[fa5724]: / ProjectExecutableFiles6 / TrainingfileMiniProFetalAI.pynb.ipynb

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{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ct_1uRlXLwZE"
      },
      "source": [
        "**Importing the libraries**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "84yyK7lnRgRO"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "sns.set_style('darkgrid')\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.svm import LinearSVC,SVC\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
        "from sklearn.metrics import confusion_matrix\n",
        "#from sklearn.metrics import plot_confusion_matrix\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(action='ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fv9KsZ3xWQds"
      },
      "source": [
        "**Read the Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "0Io_YF9sRb3b"
      },
      "outputs": [],
      "source": [
        "data=pd.read_csv('/content/fetalhealth.csv')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "5S0fn20tXv96",
        "outputId": "552aff0b-5eb8-485a-8377-b41c11a5167a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   baseline value  accelerations  fetal_movement  uterine_contractions  \\\n",
              "0           120.0          0.000             0.0                 0.000   \n",
              "1           132.0          0.006             0.0                 0.006   \n",
              "2           133.0          0.003             0.0                 0.008   \n",
              "3           134.0          0.003             0.0                 0.008   \n",
              "4           132.0          0.007             0.0                 0.008   \n",
              "\n",
              "   light_decelerations  severe_decelerations  prolongued_decelerations  \\\n",
              "0                0.000                   0.0                       0.0   \n",
              "1                0.003                   0.0                       0.0   \n",
              "2                0.003                   0.0                       0.0   \n",
              "3                0.003                   0.0                       0.0   \n",
              "4                0.000                   0.0                       0.0   \n",
              "\n",
              "   abnormal_short_term_variability  mean_value_of_short_term_variability  \\\n",
              "0                             73.0                                   0.5   \n",
              "1                             17.0                                   2.1   \n",
              "2                             16.0                                   2.1   \n",
              "3                             16.0                                   2.4   \n",
              "4                             16.0                                   2.4   \n",
              "\n",
              "   percentage_of_time_with_abnormal_long_term_variability  ...  histogram_min  \\\n",
              "0                                               43.0       ...           62.0   \n",
              "1                                                0.0       ...           68.0   \n",
              "2                                                0.0       ...           68.0   \n",
              "3                                                0.0       ...           53.0   \n",
              "4                                                0.0       ...           53.0   \n",
              "\n",
              "   histogram_max  histogram_number_of_peaks  histogram_number_of_zeroes  \\\n",
              "0          126.0                        2.0                         0.0   \n",
              "1          198.0                        6.0                         1.0   \n",
              "2          198.0                        5.0                         1.0   \n",
              "3          170.0                       11.0                         0.0   \n",
              "4          170.0                        9.0                         0.0   \n",
              "\n",
              "   histogram_mode  histogram_mean  histogram_median  histogram_variance  \\\n",
              "0           120.0           137.0             121.0                73.0   \n",
              "1           141.0           136.0             140.0                12.0   \n",
              "2           141.0           135.0             138.0                13.0   \n",
              "3           137.0           134.0             137.0                13.0   \n",
              "4           137.0           136.0             138.0                11.0   \n",
              "\n",
              "   histogram_tendency  fetal_health  \n",
              "0                 1.0           2.0  \n",
              "1                 0.0           1.0  \n",
              "2                 0.0           1.0  \n",
              "3                 1.0           1.0  \n",
              "4                 1.0           1.0  \n",
              "\n",
              "[5 rows x 22 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>baseline value</th>\n",
              "      <th>accelerations</th>\n",
              "      <th>fetal_movement</th>\n",
              "      <th>uterine_contractions</th>\n",
              "      <th>light_decelerations</th>\n",
              "      <th>severe_decelerations</th>\n",
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              "      <th>abnormal_short_term_variability</th>\n",
              "      <th>mean_value_of_short_term_variability</th>\n",
              "      <th>percentage_of_time_with_abnormal_long_term_variability</th>\n",
              "      <th>...</th>\n",
              "      <th>histogram_min</th>\n",
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              "      <th>histogram_number_of_peaks</th>\n",
              "      <th>histogram_number_of_zeroes</th>\n",
              "      <th>histogram_mode</th>\n",
              "      <th>histogram_mean</th>\n",
              "      <th>histogram_median</th>\n",
              "      <th>histogram_variance</th>\n",
              "      <th>histogram_tendency</th>\n",
              "      <th>fetal_health</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>120.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>43.0</td>\n",
              "      <td>...</td>\n",
              "      <td>62.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>137.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>132.0</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>68.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>136.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>133.0</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>68.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>135.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>134.0</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>53.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>137.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>137.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>132.0</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>16.0</td>\n",
              "      <td>2.4</td>\n",
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              "      <td>53.0</td>\n",
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              "      <td>9.0</td>\n",
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              "  </tbody>\n",
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              "<p>5 rows × 22 columns</p>\n",
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    {
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      "metadata": {
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        "id": "SJA26H4zX4D5",
        "outputId": "d4d47057-1287-4a3b-f8ef-34e79a5129ec"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2126, 22)"
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          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SgEpUZdKYDM-"
      },
      "source": [
        "**Data Preparation**\n",
        "   1.**Handling Missing Values**:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vixn8E7bYUDa",
        "outputId": "59648d69-f793-4dea-ff10-2fb4503a0219"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2126 entries, 0 to 2125\n",
            "Data columns (total 22 columns):\n",
            " #   Column                                                  Non-Null Count  Dtype  \n",
            "---  ------                                                  --------------  -----  \n",
            " 0   baseline value                                          2126 non-null   float64\n",
            " 1   accelerations                                           2126 non-null   float64\n",
            " 2   fetal_movement                                          2126 non-null   float64\n",
            " 3   uterine_contractions                                    2126 non-null   float64\n",
            " 4   light_decelerations                                     2126 non-null   float64\n",
            " 5   severe_decelerations                                    2126 non-null   float64\n",
            " 6   prolongued_decelerations                                2126 non-null   float64\n",
            " 7   abnormal_short_term_variability                         2126 non-null   float64\n",
            " 8   mean_value_of_short_term_variability                    2126 non-null   float64\n",
            " 9   percentage_of_time_with_abnormal_long_term_variability  2126 non-null   float64\n",
            " 10  mean_value_of_long_term_variability                     2126 non-null   float64\n",
            " 11  histogram_width                                         2126 non-null   float64\n",
            " 12  histogram_min                                           2126 non-null   float64\n",
            " 13  histogram_max                                           2126 non-null   float64\n",
            " 14  histogram_number_of_peaks                               2126 non-null   float64\n",
            " 15  histogram_number_of_zeroes                              2126 non-null   float64\n",
            " 16  histogram_mode                                          2126 non-null   float64\n",
            " 17  histogram_mean                                          2126 non-null   float64\n",
            " 18  histogram_median                                        2126 non-null   float64\n",
            " 19  histogram_variance                                      2126 non-null   float64\n",
            " 20  histogram_tendency                                      2126 non-null   float64\n",
            " 21  fetal_health                                            2126 non-null   float64\n",
            "dtypes: float64(22)\n",
            "memory usage: 365.5 KB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Av4OlYcYd4N",
        "outputId": "76a8fa0d-9e37-4f44-9461-8f47eef24ce6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "baseline value                                            0\n",
              "accelerations                                             0\n",
              "fetal_movement                                            0\n",
              "uterine_contractions                                      0\n",
              "light_decelerations                                       0\n",
              "severe_decelerations                                      0\n",
              "prolongued_decelerations                                  0\n",
              "abnormal_short_term_variability                           0\n",
              "mean_value_of_short_term_variability                      0\n",
              "percentage_of_time_with_abnormal_long_term_variability    0\n",
              "mean_value_of_long_term_variability                       0\n",
              "histogram_width                                           0\n",
              "histogram_min                                             0\n",
              "histogram_max                                             0\n",
              "histogram_number_of_peaks                                 0\n",
              "histogram_number_of_zeroes                                0\n",
              "histogram_mode                                            0\n",
              "histogram_mean                                            0\n",
              "histogram_median                                          0\n",
              "histogram_variance                                        0\n",
              "histogram_tendency                                        0\n",
              "fetal_health                                              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LfBskx8TYt9o"
      },
      "source": [
        "2.**Handling Imbalance Data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yZ0JdvPVZ0KI",
        "outputId": "30863a86-d868-41fb-e767-e9a8730b3b39"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fetal_health\n",
              "1.0    1655\n",
              "2.0     295\n",
              "3.0     176\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "#Evaluating the target and find out if our data is imbalanced or not\n",
        "data['fetal_health'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "p8ysab2GcLs5",
        "outputId": "4c5a5ca8-1cb6-4de1-fec6-de9b92071f39"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='fetal_health', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "colours=[\"#f7b2b0\",\"#8f7198\", \"#003f5c\"]\n",
        "sns.countplot(data= data, x=\"fetal_health\",palette=colours)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N7wwiy_qcnGd"
      },
      "source": [
        "# **Descriptive Statistics**\n",
        "Descriptive analysis is to study the basic features of data with the statistical process. Here pandas has a worthy function called describe.With this describe function we can understand the unique, top and frequent values of categorical features. And we can find mean, std, min, max and percentile values of continuous features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 759
        },
        "id": "Aooo8JM4cnoz",
        "outputId": "639486ea-3cbd-490f-9099-1224cf481e68"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f468ffd0e80>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
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              "  background-color: #f9f9f9;\n",
              "  color: #4CAF50;\n",
              "  font-weight: bold;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_a06c9\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_a06c9_level0_col0\" class=\"col_heading level0 col0\" >count</th>\n",
              "      <th id=\"T_a06c9_level0_col1\" class=\"col_heading level0 col1\" >mean</th>\n",
              "      <th id=\"T_a06c9_level0_col2\" class=\"col_heading level0 col2\" >std</th>\n",
              "      <th id=\"T_a06c9_level0_col3\" class=\"col_heading level0 col3\" >min</th>\n",
              "      <th id=\"T_a06c9_level0_col4\" class=\"col_heading level0 col4\" >25%</th>\n",
              "      <th id=\"T_a06c9_level0_col5\" class=\"col_heading level0 col5\" >50%</th>\n",
              "      <th id=\"T_a06c9_level0_col6\" class=\"col_heading level0 col6\" >75%</th>\n",
              "      <th id=\"T_a06c9_level0_col7\" class=\"col_heading level0 col7\" >max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row0\" class=\"row_heading level0 row0\" >baseline value</th>\n",
              "      <td id=\"T_a06c9_row0_col0\" class=\"data row0 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row0_col1\" class=\"data row0 col1\" >133.303857</td>\n",
              "      <td id=\"T_a06c9_row0_col2\" class=\"data row0 col2\" >9.840844</td>\n",
              "      <td id=\"T_a06c9_row0_col3\" class=\"data row0 col3\" >106.000000</td>\n",
              "      <td id=\"T_a06c9_row0_col4\" class=\"data row0 col4\" >126.000000</td>\n",
              "      <td id=\"T_a06c9_row0_col5\" class=\"data row0 col5\" >133.000000</td>\n",
              "      <td id=\"T_a06c9_row0_col6\" class=\"data row0 col6\" >140.000000</td>\n",
              "      <td id=\"T_a06c9_row0_col7\" class=\"data row0 col7\" >160.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row1\" class=\"row_heading level0 row1\" >accelerations</th>\n",
              "      <td id=\"T_a06c9_row1_col0\" class=\"data row1 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row1_col1\" class=\"data row1 col1\" >0.003178</td>\n",
              "      <td id=\"T_a06c9_row1_col2\" class=\"data row1 col2\" >0.003866</td>\n",
              "      <td id=\"T_a06c9_row1_col3\" class=\"data row1 col3\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row1_col4\" class=\"data row1 col4\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row1_col5\" class=\"data row1 col5\" >0.002000</td>\n",
              "      <td id=\"T_a06c9_row1_col6\" class=\"data row1 col6\" >0.006000</td>\n",
              "      <td id=\"T_a06c9_row1_col7\" class=\"data row1 col7\" >0.019000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row2\" class=\"row_heading level0 row2\" >fetal_movement</th>\n",
              "      <td id=\"T_a06c9_row2_col0\" class=\"data row2 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row2_col1\" class=\"data row2 col1\" >0.009481</td>\n",
              "      <td id=\"T_a06c9_row2_col2\" class=\"data row2 col2\" >0.046666</td>\n",
              "      <td id=\"T_a06c9_row2_col3\" class=\"data row2 col3\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row2_col4\" class=\"data row2 col4\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row2_col5\" class=\"data row2 col5\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row2_col6\" class=\"data row2 col6\" >0.003000</td>\n",
              "      <td id=\"T_a06c9_row2_col7\" class=\"data row2 col7\" >0.481000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row3\" class=\"row_heading level0 row3\" >uterine_contractions</th>\n",
              "      <td id=\"T_a06c9_row3_col0\" class=\"data row3 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row3_col1\" class=\"data row3 col1\" >0.004366</td>\n",
              "      <td id=\"T_a06c9_row3_col2\" class=\"data row3 col2\" >0.002946</td>\n",
              "      <td id=\"T_a06c9_row3_col3\" class=\"data row3 col3\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row3_col4\" class=\"data row3 col4\" >0.002000</td>\n",
              "      <td id=\"T_a06c9_row3_col5\" class=\"data row3 col5\" >0.004000</td>\n",
              "      <td id=\"T_a06c9_row3_col6\" class=\"data row3 col6\" >0.007000</td>\n",
              "      <td id=\"T_a06c9_row3_col7\" class=\"data row3 col7\" >0.015000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row4\" class=\"row_heading level0 row4\" >light_decelerations</th>\n",
              "      <td id=\"T_a06c9_row4_col0\" class=\"data row4 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row4_col1\" class=\"data row4 col1\" >0.001889</td>\n",
              "      <td id=\"T_a06c9_row4_col2\" class=\"data row4 col2\" >0.002960</td>\n",
              "      <td id=\"T_a06c9_row4_col3\" class=\"data row4 col3\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row4_col4\" class=\"data row4 col4\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row4_col5\" class=\"data row4 col5\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row4_col6\" class=\"data row4 col6\" >0.003000</td>\n",
              "      <td id=\"T_a06c9_row4_col7\" class=\"data row4 col7\" >0.015000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row5\" class=\"row_heading level0 row5\" >severe_decelerations</th>\n",
              "      <td id=\"T_a06c9_row5_col0\" class=\"data row5 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row5_col1\" class=\"data row5 col1\" >0.000003</td>\n",
              "      <td id=\"T_a06c9_row5_col2\" class=\"data row5 col2\" >0.000057</td>\n",
              "      <td id=\"T_a06c9_row5_col3\" class=\"data row5 col3\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row5_col4\" class=\"data row5 col4\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row5_col5\" class=\"data row5 col5\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row5_col6\" class=\"data row5 col6\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row5_col7\" class=\"data row5 col7\" >0.001000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row6\" class=\"row_heading level0 row6\" >prolongued_decelerations</th>\n",
              "      <td id=\"T_a06c9_row6_col0\" class=\"data row6 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row6_col1\" class=\"data row6 col1\" >0.000159</td>\n",
              "      <td id=\"T_a06c9_row6_col2\" class=\"data row6 col2\" >0.000590</td>\n",
              "      <td id=\"T_a06c9_row6_col3\" class=\"data row6 col3\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row6_col4\" class=\"data row6 col4\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row6_col5\" class=\"data row6 col5\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row6_col6\" class=\"data row6 col6\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row6_col7\" class=\"data row6 col7\" >0.005000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row7\" class=\"row_heading level0 row7\" >abnormal_short_term_variability</th>\n",
              "      <td id=\"T_a06c9_row7_col0\" class=\"data row7 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row7_col1\" class=\"data row7 col1\" >46.990122</td>\n",
              "      <td id=\"T_a06c9_row7_col2\" class=\"data row7 col2\" >17.192814</td>\n",
              "      <td id=\"T_a06c9_row7_col3\" class=\"data row7 col3\" >12.000000</td>\n",
              "      <td id=\"T_a06c9_row7_col4\" class=\"data row7 col4\" >32.000000</td>\n",
              "      <td id=\"T_a06c9_row7_col5\" class=\"data row7 col5\" >49.000000</td>\n",
              "      <td id=\"T_a06c9_row7_col6\" class=\"data row7 col6\" >61.000000</td>\n",
              "      <td id=\"T_a06c9_row7_col7\" class=\"data row7 col7\" >87.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row8\" class=\"row_heading level0 row8\" >mean_value_of_short_term_variability</th>\n",
              "      <td id=\"T_a06c9_row8_col0\" class=\"data row8 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row8_col1\" class=\"data row8 col1\" >1.332785</td>\n",
              "      <td id=\"T_a06c9_row8_col2\" class=\"data row8 col2\" >0.883241</td>\n",
              "      <td id=\"T_a06c9_row8_col3\" class=\"data row8 col3\" >0.200000</td>\n",
              "      <td id=\"T_a06c9_row8_col4\" class=\"data row8 col4\" >0.700000</td>\n",
              "      <td id=\"T_a06c9_row8_col5\" class=\"data row8 col5\" >1.200000</td>\n",
              "      <td id=\"T_a06c9_row8_col6\" class=\"data row8 col6\" >1.700000</td>\n",
              "      <td id=\"T_a06c9_row8_col7\" class=\"data row8 col7\" >7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row9\" class=\"row_heading level0 row9\" >percentage_of_time_with_abnormal_long_term_variability</th>\n",
              "      <td id=\"T_a06c9_row9_col0\" class=\"data row9 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row9_col1\" class=\"data row9 col1\" >9.846660</td>\n",
              "      <td id=\"T_a06c9_row9_col2\" class=\"data row9 col2\" >18.396880</td>\n",
              "      <td id=\"T_a06c9_row9_col3\" class=\"data row9 col3\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row9_col4\" class=\"data row9 col4\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row9_col5\" class=\"data row9 col5\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row9_col6\" class=\"data row9 col6\" >11.000000</td>\n",
              "      <td id=\"T_a06c9_row9_col7\" class=\"data row9 col7\" >91.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row10\" class=\"row_heading level0 row10\" >mean_value_of_long_term_variability</th>\n",
              "      <td id=\"T_a06c9_row10_col0\" class=\"data row10 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row10_col1\" class=\"data row10 col1\" >8.187629</td>\n",
              "      <td id=\"T_a06c9_row10_col2\" class=\"data row10 col2\" >5.628247</td>\n",
              "      <td id=\"T_a06c9_row10_col3\" class=\"data row10 col3\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row10_col4\" class=\"data row10 col4\" >4.600000</td>\n",
              "      <td id=\"T_a06c9_row10_col5\" class=\"data row10 col5\" >7.400000</td>\n",
              "      <td id=\"T_a06c9_row10_col6\" class=\"data row10 col6\" >10.800000</td>\n",
              "      <td id=\"T_a06c9_row10_col7\" class=\"data row10 col7\" >50.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row11\" class=\"row_heading level0 row11\" >histogram_width</th>\n",
              "      <td id=\"T_a06c9_row11_col0\" class=\"data row11 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row11_col1\" class=\"data row11 col1\" >70.445908</td>\n",
              "      <td id=\"T_a06c9_row11_col2\" class=\"data row11 col2\" >38.955693</td>\n",
              "      <td id=\"T_a06c9_row11_col3\" class=\"data row11 col3\" >3.000000</td>\n",
              "      <td id=\"T_a06c9_row11_col4\" class=\"data row11 col4\" >37.000000</td>\n",
              "      <td id=\"T_a06c9_row11_col5\" class=\"data row11 col5\" >67.500000</td>\n",
              "      <td id=\"T_a06c9_row11_col6\" class=\"data row11 col6\" >100.000000</td>\n",
              "      <td id=\"T_a06c9_row11_col7\" class=\"data row11 col7\" >180.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row12\" class=\"row_heading level0 row12\" >histogram_min</th>\n",
              "      <td id=\"T_a06c9_row12_col0\" class=\"data row12 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row12_col1\" class=\"data row12 col1\" >93.579492</td>\n",
              "      <td id=\"T_a06c9_row12_col2\" class=\"data row12 col2\" >29.560212</td>\n",
              "      <td id=\"T_a06c9_row12_col3\" class=\"data row12 col3\" >50.000000</td>\n",
              "      <td id=\"T_a06c9_row12_col4\" class=\"data row12 col4\" >67.000000</td>\n",
              "      <td id=\"T_a06c9_row12_col5\" class=\"data row12 col5\" >93.000000</td>\n",
              "      <td id=\"T_a06c9_row12_col6\" class=\"data row12 col6\" >120.000000</td>\n",
              "      <td id=\"T_a06c9_row12_col7\" class=\"data row12 col7\" >159.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row13\" class=\"row_heading level0 row13\" >histogram_max</th>\n",
              "      <td id=\"T_a06c9_row13_col0\" class=\"data row13 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row13_col1\" class=\"data row13 col1\" >164.025400</td>\n",
              "      <td id=\"T_a06c9_row13_col2\" class=\"data row13 col2\" >17.944183</td>\n",
              "      <td id=\"T_a06c9_row13_col3\" class=\"data row13 col3\" >122.000000</td>\n",
              "      <td id=\"T_a06c9_row13_col4\" class=\"data row13 col4\" >152.000000</td>\n",
              "      <td id=\"T_a06c9_row13_col5\" class=\"data row13 col5\" >162.000000</td>\n",
              "      <td id=\"T_a06c9_row13_col6\" class=\"data row13 col6\" >174.000000</td>\n",
              "      <td id=\"T_a06c9_row13_col7\" class=\"data row13 col7\" >238.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row14\" class=\"row_heading level0 row14\" >histogram_number_of_peaks</th>\n",
              "      <td id=\"T_a06c9_row14_col0\" class=\"data row14 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row14_col1\" class=\"data row14 col1\" >4.068203</td>\n",
              "      <td id=\"T_a06c9_row14_col2\" class=\"data row14 col2\" >2.949386</td>\n",
              "      <td id=\"T_a06c9_row14_col3\" class=\"data row14 col3\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row14_col4\" class=\"data row14 col4\" >2.000000</td>\n",
