsUI+Pj6SpMaNG+vEiROSpB07dujy5cv6/PPP5ejoaKg9pnYAAAAg3Zg7d67GjRunGjVqyNXVVe3atVP79u0lSf7+/oqIiJAkffPNN7p582aykwtbtGihKVOmpKotgjQAAADSjRw5cmjp0qUpPnfx4kXL/1O6ScvTYmoHAAAAYABBGgAAADCAIA0AAAAYQJAGAAAADCBIAwAAAAYQpAEAAAADCNIAAACAAQRpAAAAwACCNAAAAGAAQRoAAAAwgCANAAAAGECQBgAAAAwgSAMAAAAGEKQBAAAAAwjSAAAAgAEEaQAAAMAAgjQAAABgAEEaAAAAMIAgDQAAABhAkAYAAAAMIEgDAAAABhCkAQAAAAMI0gAAAIABBGkAAADAAII0AAAAYABBGgAAADCAIA0AAAAYQJAGAAAADCBIAwAAAAYQpAEAAAADCNIAAACAAXZp3QEAAACkDyY5yMm21DNe551nur5niRFpAAAAwACCNAAAAGAAQRoAAAAwgCANAAAAGECQBgAAAAwgSAMAAAAGEKQBAAAAAwjSAAAAgAEEaQAAAMAAgjQAAABgAEEaAAAAMIAgDQAAABhAkAYAAAAMIEgDAAAABhCkAQAAAAMI0gAAAIABBGkAAADAAII0AAAAYABBGgAAADCAIA0AAAAYQJAGAAAADCBIAwAAAAYQpAEAAAADCNIAAACAAQRpAAAAwACCNAAAAGAAQRoAAAAwgCANAAAAGECQBgAAAAwgSAMAAAAGEKQBAAAAAwjSAAAAgAEEaQAAAMAAgjQAAABgAEEaAAAAMIAgDQAAABhAkAYAAAAMIEgDAAAABhCkAQAAAAMI0gAAAIABBGkAAADAAII0AAAAYABBGgAAAOnGzZs31atXL1WpUkV169bVjBkzlJCQkGLdr776So0aNVL58uXl4+OjM2fOPFVbBGkAAACkG/3795enp6f279+vFStWaP/+/Vq1alWyegcOHNC8efM0ffp0/fLLL6pbt6769OmjiIiIVLdFkAYAAEC64OvrqwsXLmjo0KFyc3NTgQIF1LVrV23YsCFZ3Q0bNuidd95R2bJl5eTkpJ49e0qSDh48mOr27J5Zz1MpNjZWZrNZp2cm3yDACF9f3//+r2ya9gPph68S31OvqUEa9wTpRdLnFO8pPCsxMTEymUxp3Y1kYmLi5et755mv08HBNlV1z549q9y5c8vd3d1SVrJkSfn7+yssLEyurq5WdZs0aWJ5bGNjo+LFi8vX11dNmzZNVXsvPEi/jC86AADAq8RkMr10mcrBweE5rdc21esODQ1VxowZrcqSQnVISIhVkA4NDbUK3El1Q0JCUt23Fx6kvb29X3STAAAAeM68vLzSuguSJLPZ/FzqpoQ50gAAAEgXPDw8FBoaalUWGhoqk8kkDw8Pq/LMmTOnWPev9f4XgjQAAADShVKlSikwMFDBwcGWMl9fXxUpUkQuLi7J6p49e9byOD4+XufOnVPZsqk/54ogDQAAgHShRIkSKl26tGbOnKmwsDD5+flpxYoV8vHxkSQ1btxYJ06ckCT5+Pho27Zt+v333xUZGamFCxfKwcFBderUSXV7L3yONAAAAPC8zJ07V+PGjVONGjXk6uqqdu3aqX379pIkf39/y3Wia9WqpcGDB2vgwIG6f/++SpcurSVLlsjJySnVbZnM/3SWNQAAAPAvxNQOAAAAwACCNAAAAGAAQRoAAAAwgCANAAAAGECQBmCF848BvKr4/MKLRpAGIEmKiopSdHS0TCaTEhIS0ro7APDU+PzCi0aQBqC4uDj17NlTjRs3VmRkpGxsbPgyQjIRERFau3at7ty5k9ZdAazMnj1b3333nSRp586dCg8PT+Me4d+CIJ2OxcfHJyvjsBdSYjab1bBhQ0VFRalDhw6EaSSTkJCgQYMGafLkyVq3bp3V7XeBtBQaGqqYmBgtXrxYZcuW1enTp+Xi4sL3HV4IgnQ6FRcXJ1tbW8XExOjEiRPy8/NTZGQkh71gJWmEMSEhQe3atdPgwYN148YNwjSSsbGxUWRkpIoVK6bFixdryZIlCgkJSetu4V8uISFBmTJlUu3atXX16lXZ2dmpatWqkhKneRCm8bxxi/B0KCEhQXZ2dgoLC1OPHj109epV2djYqFmzZurXr58yZcqkhIQE2djwO+rfbtOmTfr00091//599enTRy1atJAkzZgxQx06dNDatWuVIUMG3i//YpGRkfrjjz/k5eWlGzduaPLkybp06ZKmT58uk8mkXr16KXPmzGndTfwLJX0uRUVFaffu3Xr99dfl7++v2bNnKz4+Xo0aNbKEaZPJlNbdRTrFN2M6k/TBEhMTo169eilbtmyaPn26ypcvr+3bt2vy5MkKCQlhpBGSpJYtW+q9997TwoULtWDBAklSixYtNGzYMEamocjISLVu3VpHjhxRbGysXF1dlSNHDr377rt6//33tXLlSkamkSbi4+Mt33U+Pj7Kly+f5s+fr+HDhyshIUGff/659u3bJ4kTEPF8MSKdziR9sNy+fVtFihTR4MGDLYe9Jk2apD179mjKlCkaO3asMmfOzEjjv5y7u7t69eqlhIQELVmyRJL0/vvvMzINSdLevXt19epVderUSVmzZlXLli1VuHBhSVLXrl0lyfID7L333pOHh0dadRX/Mra2toqKitL58+dVokQJde7cWZJUu3ZtJSQkaMaMGZozZ45sbGzUoEEDhYeHKzY2lvconjmCdDqTkJCgLl266Pr16ypRooTs7P7/JR43bpwkWcL0mDFj5OHhwWGvfzk3Nzf16tVLZrNZixcvlkSYRqJ8+fLJyclJI0aMkJOTk5ydnRURESFnZ2e5urqqW7dukpKH6WnTpilr1qzq0aNHWnYf6Vh8fLyGDx+uffv2ydvbW5GRkbK3t5fJZFLdunVlMpk0ffp0zZgxQ9euXdO5c+dUpUoVtW3bNq27jnSGIJ3OmM1mdenSRbNmzdKZM2d09uxZVapUSTY2NjKZTBo3bpxMJpP27t2rYcOGac6cOXJzc0vrbuMFio+Pl62trVWZu7u7+vbtK7PZnOLI9OzZs9WsWTPt3LlTTk5OL7zPSBtlypTRhx9+qKlTp8rOzk6//PKLnJ2dFRMTIwcHB7m4uFiNTDs6Oio6OlorV67U+vXr07bzSNdsbGxUq1Yt3b9/XxcvXtSVK1fk7e2t2NhY2dvbq06dOjKbzZo1a5Y+//xz5c6dW9OmTUvrbiMdIki/4v4aimxtbdWwYUM5Ojpq1KhRmj9/viZOnGg5HGsymTR27Fg9fPhQDx8+lIuLS1p1HWng8ffLunXrdO/ePdna2qpbt25yc3PTBx98IEnJwnR0dLSWL1+uu3fvKm/evGnWf7xYdnZ2Onv2rDJlyqSHDx9q0KBBWrp0qRwcHCyBxdXVVV27dpWjo6NmzpwpZ2dnffPNNypRokRadx/pyF+/60wmk9555x1lzJhR06dP14ABA7Ru3TrlyZPH8t6sW7euChcurJCQEJUqVUq2trYpDiQA/4TJzLVhXllxcXGys7NTZGSk9u3bp4CAABUvXlxeXl7KkyePfvjhB40aNUrFihXThAkTLGE6SdIheg7V/zs8/jp/8MEHunTpknLkyKFTp07p9ddf15AhQ1SkSBGFh4dr/vz5Wrlypfr27avevXtbzozn6MW/z9WrV2Vvb6/vv/9en332mapUqaJly5ZJkmVkWpJmzpyptWvXatOmTck+a4B/4vHvum3btunGjRvKnTu36tevL09PTx04cEBTpkxRfHy81q5daxWmH0eIxvNAkH5FJX0ghIWFycfHRwkJCbp//74ePXqkkiVLasiQIapSpYolTBcvXlzjx49/YpjGv8fo0aN18uRJrV+/XpkzZ9bo0aO1ZcsW1ahRQ2PGjFGhQoUUHh6uL774QsuWLdOAAQPUr1+/tO420tiDBw+0YcMGzZ071ypMS4nz6JcvX67NmzerZMmSadhLpDePf9e1bdvWEqhv3ryp4sWLq1+/fqpfv74lTCckJGjNmjXKkycP3294IXiHvaKSzlju0KGDihQpopUrV+ro0aOaOHGiIiMjNWrUKB0/flz169fX1KlT9eeff2rgwIG6efOm1Xr4kPl3CQgIkK+vryZOnKjMmTNr6dKl+uGHH9SvXz+dOXNGU6dOlZ+fn1xcXNS+fXsNGTJEjRo1Sutu4yXg7u4uHx8fDRgwQL/++qvViYQ2NjbasGEDIRrPnK2traKjo9W5c2cVLlxYX3/9tbZv3661a9fq9u3bmj59ug4ePKh69epp1KhRcnR0VKNGjXT37l2+3/BCMEf6FXHv3j1lzZrVquzIkSNKSEjQ4MGDlSVLFklSmzZt5O7urnnz5mn27NmaNWuW6tatq3HjxmnLli3KkSNHWnQfL4nw8HAFBAQoc+bM2r59u1atWqXZs2erevXqkqQvvvhCYWFhypIli/Lnz68PPviAkwth4ebmJh8fH0nSwoUL1aZNG23atElDhgxJ454hPfv5558VFxen4cOHy9XVVWazWd7e3lq2bJm6d++uRYsWqVy5cqpfv77Cw8N18OBBLnOHF4afa6+Ay5cvq27durp06ZJV+Y0bN3Tt2jVly5ZNNjY2io2NlSQ1bNhQHTt21OnTp3X9+nWZTCY1bNhQS5cutZxsgfQvpdc5V65c+uqrr5Q5c2Zt3rxZ7dq1s4TounXrqnLlygoPD9fZs2fVvHlzQjSSSQrT3bp1082bN3Xr1q207hLSubCwMAUEBCgmJsZSFh8fLy8vL82dO1enT5/WoUOHZGNjoxYtWujzzz/nuw4vDEH6FWAymTRz5kwVLVpUcXFxlvLChQvLwcFBGzZsUEJCguzt7S1hum3btnJ0dLSE779e2QPp2+Mn1fz4449as2aNDh06JJPJpDJlyshsNuv8+fNW1w+/d++eypQpo2+//VbffvutihYtmlbdx0vOzc1N3bt3165du5QrV6607g7SkcfvQJh0CpeLi4tiY2N19OhRRUVFyWQyWaZtFClSRJ6engoODpYkq880vuvwIhCkXwGFCxdWw4YNFR0drbZt22rdunWSpGLFiilfvnxau3at9u3bJ7PZbDlL+caNG8qWLRuXKvsXMpvNli+QAQMGaMaMGVq2bJmWLFmiy5cvS5Kio6Pl6uoqX19fnTt3TpJ05swZBQcHKz4+Xq6urmnWf7waXF1dlTlz5rTuBtKRuLg4y915AwMDde3aNUlSgwYNVLNmTc2bN09HjhyxhOmkZVxdXeXp6ZmWXce/GFfteMklvTwmk0nXr1/XuHHj9Pvvv2v8+PFq1aqVrl+/rnbt2snV1VXt27dX69atFRwcrE8++UT379/X+vXr+VX+LzV9+nQdPnxYCxYsUI4cORQUFKR8+fJZnt+4caMmTJigHDlyKHv27PLz89PatWvl5eWVhr0G8G+UdIWNsLAw9erVSwEBAYqKilLdunU1adIk3b9/XyNHjtSlS5fUv39/VaxYUS4uLvroo48UEhKijRs38l2HNEGQfomldM3LGzduaMaMGdq/f78mTpyoNm3a6Pr16xo2bJiuXr2qiIgI5cuXT87Ozvr6669lb2/PtTP/haKjo9WnTx/VqlXLchvnx28Ff/PmTeXMmVOHDh3STz/9pPj4eHXp0oXr/wJ44ZI+m6Kjo+Xj46Ps2bOrbt26+vPPP7V582aVK1dO48aNk4uLiyZPnqyff/5ZZrNZ+fPnl7Ozs9asWcN3HdIMV+14ST1+AfoNGzYoICBA1atXV/369TVgwABJ0sSJEyUlXqlj8eLF8vPz05UrV5QzZ05Vq1ZNtra2lvXg3yU2NlbBwcG6c+eOpcxkMikhIUGRkZGaMWOGKlasqI4dO6pu3bpWIRsAXpSkz574+HidPHlSRYoU0YQJE+Ti4qLo6GhVq1ZN48aN0+TJk7V06VItWLBAhw4dUlRUlDJkyKAaNWrwXYc0xbvuJRQfHy87OzuFhYWpTZs2ypYtmyIjI9W4cWOZzWYVLlxYAwYMUEJCgiZOnCgbGxu1atVKFSpUUIUKFZKtB+lbSjcdcHV1Vf78+fXdd9+pVatWKlKkiKTE6/26uLjo9u3bOnnypDp27ChJhGgAacJkMik2NlbvvvuubG1tVaBAAbm4uEiSHB0dVadOHU2aNElDhw7V5MmTNWnSJNWuXdtqHXzXIS1xsuFLKOlmK127dlXx4sW1dOlSrVmzRpUqVVJkZKSkxBMQx40bp7p16+qjjz6ynID41/UgfYuPj7eE6Bs3bujq1asKDAyUJI0aNUo2NjYaPny4/P39rZbz9PS0hGsASEv29vZ6/fXX5evrq+PHj1tOgJYSv8dq1qyp5s2b6+eff1ZQUJD+OiOV7zqkJYL0S+DYsWPavHmzVdnRo0eVkJCg8ePHy9HRUY6Ojtq/f78GDBigdu3aacyYMcqUKZPGjh0rb29v7dq1K9mHC9K3x+cDjho1Sh9++KHeeust9ejRQ4sWLVLOnDk1btw4PXz4UO+9957WrFmjH3/8UbNnz9aRI0fUuHHjNN4CAP9GKV3fefDgwRo8eLCCgoL09ddfKyAgwPKco6OjXnvtNcvRN46g4WXCsZA0FhUVpY0bNyYbHYyIiFBwcLDOnz+v8PBw/fDDD9q6dasaNWqkjBkz6rffftPy5cvVp08fTZkyRblz55bJZGKu679IUogeNmyYzpw5oxEjRujevXu6dOmS5syZo3v37mnQoEH64osvNHnyZM2ZM0cuLi7KmjWrVq5cyYmFAF64pLnMERERWrNmjWJiYpQrVy41b95cvXr1UkxMjObPny8bGxv5+PioePHievDggfbv369ChQolu8MvkNYI0mnMyclJY8eOVaZMmRQeHq5ffvlFb7zxhipWrChJ6t+/v5ycnJQvXz4tXbpUNWvWVGxsrHr27KmwsDBJslwrOqW5skh/Hv+x9OOPP8rX11cLFiywBOPTp09r7dq1khJvZFC0aFGtXr1aFy5ckKurq1xdXZUpU6a06j6Af7Gk839atWolk8mk+/fvKz4+Xjt37tSiRYv0wQcfyGw2a8GCBTpw4IAqVapkubb0qlWrZGNjw3cdXioE6ZdApkyZZDabtWjRIi1dulQfffSR3n33XW3atElHjhxRgQIFlD17duXIkcOyjI2NjeVmCEnBig+W9O/xL5CYmBiFhITowYMHlrKbN2+qf//+atu2rcaOHasHDx7o3LlzqlatmooVK5aWXQfwL/b4VTWmTJmikiVLaty4cYqPj9f69eu1Zs0atWnTRps2bVL//v3l6OioWbNm6d69e+rSpYsaNGggKfFzz8HBIS03BbBCkE5Dj4cik8mkt99+W7du3dKkSZNkNpvVrl07NW/e3Kr+vXv3NH78eD148EBdunSxLIv0z2w2W94v/fv3V+3atZWQkKCQkBC5uLgoMjJS7dq1U926dTVhwgTFxMRo5MiRypw5s6pVq5bGvQfwb2ZnZ6fw8HDt27dPuXLlUp06dSyDQT169JCnp6dmzpyp999/XwsXLlSvXr0kSbNmzdJrr72m0qVLy9PTkxCNlw5BOo0k/TqPjY1VUFCQ4uLilC9fPo0YMUJms1mTJk2Sra2t2rRpo8jISK1bt05bt26Vg4ODbG1ttWHDBtnZ2XEB+n+Jx6dzHDp0SEFBQapZs6bs7Oy0evVqvfvuu4qJidFbb72loUOHSko8ahEZGakaNWokWwcAvGjffPONPvnkE9na2qpEiRKSEgeIMmTIoLfeektBQUGWk6IbNGigXr16KSEhQfPmzVN4eLg+/PBD5cqVK423ArBGkE4Dj18neuDAgbp586YiIiLUrl079e3bVx988IFMJpMmTJggKfGGK1WqVFFISIiKFSumxo0bcwH6f5mkADx16lRdunRJ2bJlk6enpxISEtSpUyctW7ZMjx49UpcuXSx3+Jo7d64uXbqkyZMnW60DAF6EpIGepKt0vPXWW3r06JEWL16svXv3qnr16nJ2draE6bZt22r58uW6du2aZR19+vRRZGSkvv76a8sgAfAyIYWlAVtbW0VGRurdd9/Va6+9pgkTJigoKMgyh7VQoULq16+fEhIS9NFHH0lKDNMlS5a0rIML0P871alTRxs2bFB8fLyOHz+uSpUqqVWrVoqNjdXq1avVokULFS9eXCaTSRcvXtSyZcssJ6MCwItka2ur6Oho9ezZU82bN9fbb7+tDh06KCYmRosXL1bWrFn14YcfysnJSVLiUTQPDw/LUdako2iDBg1S165dLVNBgJeJyczFh9PEjh07tG7dOi1evFhubm7JnjebzQoODtbHH3+s3bt364svvlC9evXSoKd42Zw+fVrt27eXt7e3hg0bpjJlyshsNuvixYv6/vvvdePGDeXLl09NmzZVgQIF0rq7AP7FHj58qLZt2+rRo0caMWKEmjRpovDwcC1btkxLlixRs2bNVK9ePWXNmlXLly9XQECAtm3bxkARXhkE6TSydOlSrVq1Svv375eTk5PViYdHjx7Vt99+q8mTJ+vatWv67rvv1KdPHz5YYPH777+rZ8+eqlChgt5//32VKVMmrbsEAFbfZUlTO4KDg/XBBx/Iz89PY8aMsYTp5cuXa/ny5YqNjVXv3r314MEDjRkzxjI9jfN/8CrgemkvWNJcsdy5c0uSDhw4YPngSUhIkJR4CbOff