heart_disease_prediction.ipynb

{
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.10.12",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat_minor": 0,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Title: Heart Disease Prediction\n",
        "\n",
        "### Context\n",
        "\n",
        "This is a multivariate type of dataset which means providing or involving a variety of separate mathematical or statistical variables, multivariate numerical data analysis. It is composed of 14 attributes which are age, sex, chest pain type, resting blood pressure, serum cholesterol, fasting blood sugar, resting electrocardiographic results, maximum heart rate achieved, exercise-induced angina, oldpeak — ST depression induced by exercise relative to rest, the slope of the peak exercise ST segment, number of major vessels and Thalassemia. This database includes 76 attributes, but all published studies relate to the use of a subset of 14 of them. The Cleveland database is the only one used by ML researchers to date. One of the major tasks on this dataset is to predict based on the given attributes of a patient that whether that particular person has heart disease or not and other is the experimental task to diagnose and find out various insights from this dataset which could help in understanding the problem more.\n",
        "\n"
      ],
      "metadata": {
        "id": "_Oq4fobGz4dT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "| Column  | Description                                                      | Values (Categorical)                                     |\n",
        "|---------|------------------------------------------------------------------|---------------------------------------------|\n",
        "| id      | Unique id for each patient                                       |                                             |\n",
        "| age     | Age of the patient in years                                      |                                           |\n",
        "| origin  | Place of study                                                   |Cleveland, Hungary, Switzerland, VA Long Beach                                             |\n",
        "| sex     | Male/Female                                                      |Male, Female                                             |\n",
        "| cp      | Chest pain type                                                  | Typical Angina, Atypical Angina, Non-Anginal, Asymptomatic|\n",
        "| trestbps| Resting blood pressure (in mm Hg on admission to the hospital)   |                                             |\n",
        "| chol    | Serum cholesterol in mg/dl                                       |                                             |\n",
        "| fbs     | Fasting blood sugar (> 120 mg/dl)                                |                                             |\n",
        "| restecg | Resting electrocardiographic results                             | Normal, ST-T Abnormality, LV Hypertrophy    |\n",
        "| thalach | Maximum heart rate achieved                                      |                                             |\n",
        "| exang   | Exercise-induced angina (True/False)                             | True, False                                 |\n",
        "| oldpeak | ST depression induced by exercise relative to rest               |                                             |\n",
        "| slope   | Slope of the peak exercise ST segment                            |                                             |\n",
        "| ca      | Number of major vessels (0-3) colored by fluoroscopy             | 0, 1, 2, 3                                  |\n",
        "| thal    | Thalassemia                                                      | Normal, Fixed Defect, Reversible Defect    |\n",
        "| num     | Predicted attribute                                              |0, 1, 2, 3, 4                                             |\n"
      ],
      "metadata": {
        "id": "4zFTkFCZz4dV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder\n",
        "from sklearn.impute import SimpleImputer, KNNImputer\n",
        "from sklearn.experimental import enable_iterative_imputer\n",
        "from sklearn.impute import IterativeImputer\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, StratifiedKFold\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "from sklearn.metrics import mean_absolute_error, precision_score, r2_score, mean_squared_error\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "from xgboost import XGBClassifier\n",
        "import warnings\n",
        "\n",
        "warnings.filterwarnings('ignore')\n",
        "pd.set_option('display.max_columns',None)\n",
        "pd.set_option('display.max_rows',None)"
      ],
      "metadata": {
        "id": "JTqocaLDz4dW"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('heart_disease_uci.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4MN9jq0Yz4dX",
        "outputId": "b9247a12-d6d9-4de1-d16d-c56702925c23"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  age     sex    dataset               cp  trestbps   chol    fbs  \\\n",
              "0   1   63    Male  Cleveland   typical angina     145.0  233.0   True   \n",
              "1   2   67    Male  Cleveland     asymptomatic     160.0  286.0  False   \n",
              "2   3   67    Male  Cleveland     asymptomatic     120.0  229.0  False   \n",
              "3   4   37    Male  Cleveland      non-anginal     130.0  250.0  False   \n",
              "4   5   41  Female  Cleveland  atypical angina     130.0  204.0  False   \n",
              "\n",
              "          restecg  thalch  exang  oldpeak        slope   ca  \\\n",
              "0  lv hypertrophy   150.0  False      2.3  downsloping  0.0   \n",
              "1  lv hypertrophy   108.0   True      1.5         flat  3.0   \n",
              "2  lv hypertrophy   129.0   True      2.6         flat  2.0   \n",
              "3          normal   187.0  False      3.5  downsloping  0.0   \n",
              "4  lv hypertrophy   172.0  False      1.4    upsloping  0.0   \n",
              "\n",
              "                thal  num  \n",
              "0       fixed defect    0  \n",
              "1             normal    2  \n",
              "2  reversable defect    1  \n",
              "3             normal    0  \n",
              "4             normal    0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f09dd95b-3af5-4ee3-9d5c-f638007a170e\" 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>id</th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>dataset</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalch</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>63</td>\n",
              "      <td>Male</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>typical angina</td>\n",
              "      <td>145.0</td>\n",
              "      <td>233.0</td>\n",
              "      <td>True</td>\n",
              "      <td>lv hypertrophy</td>\n",
              "      <td>150.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2.3</td>\n",
              "      <td>downsloping</td>\n",
              "      <td>0.0</td>\n",
              "      <td>fixed defect</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>67</td>\n",