              "      <td id=\"T_a06c9_row14_col5\" class=\"data row14 col5\" >3.000000</td>\n",
              "      <td id=\"T_a06c9_row14_col6\" class=\"data row14 col6\" >6.000000</td>\n",
              "      <td id=\"T_a06c9_row14_col7\" class=\"data row14 col7\" >18.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row15\" class=\"row_heading level0 row15\" >histogram_number_of_zeroes</th>\n",
              "      <td id=\"T_a06c9_row15_col0\" class=\"data row15 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row15_col1\" class=\"data row15 col1\" >0.323612</td>\n",
              "      <td id=\"T_a06c9_row15_col2\" class=\"data row15 col2\" >0.706059</td>\n",
              "      <td id=\"T_a06c9_row15_col3\" class=\"data row15 col3\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row15_col4\" class=\"data row15 col4\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row15_col5\" class=\"data row15 col5\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row15_col6\" class=\"data row15 col6\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row15_col7\" class=\"data row15 col7\" >10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row16\" class=\"row_heading level0 row16\" >histogram_mode</th>\n",
              "      <td id=\"T_a06c9_row16_col0\" class=\"data row16 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row16_col1\" class=\"data row16 col1\" >137.452023</td>\n",
              "      <td id=\"T_a06c9_row16_col2\" class=\"data row16 col2\" >16.381289</td>\n",
              "      <td id=\"T_a06c9_row16_col3\" class=\"data row16 col3\" >60.000000</td>\n",
              "      <td id=\"T_a06c9_row16_col4\" class=\"data row16 col4\" >129.000000</td>\n",
              "      <td id=\"T_a06c9_row16_col5\" class=\"data row16 col5\" >139.000000</td>\n",
              "      <td id=\"T_a06c9_row16_col6\" class=\"data row16 col6\" >148.000000</td>\n",
              "      <td id=\"T_a06c9_row16_col7\" class=\"data row16 col7\" >187.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row17\" class=\"row_heading level0 row17\" >histogram_mean</th>\n",
              "      <td id=\"T_a06c9_row17_col0\" class=\"data row17 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row17_col1\" class=\"data row17 col1\" >134.610536</td>\n",
              "      <td id=\"T_a06c9_row17_col2\" class=\"data row17 col2\" >15.593596</td>\n",
              "      <td id=\"T_a06c9_row17_col3\" class=\"data row17 col3\" >73.000000</td>\n",
              "      <td id=\"T_a06c9_row17_col4\" class=\"data row17 col4\" >125.000000</td>\n",
              "      <td id=\"T_a06c9_row17_col5\" class=\"data row17 col5\" >136.000000</td>\n",
              "      <td id=\"T_a06c9_row17_col6\" class=\"data row17 col6\" >145.000000</td>\n",
              "      <td id=\"T_a06c9_row17_col7\" class=\"data row17 col7\" >182.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row18\" class=\"row_heading level0 row18\" >histogram_median</th>\n",
              "      <td id=\"T_a06c9_row18_col0\" class=\"data row18 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row18_col1\" class=\"data row18 col1\" >138.090310</td>\n",
              "      <td id=\"T_a06c9_row18_col2\" class=\"data row18 col2\" >14.466589</td>\n",
              "      <td id=\"T_a06c9_row18_col3\" class=\"data row18 col3\" >77.000000</td>\n",
              "      <td id=\"T_a06c9_row18_col4\" class=\"data row18 col4\" >129.000000</td>\n",
              "      <td id=\"T_a06c9_row18_col5\" class=\"data row18 col5\" >139.000000</td>\n",
              "      <td id=\"T_a06c9_row18_col6\" class=\"data row18 col6\" >148.000000</td>\n",
              "      <td id=\"T_a06c9_row18_col7\" class=\"data row18 col7\" >186.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row19\" class=\"row_heading level0 row19\" >histogram_variance</th>\n",
              "      <td id=\"T_a06c9_row19_col0\" class=\"data row19 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row19_col1\" class=\"data row19 col1\" >18.808090</td>\n",
              "      <td id=\"T_a06c9_row19_col2\" class=\"data row19 col2\" >28.977636</td>\n",
              "      <td id=\"T_a06c9_row19_col3\" class=\"data row19 col3\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row19_col4\" class=\"data row19 col4\" >2.000000</td>\n",
              "      <td id=\"T_a06c9_row19_col5\" class=\"data row19 col5\" >7.000000</td>\n",
              "      <td id=\"T_a06c9_row19_col6\" class=\"data row19 col6\" >24.000000</td>\n",
              "      <td id=\"T_a06c9_row19_col7\" class=\"data row19 col7\" >269.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row20\" class=\"row_heading level0 row20\" >histogram_tendency</th>\n",
              "      <td id=\"T_a06c9_row20_col0\" class=\"data row20 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row20_col1\" class=\"data row20 col1\" >0.320320</td>\n",
              "      <td id=\"T_a06c9_row20_col2\" class=\"data row20 col2\" >0.610829</td>\n",
              "      <td id=\"T_a06c9_row20_col3\" class=\"data row20 col3\" >-1.000000</td>\n",
              "      <td id=\"T_a06c9_row20_col4\" class=\"data row20 col4\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row20_col5\" class=\"data row20 col5\" >0.000000</td>\n",
              "      <td id=\"T_a06c9_row20_col6\" class=\"data row20 col6\" >1.000000</td>\n",
              "      <td id=\"T_a06c9_row20_col7\" class=\"data row20 col7\" >1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a06c9_level0_row21\" class=\"row_heading level0 row21\" >fetal_health</th>\n",
              "      <td id=\"T_a06c9_row21_col0\" class=\"data row21 col0\" >2126.000000</td>\n",
              "      <td id=\"T_a06c9_row21_col1\" class=\"data row21 col1\" >1.304327</td>\n",
              "      <td id=\"T_a06c9_row21_col2\" class=\"data row21 col2\" >0.614377</td>\n",
              "      <td id=\"T_a06c9_row21_col3\" class=\"data row21 col3\" >1.000000</td>\n",
              "      <td id=\"T_a06c9_row21_col4\" class=\"data row21 col4\" >1.000000</td>\n",
              "      <td id=\"T_a06c9_row21_col5\" class=\"data row21 col5\" >1.000000</td>\n",
              "      <td id=\"T_a06c9_row21_col6\" class=\"data row21 col6\" >1.000000</td>\n",
              "      <td id=\"T_a06c9_row21_col7\" class=\"data row21 col7\" >3.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "data.describe().T.style.set_properties(**{'background-color': '#f9f9f9', 'color': '#4CAF50', 'font-weight': 'bold'})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sNFZMg2f0dte"
      },
      "source": [
        "**Visual Analysis**\n",
        "**Univariate Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MmHGFiQ50nK3",
        "outputId": "df61daeb-9bbf-45d4-e80e-07281a94ba4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'baseline value'}>,\n",
              "        <Axes: title={'center': 'accelerations'}>,\n",
              "        <Axes: title={'center': 'fetal_movement'}>,\n",
              "        <Axes: title={'center': 'uterine_contractions'}>,\n",
              "        <Axes: title={'center': 'light_decelerations'}>],\n",
              "       [<Axes: title={'center': 'severe_decelerations'}>,\n",
              "        <Axes: title={'center': 'prolongued_decelerations'}>,\n",
              "        <Axes: title={'center': 'abnormal_short_term_variability'}>,\n",
              "        <Axes: title={'center': 'mean_value_of_short_term_variability'}>,\n",
              "        <Axes: title={'center': 'percentage_of_time_with_abnormal_long_term_variability'}>],\n",
              "       [<Axes: title={'center': 'mean_value_of_long_term_variability'}>,\n",
              "        <Axes: title={'center': 'histogram_width'}>,\n",
              "        <Axes: title={'center': 'histogram_min'}>,\n",
              "        <Axes: title={'center': 'histogram_max'}>,\n",
              "        <Axes: title={'center': 'histogram_number_of_peaks'}>],\n",
              "       [<Axes: title={'center': 'histogram_number_of_zeroes'}>,\n",
              "        <Axes: title={'center': 'histogram_mode'}>,\n",
              "        <Axes: title={'center': 'histogram_mean'}>,\n",
              "        <Axes: title={'center': 'histogram_median'}>,\n",
              "        <Axes: title={'center': 'histogram_variance'}>],\n",
              "       [<Axes: title={'center': 'histogram_tendency'}>,\n",
              "        <Axes: title={'center': 'fetal_health'}>, <Axes: >, <Axes: >,\n",
              "        <Axes: >]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1700x1700 with 25 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "data.hist(figsize=(17,17),layout=(5,5),sharex=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y3-AlW8h1MXR"
      },
      "source": [
        "**Bivariate Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "55ZHHKM41PkK",
        "outputId": "ee2522ef-9bf0-4431-80d2-a74ba346d5fb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='fetal_movement', ylabel='fetal_health'>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.scatterplot(x='fetal_movement',y='fetal_health',data=data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6XgApWtV6a-9"
      },
      "source": [
        "**Multivariate analysis**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9d0HNWbY6bqD",
        "outputId": "4fe85dd4-ad71-48f8-d7dd-f86cfcfbab65"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#correlation matrix\n",
        "corrmat= data.corr()\n",
        "plt.figure(figsize=(18,18))\n",
        "\n",
        "cmap = sns.light_palette(\"seagreen\",as_cmap=True)\n",
        "\n",
        "sns.heatmap(corrmat,annot=True, cmap=cmap, center=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jFOjv-uW85LV"
      },
      "source": [
        "\n",
        "**Feature Selection**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop(columns=['histogram_mean'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "Iztf_9aznQEp"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JW-KDc5footQ",
        "outputId": "b3732872-f72d-42c4-c2fd-d8804c53a514"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2126, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0sAYywK39xQ7",
        "outputId": "1a2d08e4-41ce-470e-c40f-37506ccc01e0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fetal_health                                              1.000000\n",
              "prolongued_decelerations                                  0.484859\n",
              "abnormal_short_term_variability                           0.471191\n",
              "percentage_of_time_with_abnormal_long_term_variability    0.426146\n",
              "histogram_variance                                        0.206630\n",
              "baseline value                                            0.148151\n",
              "severe_decelerations                                      0.131934\n",
              "fetal_movement                                            0.088010\n",
              "histogram_min                                             0.063175\n",
              "light_decelerations                                       0.058870\n",
              "histogram_number_of_zeroes                               -0.016682\n",
              "histogram_number_of_peaks                                -0.023666\n",
              "histogram_max                                            -0.045265\n",
              "histogram_width                                          -0.068789\n",
              "mean_value_of_short_term_variability                     -0.103382\n",
              "histogram_tendency                                       -0.131976\n",
              "uterine_contractions                                     -0.204894\n",
              "histogram_median                                         -0.205033\n",
              "mean_value_of_long_term_variability                      -0.226797\n",
              "histogram_mode                                           -0.250412\n",
              "accelerations                                            -0.364066\n",
              "Name: fetal_health, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "data.corr()[\"fetal_health\"].sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "e8uzJvJZE51t"
      },
      "outputs": [],
      "source": [
        "new_data=data.loc[:,[\"prolongued_decelerations\",\"abnormal_short_term_variability\",\n",
        "\"percentage_of_time_with_abnormal_long_term_variability\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "11qJRHQtFeC2",
        "outputId": "6ab7426e-532c-4c08-d5d5-1a7ca321d905"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   prolongued_decelerations  abnormal_short_term_variability  \\\n",
              "0                       0.0                             73.0   \n",
              "1                       0.0                             17.0   \n",
              "2                       0.0                             16.0   \n",
              "3                       0.0                             16.0   \n",
              "4                       0.0                             16.0   \n",
              "\n",
              "   percentage_of_time_with_abnormal_long_term_variability  \n",
              "0                                               43.0       \n",
              "1                                                0.0       \n",
              "2                                                0.0       \n",
              "3                                                0.0       \n",
              "4                                                0.0       "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cf58d1d3-ed77-4501-9216-5eb6e53b9a32\" 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>prolongued_decelerations</th>\n",
              "      <th>abnormal_short_term_variability</th>\n",