/5Zt2/fVuHChfXBBx/Izs5OcXFxadZvvFzKlSunpUuX6uTJk5o/f77VLXUlcZdLAC9c0ndZTEyMJFnO5fHw8ND8+fNVqFAhy1FWFxcXde3aVb1795adnZ1sbGw0btw42dvbKy4ujhCNVwZB+jlLCs5JwSbpcbVq1ZQhQwYtXLhQx44dU3x8vOVXfK5cuZQ5c+ZkJ4cxIo3HeXt7a+nSpTp9+rSmTJmiCxcuWJ7jxEIAL1pSiO7YsaNGjBghSZZBIA8PDy1YsEAFCxbU1KlTtXv3brm6uqpjx47q1q2bFi5cqFmzZnESPV45BOnnKOnQVHh4uD755BP17dtX/fr10/bt25U5c2YtWbJEISEh+vjjj7V9+3aFhYUpODhYX331lXLnzq2cOXOm9SbgJeft7a158+YpKChI7u7uad0dAP9CSQNEkhQZGakcOXLohx9+0JQpUyRZh+mPPvpItra2Wrx4sbZu3Sp3d3d17txZffv21bJly/TFF1+k1WYAhjBH+jlJOts4PDxcb7/9tvLmzatMmTIpJCREv/zyi95++20NGDBAMTEx6tWrl4KDgyUlTvkwmUzauHGj7O3tuRUqUiUqKspy5jsAvChJI8jR0dE6efKkwsLClCtXLq1bt067d+9Wq1atNHbsWEv90NBQDRo0SGfOnFGpUqW0YsUKSdLt27f1zTffqFGjRipcuHBabQ7w1AjSz1FcXJxGjRqliIgIzZ4923JHpiFDhmjv3r1avXq1vL29FRoaqiNHjuj27dvKnj27GjVqxHWiAQAvtaSjrmFhYercubOkxHN8NmzYIJPJpCVLlmjnzp1q3bq1xo0bJ0l68OCB5s6dq8GDBytDhgxWA0WcYIhXESntGXv87nEPHz7U+fPn1apVK0uI3rt3r3766SeNHTtW3t7eCg4OloeHh958802r9XCdaADAy8zW1lYRERHy8fHRa6+9pqFDh8rJyUkeHh6SpEGDBkmStm7dqtu3b6t58+Zav369IiMjLSH68aOuhGi8ikhqz1DSr+nY2FiZTCYlJCQoNDRUoaGhkqR9+/Zp5MiRGjFihNq1a6dLly5pwIABWrJkifLkyWP1y5wPFADAy27Tpk1yd3fX9OnTLYM/R48e1cGDB+Xs7KwiRYqoffv22rp1q3777TcVLlxYX331lWxsbGQ2m5m6iFceQfoZSUhIsPw679Onj/r27atq1aqpRIkS2rt3r7JkyaLPP//cEqIl6dSpU8qUKZPy5s3LVRYAAK+cwMBAJSQkyGw267ffftPevXu1atUqlSpVSnfu3FH+/PnVr18/tWvXTrdu3VLFihVlY2PD1EWkG7yLn4GkQ1OxsbH65ZdflClTJlWrVk2SNHjwYH344Yf65JNPNGDAAEuIjomJ0b59+wjRAIBXVo0aNbRq1So1bdrUMsI8Z84cNWzYUHfv3tWbb76pO3fuqFq1asqTJ48kpi4ifeGd/AwkXTvzww8/1K1bt1SuXDlJifOlCxUqpKFDh2r27NmW2546Oztr//79unPnjhYtWmSpS6AGALxKXn/9dS1atEg//fSTKlasqGLFiqlgwYKSpAwZMqh48eJyc3OT9P/fc0xdRHpCkP4HHj9JIjg4WPb29nr06JGuXr0qKfGmGA4ODqpdu7YKFSqkGTNmaMOGDcqUKZMKFy6spUuXWu7ixK9zAMCrxmQyqXbt2qpdu7ZVeVhYmEaOHKmEhATLcwwWIT3i8ncGJYXfmJgYBQQEyMnJSXZ2dpo7d66++eYbtWnTRpMnT5ZkHbjv3r0rZ2dnOTs7y2QyEaIBAOlCWFiYtm/frt9++023b9/Ww4cPtXnzZtnb23NpO6RbJDgDzGaz7OzsFBYWpi5duigwMFDBwcFq1aqVqlatKrPZrL1798re3l7jx4+3TP1wcHBQtmzZkq0HAIBXnYODg6KiohQREaEqVaqoT58+lrsa8l2H9IoR6af0+ImF7du3V44cOSwnU8yfP19eXl5q2LChLl68qL179+rtt9+23NWJuxQCANKzv57vw0g00jt+Ij6FpDOSY2JidOTIEb322msaPXq0XF1dJUnVqlVT586dZTKZ1L59e5lMJn3zzTcKCwvT1KlTCdEAgHTtr/OgCdFI7wjST8FkMik+Pl69evVScHCwsmXLZgnRsbGx8vLy0qpVq9ShQwedPHlSI0eO1MOHDy3X2SRIAwAApB9M7TDg2LFj6t27t6Kjo7V69WpVqFBBUmKYtre315IlSzRr1iz9/PPPSkhIUNasWZPdChUAAACvNlKdAZUrV9aqVatkY2OjefPm6cyZM5Ike3t7SVL27NmVNWtWhYeHK3v27IRoAACAdIhkZ1CZMmW0evVqnTlzRrNmzdLJkyclSaGhodqxY4fy5cunvHnzWuoTogEAANIXpnb8Q6dOnVKnTp2UkJCgihUrKleuXLpx44aWL18uBwcHRqIBAADSKRLeP+Tt7a2vv/5adnZ2io6OVq1atbRmzRo5ODgoJiaGEA0AAJBOkfKegTJlymjVqlW6evWqvv32W124cEFS4sXpAQAAkD4xteMZOnXqlPr27atChQpp/PjxKlasWFp3CQAAAM8JI9LPkLe3t+bNm6egoCC5u7undXcAAADwHDEi/RxERUXJyckprbsBAACA54ggDQAAABjA1A4AAADAAII0AAAAYABBGgAAADCAIA0AAAAYQJAGAAAADCBIAwAAAAYQpAEAAAADCNIAAACAAf8HbC9bCs4eGMgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'DecisionTreeClassifier Classification Report'}>"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Decision Tree Classifier with Hyperparameter"
      ],
      "metadata": {
        "id": "Ejp86EEkpzCD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "param_dist = {\n",
        "    'criterion': ['gini', 'entropy'],\n",
        "    'max_depth': randint(1, 10),\n",
        "    'min_samples_split': randint(2, 10),\n",
        "    'min_samples_leaf': randint(1, 10),\n",
        "}\n",
        "\n",
        "dt = DecisionTreeClassifier()\n",
        "\n",
        "search = RandomizedSearchCV(\n",
        "    dt,\n",
        "    param_distributions=param_dist,\n",
        "    n_iter=100,\n",
        "    cv=5,\n",
        "    random_state=1,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "search.fit(X_train, y_train)\n",
        "\n",
        "print(\"Best hyperparameters:\", search.best_params_)\n",
        "print(\"Best score:\", search.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "obrEBPznp2vu",
        "outputId": "4a7e072e-e056-42b3-d5de-6c11985be160"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best hyperparameters: {'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 7, 'min_samples_split': 9}\n",
            "Best score: 0.8484848484848484\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Random Forest Classifier"
      ],
      "metadata": {
        "id": "LeS98ky3zNUo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cskcsS-U11K7",