              "      <td>Male</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>asymptomatic</td>\n",
              "      <td>160.0</td>\n",
              "      <td>286.0</td>\n",
              "      <td>False</td>\n",
              "      <td>lv hypertrophy</td>\n",
              "      <td>108.0</td>\n",
              "      <td>True</td>\n",
              "      <td>1.5</td>\n",
              "      <td>flat</td>\n",
              "      <td>3.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>67</td>\n",
              "      <td>Male</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>asymptomatic</td>\n",
              "      <td>120.0</td>\n",
              "      <td>229.0</td>\n",
              "      <td>False</td>\n",
              "      <td>lv hypertrophy</td>\n",
              "      <td>129.0</td>\n",
              "      <td>True</td>\n",
              "      <td>2.6</td>\n",
              "      <td>flat</td>\n",
              "      <td>2.0</td>\n",
              "      <td>reversable defect</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>37</td>\n",
              "      <td>Male</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>non-anginal</td>\n",
              "      <td>130.0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>False</td>\n",
              "      <td>normal</td>\n",
              "      <td>187.0</td>\n",
              "      <td>False</td>\n",
              "      <td>3.5</td>\n",
              "      <td>downsloping</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>41</td>\n",
              "      <td>Female</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>atypical angina</td>\n",
              "      <td>130.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>False</td>\n",
              "      <td>lv hypertrophy</td>\n",
              "      <td>172.0</td>\n",
              "      <td>False</td>\n",
              "      <td>1.4</td>\n",
              "      <td>upsloping</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>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-f09dd95b-3af5-4ee3-9d5c-f638007a170e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f09dd95b-3af5-4ee3-9d5c-f638007a170e 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-f09dd95b-3af5-4ee3-9d5c-f638007a170e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-575fe99a-c50e-4cee-9a63-eebf29a1a231\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-575fe99a-c50e-4cee-9a63-eebf29a1a231')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-575fe99a-c50e-4cee-9a63-eebf29a1a231 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploratory Data Analysis (EDA)"
      ],
      "metadata": {
        "id": "MBREo8-Oz4dY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QzlAw8Naz4dY",
        "outputId": "b9522333-6bcc-496f-dec8-74d4b806f8ad"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 920 entries, 0 to 919\n",
            "Data columns (total 16 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   id        920 non-null    int64  \n",
            " 1   age       920 non-null    int64  \n",
            " 2   sex       920 non-null    object \n",
            " 3   dataset   920 non-null    object \n",
            " 4   cp        920 non-null    object \n",
            " 5   trestbps  861 non-null    float64\n",
            " 6   chol      890 non-null    float64\n",
            " 7   fbs       830 non-null    object \n",
            " 8   restecg   918 non-null    object \n",
            " 9   thalch    865 non-null    float64\n",
            " 10  exang     865 non-null    object \n",
            " 11  oldpeak   858 non-null    float64\n",
            " 12  slope     611 non-null    object \n",
            " 13  ca        309 non-null    float64\n",
            " 14  thal      434 non-null    object \n",
            " 15  num       920 non-null    int64  \n",
            "dtypes: float64(5), int64(3), object(8)\n",
            "memory usage: 115.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rNes07tTz4dY",
        "outputId": "cebebd98-888a-447b-afa7-6db17ab5272a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(920, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Summary Statistics"
      ],
      "metadata": {
        "id": "nSZJ1ocFz4dY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "8b50-rXvz4dY",
        "outputId": "a16e40dd-b481-49af-a145-b802f7340211"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               id         age    trestbps        chol      thalch     oldpeak  \\\n",
              "count  920.000000  920.000000  861.000000  890.000000  865.000000  858.000000   \n",
              "mean   460.500000   53.510870  132.132404  199.130337  137.545665    0.878788   \n",
              "std    265.725422    9.424685   19.066070  110.780810   25.926276    1.091226   \n",
              "min      1.000000   28.000000    0.000000    0.000000   60.000000   -2.600000   \n",
              "25%    230.750000   47.000000  120.000000  175.000000  120.000000    0.000000   \n",
              "50%    460.500000   54.000000  130.000000  223.000000  140.000000    0.500000   \n",
              "75%    690.250000   60.000000  140.000000  268.000000  157.000000    1.500000   \n",
              "max    920.000000   77.000000  200.000000  603.000000  202.000000    6.200000   \n",
              "\n",
              "               ca         num  \n",
              "count  309.000000  920.000000  \n",
              "mean     0.676375    0.995652  \n",
              "std      0.935653    1.142693  \n",
              "min      0.000000    0.000000  \n",
              "25%      0.000000    0.000000  \n",
              "50%      0.000000    1.000000  \n",
              "75%      1.000000    2.000000  \n",
              "max      3.000000    4.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4f8abecc-a2d7-42c9-a598-68889f689ae2\" 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>id</th>\n",
              "      <th>age</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>thalch</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>ca</th>\n",
              "      <th>num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>920.000000</td>\n",
              "      <td>920.000000</td>\n",
              "      <td>861.000000</td>\n",
              "      <td>890.000000</td>\n",
              "      <td>865.000000</td>\n",
              "      <td>858.000000</td>\n",
              "      <td>309.000000</td>\n",
              "      <td>920.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>460.500000</td>\n",
              "      <td>53.510870</td>\n",
              "      <td>132.132404</td>\n",
              "      <td>199.130337</td>\n",
              "      <td>137.545665</td>\n",
              "      <td>0.878788</td>\n",
              "      <td>0.676375</td>\n",
              "      <td>0.995652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>265.725422</td>\n",
              "      <td>9.424685</td>\n",
              "      <td>19.066070</td>\n",
              "      <td>110.780810</td>\n",
              "      <td>25.926276</td>\n",
              "      <td>1.091226</td>\n",
              "      <td>0.935653</td>\n",