              "      <th>percentage_of_time_with_abnormal_long_term_variability</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>43.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
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              "    async function quickchart(key) {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "new_data",
              "summary": "{\n  \"name\": \"new_data\",\n  \"rows\": 2126,\n  \"fields\": [\n    {\n      \"column\": \"prolongued_decelerations\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0005899475163513611,\n        \"min\": 0.0,\n        \"max\": 0.005,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.0,\n          0.002,\n          0.005\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"abnormal_short_term_variability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.192813718571703,\n        \"min\": 12.0,\n        \"max\": 87.0,\n        \"num_unique_values\": 75,\n        \"samples\": [\n          29.0,\n          13.0,\n          21.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percentage_of_time_with_abnormal_long_term_variability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.396879675177722,\n        \"min\": 0.0,\n        \"max\": 91.0,\n        \"num_unique_values\": 87,\n        \"samples\": [\n          70.0,\n          43.0,\n          75.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "new_data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qn6MQFmeGrMt"
      },
      "source": [
        "**Scaling Data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "93MHP1ByGtj2",
        "outputId": "0c30cc88-fc27-4ca6-c200-fbed5220a12f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   baseline value  accelerations  fetal_movement  uterine_contractions  \\\n",
              "0        0.259259       0.000000             0.0              0.000000   \n",
              "1        0.481481       0.315789             0.0              0.400000   \n",
              "2        0.500000       0.157895             0.0              0.533333   \n",
              "3        0.518519       0.157895             0.0              0.533333   \n",
              "4        0.481481       0.368421             0.0              0.533333   \n",
              "\n",
              "   light_decelerations  severe_decelerations  prolongued_decelerations  \\\n",
              "0                  0.0                   0.0                       0.0   \n",
              "1                  0.2                   0.0                       0.0   \n",
              "2                  0.2                   0.0                       0.0   \n",
              "3                  0.2                   0.0                       0.0   \n",
              "4                  0.0                   0.0                       0.0   \n",
              "\n",
              "   abnormal_short_term_variability  mean_value_of_short_term_variability  \\\n",
              "0                         0.813333                              0.044118   \n",
              "1                         0.066667                              0.279412   \n",
              "2                         0.053333                              0.279412   \n",
              "3                         0.053333                              0.323529   \n",
              "4                         0.053333                              0.323529   \n",
              "\n",
              "   percentage_of_time_with_abnormal_long_term_variability  \\\n",
              "0                                           0.472527        \n",
              "1                                           0.000000        \n",
              "2                                           0.000000        \n",
              "3                                           0.000000        \n",
              "4                                           0.000000        \n",
              "\n",
              "   mean_value_of_long_term_variability  histogram_width  histogram_min  \\\n",
              "0                             0.047337         0.344633       0.110092   \n",
              "1                             0.205128         0.717514       0.165138   \n",
              "2                             0.264300         0.717514       0.165138   \n",
              "3                             0.453649         0.644068       0.027523   \n",
              "4                             0.392505         0.644068       0.027523   \n",
              "\n",
              "   histogram_max  histogram_number_of_peaks  histogram_number_of_zeroes  \\\n",
              "0       0.034483                   0.111111                         0.0   \n",
              "1       0.655172                   0.333333                         0.1   \n",
              "2       0.655172                   0.277778                         0.1   \n",
              "3       0.413793                   0.611111                         0.0   \n",
              "4       0.413793                   0.500000                         0.0   \n",
              "\n",
              "   histogram_mode  histogram_median  histogram_variance  histogram_tendency  \n",
              "0        0.472441          0.403670            0.271375                 1.0  \n",
              "1        0.637795          0.577982            0.044610                 0.5  \n",
              "2        0.637795          0.559633            0.048327                 0.5  \n",
              "3        0.606299          0.550459            0.048327                 1.0  \n",
              "4        0.606299          0.559633            0.040892                 1.0  "
            ],
            "text/html": [
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>baseline value</th>\n",
              "      <th>accelerations</th>\n",
              "      <th>fetal_movement</th>\n",
              "      <th>uterine_contractions</th>\n",
              "      <th>light_decelerations</th>\n",
              "      <th>severe_decelerations</th>\n",
              "      <th>prolongued_decelerations</th>\n",
              "      <th>abnormal_short_term_variability</th>\n",
              "      <th>mean_value_of_short_term_variability</th>\n",
              "      <th>percentage_of_time_with_abnormal_long_term_variability</th>\n",
              "      <th>mean_value_of_long_term_variability</th>\n",
              "      <th>histogram_width</th>\n",
              "      <th>histogram_min</th>\n",
              "      <th>histogram_max</th>\n",
              "      <th>histogram_number_of_peaks</th>\n",
              "      <th>histogram_number_of_zeroes</th>\n",
              "      <th>histogram_mode</th>\n",
              "      <th>histogram_median</th>\n",
              "      <th>histogram_variance</th>\n",
              "      <th>histogram_tendency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.259259</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.813333</td>\n",
              "      <td>0.044118</td>\n",
              "      <td>0.472527</td>\n",
              "      <td>0.047337</td>\n",
              "      <td>0.344633</td>\n",
              "      <td>0.110092</td>\n",
              "      <td>0.034483</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.472441</td>\n",
              "      <td>0.403670</td>\n",
              "      <td>0.271375</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.481481</td>\n",
              "      <td>0.315789</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.2</td>\n",
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              "      <td>0.066667</td>\n",
              "      <td>0.279412</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.205128</td>\n",
              "      <td>0.717514</td>\n",
              "      <td>0.165138</td>\n",
              "      <td>0.655172</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.637795</td>\n",
              "      <td>0.577982</td>\n",
              "      <td>0.044610</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.157895</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.533333</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.053333</td>\n",
              "      <td>0.279412</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.264300</td>\n",
              "      <td>0.717514</td>\n",
              "      <td>0.165138</td>\n",
              "      <td>0.655172</td>\n",
              "      <td>0.277778</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.637795</td>\n",
              "      <td>0.559633</td>\n",
              "      <td>0.048327</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.518519</td>\n",
              "      <td>0.157895</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.533333</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.053333</td>\n",
              "      <td>0.323529</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.453649</td>\n",
              "      <td>0.644068</td>\n",
              "      <td>0.027523</td>\n",
              "      <td>0.413793</td>\n",
              "      <td>0.611111</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.606299</td>\n",
              "      <td>0.550459</td>\n",
              "      <td>0.048327</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.481481</td>\n",
              "      <td>0.368421</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.533333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.053333</td>\n",
              "      <td>0.323529</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.392505</td>\n",
              "      <td>0.644068</td>\n",
              "      <td>0.027523</td>\n",
              "      <td>0.413793</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.606299</td>\n",
              "      <td>0.559633</td>\n",
              "      <td>0.040892</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f51802be-0248-44f3-8474-f72657d0294b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f51802be-0248-44f3-8474-f72657d0294b 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-f51802be-0248-44f3-8474-f72657d0294b');\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",
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              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-850978d7-6a4a-4d23-a22f-ffa225274b42')\"\n",
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              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-850978d7-6a4a-4d23-a22f-ffa225274b42 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x_scaled",
              "summary": "{\n  \"name\": \"x_scaled\",\n  \"rows\": 2126,\n  \"fields\": [\n    {\n      \"column\": \"baseline value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.18223785662446285,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          0.7222222222222223,\n          0.7592592592592591,\n          0.8888888888888888\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accelerations\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2034521554892359,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.0,\n          0.8421052631578948,\n          0.8947368421052633\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fetal_movement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09701838766213082,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          0.006237006237006237,\n          0.17463617463617465,\n          0.08939708939708939\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"uterine_contractions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19640460885892652,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          0.0,\n          0.4,\n          0.13333333333333336\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"light_decelerations\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1973472384539724,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        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 \"column\": \"histogram_tendency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3054143150276459,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          0.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "x=data.drop(columns=['fetal_health'])\n",
        "y=data[\"fetal_health\"]\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scale=MinMaxScaler()\n",
        "x_scaled=pd.DataFrame(scale.fit_transform(x),columns=x.columns)\n",
        "x_scaled.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "id": "-jiJBYSeKu7X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c9fcec0-5198-4b54-c4a1-19f453eb71ab"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2126, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RksyXlRLIHFy"
      },
      "source": [
        "**Splitting data into Train and Test**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "HZzfiLN7IKQM"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WCURnYcNIdie",
        "outputId": "3cc8a1c4-9417-4c6f-c3f1-a78db80d3721"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1488, 20), (638, 20))"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=42)\n",
        "x_train.shape,x_test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nKz209MJJU2A"
      },
      "source": [
        "**Applying SMOTE for balancing the Data**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o2j5hDyEJYA2",
        "outputId": "15d7a4ad-8ce2-4524-a347-36dd265211bb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: imblearn in /usr/local/lib/python3.10/dist-packages (0.0)\n",
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.10/dist-packages (from imblearn) (0.10.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (1.11.4)\n",
            "Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (1.2.2)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn->imblearn) (3.5.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install imblearn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "DhAtCva-JuI_"