        "outputId": "7eee065b-cce2-4019-f999-6e4ae8257862"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 0.7619047619047619\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rt = RandomForestClassifier(n_estimators = 100)\n",
        "\n",
        "rt.fit(X_train , y_train)\n",
        "y_pred = rt.predict(X_test)\n",
        "\n",
        "print(\"Accuracy\", metrics.accuracy_score(y_test , y_pred))\n",
        "\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from scipy.stats import randint\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from yellowbrick.classifier import ClassificationReport\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "rt = RandomForestClassifier(n_estimators = 100)\n",
        "rt.fit(X_train , y_train)\n",
        "y_pred = rt.predict(X_test)\n",
        "\n",
        "print(\"Accuracy\", metrics.accuracy_score(y_test , y_pred))\n",
        "\n",
        "# Plot the confusion matrix as a heatmap\n",
        "conf_mat = confusion_matrix(y_test, y_pred)\n",
        "plt.figure(figsize=(4,3))\n",
        "sns.heatmap(conf_mat, annot=True, cmap='Blues', fmt='g', cbar=False)\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n",
        "\n",
        "# Calculate the classification report, which includes precision, recall, and f1 score\n",
        "report = classification_report(y_test, y_pred, output_dict=True)\n",
        "\n",
        "# Plot the classification report\n",
        "class_names = ['negative', 'positive']  # replace with your list of class names\n",
        "visualizer = ClassificationReport(rt, classes=class_names, support=True, cmap='YlGn')\n",
        "visualizer.score(X_test, y_test)\n",
        "visualizer.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 900
        },
        "id": "3tJ6c3OmRvud",
        "outputId": "6565952c-f735-4f55-e6d3-232664212bcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 0.7857142857142857\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'RandomForestClassifier Classification Report'}>"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Random Forest Classifier with Hyperparameter"
      ],
      "metadata": {