              "      <td>1.142693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>-2.600000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>230.750000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>460.500000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>223.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>690.250000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>268.000000</td>\n",
              "      <td>157.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>920.000000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>603.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</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-4f8abecc-a2d7-42c9-a598-68889f689ae2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4f8abecc-a2d7-42c9-a598-68889f689ae2 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-4f8abecc-a2d7-42c9-a598-68889f689ae2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-62dc3cf1-0be3-49e8-a923-f36d7e4ba36d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-62dc3cf1-0be3-49e8-a923-f36d7e4ba36d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-62dc3cf1-0be3-49e8-a923-f36d7e4ba36d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Correlation Matrix of Numeric Columns"
      ],
      "metadata": {
        "id": "kLxFFXsgz4dZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_num = df.select_dtypes(include=['float64', 'int64']).columns.tolist()\n",
        "correlation_matrix  = df[df_num].corr()\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap=sns.cubehelix_palette(as_cmap=True), fmt=\".2f\")\n",
        "plt.title('Correlation Matrix Heatmap')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "yhjcLgu0z4dZ",
        "outputId": "bb6f7c55-a672-42a0-ff96-3598d7b86acf"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Unique values in Categorical Columns"
      ],
      "metadata": {
        "id": "5iJPRalYz4dZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for col in df.columns:\n",
        "    if df[col].dtype == 'object':\n",
        "        a = df[col].unique()\n",
        "        print(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xY1zIvvkz4dZ",
        "outputId": "736e8f89-15f3-42ed-fcbb-1fa2ffb11882"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Male' 'Female']\n",
            "['Cleveland' 'Hungary' 'Switzerland' 'VA Long Beach']\n",
            "['typical angina' 'asymptomatic' 'non-anginal' 'atypical angina']\n",
            "[True False nan]\n",
            "['lv hypertrophy' 'normal' 'st-t abnormality' nan]\n",
            "[False True nan]\n",
            "['downsloping' 'flat' 'upsloping' nan]\n",
            "['fixed defect' 'normal' 'reversable defect' nan]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['num'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cpy_7uPLz4da",
        "outputId": "609f56f1-a471-4697-e2ca-6cc84bacc395"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 2, 1, 3, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['age'].min(), df['age'].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vP-xOEBZz4da",
        "outputId": "fddbd6a5-5a57-4baa-ed75-111597bae44a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 77)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df, x = 'age', kde= True, color='black')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "9cXp7ekvz4da",
        "outputId": "0b521894-c795-45c0-814b-3b19c14bf0a5"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='age', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = df['age'].mode()[0]\n",
        "b = df['age'].mean()\n",
        "c = df['age'].median()\n",
        "\n",
        "print(f\"Mode of Age: {a},\\nMean of Age: {b}, \\nMedian of Age: {c}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4YYDFAjiz4da",
        "outputId": "b03b0977-dd39-4501-9a40-e8993b30aee2"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mode of Age: 54,\n",
            "Mean of Age: 53.51086956521739, \n",
            "Median of Age: 54.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.histogram(df, x = 'age', color='sex')\n",
        "fig.update_layout(width=800, height=400, margin=dict(l=50, r=50, t=50, b=50))\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "rHS9G7ioz4da",
        "outputId": "bd4256d1-93ea-47aa-e97e-d095a2440931"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.24.1.min.js\"></script>                <div id=\"dff03a52-a103-4822-8edb-10d4fde9d484\" class=\"plotly-graph-div\" style=\"height:400px; width:800px;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"dff03a52-a103-4822-8edb-10d4fde9d484\")) {                    Plotly.newPlot(                        \"dff03a52-a103-4822-8edb-10d4fde9d484\",                        [{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"sex=Male\\u003cbr\\u003eage=%{x}\\u003cbr\\u003ecount=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"Male\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Male\",\"offsetgroup\":\"Male\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[63,67,67,37,56,63,53,57,56,44,52,57,48,54,49,64,58,58,60,43,40,60,64,59,44,42,43,57,55,61,40,59,58,51,50,53,65,44,44,60,54,50,41,54,51,58,54,60,60,54,59,46,67,62,65,44,60,48,58,45,39,68,52,44,47,66,62,52,59,52,48,45,34,49,54,59,57,61,39,56,52,41,58,63,65,48,51,55,65,54,44,54,51,29,51,70,62,35,51,59,59,52,64,58,47,57,41,45,52,55,64,70,51,58,60,68,46,77,48,57,52,35,70,53,64,57,52,56,43,53,48,42,59,42,66,54,69,50,51,43,67,69,59,57,43,45,58,50,55,38,41,52,56,59,53,47,54,66,49,54,56,46,42,41,61,67,58,47,52,62,57,58,64,43,70,57,44,61,42,52,59,40,42,61,66,46,59,64,57,57,47,35,61,58,58,56,56,67,44,63,41,59,45,68,57,38,28,29,29,32,32,33,34,34,35,35,36,36,36,36,37,37,37,37,38,38,39,39,39,39,39,39,39,39,39,40,40,40,40,40,41,41,41,41,42,42,42,42,42,42,43,44,44,44,45,45,45,45,46,46,46,46,46,46,47,47,47,48,48,48,48,49,49,49,49,50,50,50,50,50,51,51,51,52,52,52,52,53,53,53,53,53,53,54,54,54,54,54,54,54,54,54,55,55,55,55,55,55,55,56,56,56,56,57,58,58,58,58,59,59,59,59,60,61,62,31,34,35,36,37,38,38,38,40,41,41,43,46,46,46,47,47,48,48,48,49,49,50,52,54,54,55,57,58,60,63,65,32,38,39,43,45,46,46,48,48,48,48,50,52,52,53,54,54,54,54,54,55,56,57,58,58,41,43,44,44,46,49,49,51,52,52,52,52,53,53,54,55,55,55,56,56,58,59,59,65,66,41,43,44,47,49,49,50,50,52,52,56,65,32,34,35,36,38,38,38,38,40,41,42,42,43,43,43,45,46,46,47,47,47,47,48,50,50,50,51,51,51,51,51,51,52,52,52,52,53,53,53,53,53,53,53,53,54,54,54,55,55,55,55,56,56,56,56,56,56,56,56,57,57,57,57,57,57,58,58,58,59,59,59,59,59,60,60,60,60,60,60,61,61,61,61,61,61,61,61,61,62,62,62,62,62,63,63,63,63,63,64,65,65,65,65,66,67,68,68,69,69,70,70,72,74,63,44,60,55,66,66,65,60,60,60,56,59,62,63,57,62,63,46,63,60,58,64,63,74,52,69,51,60,56,55,54,77,63,55,52,64,60,60,58,59,61,40,61,41,57,63,59,51,59,42,55,62,56,53,68,53,60,62,59,51,61,57,56,58,69,67,58,65,63,55,57,65,54,72,75,49,51,60,58,61,67,62,65,63,69,51,62,55,75,40,67,58,60,63,35,62,43,63,68,65,48,63,64,61,50,59,55,45,65,61,49,72,50,64,55,63,59,56,62,74,54,57,62,76,70,48,48,61,66,68,55,62,71,74,53,58,75,56,58,64,54,54,59,55,57,61,41,71,38,55,56,69,64,72,69,56,62,67,57,69,51,48,69,69,64,57,53,37,67,74,63,58,61,64,58,60,57,55,55,56,57,61,61,58,74,68,62,53,62,46,62,55,58,62],\"xaxis\":\"x\",\"yaxis\":\"y\",\"type\":\"histogram\"},{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"sex=Female\\u003cbr\\u003eage=%{x}\\u003cbr\\u003