      },
      "outputs": [],
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "smote=SMOTE()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "EqQeQTF4KF3w"
      },
      "outputs": [],
      "source": [
        "x_train_smote,y_train_smote=smote.fit_resample(x_train.astype('float'),y_train)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Um8fW6SQAOpG",
        "outputId": "2cc168d3-9c2e-45ef-d03d-8f3752ae2e0f"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['baseline value', 'accelerations', 'fetal_movement',\n",
            "       'uterine_contractions', 'light_decelerations', 'severe_decelerations',\n",
            "       'prolongued_decelerations', 'abnormal_short_term_variability',\n",
            "       'mean_value_of_short_term_variability',\n",
            "       'percentage_of_time_with_abnormal_long_term_variability',\n",
            "       'mean_value_of_long_term_variability', 'histogram_width',\n",
            "       'histogram_min', 'histogram_max', 'histogram_number_of_peaks',\n",
            "       'histogram_number_of_zeroes', 'histogram_mode', 'histogram_median',\n",
            "       'histogram_variance', 'histogram_tendency'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-CFOS6OKVkH",
        "outputId": "0092a980-e601-476b-e918-fb4a208d5c43"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before SMOTE: Counter({1.0: 1159, 2.0: 194, 3.0: 135})\n",
            "After SMOTE: Counter({1.0: 1159, 3.0: 1159, 2.0: 1159})\n"
          ]
        }
      ],
      "source": [
        "from collections import Counter\n",
        "print(\"Before SMOTE:\",Counter(y_train))\n",
        "print(\"After SMOTE:\",Counter(y_train_smote))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kjHQ21OPK9l2"
      },
      "source": [
        "After applying SMOTE, the dataset is balanced. And now we will train the model after balancing the dataset to check the accuracy.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h3xR0zSSLEHa"
      },
      "source": [
        "**Model Building**\n",
        "*Training The Model In Multiple Algorithms*\n",
        "Now our data is cleaned and it’s time to build the model. We can train our data on different algorithms. For this project we are applying four classification algorithms. The best model is saved based on its performance.\n",
        "\n",
        "\n",
        "## **Random Forest Model**\n",
        "\n",
        "A function named randomForest is created and train and test data are passed as the parameters. Inside the function, the RandomForestClassifier algorithm is initialized and training data is passed to the model with the .fit() function. Test data is predicted with .predict() function and saved in a new variable. For evaluating the model, a confusion matrix and classification report is done.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Random Forest Model RF**"
      ],
      "metadata": {
        "id": "1QqGm-JHnHej"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-IwVpPqwLdtD",
        "outputId": "93684a25-74d4-45fc-8335-55362a08315e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9420062695924765\n"
          ]
        }
      ],
      "source": [
        "RF_model=RandomForestClassifier()\n",
        "RF_model.fit(x_train_smote,y_train_smote)\n",
        "predictions=RF_model.predict(x_test)\n",
        "print(accuracy_score(y_test,predictions))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"For the amounts of training data is:\", x_train_smote.shape[0]) # Assuming you want to print the number of samples in the training data\n",
        "print(\"Accuracy of RandomForestClassifier:\",RF_model.score(x_test,y_test))\n",
        "cm=confusion_matrix(y_test,predictions)\n",
        "cm_display=ConfusionMatrixDisplay(cm).plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "upS8XkQDPc1m",
        "outputId": "9a1bf008-4746-4457-9aae-c95f80a95fd8"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For the amounts of training data is: 3477\n",
            "Accuracy of RandomForestClassifier: 0.9420062695924765\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-learn\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test,predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SEXXDv9ZDpMc",
        "outputId": "c99d80c2-3aea-4519-ada0-f2a7d38191df"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.11.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.97      0.96      0.97       496\n",
            "         2.0       0.83      0.84      0.84       101\n",
            "         3.0       0.88      0.93      0.90        41\n",
            "\n",
            "    accuracy                           0.94       638\n",
            "   macro avg       0.90      0.91      0.90       638\n",
            "weighted avg       0.94      0.94      0.94       638\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jX_Lj68uKxgk",
        "outputId": "7eecd13b-deaf-4de3-cef0-e06f49d4bd0d"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.97      0.96      0.97       496\n",
            "         2.0       0.83      0.84      0.84       101\n",
            "         3.0       0.88      0.93      0.90        41\n",
            "\n",
            "    accuracy                           0.94       638\n",
            "   macro avg       0.90      0.91      0.90       638\n",
            "weighted avg       0.94      0.94      0.94       638\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1PcfZPeAF4Cv",
        "outputId": "5c71dc36-325a-41b1-b310-b629eccb85b4"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[478,  17,   1],\n",
              "       [ 12,  85,   4],\n",
              "       [  3,   0,  38]])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nJTCdorJODgh"
      },
      "source": [
        "**Decision Tree**\n",
        "A function named decisionTree is created and train and test data are passed as the parameters. Inside the function, DecisionTreeClassifier algorithm is initialized and training data is passed to the model with the .fit() function. Test data is predicted with .predict() function and saved in a new variable. For evaluating the model, a confusion matrix and classification report is done."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Decision Tree DT**"
      ],
      "metadata": {
        "id": "s76RORgXmujc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t--puvj5OEmY",
        "outputId": "dccc7b6c-c2bb-4a13-9fb5-c439a8926fcb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9028213166144201\n"
          ]
        }
      ],
      "source": [
        "DT_model=DecisionTreeClassifier()\n",
        "DT_model.fit(x_train_smote,y_train_smote)\n",
        "predictions=DT_model.predict(x_test)\n",
        "print(accuracy_score(y_test,predictions))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "cLwpgbR4PAnG",
        "outputId": "2b3f3d10-3eb3-4e52-cdc8-1f1cebb89e84"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For the amounts of training data is: 3477\n",
            "Accuracy of DecisionTreeClassifier: 0.9028213166144201\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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R7AEAluAw3Bt3dxgeDKaK0cYHAMDkqOwBAJbgcHOCnjv7ehvJHgBgCQ7Z5HBj3N2dfb3Nd7+mAACACqGyBwBYAnfQAwDA5Kw8Zu+7kQMAgAqhsgcAWIJDbt4b34cn6JHsAQCWYLg5G98g2QMAcGmz8lPvGLMHAMDkqOwBAJZg5dn4JHsAgCXQxgcAAKZFZQ8AsAQr3xufZA8AsATa+AAAwLSo7AEAlmDlyp5kDwCwBCsne9r4AACYHJU9AMASrFzZk+wBAJZgyL3L5wzPhVLlSPYAAEuwcmXPmD0AACZHZQ8AsAQrV/YkewCAJVg52dPGBwDA5KjsAQCWYOXKnmQPALAEw7DJcCNhu7Ovt9HGBwDA5KjsAQCWwPPsAQAwOSuP2dPGBwDA5KjsAQCWYOUJeiR7AIAlWLmNT7IHAFiClSt7xuwBADA5U1T2fRK7qCD3rLfDqDKhEcFacdR6521vU8/bIVS50tDAsj+b1FFpQbGXo6lathNZ3g4BJmO42cb35creFMkeAIALMSQZhnv7+yra+AAAmBzJHgBgCefuoOfO4orXX39dt9xyi9q3b6/27durb9+++uKLL5yvFxUVKSUlRV26dFFiYqKGDx+ukydPljvG0aNHNXjwYLVt21Zdu3bVjBkzVFpa6vK5k+wBAJZwbja+O4sr6tatqyeeeELvvvuuli9friuvvFKPPPKI9u3bJ0maPn26Pv/8c82ePVuLFy/WiRMnNGzYMOf+drtdQ4YMUUlJiZYtW6a0tDStWLFCL7zwgsvnTrIHAMAFeXl55Zbi4t+fPNujRw8lJyerSZMmuuyyy/TYY48pNDRU27dvV25urpYvX64xY8aoa9euSkhI0PTp07Vt2zZt375dkrRhwwbt379fzzzzjFq2bKnk5GSNHDlSS5cu/cP3/CMkewCAJZy7qY47iyR1795dHTp0cC7p6ekXfG+73a6VK1eqoKBAiYmJ2rlzp0pKStStWzfnNs2aNVNsbKwz2W/fvl1xcXGKjo52bpOUlKS8vDzt37/fpXNnNj4AwBIMw83Z+L/su379+nLrAwMD/3CfvXv36q677lJRUZFCQ0M1d+5cNW/eXLt371ZAQICqV69ebvuoqChlZZVddnry5MlyiV6S8+dz21QUyR4AABeEh4dXeNvLLrtM7733nnJzc/XPf/5To0eP1pIlSyoxut9HsgcAWII3bpcbGBioxo0bS5ISEhL07bffatGiRbrxxhtVUlKinJycctV9dna2YmJiJJVV8Tt27Ch3vHOz9c9tU1GM2QMALKGqZ+P/HofDoeLiYiUkJCggIEAbN250vnbw4EEdPXpU7dq1kyS1a9dO33//vbKzs53bfPnllwoPD1fz5s1del8qewCAJVT1U++effZZde/eXfXq1VN+fr4++ugjbd68WfPnz1dERIT69OmjtLQ0RUZGKjw8XFOnTlViYqIz2SclJal58+Z68sknNWrUKGVlZWn27Nnq16/f/5wn8HtI9gAAVILs7GyNHj1aJ06cUEREhOLj4zV//nxdddVVkqSxY8fKz89PI0aMUHFxsZKSkjRx4kTn/v7+/po3b54mTZqkvn37KiQkRL169dKIESNcjoVkDwCwBE/Nxq+o6dOn/8/Xg4KCNHHixHIJ/r/Vr19fr776qmtv/DtI9gAASyhL9u5M0PNgMFWMCXoAAJgclT0AwBK8cendpYJkDwCwBEPuPZPeh7v4tPEBADA7KnsAgCXQxgcAwOws3Mcn2QMArMHdW976cGXPmD0AACZHZQ8AsISqvoPepYRkDwCwBCtP0KONDwCAyVHZAwCswbC5N8nOhyt7kj0AwBKsPGZPGx8AAJOjsgcAWAM31QEAwNysPBu/Qsn+008/rfABr7322osOBgAAeF6Fkv0jjzxSoYPZbDbt3r3brYAAAKg0PtyKd0eFkv2ePXsqOw4AACqVldv4bs3GLyoq8lQcAABULsMDi49yOdnb7XbNnTtXV199tRITE3X48GFJ0uzZs/X22297PEAAAOAel5P9yy+/rBUrVmjUqFEKCAhwro+Li9M777zj0eAAAPAcmwcW3+Rysn///fc1ZcoU3XrrrfLz+3X3+Ph4HTx40KPBAQDgMbTxK+748eNq1KjReesNw1BpaalHggIAAJ7jcrJv3ry5tm7det76jz/+WC1btvRIUAAAeJyFK3uX76D38MMPa8yYMTp+/LgMw9Ann3yiQ4cO6b333lN6enplxAgAgPss/NQ7lyv76667TvPmzdPGjRsVEhKiF154QQcOHNC8efN01VVXVUaMAADADRd1b/yOHTtqwYIFno4FAIBKY+VH3F70g3C+/fZbHThwQFLZOH5CQoLHggIAwON46l3F/fTTT/rb3/6m//znP6pevbokKScnR4mJifr73/+uunXrejxIAABw8Vwes3/66adVWlqqVatWafPmzdq8ebNWrVolwzD09NNPV0aMAAC479wEPXcWH+VyZb9lyxYtW7ZMTZs2da5r2rSpxo0bp379+nk0OAAAPMVmlC3u7O+rXE729erV+92b5zgcDtWuXdsjQQEA4HEWHrN3uY0/atQoTZkyRd9++61z3bfffqtp06Zp9OjRHg0OAAC4r0KVfadOnWSz/TpWUVBQoDvvvFP+/v6Syp6E5+/vr7Fjx+q6666rnEgBAHCHhW+qU6FkP3bs2MqOAwCAymXhNn6Fkn2vXr0qOw4AAFBJLvqmOpJUVFSkkpKScuvCw8PdCggAgEpBZV9xBQUFmjVrllavXq3Tp0+f9/ru3bs9ERcAAJ5l4WTv8mz8Z555Rl999ZUmTZqkwMBATZ06VcOHD1ft2rU1Y8aMyogRAAC4weVk//nnn2vixInq2bOn/P391bFjRz388MN67LHH9OGHH1ZGjAAAuM/Cd9BzOdmfOXNGDRs2lFQ2Pn/mzBlJUocOHbR161bPRgcAgIecu4OeO4uvcnnMvkGDBsrMzFRsbKyaNm2q1atXq02bNvr8888VERFRGTFaUkKnM/rLA0fVvFWeouqUaPLQeG1cG+V83XDk64HRe9UpOUsRNUp1PDNI7y+qp1Vv8CAiX7Po5eWqWzv/vPUfrI7Xa69fLaM0Q2NGrlGLy39SQIBDW7fHau4/Ouv0mRAvRIvKZOSla8WejVrxj2jNm9jA2+HARFxO9n369NGePXvUuXNnDR48WA899JCWLFmi0tJSjRkzxqVjbdmyRfPnz9fOnTuVlZWluXPnclOeXwSHOHRwT5g+eae2xr+097zXjdxUtev2s2Y+frmOHwlSh6TTemTSQWUfD9Smz2p5IWJcrOGjb5af368lQ5NGpzRj4lqt39hYQUElMk7dL8Pw05OTbpAk3Xf3dk1+6jONfOomGT7cVkR5zRPyZBS+qUN7Qr0dinlZeIKey8n+vvvuc/53t27dtHr1au3atUuNGjVSixYtXDpWQUGB4uPj1adPHw0bNszVUExt6/qa2rq+5h9vULJN6z6sq283R0qSVr9ZVzfedVzxbfNI9j7mTE5wuZ/79tqpI8citGNXHXXrfFyyH9ELr9yj7J/LXp855yq9u3CZ2rU+pm07Yr0QMTwtONSux2btk636y8rPGentcGBCbl1nL0n169dX/fr1L2rf5ORkJScnuxuCNQUkqlPyaq18vZayjweqTZcc1W9SqFemN/F2ZHBDtWp2Xdv9oJZ/eIUkmwIC7JJsKin1l2SXJJUU+8swbEpocYJkbxLDpmdq67qaanDlVd4OxdRscvOpdx6LpOpVKNkvWrSowgccMGDARQdzsULCgy+8kY8LCglUaETZeYaEB8tWfYKOHf63lmz4WqUlNhmG9PKUFjq4p45CTTp1wh4a6O0QKt1VnQ8qPKxY//qqhUJDA/XjkQaSLUSD7vlaC15vL5vN0IA7t8nf31DtmGKFmvh3YouwxpyEpJtOKq7tWU24v7VuHyP5+fupWkA1hVrk/EMizP/v96XAZhjGBb/n9OjRo2IHs9n06aefXlQg8fHxjNn/AcdPcbLVmCtb8PXOdUb+fBkFb8kWMVryj5WKt8jIe062Gi/KFkR14KscPw+UbAHyq5nuXGcUbZCRM1GyZ0ryk4JvlkoPSAFt5BeZ4r1g4TbDfkxGdm/Zai6QLaBsGNSRfY8U0FJ+1Z/2cnTm0+blF5VXXHzR+4cHBmrHUN8ccq5QZf/ZZ59Vdhxu+WvcoyrMO+vtMCrN8v9IaQPnafO65ZKk6rUCtGDtZ5r1ZFttXPuWc7uh46srqs7jmjqsnZcirVz2Vk29HUKlionK1bzn/q0Zz1+nzf+ZKUkKCQ3Um+8/qrseuEXV/HLkcNiUXxCkBXPW6P3VYXpv1UwvR115bFt2eTuEStf52p/11NxslR6/VZJN/tX8JNnlKNose85C3dnmSjkcvtw8vrCQiGAtO5x+4Q09gQl6vq0w76wKcs2b7CWpqLDYeY4hYaWSSnS2oKTceRcXOWQ47Kb9XdgLLv4buS/o/ufdOn0mWOu/rCOHo/y5FhYUq6Cg7LYY7RJ+VGT1Qq3/sp4KTPw7seUWejuESrdpTaAG94iXJIWEBeuFL6fp+3V99eP3gXprbm3lnTHn32VUPVMkezMKDrUrtvGvf9HrNChS05b5yj1dTfl5wVJAZw14dJdyTjXRiaNBat05R9fenqVXU5t4L2hcNJvN0A09DmjNuqZyOMrf68ooWK64Zid0/IS/rojP0tCBm/XuR1co82ikl6KFpxTm++vHvWVj86ERIbIFxKmo0F+5p35dDw+isveO/Px8ZWRkOH/OzMzU7t27FRkZqdhYa88yvjwhTzOX/trGHPL0D5KkNe/GaN7U1rLV+Lv2f9dHTz67TxE1SnXiSJAWPtdIK1+v46WI4Y72bY6pTky+/vnp5ee9ZtgP6qlH1yg8rEjHs8L0xvI2Wv5hSy9ECfg2d++CZ6k76HnSzp07y83eT01NlST16tVLaWlp3grrkvDt5kjdeHm3330tNEKy+cdo7qQrTNuyt5qvv4nVDX1+/0oWv4hRun+4YeqWPX41fkArFVhgCANVy6vJvkuXLtq79/y7wwEA4HEWbuO7/CAcSdq6daueeOIJ9e3bV8ePH5ckvffeezwIBwBw6TI8sPgol5P9P//5Tw0aNEjBwcH67rvvVPzLNYt5eXlKT6+iyycAAECFuZzsX375ZaWkpGjq1KmqVu3XUYD27dvru+++82hwAAB4Co+4dcGhQ4fUsWPH89ZHREQoJyfHI0EBAOBxhq1scWd/H+VyZR8dHV3ucrlzvv76azVs2NAjQQEA4HGM2VfcnXfeqWnTpumbb76RzWbT8ePH9cEHH2jGjBm6++67KyNGAADgBpfb+IMHD5bD4dB9992nwsJC3XPPPQoMDNTAgQPVv3//yogRAAC3cVMdF9hsNg0dOlSDBg1SRkaGCgoK1KxZM4WFhVVGfAAAeIaFr7O/6JvqBAYGqnnz5p6MBQAAVAKXk33//v1ls/3xjMRFixa5FRAAAJXC3cvnrFTZt2xZ/gEcpaWl2r17t/bt26fbb7/dU3EBAOBZtPErbuzYsb+7fs6cOSooKHA7IAAA4FkXdW/833Prrbdq+fLlnjocAACeZeHr7D321Ltt27YpMDDQU4cDAMCjuPTOBcOGDSv3s2EYysrK0s6dO/Xwww97LDAAAHxZenq6PvnkEx08eFDBwcFKTEzUE088oaZNmzq3KSoqUlpamlatWqXi4mIlJSVp4sSJio6Odm5z9OhRTZo0SZs2bVJoaKhuv/12Pf744+WeT3MhLrfxIyIiyi2RkZHq3LmzXnnllfO+CAAAYFWbN29Wv3799NZbb2nBggUqLS3VoEGDys1vmz59uj7//HPNnj1bixcv1okTJ8rlUrvdriFDhqikpETLli1TWlqaVqxYoRdeeMGlWFyq7O12u3r37q24uDhFRka69EYAAHhVFc/Gnz9/frmf09LS1LVrV+3atUudOnVSbm6uli9frlmzZqlr166SypL/TTfdpO3bt6tdu3basGGD9u/frwULFig6OlotW7bUyJEjNWvWLA0bNqzCw+cuVfb+/v4aOHAgT7cDAPgcTz3iNi8vr9xSXFxcoffPzc2VJGexvHPnTpWUlKhbt27ObZo1a6bY2Fht375dkrR9+3bFxcWVa+snJSUpLy9P+/fvr/C5u9zGv/zyy5WZmenqbgAAmEL37t3VoUMH55Kenn7BfRwOh6ZPn6727dsrLi5OknTy5EkFBASoevXq5baNiopSVlaWc5vfJnpJzp/PbVMRLk/Qe/TRRzVjxgyNHDlSrVq1UmhoaLnXw8PDXT0kAABVwwMz6tevX1/u54q00lNSUrRv3z69/vrr7gdwESqc7F988UUNHDhQgwcPliQNHTq03G1zDcOQzWbT7t27PR8lAADu8tCYvatF7eTJk7Vu3TotWbJEdevWda6Pjo5WSUmJcnJyylX32dnZiomJcW6zY8eOcsc7efKkJDm3qYgKJ/u5c+fq7rvv5t73AABUgGEYmjJlitasWaPFixerYcOG5V5PSEhQQECANm7cqJ49e0qSDh48qKNHj6pdu3aSpHbt2mnevHnKzs5WVFSUJOnLL79UeHi4Sw+jq3CyN4yyrzSdO3eu8MEBALhUVPVNdVJSUvTRRx/ppZdeUlhYmHOMPSIiQsHBwYqIiFCfPn2UlpamyMhIhYeHa+rUqUpMTHQm+6SkJDVv3lxPPvmkRo0apaysLM2ePVv9+vVz6UZ2Lo3Z/6+n3QEAcEmr4kvv3njjDUllT4v9rdTUVPXu3VtS2fNm/Pz8NGLEiHI31TnH399f8+bN06RJk9S3b