        "id": "aWI3Dtpkppyy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the hyperparameters and their ranges\n",
        "print(\"With Hyperparameter\")\n",
        "param_dist = {\n",
        "    'n_estimators': randint(10, 500),\n",
        "    'max_features': ['auto', 'sqrt', 'log2', None],\n",
        "    'max_depth': [None] + list(range(1, 20)),\n",
        "    'min_samples_split': randint(2, 20),\n",
        "    'min_samples_leaf': randint(1, 20),\n",
        "    'bootstrap': [True, False]\n",
        "}\n",
        "\n",
        "# Create a Random Forest Classifier object\n",
        "rf = RandomForestClassifier(n_estimators=100)\n",
        "\n",
        "# Create an instance of RandomizedSearchCV\n",
        "rf_random = RandomizedSearchCV(estimator=rf,\n",
        "                               param_distributions=param_dist,\n",
        "                               n_iter=100,\n",
        "                               cv=5,\n",
        "                               random_state=42,\n",
        "                               n_jobs=-1)\n",
        "\n",
        "# Fit the RandomizedSearchCV object to the training data\n",
        "rf_random.fit(X_train, y_train)\n",
        "\n",
        "# Print the best hyperparameters and their corresponding accuracy score\n",
        "print(\"Best parameters:\", rf_random.best_params_)\n",
        "print(\"Best score randomforest:\", rf_random.best_score_)\n",
        "\n",
        "# Use the best hyperparameters to build the final Random Forest Classifier model\n",
        "rt = RandomForestClassifier(**rf_random.best_params_)\n",
        "rt.fit(X_train, y_train)\n",
        "y_pred = rt.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r9BM0M_honO7",
        "outputId": "b08faaa1-3c2f-47a2-c841-b131ffaa1b10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "With Hyperparameter\n",
            "Best parameters: {'bootstrap': True, 'max_depth': 14, 'max_features': 'sqrt', 'min_samples_leaf': 3, 'min_samples_split': 2, 'n_estimators': 14}\n",
            "Best score randomforest: 0.8727272727272727\n"
          ]
        }
      ]
    }
  ],
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