ecount=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"Female\",\"marker\":{\"color\":\"#EF553B\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Female\",\"offsetgroup\":\"Female\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[41,62,57,56,48,58,50,58,66,69,65,71,61,65,41,51,46,54,65,65,51,53,53,53,51,62,44,63,60,57,71,61,43,62,35,63,45,56,62,43,55,60,42,67,54,58,54,45,59,62,56,60,63,62,68,45,50,50,64,64,62,37,66,46,46,64,41,54,39,63,34,67,52,55,74,54,49,41,49,60,51,42,67,76,44,58,60,71,66,39,58,55,58,55,63,57,57,30,31,32,34,35,35,37,37,37,38,39,41,41,41,42,43,43,43,43,43,43,43,44,45,45,45,46,47,47,48,48,48,48,48,48,48,49,49,49,49,50,50,51,51,51,51,52,52,52,52,53,53,53,54,54,54,54,54,54,54,55,55,55,56,57,57,59,61,62,33,49,50,51,59,40,47,48,56,47,54,58,38,38,50,51,62,62,64,64,66,73,63,64,54,61,51,54],\"xaxis\":\"x\",\"yaxis\":\"y\",\"type\":\"histogram\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"age\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"count\"}},\"legend\":{\"title\":{\"text\":\"sex\"},\"tracegroupgap\":0},\"margin\":{\"t\":50,\"l\":50,\"r\":50,\"b\":50},\"barmode\":\"relative\",\"width\":800,\"height\":400},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('dff03a52-a103-4822-8edb-10d4fde9d484');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['sex'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YTamkRiPz4db",
        "outputId": "a712f3a1-f773-4328-9058-98da8382f852"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Male      726\n",
              "Female    194\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['dataset'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wLaXBaM2z4db",
        "outputId": "da8a47ef-bbe0-4594-fcac-07ab221770e4"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cleveland        304\n",
              "Hungary          293\n",
              "VA Long Beach    200\n",
              "Switzerland      123\n",
              "Name: dataset, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.groupby('sex')['dataset'].value_counts())\n",
        "sns.countplot(df, x = 'dataset', hue= 'sex')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "dj3UMJa4z4db",
        "outputId": "b39ef857-d521-4c14-a36f-1acef10a9ed7"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sex     dataset      \n",
            "Female  Cleveland         97\n",
            "        Hungary           81\n",
            "        Switzerland       10\n",
            "        VA Long Beach      6\n",
            "Male    Hungary          212\n",
            "        Cleveland        207\n",
            "        VA Long Beach    194\n",
            "        Switzerland      113\n",
            "Name: dataset, dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='dataset', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Mean of Data set: {df.groupby('dataset')['age'].mean()}\")\n",
        "print('-----------------------------')\n",
        "print(f\"Median of Data set: {df.groupby('dataset')['age'].median()}\")\n",
        "print('-----------------------------')\n",
        "print(f\"Mode of Data set: {df.groupby('dataset')['age'].agg(pd.Series.mode)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "09wgFop1z4db",
        "outputId": "2356a7cc-fa4d-47e4-e18f-192be238f54c"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean of Data set: dataset\n",
            "Cleveland        54.351974\n",
            "Hungary          47.894198\n",
            "Switzerland      55.317073\n",
            "VA Long Beach    59.350000\n",
            "Name: age, dtype: float64\n",
            "-----------------------------\n",
            "Median of Data set: dataset\n",
            "Cleveland        55.5\n",
            "Hungary          49.0\n",
            "Switzerland      56.0\n",
            "VA Long Beach    60.0\n",
            "Name: age, dtype: float64\n",
            "-----------------------------\n",
            "Mode of Data set: dataset\n",
            "Cleveland              58\n",
            "Hungary                54\n",
            "Switzerland            61\n",
            "VA Long Beach    [62, 63]\n",
            "Name: age, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Lets explore chest pain (ca) column"
      ],
      "metadata": {
        "id": "b552101rz4db"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig1 = px.histogram(df, x = 'age', color = 'cp')\n",
        "fig1.update_layout(width=800, height=400, margin=dict(l=50, r=50, t=50, b=50))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "fe8D6v4mz4db",
        "outputId": "c9472a28-b98d-4c62-ee66-ec819ddf03b6"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.24.1.min.js\"></script>                <div id=\"0d704579-8d6a-4028-afd6-eb9a7bf0d0c9\" class=\"plotly-graph-div\" style=\"height:400px; width:800px;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"0d704579-8d6a-4028-afd6-eb9a7bf0d0c9\")) {                    Plotly.newPlot(                        \"0d704579-8d6a-4028-afd6-eb9a7bf0d0c9\",                        [{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"cp=typical angina\\u003cbr\\u003eage=%{x}\\u003cbr\\u003ecount=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"typical angina\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"typical angina\",\"offsetgroup\":\"typical angina\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[63,64,58,66,69,40,51,34,52,65,59,52,42,59,69,59,38,56,61,60,59,64,45,30,35,43,47,54,57,62,34,43,46,55,32,62,62,67,67,65,49,62,61,68,74,62],\"xaxis\":\"x\",\"yaxis\":\"y\",\"type\":\"histogram\"},{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"cp=asymptomatic\\u003cbr\\u003eage=%{x}\\u003cbr\\u003ecount=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"asymptomatic\",\"marker\":{\"color\":\"#EF553B\",\"pattern\":{\"shape\":\"\"}},\"name\":\"asymptomatic\",\"offsetgroup\":\"asymptomatic\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[67,67,62,57,63,53,57,54,60,43,40,60,59,42,43,57,55,65,61,50,65,44,60,54,41,51,58,54,60,59,67,62,65,44,60,58,45,53,53,66,62,52,59,60,48,45,57,59,61,39,61,56,43,35,63,65,48,63,55,56,54,62,51,70,35,57,42,55,64,70,51,58,60,77,58,57,35,53,59,62,64,57,52,56,48,56,60,51,43,62,67,45,50,64,43,45,58,50,62,66,52,46,64,59,53,63,47,54,66,55,54,56,46,49,67,58,47,52,57,58,64,43,67,61,42,52,40,61,66,46,71,58,57,55,61,58,58,44,63,63,59,57,68,57,35,37,37,37,39,39,41,42,44,44,45,45,45,46,46,46,46,47,48,48,48,49,49,50,50,50,51,52,53,53,53,54,55,55,56,57,58,59,61,61,31,33,37,38,38,38,40,41,41,43,46,46,46,47,48,48,48,49,50,51,52,54,54,55,59,60,63,65,32,38,39,40,45,46,46,48,48,48,48,50,52,52,53,54,54,54,54,55,56,57,58,41,43,44,48,49,49,52,52,52,52,53,54,55,56,56,59,59,65,66,41,43,44,47,47,49,49,50,50,52,52,56,65,34,35,36,38,38,38,38,40,41,42,42,43,43,43,46,46,47,47,48,50,50,50,50,51,51,51,51,51,51,51,52,52,52,52,53,53,53,53,54,54,54,55,55,55,56,56,56,56,56,57,57,57,57,57,58,58,58,59,59,59,59,60,60,60,60,60,61,61,61,61,61,61,61,61,62,62,62,62,63,63,63,63,63,64,64,64,65,65,65,65,66,66,68,68,69,69,70,70,63,44,60,55,65,59,62,57,62,63,46,63,60,58,64,69,51,60,56,54,77,63,64,60,60,58,59,40,61,41,57,63,59,51,62,53,68,53,59,51,61,57,58,65,57,54,75,60,64,58,61,67,62,65,63,69,51,62,75,67,58,60,63,43,65,63,64,61,50,59,55,65,72,50,64,55,63,59,56,74,54,57,54,70,48,66,55,75,64,59,55,57,61,41,71,38,55,56,