1+FhISoV69eGjFihEuxuJTse/bsecGEv3nzZpcCAADAjPbu3XvBbYKCgjRx4sRyCf6/1a9fX6+++qpbsbiU7IcPH66IiAi33hAAAG/g3vgVdPPNNzsnCAAA4FMs/Dz7Ct9Uh/F6AAB8k8uz8QEA8EkWruwrnOz37NlTmXEAAFCpGLMHAMDsLFzZu/wgHAAA4Fuo7AEA1mDhyp5kDwCwBCuP2dPGBwDA5KjsAQDWQBsfAABzo40PAABMi8oeAGANtPEBADA5Cyd72vgAAJgclT0AwBJsvyzu7O+rSPYAAGuwcBufZA8AsAQuvQMAAKZFZQ8AsAba+AAAWIAPJ2x30MYHAMDkqOwBAJZg5Ql6JHsAgDVYeMyeNj4AACZHZQ8AsATa+AAAmB1tfAAAYFamqOwd+QVy5Bd6O4wq4/BzlP1psfO2bfzG2yFUOVtESNmfm3fKlmudz1qSqtWP9XYIVapaeFDZn/XqqFpEkZejqTrnzrsq0MYHAMDsLNzGJ9kDAKzBwsmeMXsAAEyOyh4AYAmM2QMAYHa08QEAgFlR2QMALMFmGLIZF1+eu7Ovt5HsAQDWQBsfAACYFZU9AMASmI0PAIDZ0cYHAABmRWUPALAE2vgAAJidhdv4JHsAgCVYubJnzB4AAJOjsgcAWANtfAAAzM+XW/HuoI0PAIDJUdkDAKzBMMoWd/b3USR7AIAlMBsfAACYFpU9AMAamI0PAIC52Rxlizv7+yra+AAAmByVPQDAGmjjAwBgblaejU+yBwBYg4Wvs2fMHgAAk6OyBwBYAm18AADMzsIT9GjjAwBgclT2AABLoI0PAIDZMRsfAACYFZU9AMASaOMDAGB2zMYHAABmRWUPALAE2vgAAJidwyhb3NnfR9HGBwBYg+GBxQVbtmzRQw89pKSkJMXHx2vt2rXlwzEMPf/880pKSlKbNm1033336Ycffii3zenTp/X444+rffv26tixo8aOHav8/HwXT5xkDwBApSgoKFB8fLwmTpz4u6+/+uqrWrx4sSZNmqS33npLISEhGjRokIqKipzbPPHEE9q/f78WLFigefPmaevWrZowYYLLsdDGBwBYgk1ujtm7uH1ycrKSk5N/9zXDMLRo0SINHTpU1113nSRp5syZ6tatm9auXaubb75ZBw4c0L/+9S+98847at26tSRp3LhxGjx4sJ588knVqVOnwrFQ2QMArOHcHfTcWSTl5eWVW4qLi10OJTMzU1lZWerWrZtzXUREhNq2batt27ZJkrZt26bq1as7E70kdevWTX5+ftqxY4dL70dlDwCAC7p3715u3HzYsGEaPny4S8fIysqSJEVFRZVbHxUVpZMnT0qSTp48qVq1apV7vVq1aoqMjHTuX1EkewCAJXjq0rv169eXWx8YGOhGVFWDZA8AsAYP3UEvPDzc7VBiYmIkSdnZ2apdu7ZzfXZ2tlq0aCFJio6O1s8//1xuv9LSUp05c8a5f0UxZg8AQBVr0KCBYmJitHHjRue6vLw8ffPNN0pMTJQkJSYmKicnRzt37nRu89VXX8nhcKhNmzYuvR+VPQDAEmyGIZsbj6l1dd/8/HxlZGQ4f87MzNTu3bsVGRmp2NhYDRgwQC+//LIaN26sBg0a6Pnnn1ft2rWds/ObNWumq6++WuPHj1dKSopKSko0ZcoU3XzzzS7NxJdI9gAAq3D8srizvwt27typAQMGOH9OTU2VJPXq1UtpaWl68MEHVVhYqAkTJignJ0cdOnTQP/7xDwUFBTn3mTVrlqZMmaJ7771Xfn5+uuGGGzRu3DiXQyfZAwBQCbp06aK9e/f+4es2m00jR47UyJEj/3CbGjVq6Nlnn3U7FpI9AMASqrqNfykh2QMArMHCz7Mn2QMArOE3d8G76P19FJfeAQBgclT2AABL8NQd9HwRyd5H9B12XFfddEYNmxeppMhPjlNDFXtZofa79iwE+Ihzn3ejy4vkOHGlxsw19EpKbWUeCPZ2aHDDTX1+1E19DqtOvQJJ0o8HI/TG/Ob6+ssYxdTJl+OnOL39+fn7pY5ppw2f1qviaE3Iwm18kr2PaNM1Xx/+X7S+3x6qsOqB+vs/SzVx/m490D1ORYX+3g4PHnbu887YV1Nzvpoi/2r3avobB/Vgcjyftw87eSJY//dinI4eDpNs0nU3H9H4WV9rxD1XKTsrSraYf+uBq6eosKDsKWp/6pWh3vcc0tYvXbs1KvDfvJrs09PT9cknn+jgwYMKDg5WYmKinnjiCTVt2tSbYV2Snu736+8kNCJEtsgZql3/Sl3eplA7N7l/n2ZcWs593qERIbIFtNScMc208Kuv+bx93OZ/lb/r2aKX43RTnwy1SDitDZ9Fy+Yfo9OnglWQV/bk9K7XHNeGtfV0tpC6zBNsjrLFnf19lVcn6G3evFn9+vXTW2+9pQULFqi0tFSDBg1SQUGBN8PyDY5cSVLuaao8KwiNsEvi8zYTPz9D3a8/quCQUu3+tsZ5rzdvcUbN4nP1yQcNqj44s/LQ8+x9kVe/Ls6fP7/cz2lpaeratat27dqlTp06Vfg4IRHWGscMrR4kI3ea9m6vrqyjtRQa4e2IUFlCIoJlGA49OCFDu7+OsNTnXS086MIb+aBGl53RtLnrFRDo0NlCfz0zoYuys6IVElZ2vuf+vOkvx5T5Q4R+PFhXoSZu5pw7X1Qum2FcOl9VfvzxR91www368MMPFRcX5+1wLlmOMxOlovWyRb0hm39db4eDSsbnbS6GUSzZj0lGroyzH0sFb8sWtVS2as1/s81ZGSeuki38YdnCBnkxWnO57bqZKvhlPsTFCA0N1Ptrn/RgRFXnkhkIcjgcmj59utq3b+9yor+rwWAV5p6tpMguLQ+OP6Qu151WdMIq3d14gmXO26qGTsnQDXc5NPLPjZWx1zf/kblY1WKt8cVm/KxqOn70YS1O76IlW6fono7j1anbPg0dlach1+5Qzhlzf+4hYUFasnVKlbwXt8u9BKSkpGjfvn16/fXXXd63MPesCnILKyGqS4mhR6YdUedrz2j8gFaat6OhRc7bqso+7w7JObLVWqmMvRMs91lXyyvydghVwnDYZbOVqDC/7HwL84uU3PMHfbW+tn46IknW+D2gcl0Sd9CbPHmy1q1bp4ULF6puXWt8m3fVsOlH1KP3KaU90liF+f4y7FmqEV2swGAfnh6KP3Tu8/7745dLtjDViC5WzZgSPm8fd+8je9Uq8WfVrlegxs1yde8je9W6w8/6fHWsc5u6sXlKSPxZn7zf0IuRmhQT9LzDMAxNmTJFa9as0eLFi9WwIf9z/5Fb7suWJM1694Akyci6Sgv+Lc16tKHWvFXLm6GhEpz7vKcu+c75WUt83r6uRs1iPT5ph2pFn1V+XoB+2B+h8cM7afvmaOckvP93U4ZOngjWf76K9m6wZmTIvefZ+26u926yT0lJ0UcffaSXXnpJYWFhysrKkiRFREQoONhaM+wvpGdsW+d/h0aE6P0zi3Rb5ADLtXat4tznzWdtLs9PbX3Bbd74xxWaP7tZFURjPYzZe8kbb7whSerfv3+59ampqerdu7c3QgIAwHS8muz37t3rzbcHAFiJITfvje+xSKrcJTMbHwCASmXhB+FcErPxAQBA5aGyBwBYg0Puzcb34StfSfYAAEuw8mx82vgAAJgclT0AwBosPEGPZA8AsAYLJ3va+AAAmByVPQDAGixc2ZPsAQDWwKV3AACYG5feAQAA06KyBwBYA2P2AACYnMMoW9zZ30fRxgcAwOSo7AEA1kAbHwAAs3Mz2ct3kz1tfAAATI7KHgBgDbTxAQAwOWbjAwAAs6KyBwBYg+EoW9zZ30eR7AEA1sCYPQAAJseYPQAAMCsqewCANdDGBwDA5Ay5mew9FkmVo40PAIDJUdkDAKyBNj4AACbncJQt7uzvo2jjAwBgclT2AABroI0PAIDJWTjZ08YHAMDkqOwBANZg4dvlkuwBAJZgGA4Zbjy5zp19vY1kDwCwBsPNyp4xewAAcKmisgcAWIOFZ+OT7AEA1sAd9AAAgFlR2QMArIE2PgAA5mY4HDLcaMW7s6+30cYHAMDkqOwBANZAGx8AAJOz8O1yaeMDAGByVPYAAGswDMmd+9vTxgcA4NJmOAwZbrTi3dnX20j2AABrMBxuVvZcegcAAC5RVPYAAEugjQ8AgNlZuI1vimQfEhHs7RCq1Lnztdp5W5GVP+tq4UHeDqFKhYQFlfvTKqryfEOrh3h1f2+yGYYPX0sAAAAuiAl6AACYHMkeAACTI9kDAGByJHsAAEyOZA8AgMmR7AEAMDmSPQAAJkeyBwDA5Ej2AACYHMkeAACTI9n7mKVLl6pHjx5q3bq17rjjDu3YscPbIaESbNmyRQ899JCSkpIUHx+vtWvXejskVJL09HT16dNHiYmJ6tq1qx5++GEdPHjQ22HBZEj2PmTVqlVKTU3VI488ohUrVqhFixYaNGiQsrOzvR0aPKygoEDx8fGaOHGit0NBJdu8ebP69eunt956SwsWLFBpaakGDRqkgoICb4cGE+FBOD7kjjvuUOvWrTVhwgRJksPhUHJysvr376/Bgwd7OTpUlvj4eM2dO1fXXXedt0NBFfj555/VtWtXLVmyRJ06dfJ2ODAJKnsfUVxcrF27dqlbt27OdX5+furWrZu2bdvmxcgAeFJubq4kKTIy0suRwExI9j7i1KlTstvtioqKKrc+KipKJ0+e9FJUADzJ4XBo+vTpat++veLi4rwdDkykmrcDAACUSUlJ0b59+/T66697OxSYDMneR9SsWVP+/v7nTcbLzs5WdHS0l6IC4CmTJ0/WunXrtGTJEtWtW9fb4cBkaOP7iMDAQLVq1UobN250rnM4HNq4caMSExO9GBkAdxiGocmTJ2vNmjVauHChGjZs6O2QYEJU9j7k/vvv1+jRo5WQkKA2bdpo4cKFKiwsVO/evb0dGjwsPz9fGRkZzp8zMzO1e/duRUZGKjY21ouRwdNSUlL00Ucf6aWXXlJYWJiysrIkSREREQoODvZydDALLr3zMUuWLNH8+fOVlZWlli1baty4cWrbtq23w4KHbdq0SQMGDDhvfa9evZSWluaFiFBZ4uPjf3d9amoqX+ThMSR7AABMjjF7AABMjmQPAIDJkewBADA5kj0AACZHsgcAwORI9gAAmBzJHgAAkyPZAwBgciR7wE1jxozRww8/7Py5f//+mjZtWpXHsWnTJsXHxysnJ+cPt4mPj9fatWsrfMw5c+botttucyuuzMxMxcfHa/fu3W4dB8DF4974MKUxY8ZoxYoVkqSAgADVq1dPt912mx566CFVq1a5/9vPmTOnwu9x7ra4W7ZsUfXq1Ss1LgDWRbKHaV199dVKTU1VcXGxvvjiC02ePFkBAQEaMmTIedsWFxcrMDDQI+9bo0YNjxwHADyFNj5MKzAwUDExMapfv77++te/qlu3bvrss88k/dp6f/nll5WUlKQ//elPkqRjx45p5MiR6tixozp37qyhQ4cqMzPTeUy73a7U1FR17NhRXbp00cyZM/Xfj5f47zZ+cXGxnnnmGSUnJyshIUHXX3+93n77bWVmZjofdtOpUyfFx8drzJgxksoeX5yenq4ePXqoTZs2uvXWW/Xxxx+Xe58vvvhCPXv2VJs2bdS/f38dOXLE5d/RM888o549e6pt27a69tprNXv2bJWUlJy33bJly5ScnKy2bdtq5MiRys3NLff622+/rRtvvFGtW7fWn/70Jy1dutTlWABUHip7WEZQUJBOnz7t/Hnjxo0KDw/XggULJEklJSUaNGiQ2rVrp6VLl6patWp66aWX9MADD+iDDz5QYGCgXnvtNa1YsULTp09Xs2bN9Nprr2nNmjW68sor//B9n3zySW3fvl3jxo1TixYtlJmZqVOnTqlevXqaM2eOhg8fro8//ljh4eHOR5qmp6frgw8+UEpKipo0aaItW7Zo1KhRqlWrljp37qxjx45p2LBh6tevn+68807t3LlTM2bMcPl3EhYWptTUVNWuXVvff/+9xo8fr7CwMD344IPObTIyMrR69WrNmzdPeXl5evrppzVp0iQ9++yzkqQPPvhAzz//vCZMmKCWLVtq9+7dGj9+vEJDQ9WrVy+XYwLgeSR7mJ5hGNq4caM2bNige+65x7k+NDRUU6dOdbbv33//fTkcDk2bNk02m01S2WNGO3XqpM2bNyspKUkLFy7U4MGDdcMNN0gqexb5hg0b/vC9Dx06pNWrV2vBggXq1q2bJKlhw4bO1yMjIyVJUVFRzjH74uJipaena8GCBUpMTHTu8/XXX+vNN99U586d9cYbb6hRo0bOTkDTpk31/fff69VXX3Xpd/PbiYUNGjTQoUOHtHLlynLJvqioSDNnzlSdOnUkSePGjdOQIUM0ZswYxcTEaM6cORozZozzd9KwYUPt379fb775JskeuESQ7GFa69atU2JiokpKSmQYhv785z9r+PDhztfj4uLKjdPv2bNHGRkZat++fbnjFBUVKSMjQ7m5ucrKylLbtm2dr1WrVk0JCQnntfLP2b17t/z9/dWpU6cKx/3jjz+qsLBQAwcOLLe+pKRELVu2lCQdOHBAbdq0Kfd6u3btKvwe56xatUqLFi3S4cOHVVBQoNLSUoWHh5fbpl69es5EL0mJiYlyOBw6dOiQwsLClJGRoaefflrjx493blNaWqqIiAiX4wFQOUj2MK0uXbpo0qRJCggIUO3atc+bIR8SElLu54KCArVq1UqzZs0671i1atW6qBjOteVdUVBQIKmslf/bJCvJY5MIJWnbtm164oknNHz4cCUlJSkiIkIrV650Dmu4EuuUKVPKfQmSJD8/pgQBlwqSPUwrJCREjRs3rvD2rVq10urVqxUVFXVedXtOTEyMvvnmG2elXlpaql27dumKK6743e3j4uLkcDi0ZcsWZxv/twICAiSVTfw7p1mzZgoMDNTRo0fVuXPn3z1us2bNnJMNz/nmm28ufJK/sW3bNsXGxmro0KHOdUePHj1vu2PHjun48ePOLx7bt2+Xn5+fLrvsMkVHR6t27do6fPiwbr31VpfeH0DV4as38ItbbrlFNWvW1NChQ7V161YdPnxYmzZt0tSpU/XTTz9JkgYMGKBXX31Va9eu1YEDB5SSkvI/b2LToEED9erVS2PHjtXatWudx1y1apUkqX79+rLZbFq3bp1+/vln5efnKzw8XAMHDlRqaqpWrFihjIwM7dq1S4sXL3beO+Cuu+7SDz/8oBkzZujgwYP68MMPna9VVOPGjXXs2DGtXLlSGRkZWrRo0e/ecCcoKEhjxozRnj17tHXrVk2dOlU33nijYmJiJEkjRozQK6+8okWLFunQoUPau3evli9f7lKHAEDlorIHfhESEqIlS5Zo1qxZGjZsmPLz81WnTh117drVWekPHDhQWVlZGj16tPz8/NSnTx9df/31512K9luTJk3Sc889p0mTJun06dOKjY11Xutfp04dDR8+XM8++6yeeuop3X777UpLS9Ojjz6qWrVqKT09XZmZmYqIiNAVV1yhhx56SJIUGxurOXPmKDU1VUuWLFGbNm302GOPaezYsRU+32uvvVb33nuvJk+erOLiYl1zzTUaOnSoXnzxxXLbNWrUSNdff70efPBBnTlzRtdcc40mTpzofP2OO+5QcHCw5s+fr5kzZyo0NFRxcXG69957KxwLgMplM/5oZhEAADAF2vgAAJgcyR4AAJMj2QMAYHIkewAATI5kDwCAyZHsAQAwOZI9AAAmR7IHAMDkSPYAAJgcyR4AAJMj2QMAYHL/Hza0nohGqhjDAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "print(\"For the amounts of training data is:\",x_train_smote.shape[0])\n",
        "print(\"Accuracy of DecisionTreeClassifier:\",DT_model.score(x_test,y_test))\n",
        "cm=confusion_matrix(y_test,predictions)\n",
        "cm_display=ConfusionMatrixDisplay(cm).plot()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,predictions))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6_lXdByAGXpV",
        "outputId": "77d490f4-0f65-4706-ba1a-5d099c6dd27d"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.96      0.93      0.94       496\n",
            "         2.0       0.71      0.78      0.74       101\n",
            "         3.0       0.80      0.90      0.85        41\n",
            "\n",
            "    accuracy                           0.90       638\n",
            "   macro avg       0.82      0.87      0.84       638\n",
            "weighted avg       0.91      0.90      0.90       638\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_e4hBbWhGg3D",
        "outputId": "1b845fc7-9860-4908-8070-1868863d50f9"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[460,  31,   5],\n",
              "       [ 18,  79,   4],\n",
              "       [  2,   2,  37]])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oPe60A8wPcFq"
      },
      "source": [
        "***Logistic Regression***\n",
        "\n",
        "A function named LogisticRegression() is created and train and test data are passed as the parameters. Inside the function, LogisticRegression algorithm is initialized and training data is passed to the model with the .fit() function. Test data is predicted with .predict() function and saved in a new variable. For evaluating the model, a confusion matrix and classification report is done."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**[Logistic Regression] LR**\n"
      ],
      "metadata": {
        "id": "rQJIY5XymHJb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MYtd1R9PPfcn",
        "outputId": "09eebb5c-9e29-4820-8516-db1dbb368388"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.780564263322884\n"
          ]
        }
      ],
      "source": [
        "LR_model=LogisticRegression()\n",
        "LR_model.fit(x_train_smote,y_train_smote)\n",
        "predictions=LR_model.predict(x_test)\n",
        "print(accuracy_score(y_test,predictions))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "gKE6QuKsP892",
        "outputId": "16fd5bc8-fcb5-40a0-fab1-3efc07e2538c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For the amounts of training data is: 3477\n",
            "Accuracy of LogisticRegression: 0.780564263322884\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "print(\"For the amounts of training data is:\",x_train_smote.shape[0])\n",
        "print(\"Accuracy of LogisticRegression:\",LR_model.score(x_test,y_test))\n",
        "cm=confusion_matrix(y_test,predictions)\n",
        "cm_display=ConfusionMatrixDisplay(cm).plot()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NApgMTEBGsdj",
        "outputId": "ce496922-0a55-4ede-f775-d002195ea008"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.96      0.78      0.86       496\n",
            "         2.0       0.45      0.74      0.56       101\n",
            "         3.0       0.52      0.90      0.66        41\n",
            "\n",
            "    accuracy                           0.78       638\n",
            "   macro avg       0.65      0.81      0.69       638\n",
            "weighted avg       0.85      0.78      0.80       638\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uUn_0JuwG1r_",
        "outputId": "9152fac3-514d-4d21-c872-32fe27a3bbd9"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[386,  88,  22],\n",
              "       [ 14,  75,  12],\n",
              "       [  1,   3,  37]])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "APcWmqY6Qf3A"
      },
      "source": [
        "**K-Nearest Neighbors KNN**\n",
        "\n",
        "A function named KNeighborsClassifier() is created and train and test data are passed as the parameters. Inside the function, KNeighbors algorithm is initialized and training data is passed to the model with the .fit() function. Test data is predicted with .predict() function and saved in a new variable. For evaluating the model, a confusion matrix and classification report is done.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**KNN K-Nearest Neighbors**"
      ],
      "metadata": {
        "id": "DAovVxWzl0a-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q4EgugzDQnGD",
        "outputId": "466285ac-0750-4b6b-e827-bebfa33e5377"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.835423197492163\n"
          ]
        }
      ],
      "source": [
        "KNN_model=KNeighborsClassifier()\n",
        "KNN_model.fit(x_train_smote,y_train_smote)\n",
        "predictions=KNN_model.predict(x_test)\n",
        "print(accuracy_score(y_test,predictions))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "WVdXmmovRHrx",
        "outputId": "1320dd36-6ea8-4adf-c764-7b1882486f55"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For the amounts of training data is: 3477\n",
            "Accuracy of KNeighborsClassifier: 0.835423197492163\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "print(\"For the amounts of training data is:\",x_train_smote.shape[0])\n",
        "print(\"Accuracy of KNeighborsClassifier:\",KNN_model.score(x_test,y_test))\n",
        "cm=confusion_matrix(y_test,predictions)\n",
        "cm_display=ConfusionMatrixDisplay(cm).plot()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ykqdYuFTHAtX",
        "outputId": "2adf8f2b-85b1-43dd-b11c-39784ac028cc"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.96      0.85      0.90       496\n",
            "         2.0       0.53      0.78      0.63       101\n",
            "         3.0       0.69      0.80      0.74        41\n",
            "\n",
            "    accuracy                           0.84       638\n",
            "   macro avg       0.72      0.81      0.76       638\n",
            "weighted avg       0.87      0.84      0.85       638\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UDYNtqk9HWkF",