69,64,72,69,56,62,67,57,69,51,48,69,64,53,67,58,61,64,58,60,57,55,56,57,74,51,62,53,62,46,54,55,58],\"xaxis\":\"x\",\"yaxis\":\"y\",\"type\":\"histogram\"},{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"cp=non-anginal\\u003cbr\\u003eage=%{x}\\u003cbr\\u003ecount=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"non-anginal\",\"marker\":{\"color\":\"#00cc96\",\"pattern\":{\"shape\":\"\"}},\"name\":\"non-anginal\",\"offsetgroup\":\"non-anginal\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[37,56,52,57,48,58,50,58,64,44,61,59,58,51,65,53,50,54,46,54,60,54,46,65,65,51,39,68,44,47,53,51,62,44,63,71,49,57,62,58,51,54,51,43,51,64,58,47,41,60,67,68,54,48,52,70,43,53,42,69,50,68,57,64,37,41,41,54,39,67,52,49,54,60,51,42,76,44,44,59,42,66,39,47,67,38,33,36,36,36,37,37,38,39,39,39,40,40,40,42,42,42,43,45,46,46,47,48,48,49,49,51,51,51,52,52,53,53,54,54,55,55,56,56,56,58,59,59,60,47,49,49,50,58,54,58,47,51,53,54,38,38,45,47,47,53,53,56,56,56,57,59,60,61,62,72,73,66,66,60,60,63,63,74,52,55,55,52,61,59,42,55,60,56,69,72,51,55,40,35,63,68,48,45,49,62,62,76,48,62,71,53,58,56,58,54,69,37,74,55,61,61,58,68],\"xaxis\":\"x\",\"yaxis\":\"y\",\"type\":\"histogram\"},{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"cp=atypical angina\\u003cbr\\u003eage=%{x}\\u003cbr\\u003ecount=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"atypical angina\",\"marker\":{\"color\":\"#ab63fa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"atypical angina\",\"offsetgroup\":\"atypical angina\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[41,56,56,44,48,49,58,71,41,44,48,52,52,54,41,45,44,29,55,62,59,52,45,46,54,45,63,66,54,50,55,46,34,74,49,42,41,41,62,70,57,58,57,35,58,56,56,55,41,57,28,29,29,31,32,32,32,34,34,34,35,35,36,37,37,38,38,39,39,39,39,39,40,40,41,41,41,41,41,41,42,42,42,43,43,43,43,43,43,44,44,45,45,45,46,47,47,48,48,48,48,48,48,49,49,49,49,50,50,50,50,51,51,51,52,52,52,52,52,53,53,53,53,54,54,54,54,54,54,54,54,54,54,54,54,55,55,55,55,55,55,56,57,58,58,59,59,62,35,36,57,44,55,56,58,58,53,53,55,74,60,56,63,56,62,58,63,55,61,61,54,57,63,62],\"xaxis\":\"x\",\"yaxis\":\"y\",\"type\":\"histogram\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"age\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"count\"}},\"legend\":{\"title\":{\"text\":\"cp\"},\"tracegroupgap\":0},\"margin\":{\"t\":50,\"l\":50,\"r\":50,\"b\":50},\"barmode\":\"relative\",\"width\":800,\"height\":400},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('0d704579-8d6a-4028-afd6-eb9a7bf0d0c9');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Lets Explore thalch column"
      ],
      "metadata": {
        "id": "DaQniQq4z4db"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['thalch'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MJ9RVgr1z4db",
        "outputId": "5ab0687d-f974-4954-d05f-dc8330958940"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    865.000000\n",
              "mean     137.545665\n",
              "std       25.926276\n",
              "min       60.000000\n",
              "25%      120.000000\n",
              "50%      140.000000\n",
              "75%      157.000000\n",
              "max      202.000000\n",
              "Name: thalch, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['thalch'].corr(df['num'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jN0s6OZnz4dc",
        "outputId": "154cc92d-f89a-4e6f-bc68-e643df9a7d6f"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.36626539944425285"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## *Dealing with Outliers*"
      ],
      "metadata": {
        "id": "zXxcEXsKz4dc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop('id', axis = 1)"
      ],
      "metadata": {
        "id": "AORKROS7z4dc"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "numerical_columns = [col for col in df.columns if df[col].dtype != 'object']\n",
        "\n",
        "plt.figure(figsize=(20, 18))\n",
        "\n",
        "colors = ['red', 'green', 'blue','orange', 'purple', 'grey', 'yellow']\n",
        "\n",
        "for i, col in enumerate(numerical_columns):\n",
        "    plt.subplot(4, 2, i+1)\n",
        "    sns.boxplot(x = df[col], color = colors[i])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "U8f0HJ6hz4dc",
        "outputId": "33da869d-4440-432b-c6e4-73f849609605"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1800 with 7 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## *Data Distributions*"
      ],
      "metadata": {
        "id": "ea0psPqCz4dc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 20))\n",
        "\n",
        "colors = ['red', 'green', 'blue','orange', 'purple', 'grey', 'yellow']\n",
        "\n",
        "for i, col in enumerate(numerical_columns):\n",
        "    plt.subplot(4, 2, i+1)\n",
        "    sns.histplot(x = df[col], color = colors[i])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "R5AvY97Yz4dc",
        "outputId": "c0ce003f-2ec7-4327-f5e1-ee6a86a3ebfc"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x2000 with 7 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.box(df, y = numerical_columns, color_discrete_sequence=colors)\n",
        "fig.update_layout(title = \"Box Plots for Numeric Columns\", height = 1000, width = 1000)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "RYPvbnGnz4dc",
        "outputId": "6bfe3134-9c37-4829-c187-49adc69b0873"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.24.1.min.js\"></script>                <div id=\"9c6448d3-fd57-40bb-bd6a-b1156c0cef0f\" class=\"plotly-graph-div\" style=\"height:1000px; width:1000px;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"9c6448d3-fd57-40bb-bd6a-b1156c0cef0f\")) {                    Plotly.newPlot(                        \"9c6448d3-fd57-40bb-bd6a-b1156c0cef0f\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"variable=%{x}\\u003cbr\\u003evalue=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"\",\"marker\":{\"color\":\"red\"},\"name\":\"\",\"notched\":false,\"offsetgroup\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"age\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"trestbps\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"chol\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"thalch\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"oldpeak\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"ca\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\",\"num\"],\"x0\":\" \",\"xaxis\":\"x\",\"y\":[63.0,67.0,67.0,37.0,41.0,56.0,62.0,57.0,63.0,53.0,57.0,56.0,56.0,44.0,52.0,57.0,48.0,54.0,48.0,49.0,64.0,58.0,58.0,58.0,60.0,50.0,58.0,66.0,43.0,40.0,69.0,60.0,64.0,59.0,44.0,42.0,43.0,57.0,55.0,61.0,65.0,40.0,71.0,59.0,61.0,58.0,51.0,50.0,65.0,53.0,41.0,65.0,44.0,44.0,60.0,54.0,50.0,41.0,54.0,51.0,51.0,46.0,58.0,54.0,54.0,60.0,60.0,54.0,59.0,46.0,65.0,67.0,62.0,65.0,44.0,65.0,60.0,51.0,48.0,58.0,45.0,53.0,39.0,68.0,52.0,44.0,47.0,53.0,53.0,51.0,66.0,62.0,62.0,44.0,63.0,52.0,59.0,60.0,52.0,48.0,45.0,34.0,57.0,71.0,49.0,54.0,59.0,57.0,61.0,39.0,61.0,56.0,52.0,43.0,62.0,41.0,58.0,35.0,63.0,65.0,48.0,63.0,51.0,55.0,65.0,45.0,56.0,54.0,44.0,62.0,54.0,51.0,29.0,51.0,43.0,55.0,70.0,62.0,35.0,51.0,59.0,59.0,52.0,64.0,58.0,47.0,57.0,41.0,45.0,60.0,52.0,42.0,67.0,55.0,64.0,70.0,51.0,58.0,60.0,68.0,46.0,77.0,54.0,58.0,48.0,57.0,52.0,54.0,35.0,45.0,70.0,53.0,59.0,62.0,64.0,57.0,52.0,56.0,43.0,53.0,48.0,56.0,42.0,59.0,60.0,63.0,42.0,66.0,54.0,69.0,50.0,51.0,43.0,62.0,68.0,67.0,69.0,45.0,50.0,59.0,50.0,64.0,57.0,64.0,43.0,45.0,58.0,50.0,55.0,62.0,37.0,38.0,41.0,66.0,52.0,56.0,46.0,46.0,64.0,59.0,41.0,54.0,39.0,53.0,63.0,34.0,47.0,67.0,54.0,66.0,52.0,55.0,49.0,74.0,54.0,54.0,56.0,46.0,49.0,42.0,41.0,41.0,49.0,61.0,60.0,67.0,58.0,47.0,52.0,62.0,57.0,58.0,64.0,51.0,43.0,42.0,67.0,76.0,70.0,57.0,44.0,58.0,60.0,44.0,61.0,42.0,52.0,59.0,40.0,42.0,61.0,66.0,46.0,71.0,59.0,64.0,66.0,39.0,57.0,58.0,57.0,47.0,55.0,35.0,61.0,58.0,58.0,58.0,56.0,56.0,67.0,55.0,44.0,63.0,63.0,41.0,59.0,57.0,45.0,68.0,57.0,57.0,38.0,28.0,29.0,29.0,30.0,31.0,32.0,32.0,32.0,33.0,34.0,34.0,34.0,35.0,35.0,35.0,35.0,36.0,36.0,36.0,36.0,37.0,37.0,37.0,37.0,37.0,37.0,37.0,38.0,38.0,38.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,40.0,40.0,40.0,40.0,40.0,41.0,41.0,41.0,41.0,41.0,41.0,41.0,42.0,42.0,42.0,42.0,42.0,42.0,42.0,43.0,43.0,43.0,43.0,43.0,43.0,43.0,43.0,44.0,44.0,44.0,44.0,45.0,45.0,45.0,45.0,45.0,45.0,45.0,46.0,46.0,46.0,46.0,46.0,46.0,46.0,47.0,47.0,47.0,47.0,47.0,48.0,48.0,48.0,48.0,48.0,48.0,48.0,48.0,48.0,48.0,48.0,49.0,49.0,49.0,49.0,49.0,49.0,49.0,49.0,50.0,50.0,50.0,50.0,50.0,50.0,50.0,51.0,51.0,51.0,51.0,51.0,51.0,51.0,52.0,52.0,52.0,52.0,52.0,52.0,52.0,52.0,53.0,53.0,53.0,53.0,53.0,53.0,53.0,53.0,53.0,54.0,54.0,54.0,54.0,54.0,54.0,54.0,54.0,54.0,54.0,54.0,54.0,54.0,54.0,54.0,54.0,55.0,55.0,55.0,55.0,55.0,55.0,55.0,55.0,55.0,55.0,56.0,56.0,56.0,56.0,56.0,57.0,57.0,57.0,58.0,58.0,58.0,58.0,59.0,59.0,59.0,59.0,59.0,60.0,61.0,61.0,62.0,62.0,31.0,33.0,34.0,35.0,36.0,37.0,38.0,38.0,38.0,40.0,41.0,41.0,43.0,46.0,46.0,46.0,47.0,47.0,48.0,48.0,48.0,49.0,49.0,49.0,50.0,50.0,51.0,52.0,54.0,54.0,55.0,57.0,58.0,59.0,60.0,63.0,65.0,32.0,38.0,39.0,40.0,43.0,45.0,46.0,46.0,48.0,48.0,48.0,48.0,50.0,52.0,52.0,53.0,54.0,54.0,54.0,54.0,54.0,55.0,56.0,57.0,58.0,58.0,41.0,43.0,44.0,44.0,46.0,47.0,48.0,49.0,49.0,51.0,52.0,52.0,52.0,52.0,53.0,53.0,54.0,55.0,55.0,55.0,56.0,56.0,56.0,58.0,59.0,59.0,65.0,66.0,41.0,43.0,44.0,47.0,47.0,49.0,49.0,50.0,50.0,52.0,52.0,54.0,56.0,58.0,65.0,32.0,34.0,35.0,36.0,38.0,38.0,38.0,38.0,38.0,38.0,40.0,41.0,42.0,42.0,43.0,43.0,43.0,45.0,46.0,46.0,47.0,47.0,47.0,47.0,48.0,50.0,50.0,50.0,50.0,51.0,51.0,51.0,51.0,51.0,51.0,51.0,52.0,52.0,52.0,52.0,53.0,53.0,53.0,53.0,53.0,53.0,53.0,53.0,54.0,54.0,54.0,55.0,55.0,55.0,55.0,56.0,56.0,56.0,56.0,56.0,56.0,56.0,56.0,57.0,57.0,57.0,57.0,57.0,57.0,58.0,58.0,58.0,59.0,59.0,59.0,59.0,59.0,60.0,60.0,60.0,60.0,60.0,60.0,61.0,61.0,61.0,61.0,61.0,61.0,61.0,61.0,61.0,62.0,62.0,62.0,62.0,62.0,62.0,62.0,63.0,63.0,63.0,63.0,63.0,64.0,64.0,64.0,65.0,65.0,65.0,65.0,66.0,66.0,67.0,68.0,68.0,69.0,69.0,70.0,70.0,72.0,73.0,74.0,63.0,44.0,60.0,55.0,66.0,66.0,65.0,60.0,60.0,60.0,56.0,59.0,62.0,63.0,57.0,62.0,63.0,46.0,63.0,60.0,58.0,64.0,63.0,74.0,52.0,69.0,51.0,60.0,56.0,55.0,54.0,77.0,63.0,55.0,52.0,64.0,60.0,60.0,58.0,59.0,61.0,40.0,61.0,41.0,57.0,63.0,59.0,51.0,59.0,42.0,55.0,63.0,62.0,56.0,53.0,68.0,53.0,60.0,62.0,59.0,51.0,61.0,57.0,56.0,58.0,69.0,67.0,58.0,65.0,63.0,55.0,57.0,65.0,54.0,72.0,75.0,49.0,51.0,60.0,64.0,58.0,61.0,67.0,62.0,65.0,63.0,69.0,51.0,62.0,55.0,75.0,40.0,67.0,58.0,60.0,63.0,35.0,62.0,43.0,63.0,68.0,65.0,48.0,63.0,64.0,61.0,50.0,59.0,55.0,45.0,65.0,61.0,49.0,72.0,50.0,64.0,55.0,63.0,59.0,56.0,62.0,74.0,54.0,57.0,62.0,76.0,54.0,70.0,61.0,48.0,48.0,61.0,66.0,68.0,55.0,62.0,71.0,74.0,53.0,58.0,75.0,56.0,58.0,64.0,54.0,54.0,59.0,55.0,57.0,61.0,41.0,71.0,38.0,55.0,56.0,69.0,64.0,72.0,69.0,56.0,62.0,67.0,57.0,69.0,51.0,48.0,69.0,69.0,64.0,57.0,53.0,37.0,67.0,74.0,63.0,58.0,61.0,64.0,58.0,60.0,57.0,55.0,55.0,56.0,57.0,61.0,61.0,58.0,74.0,68.0,51.0,62.0,53.0,62.0,46.0,54.0,62.0,55.0,58.0,62.0,145.0,160.0,120.0,130.0,130.0,120.0,140.0,120.0,130.0,140.0,140.0,140.0,130.0,120.0,172.0,150.0,110.0,140.0,130.0,130.0,110.0,150.0,120.0,132.0,130.0,120.0,120.0,150.0,150.0,110.0,140.0,117.0,140.0,135.0,130.0,140.0,120.0,150.0,132.0,150.0,150.0,140.0,160.0,150.0,130.0,112.0,110.0,150.0,140.0,130.0,105.0,120.0,112.0,130.0,130.0,124.0,140.0,110.0,125.0,125.0,130.0,142.0,128.0,135.0,120.0,145.0,140.0,150.0,170.0,150.0,155.0,125.0,120.0,110.0,110.0,160.0,125.0,140.0,130.0,150.0,104.0,130.0,140.0,180.0,120.0,140.0,138.0,128.0,138.0,130.0,120.0,160.0,130.0,108.0,135.0,128.0,110.0,150.0,134.0,122.0,115.0,118.0,128.0,110.0,120.0,108.0,140.0,128.0,120.0,118.0,145.0,125.0,118.0,132.0,130.0,135.0,140.0,138.0,130.0,135.0,130.0,150.0,100.0,140.0,138.0,130.0,200.0,110.0,120.0,124.0,120.0,94.0,130.0,140.0,122.0,135.0,145.0,120.0,120.0,125.0,140.0,170.0,128.0,125.0,105.0,108.0,165.0,112.0,128.0,102.0,152.0,102.0,115.0,160.0,120.0,130.0,140.0,125.0,140.0,118.0,101.0,125.0,110.0,100.0,124.0,132.0,138.0,132.0,126.0,112.0,160.0,142.0,174.0,140.0,145.0,152.0,108.0,132.0,130.0,130.0,124.0,134.0,148.0,178.0,158.0,140.0,120.0,160.0,192.0,140.0,129.0,140.0,132.0,138.0,120.0,100.0,160.0,138.0,120.0,160.0,110.0,180.0,150.0,140.0,110.0,142.0,128.0,144.0,130.0,150.0,120.0,120.0,130.0,178.0,112.0,120.0,105.0,138.0,130.0,138.0,112.0,108.0,94.0,123.0,108.0,118.0,112.0,152.0,110.0,112.0,136.0,180.0,118.0,120.0,160.0,122.0,130.0,120.0,134.0,120.0,110.0,126.0,130.0,134.0,120.0,120.0,100.0,110.0,125.0,128.0,110.0,146.0,128.0,120.0,115.0,120.0,106.0,140.0,156.0,124.0,118.0,136.0,150.0,120.0,138.0,136.0,128.0,126.0,152.0,130.0,140.0,160.0,140.0,112.0,134.0,170.0,146.0,138.0,154.0,130.0,110.0,130.0,128.0,122.0,148.0,114.0,170.0,125.0,130.0,120.0,152.0,132.0,120.0,140.0,124.0,120.0,164.0,140.0,110.0,144.0,130.0,130.0,138.0,130.0,120.0,140.0,170.0,100.0,105.0,110.0,125.0,120.0,130.0,150.0,98.0,120.0,140.0,120.0,150.0,120.0,112.0,130.0,150.0,120.0,130.0,130.0,130.0,130.0,120.0,130.0,120.0,140.0,145.0,110.0,120.0,120.0,120.0,130.0,190.0,120.0,160.0,110.0,130.0,130.0,140.0,130.0,130.0,140.0,110.0,125.0,130.0,120.0,120.0,125.0,112.0,115.0,120.0,120.0,150.0,120.0,160.0,140.0,100.0,120.0,120.0,120.0,120.0,150.0,150.0,142.0,120.0,120.0,130.0,150.0,130.0,180.0,132.0,140.0,135.0,120.0,140.0,130.0,140.0,120.0,150.0,110.0,110.0,180.0,140.0,130.0,110.0,160.0,140.0,null,120.0,120.0,120.0,108.0,120.0,150.0,100.0,130.0,140.0,110.0,110.0,110.0,124.0,130.0,100.0,140.0,120.0,140.0,110.0,120.0,120.0,140.0,170.0,140.0,150.0,160.0,110.0,130.0,150.0,125.0,130.0,130.0,120.0,140.0,125.0,130.0,120.0,140.0,160.0,140.0,113.0,140.0,120.0,120.0,140.0,120.0,124.0,130.0,140.0,120.0,120.0,120.0,130.0,140.0,150.0,160.0,120.0,110.0,120.0,120.0,160.0,160.0,120.0,150.0,150.0,110.0,122.0,130.0,120.0,140.0,145.0,110.0,120.0,120.0,140.0,130.0,130.0,130.0,130.0,120.0,130.0,180.0,140.0,130.0,130.0,140.0,135.0,130.0,140.0,130.0,180.0,140.0,120.0,130.0,125.0,160.0,140.0,120.0,100.0,140.0,110.0,120.0,140.0,110.0,120.0,92.0,120.0,110.0,120.0,150.0,110.0,118.0,120.0,140.0,150.0,106.0,120.0,160.0,160.0,115.0,130.0,140.0,145.0,160.0,130.0,125.0,125.0,140.0,140.0,130.0,130.0,100.0,150.0,140.0,118.0,110.0,110.0,150.0,120.0,130.0,120.0,130.0,122.0,160.0,160.0,160.0,130.0,120.0,170.0,120.0,120.0,130.0,130.0,140.0,200.0,140.0,150.0,150.0,160.0,130.0,130.0,120.0,150.0,130.0,140.0,135.0,138.0,130.0,140.0,135.0,112.0,130.0,140.0,160.0,145.0,180.0,140.0,140.0,160.0,145.0,120.0,150.0,170.0,136.0,130.0,140.0,170.0,140.0,120.0,140.0,135.0,120.0,160.0,128.0,150.0,140.0,140.0,140.0,160.0,130.0,155.0,180.0,130.0,95.0,115.0,null,110.0,105.0,110.0,100.0,115