        "outputId": "887d5201-e5ec-44f2-d796-646fa1eeb8a5"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[421,  67,   8],\n",
              "       [ 15,  79,   7],\n",
              "       [  4,   4,  33]])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Define the Decision tree Classifier**"
      ],
      "metadata": {
        "id": "PM80JRydegmW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dt_classifier = DecisionTreeClassifier()\n"
      ],
      "metadata": {
        "id": "22mqEigRV7cB"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Define the hyperparameters and possible values**"
      ],
      "metadata": {
        "id": "6FtpGJ3iel2v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = {\n",
        "    'max_depth': [3, 5, 10],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 5, 10],\n",
        "    'criterion': ['gini', 'entropy'],\n",
        "    'splitter':['best','random']\n",
        "}"
      ],
      "metadata": {
        "id": "u52hTCIBW5CS"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Define the Random Forest Classifier**"
      ],
      "metadata": {
        "id": "-Q7GuF0be4y9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf_classifier = RandomForestClassifier()"
      ],
      "metadata": {
        "id": "NmZo5JT5b9Se"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Define the hyperparameters and possible values**\n",
        "\n",
        "> Add blockquote\n",
        "\n"
      ],
      "metadata": {
        "id": "FK0g3s4MfJtO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = {\n",
        "    'n_estimators': [100, 200, 300],\n",
        "    'max_depth': [5, 10, 15],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 5, 10],\n",
        "    'criterion': ['gini', 'entropy'],\n",
        "}"
      ],
      "metadata": {
        "id": "HMLhe9V6crMf"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Define the KNN KNeighborsClassifier**"
      ],
      "metadata": {
        "id": "I3bxjkcKfX53"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn_classifier = KNeighborsClassifier()"
      ],
      "metadata": {
        "id": "kLK4dCkUc9EW"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Define the hyperparameters and possible values**\n",
        "\n",
        "> Add blockquote\n",
        "\n"
      ],
      "metadata": {
        "id": "XmexKQVWfluW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid={\n",
        "'n_neighbors': [3, 5, 7, 9, 11], # number of nearest neighbors to consider\n",
        "'weights': ['uniform', 'distance'], # weight function to use\n",
        "'p': [1, 2], # exponent for Minkowski distance\n",
        "}\n"
      ],
      "metadata": {
        "id": "jO1-uU1mfyRM"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Define the LogisticRegression**"
      ],
      "metadata": {
        "id": "GE2EPlUsgmye"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr_classifier = LogisticRegression()"
      ],
      "metadata": {
        "id": "Wawwn3mMdgEl"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define the hyperparameters and posiible values"
      ],
      "metadata": {
        "id": "PrtEWgOjgtfQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = {\n",
        "    'penalty': ['l1', 'l2', 'elasticnet', 'none'],  # Regularization type\n",
        "    'C': [0.001, 0.01, 0.1, 1, 10, 100],          # Inverse of regularization strength\n",
        "    'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],  # Algorithm to use\n",
        "    'max_iter': [100, 200, 500]                   # Maximum number of iterations\n",
        "}"
      ],
      "metadata": {
        "id": "rGD_rB7AdmxK"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ulpy0vk6RaFA"
      },
      "source": [
        "**Testing The Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AfiTLMsMsjLd",
        "outputId": "c52a198a-938a-473a-ec29-62e9afaa2e13"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.])"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "RF_model.predict([[0.259259,\t0.000000,\t0.000000,\t0.0\t,0.813333,\t0.044118,\t0.472527,\t0.047337,\t0.344633,\t0.110092,\n",
        "                   0.034483,\t0.111111\t,0.0\t,0.472441,\t0.403670,\t0.271375,\t1.0,1,\t0.481481\t,0.315789,]])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2gd5pvvXurKT",
        "outputId": "7078ae4d-0d29-49a7-bb1b-8edd2f19c2c6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "\n",
        "RF_model.predict([[0.000,0.0,73.0,43.0,2.4,73.0,120.0,0.481481\t,0.315789\t,\t0.400000\t,\n",
        "                   0.279412\t,0.205128\t,0.717514\t,0.165138\t,0.655172\t,0.333333\t,\t0.637795\t,0.577982\t,0.044610,\t0.5]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OHtF1D4MRiZ6"
      },
      "source": [
        "Testing Model With Multiple Evaluation Metrics:\n",
        "**Comparing the Model**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_qwJP6pCtlLO",
        "outputId": "87fa435c-caeb-4d7f-9025-b44931e1c153"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.942006\n",
              "1    0.902821\n",
              "2    0.780564\n",
              "3    0.835423\n",
              "Name: score, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "df=pd.DataFrame()\n",
        "df['name']=['RandomForestClassifier','DecisionTreeClassifier','LogisticRegression','KNeighborsClassifier']\n",
        "df['name']\n",
        "\n",
        "df['score']=[RF_model.score(x_test,y_test),DT_model.score(x_test,y_test),LR_model.score(x_test,y_test),KNN_model.score(x_test,y_test)]\n",
        "df['score']\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "9av1scQWvimD",
        "outputId": "15c82f7f-0f2c-470f-f686-1784d25e307b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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e5s+fb/mw6apVq6xuF3g37du3l4ODg5YtW2aZSS5fvryaNWtmWY/coEEDffXVV3r//fd18+ZNVa5cWW+99Zblw6jOzs6aOnWq3nvvPW3btk0pKSk6fvy4pFt3Adm6datWrFih5cuX659//lGxYsXk6emphQsX3vXbKEeMGHHfsfT09NSaNWv01VdfKS4uTuXKlVNAQICGDRuWqfbsCA4O1ooVK7RhwwaFh4erZMmSql69urp06ZLtYwMAgILFZDabzbldBFDQHT58WJK04bswnQ2PyuVqAADIP9zKP6Y3R3cw5NhpP78z843ieX5NNwAAAPCoI3QDAAAABiN0AwAAAAYjdAMAAAAGI3QDAAAABiN0AwAAAAYjdAMAAAAGI3QDAAAABiN0AwAAAAYjdAMAAAAGI3QDAAAABiN0AwAAAAYjdAMAAAAGI3QDAAAABiN0AwAAAAYjdAMAAAAGI3QDAAAABiN0AwAAAAYjdAMAAAAGI3QDAAAABiN0AwAAAAYjdAMAAAAGI3QDAAAABiN0AwAAAAYjdAMAAAAGI3QDAAAABiN0AwAAAAYjdAMAAAAGI3QDAAAABiuU2wUA+D/lXRxzuwQAAPKVvPKzldAN5CEjevrndgkAAOQ7qalm2diYcrUGlpcAeURSUpLi4+NzuwxIio+P15EjRxiPPIQxyVsYj7yF8bi/3A7cEqEbyFPMZnNulwDdGof4+HjGIw9hTPIWxiNvYTweDYRuAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghG4gDzGZTLldAnRrHOzt7RmPPIQxyVsYj7yF8Xg0mMxmszm3iwAKusOHD0uSPD09c7kSAAByX2qqWTY2ef9NxIP8/C5kdDEAMm/phj0Kj4jO7TIAAMg15V0cNaKnf26XkeMI3UAeEh4RrbPhUbldBgAAyGGs6QYAAAAMRugGAAAADEboBgAAAAxG6AYAAAAMRugGAAAADEboBgAAAAxG6AYAAAAMRugGAAAADEboBgAAAAxG6AYAAAAMRugGAAAADEboBgAAAAxG6AYAAAAMRugGAAAADEboBgAAAAxG6AYAAAAMRugGAAAADEboBgAAAAxG6AYAAAAMRugGAAAADEboBgAAAAxG6AYAAAAMRugGAAAADEboBgAAAAxG6AYAAAAMRugGAAAADEboBgAAAAxG6AYAAAAMRugGAAAADPbIh+6QkBC5u7vL3d1dNWvWlLe3t5555hnNmDFDp06dMuScrVq10owZMzLdPygoSE8//XSO19GnTx/La7/bIygoKMfPm5ELFy7otddeU8uWLVWnTh01btxYAQEB2r59u6WPUdfhftzd3RUaGmq1bd68eWratKlq1qypWbNmacuWLXJ3d9e1a9ceen0AACD/K5TbBeSEokWLas2aNZKkf//9V3///bc2btyoTZs2adasWerUqVOOnm/JkiVycHDIdP/hw4frxo0bOVqDJL3++uuKi4uzPJ8+fbqKFi2qiRMnWrY5Ozvn+Hnv9Pvvv2vgwIFydnbWoEGDVK1aNcXFxWnXrl0KDAyUm5ubatasaXgdd7Nx40Y9/vjjlud79+5VaGioJk2apLp168rFxUX29vbauHHjA40rAABAZuWL0G1jY6N69epZnvv7++vFF1/U4MGDNXnyZDVo0EAVK1bMsfN5eHg8UP9KlSrl2LlvV61aNavnJUqUULFixayuxZ0SEhJUtGjRHKshMTFRY8aMUbly5fTRRx+pRIkSlrZWrVqpZ8+euR5k77wep0+fliT17dtXNjb/98uenHiDYjablZycrMKFC2f7WAAAIP945JeX3E2RIkX02muvKTk5WR9//LFl+5YtW/TMM8/I09NTzZo106JFi3Tz5k2rfa9cuaJXXnlFfn5+8vLyUrt27Swz6VL65SUnTpzQoEGD5OPjo7p166pt27ZauXKlpT2jZRXHjx9XQECA6tWrJ29vb40aNUoXL1606uPu7q6VK1cqJCREfn5+8vHx0aRJkzI9a37gwAG5u7vr+++/16hRo9SgQQONHj1akhQTE6Np06apadOmqlOnjrp06aLdu3enO8b333+vbt26ycvLS02aNNHrr79udf6vvvpKly5d0rhx46wCd5qaNWtazTLfLiIiQpMmTVLr1q3l5eWlNm3aaOHChUpKSrLqt3nzZnXs2FFeXl7y8fFRz549dejQoUy33768pE+fPpo5c6YkqVatWnJ3d9eBAwcyXF6SlJSkhQsXWpbMtG/fXp9//rlVbWlju2vXLj377LPy9PTUd999l/GAAACAAitfzHTfTbVq1VS2bFn99ttvkqT3339f8+fP10svvaSgoCCdOnXKEroDAwMlSVFRUerRo4ckaezYsapQoYLOnTun8+fP3/U8Q4cOVenSpTVr1iyVKFFC58+f1+XLl+/a/9KlS+rdu7cqVqyo+fPnKzExUYsWLVLv3r21bds2q/D6wQcfyNvbW3PmzNHZs2c1b948lSpVylJvZrz22mt69tlntXTpUtnY2CgpKUn9+/fX1atXNWbMGJUtW1bbtm3TkCFDLOFTkrZv366xY8eqS5cuGjlypCIjI7VgwQLFxMRo0aJFkqSff/5Ztra28vPzy3Q9aaKiouTk5KRJkybJwcFBZ8+eVUhIiCIjIzV79mzL8SdPnqwBAwaoefPmSkhI0KFDhxQbG5up9ju9/vrr2rRpk9asWaONGzdKuvX3JDw8PF3f0aNH6+DBgxoxYoSqVq2qXbt2acKECXJwcFDz5s0t/SIiIvTGG29o2LBhcnV1veubDAAAUHDl69AtSa6urvrnn38UFxen4OBgDRw4UOPGjZN0axmKnZ2d5syZo4CAAD322GNavXq1rl69qq+++koVKlSQJPn6+t71+NeuXdOFCxc0efJktWrVSpLUpEmTe9a0evVqpaSkaNWqVXJycpJ0a9a1Y8eO2rp1q/r06WPpW6ZMGS1YsECS9OSTT+rIkSP6+uuvHyh0t2rVShMmTLA8/+STT3Ts2DF99tlnliUqzZo107lz5/TOO+/o7bffltls1rx589ShQwfNmjXLqp7Bgwdr+PDhql69uq5cuSJnZ+csLVlxd3e3Wn/eoEED2dvbKygoSFOnTpW9vb0OHTokJycnq34tWrSw/Pl+7XeqVq2aJRTfaxnO/v379d133yk0NFRNmzaVdOvvS2RkpEJCQqxCd3R0tFauXKm6detm9qUDAIACJt8uL0ljNptlMpn022+/6caNG2rXrp1SUlIsDz8/PyUkJOjEiROSpH379qlJkyaWwH0/jz32mMqXL6+FCxdq69at95zhTvPLL7/Ix8fHErglqWrVqqpZs6Z+/fVXq753ziBXrVo1U+e43Z0hdM+ePapRo4bc3NzSXYvDhw9Lks6cOaPw8HC1b9/eqk/jxo1lY2OjP//884FqyIjZbNbq1avVoUMHeXl5qXbt2goMDFRKSorCwsIk3Vo/f/36dQUFBWnPnj2Kj4+3Osb92rNqz549cnJyUpMmTdJdo6NHj1otSXJyciJwAwCAe8r3M92XL1+Wm5uboqKiJEmdO3fOsN+lS5ckSdevX1f16tUzfXyTyaTQ0FAtWrRIM2bM0I0bN1S7dm1NmjRJjRo1ynCfmJgY1apVK932UqVKKTo62mrbnR9CtLOzS7fm+X5KlSpl9TwqKkpHjhxR7dq10/W1tbW19JGkESNGZHjMtOtVtmxZ7du3T4mJiSpSpMgD1bVmzRrNnTtXAwcOlI+PjxwcHHT48GHNmDFDiYmJkm79lmHevHlau3atAgICVKRIEbVt21avvvqqnJyc7tueVVFRUbp+/XqG10iSIiMjVa5cOUlS6dKls3weAABQMOTr0H3ixAlduXJFnTt3lqOjo6Rbt/tLC0u3S5vZdnJyUkRExAOdp3LlygoODlZycrJ+++03LVy4UEOHDtUPP/yg4sWLp+vv6Oioq1evptt+9epVubm5PdC5M8NkMqU7v7u7u9WykTulBdapU6fKy8srXbuLi4skqXHjxtq8ebP27dt3z2UdGdm+fbtatWql8ePHW7ZldG/1Tp06qVOnTrp27Zp27typ2bNnq1ChQnrzzTcz1Z4Vjo6OcnZ21ooVKzJsv/1OJ3deXwAAgDvl29CdmJiomTNnqnDhwurWrZscHBxkb2+vy5cv66mnnrrrfr6+vlq1apUuXrz4wB+Is7OzU+PGjTV48GANGzZMERERqly5crp+3t7e2rRpk6Kjoy1vBk6fPq3jx4/r+eeff7AXmgV+fn7atWuXXFxcVLZs2Qz7VKlSReXKlVNYWJh69ep112O1a9dOixYt0sKFC9WwYcN0dzA5fvy4HBwc5Orqmm7fhIQE2dnZWW278+4gt3N2dla3bt30ww8/WG779yDtD8LPz0/vvfee7OzscvUe4wAAIH/IF6E7NTVVv//+uyTpxo0bli/HCQsL05w5cyyz2KNGjdL8+fN1+fJlNW7cWLa2tgoLC9POnTsVEhIie3t79evXT5999pl69+6tYcOGqWLFigoLC9PZs2etPoyY5tixY5o7d646dOigihUrKi4uTsuXL1f58uXven/ufv36acuWLRowYICGDRumxMRELV68WK6urndd/pKTnnvuOX300Ufq27evBgwYIDc3N8XGxurIkSNKTk7W+PHjZTKZFBQUpMDAQN24cUMtWrSQvb29Ll68qF27dmns2LGqXLmyihQposWLF2vgwIF6/vnn1a9fP8uX4+zevVubNm3Sxx9/nGHo9vPz09q1a7V+/Xq5ublp27ZtOnfunFWf4OBgXb9+XY0bN1apUqX0999/68cff1S/fv0y1Z5V/v7+atmypQYOHKiBAwfK3d1d8fHxOnnypM6dO3fP3xIAAADcKV+E7oSEBMtt/ooVK6YKFSrI19dXS5YsUdWqVS39BgwYoLJly+r999/X+vXrVahQIVWqVEktWrSwzLg+9thj2rBhgxYsWKC33npL8fHxKl++vF588cUMz12mTBmVLl1ay5cv15UrV1SyZEk1bNhQ8+fPt6yPvpOrq6vWrVunefPmKTAwUDY2NvL391dQUFCG97rOaYULF9batWsVEhKiZcuWKTIyUk5OTvLw8LB6ne3bt5eDg4OWLVtmmYEuX768mjVrZrWOuV69etq6datWrFih5cuX659//lGxYsXk6emphQsX3nWmeMSIEYqKilJwcLAkqW3btpoyZYqGDh1q6ePp6ak1a9boq6++UlxcnMqVK6eAgAANGzYsU+3ZERwcrBUrVmjDhg0KDw9XyZIlVb16dXXp0iXbxwYAAAWLyWw2m3O7CKCgS7trzIbvwnQ2PCqXqwEAIPe4lX9Mb47ukNtlZEraz29PT8/79s33twwEAAAAchuhGwAAADAYoRsAAAAwGKEbAAAAMBihGwAAADAYoRsAAAAwGKEbAAAAMBihGwAAADAYoRsAAAAwGKEbAAAAMBihGwAAADAYoRsAAAAwGKEbAAAAMBihGwAAADAYoRsAAAAwGKEbAAAAMBihGwAAADAYoRsAAAAwWI6F7oiICB07dkw3btzIqUMCAAAA+UK2Q/e3336rdu3aqXnz5urcubP++OMPSdK1a9f03HPP6dtvv812kQAAAMCjLFuh+7vvvtPIkSP12GOPacSIETKbzZY2Z2dnlS1bVp988km2iwQAAAAeZdkK3UuXLlXDhg21YcMG9erVK117vXr1dPTo0eycAgAAAHjkZSt0nzhxQu3bt79re+nSpXX16tXsnAIAAAB45GUrdNvb2ys+Pv6u7WFhYXJycsrOKQAAAIBHXrZCt4+Pjz799FOlpKSka4uMjNSmTZvUtGnT7JwCAAAAeORlK3SPGTNGly9fVteuXbVx40aZTCbt3r1bixYt0jPPPCOz2awRI0bkVK0AAADAIylbobtKlSr68MMP5eTkpLfffltms1mhoaFavny5atSooQ8//FAVKlTIqVoBAACAR1Kh7B6gevXqWr16taKjo3Xu3DmZzWZVrFhRzs7OOVEfAAAA8MjLduhO4+joKC8vr5w6HFAglXdxzO0SAADIVfn1Z2GOhO6ff/5ZYWFhiomJsfqCHEkymUzq169fTpwGyPdG9PTP7RIAAMh1qalm2diYcruMHJWt0H306FGNGTNG58+fTxe20xC6gcxJSkpSfHy87O3tc7uUAi8+Pl5nzpxR5cqVGY88gjHJWxiPvCU/jkd+C9xSNkP35MmTde3aNU2fPl1eXl4qWbJkTtUFFEh3e/OKh8tsNis+Pp7xyEMYk7yF8chbGI9HQ7ZC98mTJzVq1Ch17949p+oBAAAA8p1s3TLwiSeekMmU/6b/AQAAgJyUrdA9cuRIffDBB7py5UpO1QMAAADkO9laXtKmTRslJiaqXbt2atKkicqVKydbW9t0/aZMmZKd0wAAAACPtGyF7p9++knTpk1TfHy8/ve//2XYx2QyEboBAABQoGUrdM+cOVMlSpRQcHCw6tatqxIlSuRUXQAAAEC+ka013efPn1dAQID8/f0J3AAAAMBdZCt0V6tWTbGxsTlVCwAAAJAvZSt0T5w4URs3btShQ4dyqh4AAAAg38nWmu5Vq1apePHi6tGjh6pVqyZXV1fZ2FjneJPJpHfffTdbRQIAAACPsmyF7r///luS5Orqqn///VcnT55M14cvzwEAAEBBl63Q/d133+VUHQAAAEC+la013QAAAADuL1sz3beLi4tTXFycUlNT07U9/vjjOXUaAAAA4JGT7dD94YcfavXq1QoLC7trn6NHj2b3NAAAAMAjK1vLSzZs2KAZM2aoUqVKGjNmjMxms1566SUNHjxYpUuXVs2aNTVr1qycqhXI9/jgcd5gMplkb2/PeOQhjEnewngADy5boXv9+vVq2rSp3nvvPXXv3l2S1Lx5c40dO1Zffvml/v33X12/fj0n6gTyvcKFC8ve3j63y4Ake3t7eXh4MB55CGOStzAeD1dqqjm3S0AOyNbykvPnz+vFF1+UJNnZ2UmSkpOTJUklS5ZU165d9eGHH2rAgAHZLBMoGJZu2KPwiOjcLgMAkEeUd3HUiJ7+uV0GckC2QnfJkiV18+ZNSVKJEiVkb2+vy5cvW9qLFy+uf/75J3sVAgVIeES0zoZH5XYZAAAgh2VreUn16tV17Ngxy/O6detqw4YNunLlii5duqSNGzfKzc0tuzUCAAAAj7Rshe5nn31WJ06cUFJSkiRp5MiROnXqlFq0aKFWrVrpzJkzGjNmTE7UCQAAADyysrW85Pnnn9fzzz9vee7t7a0vvvhCO3fuVKFCheTv76/KlStnu0gAAADgUZZjX47z77//KiYmRra2tmrTpo1l+8WLF/lyHAAAABRo2QrdiYmJWrJkiTZv3nzPWwPy5TgAAAAoyLIVuqdNm6ZPP/1U//nPf+Tt7S1HR8ecqgsAAADIN7IVunfs2KFu3bppxowZOVUPAAAAkO9k6+4lJpNJHh4eOVULAAAAkC9lK3S3bt1ae/fuzalaAAAAgHwpW6F7+PDhunDhgl577TX9+eefunbtmq5fv57uAQAAABRk2VrTnXZrwCNHjmjz5s137cfdSwAAAFCQZSt0jxgxQiaTKadqAQAAAPKlbIXukSNH5lQdAAAAQL6VrTXdAAAAAO6P0A0AAAAYjNANAAAAGIzQDQAAABiM0A0AAAAYjNANAAAAGIzQDQAAABiM0A0AAAAYjNANAAAAGIzQDQAAABiM0A0AAAAYjNANAAAAGIzQDQAAABiM0A0AAAAYjNANAAAAGIzQnQkhISGqX7++4ec5cOCA3N3ddfjw4UzvExISooMHD6bb7u7urtDQ0Ewf58KFC3J3d7c8PD091a5dOwUHByshISHTx3mUPKxxBQAAKJTbBeD/1K5dWxs3blTVqlUzvc+SJUtUrFgxNWjQwGr7xo0b9fjjjz9wDePGjZOPj4/i4+O1c+dOLV26VP/8849mzJjxwMfK67p166bmzZvndhkAAKAAIHTnISVKlFC9evVy5FhZPc4TTzxh2dfX11enT5/WZ599pmnTpsnGxvhfjCQkJKho0aKGn0eSypUrp3Llyj2UcwEAgIKN5SU54Pjx4woICFC9evXk7e2tUaNG6eLFi1Z9YmNjFRgYqPr168vX11cLFy7UqlWr5O7ubumT0fKSzZs3q2PHjvLy8pKPj4969uypQ4