.0,135.0,150.0,95.0,125.0,105.0,145.0,100.0,115.0,140.0,110.0,100.0,115.0,110.0,155.0,110.0,160.0,115.0,160.0,115.0,120.0,145.0,120.0,110.0,120.0,130.0,130.0,140.0,95.0,130.0,135.0,165.0,95.0,120.0,130.0,105.0,160.0,120.0,125.0,130.0,80.0,120.0,130.0,180.0,140.0,115.0,120.0,140.0,120.0,125.0,155.0,115.0,120.0,120.0,125.0,140.0,105.0,110.0,140.0,140.0,160.0,95.0,115.0,130.0,170.0,125.0,110.0,120.0,125.0,135.0,115.0,125.0,130.0,135.0,160.0,160.0,200.0,105.0,110.0,125.0,130.0,130.0,150.0,150.0,160.0,140.0,120.0,120.0,160.0,115.0,115.0,150.0,100.0,140.0,150.0,150.0,185.0,200.0,95.0,110.0,115.0,145.0,155.0,160.0,155.0,150.0,145.0,135.0,145.0,135.0,null,115.0,140.0,160.0,160.0,145.0,140.0,130.0,132.0,142.0,110.0,120.0,150.0,180.0,120.0,160.0,126.0,140.0,110.0,null,128.0,120.0,170.0,110.0,126.0,152.0,116.0,120.0,130.0,null,128.0,130.0,null,130.0,120.0,null,null,124.0,160.0,0.0,122.0,144.0,null,120.0,null,154.0,120.0,125.0,null,104.0,null,136.0,122.0,128.0,null,134.0,120.0,null,152.0,124.0,126.0,138.0,154.0,null,null,178.0,null,110.0,130.0,170.0,126.0,140.0,142.0,120.0,null,null,110.0,140.0,null,136.0,120.0,170.0,130.0,null,142.0,142.0,132.0,146.0,160.0,135.0,136.0,130.0,140.0,null,158.0,null,136.0,106.0,120.0,110.0,null,160.0,null,112.0,122.0,130.0,150.0,150.0,102.0,96.0,130.0,120.0,144.0,124.0,150.0,null,null,null,null,null,null,null,116.0,110.0,125.0,null,null,150.0,130.0,110.0,null,104.0,138.0,170.0,140.0,null,132.0,142.0,112.0,null,172.0,120.0,null,null,155.0,150.0,160.0,null,null,134.0,null,null,140.0,null,144.0,null,150.0,130.0,110.0,158.0,128.0,null,150.0,160.0,null,null,null,null,156.0,null,null,140.0,122.0,null,null,180.0,124.0,118.0,140.0,140.0,null,100.0,190.0,130.0,160.0,130.0,122.0,null,120.0,130.0,130.0,null,120.0,150.0,155.0,134.0,114.0,160.0,144.0,158.0,134.0,127.0,null,122.0,null,120.0,233.0,286.0,229.0,250.0,204.0,236.0,268.0,354.0,254.0,203.0,192.0,294.0,256.0,263.0,199.0,168.0,229.0,239.0,275.0,266.0,211.0,283.0,284.0,224.0,206.0,219.0,340.0,226.0,247.0,167.0,239.0,230.0,335.0,234.0,233.0,226.0,177.0,276.0,353.0,243.0,225.0,199.0,302.0,212.0,330.0,230.0,175.0,243.0,417.0,197.0,198.0,177.0,290.0,219.0,253.0,266.0,233.0,172.0,273.0,213.0,305.0,177.0,216.0,304.0,188.0,282.0,185.0,232.0,326.0,231.0,269.0,254.0,267.0,248.0,197.0,360.0,258.0,308.0,245.0,270.0,208.0,264.0,321.0,274.0,325.0,235.0,257.0,216.0,234.0,256.0,302.0,164.0,231.0,141.0,252.0,255.0,239.0,258.0,201.0,222.0,260.0,182.0,303.0,265.0,188.0,309.0,177.0,229.0,260.0,219.0,307.0,249.0,186.0,341.0,263.0,203.0,211.0,183.0,330.0,254.0,256.0,407.0,222.0,217.0,282.0,234.0,288.0,239.0,220.0,209.0,258.0,227.0,204.0,261.0,213.0,250.0,174.0,281.0,198.0,245.0,221.0,288.0,205.0,309.0,240.0,243.0,289.0,250.0,308.0,318.0,298.0,265.0,564.0,289.0,246.0,322.0,299.0,300.0,293.0,277.0,197.0,304.0,214.0,248.0,255.0,207.0,223.0,288.0,282.0,160.0,269.0,226.0,249.0,394.0,212.0,274.0,233.0,184.0,315.0,246.0,274.0,409.0,244.0,270.0,305.0,195.0,240.0,246.0,283.0,254.0,196.0,298.0,247.0,294.0,211.0,299.0,234.0,236.0,244.0,273.0,254.0,325.0,126.0,313.0,211.0,309.0,259.0,200.0,262.0,244.0,215.0,231.0,214.0,228.0,230.0,193.0,204.0,243.0,303.0,271.0,268.0,267.0,199.0,282.0,269.0,210.0,204.0,277.0,206.0,212.0,196.0,327.0,149.0,269.0,201.0,286.0,283.0,249.0,271.0,295.0,235.0,306.0,269.0,234.0,178.0,237.0,234.0,275.0,212.0,208.0,201.0,218.0,263.0,295.0,303.0,209.0,223.0,197.0,245.0,261.0,242.0,319.0,240.0,226.0,166.0,315.0,204.0,218.0,223.0,180.0,207.0,228.0,311.0,149.0,204.0,227.0,278.0,220.0,232.0,197.0,335.0,253.0,205.0,192.0,203.0,318.0,225.0,220.0,221.0,240.0,212.0,342.0,169.0,187.0,197.0,157.0,176.0,241.0,264.0,193.0,131.0,236.0,175.0,132.0,243.0,null,237.0,219.0,198.0,225.0,254.0,298.0,161.0,214.0,220.0,160.0,167.0,308.0,264.0,166.0,340.0,209.0,160.0,260.0,211.0,173.0,283.0,194.0,223.0,315.0,275.0,297.0,292.0,182.0,null,200.0,204.0,null,241.0,339.0,147.0,273.0,307.0,275.0,289.0,215.0,281.0,null,250.0,184.0,245.0,291.0,295.0,269.0,250.0,211.0,196.0,198.0,268.0,228.0,147.0,358.0,223.0,201.0,215.0,249.0,266.0,186.0,null,207.0,218.0,184.0,215.0,412.0,237.0,null,297.0,224.0,null,225.0,224.0,238.0,275.0,230.0,163.0,238.0,240.0,280.0,257.0,null,249.0,263.0,276.0,308.0,null,284.0,195.0,163.0,254.0,227.0,null,245.0,238.0,211.0,null,null,201.0,207.0,253.0,187.0,297.0,null,202.0,328.0,168.0,216.0,209.0,129.0,215.0,194.0,190.0,220.0,200.0,188.0,224.0,179.0,210.0,null,272.0,180.0,284.0,100.0,196.0,259.0,468.0,216.0,274.0,null,320.0,195.0,260.0,182.0,243.0,221.0,230.0,273.0,253.0,309.0,230.0,312.0,171.0,208.0,238.0,246.0,195.0,305.0,217.0,null,365.0,344.0,320.0,394.0,256.0,196.0,326.0,277.0,220.0,270.0,229.0,219.0,184.0,null,276.0,85.0,308.0,347.0,260.0,230.0,251.0,179.0,222.0,188.0,287.0,318.0,213.0,null,246.0,294.0,292.0,193.0,271.0,270.0,246.0,156.0,257.0,267.0,207.0,196.0,282.0,117.0,466.0,289.0,237.0,247.0,202.0,186.0,277.0,193.0,226.0,263.0,260.0,268.0,180.0,265.0,206.0,288.0,264.0,303.0,225.0,216.0,224.0,201.0,265.0,213.0,338.0,248.0,223.0,306.0,529.0,null,280.0,392.0,291.0,219.0,231.0,222.0,275.0,193.0,329.0,355.0,233.0,182.0,null,246.0,237.0,242.0,603.0,null,198.0,268.0,213.0,255.0,211.0,263.0,172.0,175.0,288.0,290.0,272.0,248.0,214.0,341.0,234.0,160.0,342.0,298.0,404.0,246.0,518.0,285.0,216.0,295.0,292.0,248.0,279.0,230.0,388.0,164.0,null,264.0,263.0,null,336.0,288.0,491.0,205.0,291.0,212.0,222.0,231.0,341.0,266.0,331.0,294.0,342.0,393.0,275.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,260.0,209.0,218.0,228.0,213.0,0.0,236.0,0.0,0.0,267.0,166.0,0.0,0.0,0.0,0.0,220.0,177.0,236.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,186.0,100.0,228.0,0.0,171.0,230.0,0.0,0.0,0.0,281.0,0.0,203.0,0.0,0.0,0.0,0.0,0.0,277.0,0.0,233.0,0.0,0.0,240.0,0.0,0.0,153.0,224.0,0.0,0.0,0.0,316.0,0.0,0.0,218.0,0.0,311.0,0.0,0.0,0.0,270.0,0.0,0.0,217.0,214.0,214.0,252.0,220.0,214.0,203.0,0.0,339.0,216.0,276.0,458.0,241.0,384.0,297.0,248.0,308.0,208.0,227.0,210.0,245.0,225.0,240.0,0.0,198.0,195.0,267.0,161.0,258.0,0.0,0.0,195.0,235.0,0.0,305.0,223.0,282.0,349.0,null,160.0,236.0,312.0,283.0,142.0,211.0,218.0,306.0,186.0,252.0,222.0,0.0,0.0,258.0,202.0,197.0,204.0,null,274.0,192.0,298.0,272.0,220.0,200.0,261.0,181.0,260.0,220.0,221.0,216.0,175.0,219.0,310.0,208.0,232.0,273.0,203.0,182.0,274.0,204.0,270.0,292.0,171.0,221.0,289.0,217.0,223.0,null,193.0,null,210.0,282.0,170.0,369.0,173.0,289.0,null,null,216.0,271.0,244.0,285.0,243.0,240.0,219.0,237.0,165.0,213.0,287.0,258.0,256.0,186.0,264.0,null,226.0,203.0,207.0,284.0,337.0,219.0,310.0,254.0,258.0,254.0,300.0,170.0,310.0,333.0,139.0,223.0,385.0,254.0,150.0,108.0,129.0,187.0,172.0,178.0,160.0,163.0,147.0,155.0,148.0,153.0,142.0,173.0,162.0,174.0,168.0,160.0,139.0,171.0,144.0,162.0,160.0,173.0,132.0,158.0,172.0,114.0,171.0,114.0,151.0,160.0,158.0,161.0,179.0,178.0,120.0,112.0,132.0,137.0,114.0,178.0,162.0,157.0,169.0,165.0,123.0,128.0,157.0,152.0,168.0,140.0,153.0,188.0,144.0,109.0,163.0,158.0,152.0,125.0,142.0,160.0,131.0,170.0,113.0,142.0,155.0,165.0,140.0,147.0,148.0,163.0,99.0,158.0,177.0,151.0,141.0,142.0,180.0,111.0,148.0,143.0,182.0,150.0,172.0,180.0,156.0,115.0,160.0,149.0,151.0,145.0,146.0,175.0,172.0,161.0,142.0,157.0,158.0,186.0,185.0,174.0,159.0,130.0,139.0,156.0,162.0,150.0,140.0,140.0,146.0,144.0,190.0,136.0,97.0,132.0,165.0,182.0,132.0,127.0,150.0,154.0,143.0,111.0,174.0,175.0,133.0,126.0,170.0,163.0,147.0,154.0,202.0,186.0,165.