cOSZJl33nz5lmWhRw4cMDSdufyku+//14vvPCC6tatq0aNGqlPnz46cuTIPV9brVq1lJCQoGvXrlm2xcTEaNq0aWratKnq1KmjLl26aPfu3Vb7mc1mLVmyRP7+/qpfv75GjRqlvXv3WtWYVueKFSs0f/58+fv7y9fX17J/aGio2rZtqzp16qh169ZavXq11TkuX76s0aNHy8/PT56enmrVqpXefPPNTLdntLwkPDxco0aNkre3t+rVq6eAgAAdP37cqk+rVq00Y8YMffDBB2rZsqW8vb01fPhwq2sEAABwO2a6s+nSpUvq3bu3KlasqPnz5ysxMVGLFi1S7969tW3bNpUoUUKSNGnSJO3fv18TJkxQ+fLltWnTJv3111/3PPbPP/+syZMna8CAAWrevLkSEhJ06NAhxcbGSrq1hKRHjx7q06ePnn76aUlStWrVMjzWl19+qXHjxql169ZasGCB7OzsdPDgQV25ckUeHh53reHixYsqXry4HnvsMUlSUlKS+vfvr6tXr2rMmDEqW7astm3bpiFDhmjLli2WNwLr1q3TkiVLNHDgQDVp0kT79+/XlClTMjzH2rVrVbduXc2aNUspKSmSpFmzZunjjz/W0KFDVbduXR08eFBvvfWWihQpop49e0qSXnnlFUVERGjKlCkqVaqULl26pD///NNy3Pu13ykuLk59+vSRjY2Npk+friJFiujdd9+1jKWrq6ul73fffadz585p6tSpioqK0uzZszVz5kwtWrTorscHAAAFF6E7m1avXq2UlBStWrVKTk5Okm7NDnfs2FFbt25Vnz59dPLkSe3YsUNz587Vc889J0lq1qyZ2rdvf89jHzp0SE5OTpo4caJlW4sWLSx/TlsG4urqes/lJGazWXPnzpW/v7+WLl1q2Z7ReubU1FSlpKRY1nR/8803GjNmjGxtbSVJn3/+uY4dO6bPPvvMEvCbNWumc+fO6Z133tHbb7+tmzdvasWKFerSpYsCAwMlSU2bNlVUVJQ2b96c7pyOjo5asmSJTCaTJOn8+fNav369pk+frh49ekiS/Pz8lJCQoKVLl6pHjx6ysbHR4cOHNW7cOHXo0MFyrLTrK+m+7XfasmWLLl68qC+++MKyrr5Ro0Zq2bKl1qxZo6CgIKtr+u6776pw4cKSbs2QL1++XKmpqQ9lGQ4AAHi0kA6y6ZdffpGPj48lcEtS1apVVbNmTf3666+SZFku0rp1a0sfGxsbtWzZ8p7H9vDw0PXr1xUUFKQ9e/YoPj4+SzWePn1aly9f1vPPP3/fvmPHjlXt2rXVsGFDTZw4UW3bttWgQYMs7Xv27FGNGjXk5uamlJQUy8PPz8/yOi9fvqzIyEi1atXK6ti3v/7bPfnkk5bALUl79+6VJLVp0ybdOSIjI3Xp0iVJt67PqlWr9OGHH+rcuXPpjnu/9jv98ssvql69utUHWZ2cnOTn52cZyzSNGjWyBG7p1pgnJyfr6tWr9z0PAAAoeAjd2RQTE6PSpUun216qVClFR0dLkiIjI2VnZ6eSJUta9XF2dr7nsX19fTVv3jydOHFCAQEBatKkiV555RVdv379gWpM6+/i4nLfvoGBgdq8ebPef/99tW7dWl988YU++ugjS3tUVJSOHDmi2rVrWz3effddXb582fJ6M3p9pUqVyvCcd26PioqS2WxWkyZNrM7Rv39/SbKE7kWLFqlJkyZavHix2rRpo3bt2umbb76xHOd+7XfKzFimcXBwsHqeFsATExPvenwAAFBwsbwkmxwdHTOc3bx69arc3NwkSWXKlFFycrJiY2OtgndmPnjXqVMnderUSdeuXdPOnTs1e/ZsFSpUyOoDgfeTNgsfERFx374VK1aUp6enJMnHx0ddu3bV4sWL9eyzz6pYsWJydHSUu7u7Zs2adddjlClTRlL613e3WeDbZ7mlW9fUZDLpww8/lJ2dXbr+lStXlnTrTcTs2bOVmpqqP//8U++++67Gjh2r7du3q2LFivdtv5Ojo6POnDmTbvvVq1fl6Oh419cLAABwP8x0Z5O3t7f2799vNRN6+vRpHT9+XN7e3pKkOnXqSJJ27txp6ZOamqr//e9/mT6Ps7OzunXrJn9/f50+fdqy3c7O7r6zq1WqVFG5cuW0ZcuWTJ9PkmxtbTVhwgRFRUVp06ZNkm6trQ4LC5OLi4s8PT3TPaRbt+IrU6aM1euVpG+//TZT5027g8n169czPEfah1PT2NjYyMvLS2PGjFFKSkq6pST3a0/j7e2tv//+2+r6RkdHa+/evZaxBAAAyApmujPp5s2b2r59e7rtffv21ZYtWzRgwAANGzZMiYmJWrx4sVxdXdW5c2dJUvXq1fXUU0/pjTfeUHx8vB5//HFt2rRJCQkJ6WZ5bxccHKzr16+rcePGKlWqlP7++2/9+OOP6tevn6VPlSpVtHPnTjVs2FD29vaqXLlyulBqMpk0ceJEjRs3TiNHjlSnTp1UuHBh/f777/L09Lzn2nI/Pz95e3tr9erV6tWrl5577jl99NFH6tu3rwYMGCA3NzfFxsbqyJEjSk5O1vjx42Vra6vBgwfrzTffVOnSpeXj46MDBw5o3759knTfDxpWrlxZvXr10iuvvKKAgADVrVtXycnJOnv2rA4cOKB33nlHsbGxCggIUKdOnVS5cmUlJydr3bp1cnBwkIeHx33bM9KlSxetXr1aQ4YM0ZgxYyx3LylUqJBeeumle9YMAABwL4TuTEpMTNTo0aPTbZ83b57WrVunefPmKTAwUDY2NvL391dQUJBV+H3zzTc1Y8YMzZs3T4ULF1bnzp1VvXp1ffDBB3c9p6enp9asWaOvvvpKcXFxKleunAICAjRs2DBLn6lTp+rNN9/UoEGDlJCQoLVr18rHxyfdsTp06KCiRYtq2bJlGjdunIoUKSIPDw899dRT933tL7/8svr376/PP/9cXbp00dq1axUSEqJly5YpMjJSTk5O8vDw0IsvvmjZp0+fPoqJidGHH36odevWydfXVxMmTNDYsWPTrW3PyJQpU1S5cmVt3LhRS5cuVfHixVW5cmW1a9dOklSkSBHVqFFD69at06VLl1S0aFHVqVNHoaGhcnZ2VlJS0j3bM1KiRAmtW7dOc+bM0WuvvabU1FQ1aNBA69evt7pdIAAAwIMymc1mc24XUVD16tVLNjY2WrduXW6X8lAsXrxY77//vg4cOPDQvnXyUZF255cN34XpbHhULlcDAMgr3Mo/pjdHd7hnnxs3bujo0aOqVauWihUr9pAqg/R/P7/TltjeCzPdD8nXX3+tS5cuqUaNGoqPj9d///tf/fLLL1b3zc5PTp06pW3btql+/fqys7PTTz/9pNDQUPXs2ZPADQAAChxC90NSrFgxffbZZzp79qySk5NVpUoVzZ8/X//5z39yuzRDFC1aVL/99ps2bNigf//9V2XLllVAQIBGjhyZ26UBAAA8dITuh6RZs2Zq1qxZbpfx0JQvX15r167N7TIAAADyBG4ZCAAAABiM0A0AAAAYjNANAAAAGIzQDQAAABiM0A0AAAAYjNANAAAAGIzQDQAAABiM0A0AAAAYjNANAAAAGIzQDQAAABiM0A0AAAAYjNANAAAAGIzQDQAAABiM0A0AAAAYjNANAAAAGIzQDQAAABiM0A0AAAAYjNANAAAAGIzQDQAAABiM0A0AAAAYjNANAAAAGIzQDQAAABiM0A0AAAAYjNANAAAAGIzQDQAAABiM0A0AAAAYrFBuFwDg/5R3ccztEgAAeQg/F/IPQjeQh4zo6Z/bJQAA8pjUVLNsbEy5XQayieUlQB6RlJSk+Pj43C4DkuLj43XkyBHGIw9hTPIWxuPhInDnD4RuIA8xm825XQJ0axzi4+MZjzyEMclbGA/gwRG6AQAAAIMRugEAAACDEboBAAAAgxG6AQAAAIMRugEAAACDEboBAAAAgxG6AQAAAIMRugEAAACDEboBAAAAgxG6AQAAAIMRugEAAACDEboBAAAAgxG6AQAAAIMRugEAAACDEbqBPMRkMuV2CdCtcbC3t2c88hDGJG9hPPIWxuPRYDKbzebcLgIo6A4fPixJ8vT0zOVKAAAFWWqqWTY2hPfMepCf34WMLgZA5i3dsEfhEdG5XQYAoAAq7+KoET39c7uMfIvQDeQh4RHROhseldtlAACAHMaabgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBgBSJ0h4SEqH79+um2z5kzRzVr1tTHH3+skJAQubu7q1evXun6zZo1S61atXrg8/bp00dDhgwxZL8LFy7I3d1d27dvf+DjG2Hnzp0aMGCAGjdurDp16qhVq1aaOnWqzpw5Y+nj7u6u0NDQh1rXli1b5O7urmvXrlm2hYWF6aWXXlL9+vXl7u6uo0ePZnmsAAAAMqNQbheQW+bPn6/Vq1dr+vTp6tatm0JCQiRJv/zyiw4cOCAfH59sn+P111+XjU3+f1/z1ltvaeXKlWrbtq1mzpwpZ2dnnT9/Xp988onGjh2rTz/9NNdqa9GihTZu3CgHBwfLtrffflthYWEKDg5WyZIl5ebmVmDGCgAA5I4CGboXLVqk9957T6+//rp69Ohh2V6sWDFVq1ZN77zzTo6E7mrVqmX7GLklISFBRYsWvW+/Xbt2aeXKlRo+fLhGjx5t2d6oUSM9//zz+t///mdkmffl7OwsZ2dnq22nT59Ww4YN1axZM8u2nBqrzF43AABQsBS4qb2QkBAtW7ZMr732ml588cV07cOHD9f+/ft18ODBex4nJiZG06ZNU9OmTVWnTh116dJFu3fvtuqT0ZKFHTt2qG3btvL09FT37t31119/qWHDhpaZ9ttt375dbdu2Vf369dW3b1+dP38+XZ/4+Hi9+uqr8vb2VuPGjTV79mylpKRY9Tl+/LgCAgJUr149eXt7a9SoUbp48aJVH3d3d61YsULz58+Xv7+/fH19JUknTpzQoEGD5OPjo7p166pt27ZauXKlZb9Vq1apdOnSGj58eIbXqWXLlne9ht9//7369+8vX19fNWjQQN26ddMPP/xg1ScmJkZTpkxRs2bN5OnpqebNm2vs2LGZbr99eUnakpy//vpLn332mdzd3S3LhjIaq1OnTmnYsGHy9vZWvXr1NHjw4HRjcLfrBgAAcLsCNdP97rvvasmSJZo0aZJ69+6dYZ+WLVvKw8NDS5cuvev646SkJPXv319Xr17VmDFjVLZsWW3btk1DhgyxhLyMHDlyRKNHj1bLli316quvKjw8XGPHjlVSUlK6vkePHtW1a9cUGBiomzdvas6cOZowYYI2btxo1W/hwoVq2rSpFi9erCNHjig4OFh2dnYKDAyUJF26dEm9e/dWxYoVNX/+fCUmJmrRokXq3bu3tm3bphIlSliOtXbtWtWtW1ezZs2yBPehQ4eqdOnSmjVrlkqUKKHz58/r8uXLkqSUlBQdPHhQbdq0kZ2d3X2ufnoXLlxQy5YtNWDAANnY2OiHH37Q4MGDtWbNGstvGmbPnq0ff/xR48ePV/ny5RUZGWkVzO/XfjsXFxdt3LhREydO1BNPPKHhw4ercOHCGfYNCwvTCy+8oOrVq2vOnDkymUxatmyZ+vXrp+3bt1vtl9F1AwAAuF2BCd03btzQ4sWL1a1bN/Xr1++efYcNG6aRI0fq0KFD8vLyStf++eef69ixY/rss88syxKaNWumc+fO6Z133tHbb7+d4XGXL1+uChUqKCQkxLJ+uHjx4nrllVfS9Y2NjdWnn35qWRpx48YNTZo0SZcvX1a5cuUs/SpVqqTZs2dbakhISND777+vQYMGydHRUatXr1ZKSopWrVolJycnSVKtWrXUsWNHbd26VX369LEcy9HRUUuWLJHJZJIky+zw5MmTLTPCTZo0sfS/fv26kpKS9Pjjj9/zet7N7W98UlNT5ePjo5MnT2rTpk2W0H348GE9/fTT6ty5s6Vvx44dLX++X/vtChcurHr16qlo0aJydnZWvXr17lrbkiVL5OjoqPfff19FihSRJDVo0ECtW7fWxx9/bPWB2zuvGwAAwJ0KzPKSokWLqlGjRvrvf/+rX3/99Z59n3rqKdWoUUNLly7NsH3Pnj2qUaOG3NzclJKSYnn4+fnp8OHDdz3u4cOH1aJFC6sP7LVu3TrDvjVr1rRai5wW7tNmmW+v9XZt27ZVfHy8/v77b0m3Phjq4+NjCdySVLVqVdWsWTPddXjyySetguNjjz2m8uXLa+HChdq6dWu6c6fJati8fPmyJk6cqGbNmsnDw0O1a9fW7t27re544uHhoa1btyo0NNTymm53v/as2rNnj1q1aiVbW1vL+Do4OMjDw0N//vmnVd87rxsAAMCdCsxMt42Njd5991316dNHQ4cO1fr16++6DMRkMmno0KEaN26c/vrrr3TtUVFROnLkiGrXrp2uzdbW9q41REZGpvtQX4kSJSwzqbe7/W4bkizLNxITE62233m80qVLW84l3VrzXKtWrXTHL1WqlKKjo9Ntu53JZFJoaKgWLVqkGTNm6MaNG6pdu7YmTZqkRo0aycnJSUWKFEm3PjwzUlNTNWzYMMXGxmrUqFF64oknZG9vr+DgYF26dMnS77XXXrPMOM+bN0+urq4aPHiwZT3+/dqzKioqSmvWrNGaNWvStd25lObO6wYAAHCnAhO6JalkyZIKDQ1Vz549FRAQoA0bNqhixYoZ9m3fvr1CQkL0zjvvpFs+4ejoKHd3d82aNeuBzl+mTBmr+0VLUlxcXLog/SDuPN4///xjOVdarVevXk2339WrV+Xm5ma1LaPZ2sqVKys4OFjJycn67bfftHDhQg0dOlQ//PCDihcvrgYNGmj//v1KSUlRoUKZ/+t07tw5HTlyREuXLtV//vMfy/aEhASrfiVLltTkyZM1efJkHT9+XGvXrtX06dNVo0YNNWzY8L7tWeXo6KjmzZtnGN6LFy9u9ZxZbgAAcD8FZnlJmlKlSmnVqlUymUwaMGCAZUb4TjY2Nho6dKh27typ48ePW7X5+fkpLCxMLi4u8vT0TPe4G09PT33//fdKTU21bPv222+z9Xp27Nhh9fzrr7+Wvb29atSoIUny9vbW/v37rWa1T58+rePHj8vb2zvT57Gzs1Pjxo01ePBgxcXFKSIiQpLUv39/RUZGatmyZRnut2vXrgy3p73RuH3WODw8XL/99ttda3B3d9ekSZMk3bqzyIO2PwhfX1+dOHFCHh4e6ca3SpUq2To2AAAoeArUTHeaChUqKDQ0VL1799bAgQO1fv36DPs988wzWrp0qQ4cOKDy5ctbtj/33HP66KOP1LdvXw0YMEBubm6KjY3VkSNHlJycrPHjx2d4vCFDhqhr164aOXKkunfvrosXL2rVqlUqUqRIlmdLz58/r0mTJqlDhw46cuSIVqxYoZdeekmOjo6SpH79+mnLli0aMGCAhg0bpsTERC1evFiurq5WHz7MyLFjxzR37lx16NBBFStWVFxcnJYvX67y5curUqVKkqTmzZtr4MCBCgkJ0cmTJ9WxY0c99thjunDhgj755BPFxsaqefPm6Y5dpUoVlStXTgsWLFBqaqpu3Lih4OBgubi4WPV74YUX9NRTT6l69eqytbXVp59+Kjs7O8ss9v3as2rUqFHq2rWrAgIC1L17d5UuXVr//POPfvrpJzVs2FBPP/10to4PAAAKlgIZuiWpRo0aWr58ufr3768hQ4Zk+DXxtra2Gjx4sKZMmWK1vXDhwlq7dq3lnt+RkZFycnKSh4fHPdcSe3h4aPHixVqwYIFefvlly+3o+vbtq5IlS2bpdYwdO1Y//fSTRo8eLVtbW7344otW96l2dXXVunXrNG/ePAUGBsrGxkb+/v4KCgqyul1gRsqUKaPSpUtr+fLlunLlikqWLKmGDRtq/vz5VmvXJ0yYoPr16+uDDz7Qq6++qvj4eLm4uKhp06YKCAjI8NiFCxdWSEiIZsyYodGjR8vV1VXDhg3T/v37rT6o2KBBA3366ae6cOGCbGxsVKNGDS1btkxVq1bNVHtWPfHEE/r444+1ePFiTZ8+XTdu3FCZMmXUqFGju34WAAAA4G5MZrPZnNtFFGT79u1Tv379tG7dOjVu3Di3y0EuSbvrzYbvwnQ2PCqXqwEAFERu5R/Tm6M75HYZj5S0n9/3Wl6cpsDOdOeWadOmydfXV05OTjp58qTeeecdeXh4ZHs5BAAAAPIuQvdDFhMTo5kzZ+r69esqUaKEmjVrpokTJ1rduxsAAAD5C6H7IVu4cGFulwAAAICHjOlVAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghG4AAADAYIRuAAAAwGCEbgAAAMBghXK7AAD/p7yLY26XAAAooPgZZCxCN5CHjOjpn9slAAAKsNRUs2xsTLldRr7E8hIgj0hKSlJ8fHxulwFJ8fHxOnLkCOORhzAmeQvjkbfk5HgQuI1D6AbyELPZnNslQLfGIT4+nvHIQxiTvIXxyFsYj0cDoRsAAAAwGKEbAAAAMBihGwAAADAYoRsAAAAwGKEbAAAAMJjJzEddgVx38OBBmc1m2dnZyWTidk25zWw2Kzk5mfHIQxiTvIXxyFsYj9yTlJQkk8mkBg0a3LcvX44D5AFp/0nyn2XeYDKZVLhw4dwuA7dhTPIWxiNvYTxyj8lkyvTPbma6AQAAAIOxphsAAAAwGKEbAAAAMBihGwAAADAYoRsAAAAwGKEbAAAAMBihGwAAADAYoRsAAAAwGKEbAAAAMBihGwAAADAYoRsAAAAwGKEbAAAAMBihGwAAADAYoRsw2KlTp9S/f3/Vq1dP/v7+mjdvnpKSku67n9ls1ooVK9SiRQt5eXmpR48e+v33340vOJ/LynhERERo3rx56tSpk+rXr68nn3xS48ePV3h4+EOqOn/L6r+R261evVru7u4aMmSIQVUWHNkZjytXrmjixIlq0qSJvLy81L59e23bts3givO3rI5HVFSUpk6dqhYtWqhevXp6+umntWHDhodQMe6mUG4XAORn0dHReumll+Tm5qaQkBBduXJFc+bMUUJCgqZOnXrPfVeuXKng4GAFBgbK3d1dH3zwgQYMGKDPPvtMFStWfEivIH/J6nj89ddf2rFjh55//nnVrVtXUVFRevfdd9WtWzf997//lbOz80N8FflLdv6NpImMjNTSpUtVqlQpg6vN/7IzHhEREerRo4cqV66smTNnqkSJEjpx4sQDv4HC/8nOeIwePVqnT5/WuHHj5Orqqh9++EHTpk2Tra2tunfv/pBeAayYARhm2bJl5nr16pmjoqIs2z766CNzrVq1zJcvX77rfgkJCeYGDRqYFyxYYNmWmJhobtmypfn11183sOL8LavjER0dbU5OTrbadunSJbO7u7s5NDTUqHILhKyOye0mTJhgfuWVV8y9e/c2Dx482KBKC4bsjEdgYKC5R48e5pSUFIOrLDiyOh4RERHmGjVqmD/55BOr7b169TL37dvXqHJxHywvAQz0ww8/yNfXV05OTpZt7du3V2pqqvbs2XPX/Q4ePKi4uDi1b9/esq1w4cJ66qmn9MMPPxhZcr6W1fFwcHBQoULWvxgsV66cnJ2dFRERYVS5BUJWxyTNL7/8om+//Vbjx483sMqCI6vjERcXp6+++kovvviibG1tH0KlBUNWxyMlJUWSVLJkSavtJUqUkNlsNqRW3B+hGzDQ6dOnVaVKFattDg4OKlOmjE6fPn3P/SSl27dq1aq6ePGiEhIScr7YAiCr45GRM2fO6OrVq6patWpOlljgZGdMbt68qZkzZ2ro0KFycXExsswCI6vj8ddffyk5OVmFChVS7969Vbt2bfn7+2v+/PlKTk42uux8K6vj4erqqqZNm2rZsmU6efKk4uLi9OWXX2rPnj3q1auX0WXjLljTDRgoJiZGDg4O6bY7OjoqOjr6nvsVLlxYRYoUsdru4OAgs9ms6OhoFS1aNMfrze+yOh53MpvNeuONN+Ti4qKOHTvmZIkFTnbG5MMPP1R8fLz69etnUHUFT1bH459//pEkTZkyRd27d9fLL7+sQ4cOKTg4WDY2NvwmIouy8+8jJCREY8eOtfwfZWtrqylTpqht27aG1Ir7I3QDwAMKCQnR/v379d5776lYsWK5XU6BdPXqVQUHB2vu3LkqXLhwbpdT4KWmpkqS/Pz8FBQUJElq0qSJ/v33X61atUojRoxgouAhMpvNmjRpks6ePasFCxaoTJky2rt3r9588005OjoyWZBLCN2AgRwcHBQbG5tue3R0tBwdHe+5X1JSkhITE61mu2NiYmQyme65L+4uq+Nxu02bNmnp0qWaNWuWfH19c7rEAierY/L222/L3d1dDRs2VExMjKRb61hTUlIUExOjYsWKpVuHj/vLzv9Z0q2gfTtfX18tW7ZM586dk7u7e84WWwBkdTy+//57bd++Xdu2bbNcdx8fH129elVz5swhdOcS1nQDBqpSpUq6dXexsbGKjIxMt07vzv2kW+uGb3f69Gk9/vjjzBhlUVbHI82OHTs0bdo0jRo1Sl27djWqzAIlq2Ny5swZ/fzzz2rUqJHlcfDgQe3evVuNGjXS3r17jS49X8rqeFSrVu2ex01MTMyR+gqarI7HyZMnZWtrqxo1alhtr1WrliIiIhQfH29Ivbg3QjdgoCeffFJ79+61zMRJ0vbt22VjYyN/f/+77tegQQOVKFFCX331lWVbcnKyvvnmGz355JOG1pyfZXU8JOnAgQMaN26cunXrphEjRhhdaoGR1TF59dVXtXbtWqtHzZo1Va9ePa1du1ZeXl4Po/x8J6vjUb58edWoUSPdm529e/eqaNGi9w3lyFh2xuPmzZs6fvy41fa//vpLpUqVkr29vWE14+5MZu4dAxgmOjpaHTt2VOXKlTVkyBDLFxs888wzVl9s8NJLL+nixYvasWOHZduKFSsUEhKiwMBA1ahRQxs2bNDu3bv5cpxsyOp4nDp1Sj169JCrq6umT58uG5v/m69wdnZWpUqVHvpryS+y82/kTn369FGxYsW0fPnyh1F6vpSd8fjuu+80fPhw9enTRy1atNDhw4e1ZMkSBQQEaOzYsbnxch55WR2PuLg4PfPMM7Kzs9OIESPk4uKi3bt3a9WqVRo5cqSGDx+eWy+pQGPBG2AgR0dHrVmzRjNnztSIESNUvHhxde3aNd0PoNTUVN28edNq26BBg2Q2m7Vq1Spdu3ZNtWrVUmhoKIE7G7I6Hn/88YdiY2MVGxurnj17WvXt3Lmz5syZ81Dqz4+y828EOS8749GqVSstXLhQ77zzjjZs2CAXFxeNHDlSgwcPfpgvIV/J6niUKFFCq1ev1qJFi/TWW28pNjZWFSpUUFBQkHr37v2wXwb+P2a6AQAAAIOxphsAAAAwGKEbAAAAMBihGwAAADAYoRsAAAAwGKEbAAAAMBihGwAAADAYoRsAAAAwGKEbAAAAMBihGwAAADAYoRsAAAAwGKEbAIA85saNG7ldAoAcRugGAOAOcXFxmjVrllq1aqU6derI19dX/fv3119//WXp88cff2jQoEFq1KiR6tWrp2eeeUZr1qyxOs6+ffv04osvql69emrYsKGGDRumU6dOWfUJCQmRu7u7Tp48qfHjx6tRo0Z68cUXLe2fffaZunTpIi8vLzVu3Fhjx47VpUuXjL0AAHKcyWw2m3O7CAAA8pLx48fr66+/Vu/evVW1alVdv35dv/76qzp06KBnn31We/bs0ZAhQ+Ti4qLOnTurdOnSOnXqlE6dOqX3339fkrR3714NGjRIFSpUULdu3ZSQkKD169crNTVVW7ZsUYUKFSTdCt1LlixRtWrV9MQTT6hp06Yym83q1auX3n33Xb399ttq3769GjVqpGvXrmn9+vUqVqyYPv30Uzk4OOTmZQLwAAjdAADcoWHDhnr22Wc1derUdG03b95UmzZtZDab0wVfs9ksk8kkSXruuecUERGhL7/8Uk5OTpKkY8eOqXPnznr22Wc1d+5cSf8Xup9++mktWLDAcqzw8HA99dRTGjVqlIYOHWrZ/vfff6tz584aOXKk1XYAeRvLSwAAuIODg4P++OMPXblyJV3bkSNHdOHCBfXt2zfdTHNa4I6IiNDRo0fVuXNnS+CWpJo1a8rPz0+7du1Kd9wXXnjB6vmOHTuUmpqq9u3b69q1a5ZH6dKl9cQTT+jAgQM58EoBPCyFcrsAAADymsDAQAUFBalFixaqXbu2mjdvrueee04VK1ZUWFiYJKlGjRp33f/ixYuSpMqVK6drq1q1qnbv3q0bN26oWLFilu1py03SnD17VmazWW3atMnwHIUK8SMceJTwLxYAgDt06NBBDRs21I4dO7Rnzx6FhoZq5cqVCgkJMeycRYoUsXqempoqk8mklStXytbWNl3/2wM7gLyP0A0AQAZcXFzUq1cv9erVS1evXlXnzp21bNkyvfrqq5Jura328/PLcN/HH39cknTmzJl0badPn9Zjjz1239BcqVIlmc1mVahQIcMZcwCPFtZ0AwBwm5s3byo2NtZqW6lSpeTi4qKkpCTVrl1bFSpU0Nq1axUTE2PVL+3eBC4uLqpVq5Y+/fRTqz5///239uzZo+bNm9+3jjZt2sjW1lZLlizRnfc8MJvNioqKyupLBJALmOkGAOA2//77r5o3b662bduqZs2aKlasmPbu3avDhw8rKChINjY2mjZtmoYNG6bnnntOXbp0UZkyZXT69GmdPHlSoaGhkqRXXnlFgwYNUo8ePdS1a1fLLQNLliypl19++b51VKpUSWPGjNGCBQsUHh6u//znPypevLguXLigb7/9Vt27d1dAQIDRlwNADuGWgQAA3CYpKUmLFy/Wnj17FBYWJrPZrEqVKqlHjx5WX1rz66+/aunSpfr9999lNptVsWJFde/eXb1797b02bdvn4KDg3XkyBEVKlRIjRo10oQJE1S1alVLn7RbBu7bt0/Ozs7p6vnmm2+0evVqHT16VJJUrlw5+fr6qk+fPiw7AR4hhG4AAADAYKzpBgAAAAxG6AYAAAAMRugGAAAADEboBgAAAAxG6AYAAAAMRugGAAAADEboBgAAAAxG6AYAAAAMRugGAAAADEboBgAAAAxG6AYAAAAMRugGAAAADEboBgAAAAz2/wAVc1DGvgwDJAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.set(style=\"whitegrid\")\n",
        "ax = sns.barplot(y=\"name\", x=\"score\", data=df)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(style=\"whitegrid\")\n",
        "ax = sns.scatterplot(y=\"name\", x=\"score\", data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "A6j5NDdOkgiv",
        "outputId": "fee07548-b01e-4428-fe25-10a30bfe7066"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XX_pHD947mQL"
      },
      "source": [
        "## ***Model Deployment***\n",
        "   Save The Best Model\n",
        "   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "49zxKSvWl8i-"
      },
      "outputs": [],
      "source": [
        "#saving the model\n",
        "import pickle\n",
        "pickle.dump(RF_model,open('fetalhealth.pkl','wb'))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}