0,161.0,125.0,103.0,130.0,166.0,164.0,159.0,184.0,131.0,154.0,152.0,124.0,179.0,170.0,160.0,178.0,122.0,160.0,145.0,96.0,109.0,173.0,171.0,170.0,151.0,156.0,162.0,158.0,122.0,175.0,168.0,169.0,159.0,156.0,138.0,112.0,111.0,143.0,157.0,132.0,88.0,147.0,105.0,162.0,173.0,166.0,150.0,178.0,145.0,161.0,179.0,194.0,120.0,195.0,146.0,163.0,122.0,143.0,106.0,115.0,125.0,131.0,152.0,162.0,125.0,159.0,154.0,173.0,133.0,161.0,147.0,130.0,126.0,155.0,154.0,170.0,182.0,168.0,165.0,160.0,162.0,172.0,152.0,122.0,182.0,172.0,167.0,179.0,95.0,169.0,192.0,143.0,172.0,108.0,132.0,169.0,117.0,126.0,121.0,163.0,116.0,103.0,144.0,162.0,162.0,153.0,163.0,163.0,145.0,96.0,71.0,156.0,118.0,168.0,140.0,126.0,105.0,105.0,157.0,181.0,173.0,142.0,116.0,143.0,141.0,149.0,152.0,171.0,169.0,125.0,125.0,156.0,134.0,181.0,150.0,138.0,138.0,120.0,125.0,162.0,155.0,152.0,152.0,164.0,131.0,143.0,179.0,130.0,174.0,161.0,140.0,146.0,144.0,163.0,169.0,150.0,166.0,144.0,144.0,136.0,182.0,90.0,123.0,132.0,141.0,115.0,174.0,173.0,185.0,160.0,170.0,170.0,150.0,165.0,184.0,155.0,185.0,190.0,168.0,150.0,185.0,150.0,180.0,168.0,180.0,184.0,178.0,172.0,130.0,142.0,184.0,98.0,150.0,168.0,158.0,129.0,150.0,130.0,180.0,146.0,160.0,145.0,120.0,106.0,170.0,160.0,132.0,140.0,150.0,172.0,138.0,167.0,188.0,142.0,180.0,150.0,160.0,170.0,144.0,142.0,137.0,150.0,155.0,136.0,152.0,146.0,170.0,142.0,165.0,175.0,176.0,118.0,154.0,175.0,138.0,115.0,142.0,135.0,170.0,170.0,180.0,144.0,122.0,110.0,140.0,144.0,90.0,165.0,150.0,116.0,140.0,140.0,120.0,135.0,145.0,150.0,174.0,125.0,null,148.0,120.0,125.0,175.0,110.0,130.0,100.0,160.0,118.0,138.0,160.0,160.0,164.0,135.0,174.0,172.0,132.0,130.0,145.0,110.0,160.0,170.0,116.0,135.0,140.0,170.0,120.0,160.0,120.0,145.0,150.0,100.0,148.0,140.0,139.0,140.0,118.0,138.0,165.0,170.0,127.0,142.0,130.0,132.0,162.0,140.0,112.0,148.0,155.0,138.0,140.0,150.0,155.0,140.0,130.0,130.0,137.0,142.0,154.0,110.0,130.0,175.0,137.0,122.0,134.0,160.0,155.0,150.0,137.0,150.0,155.0,160.0,134.0,140.0,110.0,164.0,100.0,114.0,128.0,140.0,98.0,126.0,140.0,150.0,110.0,160.0,100.0,124.0,150.0,120.0,100.0,140.0,135.0,120.0,115.0,116.0,152.0,153.0,150.0,180.0,140.0,160.0,130.0,166.0,170.0,134.0,152.0,170.0,138.0,130.0,150.0,124.0,125.0,145.0,98.0,110.0,115.0,103.0,156.0,175.0,170.0,140.0,150.0,150.0,120.0,140.0,122.0,130.0,145.0,140.0,130.0,125.0,115.0,87.0,130.0,150.0,150.0,130.0,155.0,130.0,115.0,112.0,150.0,102.0,92.0,99.0,121.0,150.0,126.0,116.0,150.0,91.0,125.0,118.0,142.0,128.0,125.0,92.0,92.0,140.0,130.0,120.0,150.0,100.0,175.0,170.0,108.0,120.0,140.0,150.0,96.0,110.0,124.0,82.0,130.0,120.0,105.0,136.0,143.0,96.0,150.0,124.0,122.0,99.0,125.0,119.0,112.0,94.0,118.0,135.0,135.0,98.0,158.0,96.0,122.0,140.0,125.0,134.0,94.0,100.0,150.0,110.0,115.0,127.0,154.0,130.0,125.0,166.0,156.0,179.0,128.0,150.0,120.0,144.0,176.0,128.0,99.0,122.0,145.0,140.0,138.0,133.0,113.0,120.0,118.0,149.0,124.0,128.0,110.0,120.0,156.0,139.0,127.0,92.0,104.0,170.0,163.0,60.0,126.0,120.0,128.0,122.0,82.0,95.0,120.0,115.0,122.0,120.0,120.0,135.0,141.0,155.0,110.0,150.0,150.0,155.0,92.0,83.0,97.0,98.0,99.0,82.0,100.0,148.0,103.0,121.0,148.0,131.0,120.0,100.0,98.0,182.0,138.0,100.0,105.0,175.0,94.0,115.0,119.0,115.0,143.0,110.0,130.0,63.0,99.0,149.0,70.0,110.0,113.0,105.0,115.0,77.0,105.0,117.0,145.0,143.0,123.0,134.0,72.0,128.0,72.0,78.0,109.0,149.0,86.0,154.0,98.0,140.0,145.0,114.0,93.0,67.0,154.0,122.0,90.0,108.0,125.0,120.0,136.0,130.0,null,92.0,157.0,114.0,121.0,123.0,112.0,127.0,140.0,149.0,99.0,120.0,105.0,140.0,141.0,157.0,140.0,117.0,120.0,null,148.0,86.0,84.0,125.0,120.0,118.0,124.0,106.0,111.0,null,180.0,129.0,null,140.0,120.0,null,null,110.0,105.0,155.0,110.0,122.0,null,133.0,null,131.0,80.0,165.0,86.0,111.0,null,84.0,117.0,107.0,128.0,160.0,125.0,null,97.0,161.0,106.0,130.0,140.0,null,null,120.0,null,108.0,148.0,123.0,110.0,118.0,125.0,106.0,null,null,180.0,144.0,null,140.0,102.0,108.0,145.0,null,110.0,140.0,69.0,148.0,130.0,130.0,140.0,138.0,140.0,null,112.0,null,112.0,80.0,150.0,110.0,null,88.0,null,150.0,120.0,160.0,132.0,120.0,110.0,121.0,128.0,135.0,120.0,117.0,150.0,null,null,null,null,null,null,null,102.0,140.0,135.0,null,null,130.0,112.0,100.0,null,120.0,105.0,129.0,120.0,null,162.0,100.0,140.0,null,73.0,86.0,null,null,160.0,118.0,112.0,null,null,102.0,null,null,154.0,null,160.0,null,128.0,115.0,105.0,110.0,119.0,null,135.0,130.0,null,null,120.0,null,119.0,null,null,159.0,84.0,null,null,120.0,122.0,165.0,122.0,94.0,null,110.0,150.0,130.0,113.0,140.0,100.0,null,127.0,98.0,96.0,null,98.0,118.0,112.0,151.0,96.0,108.0,128.0,138.0,126.0,154.0,null,100.0,null,93.0,2.3,1.5,2.6,3.5,1.4,0.8,3.6,0.6,1.4,3.1,0.4,1.3,0.6,0.0,0.5,1.6,1.0,1.2,0.2,0.6,1.8,1.0,1.8,3.2,2.4,1.6,0.0,2.6,1.5,2.0,1.8,1.4,0.0,0.5,0.4,0.0,2.5,0.6,1.2,1.0,1.0,1.4,0.4,1.6,0.0,2.5,0.6,2.6,0.8,1.2,0.0,0.4,0.0,0.0,1.4,2.2,0.6,0.0,0.5,1.4,1.2,1.4,2.2,0.0,1.4,2.8,3.0,1.6,3.4,3.6,0.8,0.2,1.8,0.6,0.0,0.8,2.8,1.5,0.2,0.8,3.0,0.4,0.0,1.6,0.2,0.0,0.0,0.0,0.0,0.5,0.4,6.2,1.8,0.6,0.0,0.0,1.2,2.6,0.8,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.4,3.6,1.2,1.0,1.2,0.0,3.0,1.2,0.0,0.0,1.4,1.8,2.8,0.0,4.0,1.2,5.6,1.4,0.6,4.0,2.8,0.0,0.0,0.4,0.0,0.0,0.0,0.2,1.4,2.6,1.4,1.6,2.4,0.0,0.2,0.0,1.8,0.6,0.0,1.0,0.0,0.0,0.0,1.2,0.6,1.6,0.8,2.2,2.4,1.6,0.0,1.2,1.0,0.0,0.0,1.6,1.0,0.0,0.0,0.0,0.0,0.0,0.0,2.9,0.0,0.0,1.2,2.0,1.2,0.1,2.1,1.9,0.0,0.5,1.9,0.8,4.2,0.0,0.0,0.8,0.0,0.0,2.0,0.0,4.2,0.1,1.9,1.5,0.9,0.1,0.2,1.1,0.0,0.0,0.0,0.2,0.2,0.0,0.0,3.0,0.9,0.0,1.4,0.0,3.8,2.0,1.0,0.0,1.9,0.0,0.0,2.0,0.0,0.0,0.0,0.0,2.0,1.8,0.7,0.1,0.0,0.0,0.1,0.1,3.4,0.8,0.2,0.0,3.2,1.6,0.8,0.0,0.0,0.0,0.0,0.0,2.6,0.0,1.0,0.1,1.0,1.0,0.0,1.5,2.0,0.2,0.6,1.2,0.0,0.3,1.1,0.0,0.3,0.3,0.0,0.9,0.0,3.6,1.8,1.0,2.2,0.0,0.0,1.9,2.3,1.8,1.6,0.8,0.6,0.0,0.0,0.0,0.6,3.0,0.0,2.0,0.0,0.0,4.4,2.8,0.4,0.0,0.0,0.8,1.2,2.8,4.0,0.0,0.0,1.0,0.2,1.2,3.4,1.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,2.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.5,0.0,0.0,0.0,0.0,0.0,0.0,1.5,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,1.0,0.0,1.5,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.0,0.0,0.0,1.0,0.0,1.0,0.8,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.5,1.0,0.0,0.0,3.0,1.5,0.0,0.0,2.5,1.0,0.0,1.0,2.0,0.0,0.0,1.0,1.0,1.5,0.0,2.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,2.0,0.0,2.0,3.0,1.0,0.0,1.5,1.0,0.0,1.5,0.0,1.0,0.0,2.0,0.0,1.0,0.0,0.0,2.0,3.0,1.5,2.0,2.0,0.0,1.5,0.0,1.5,1.0,1.0,0.0,2.0,1.5,1.0,3.0,0.0,2.0,2.0,1.0,3.0,2.0,2.0,0.0,1.5,1.0,1.0,2.0,1.0,1.0,2.0,4.0,0.0,1.5,1.5,0.0,2.0,2.0,1.0,1.5,2.0,2.0,0.0,0.0,2.0,1.0,3.0,2.0,0.0,2.0,3.0,0.0,2.0,5.0,2.5,2.0,2.5,0.0,3.0,1.0,1.0,0.7,0.2,null,1.0,2.8,0.0,-1.1,0.0,0.0,null,0.0,1.6,-1.5,0.0,1.5,2.0,0.5,-0.1,-2.6,1.5,0.0,1.0,2.1,0.0,0.0,0.0,0.5,0.0,0.7,1.5,0.0,0.0,-0.7,0.0,0.0,2.2,0.0,2.0,1.0,null,0.0,0.7,0.0,0.0,0.0,1.5,1.0,2.0,0.0,3.0,1.5,0.2,0.1,0.3,0.0,0.0,-2.0,0.0,-1.0,-1.0,0.0,1.0,1.8,0.3,1.4,2.0,0.0,2.0,0.7,0.5,1.0,0.0,2.6,0.0,0.0,0.9,1.0,2.4,0.1,1.1,0.5,0.5,0.4,0.0,1.5,1.4,0.0,0.0,2.5,0.0,2.0,1.0,0.0,1.7,-0.8,0.0,2.5,-0.5,2.0,-0.9,2.0,2.0,3.7,0.0,1.0,1.1,1.3,0.0,null,1.0,null,0.0,2.0,0.0,0.0,1.8,0.0,null,0.0,2.0,1.6,0.0,1.3,3.0,0.0,1.5,2.5,1.3,-0.5,0.0,1.5,2.0,0.5,0.0,1.0,0.5,null,1.0,0.0,2.5,2.0,1.5,0.0,1.0,2.0,0.0,null,3.0,1.0,null,0.5,1.5,null,null,2.0,1.0,1.5,2.0,1.0,null,2.0,null,1.5,0.0,0.0,1.5,0.0,null,0.0,1.3,0.0,2.0,0.0,2.5,null,1.6,2.0,0.0,3.0,1.5,null,null,0.0,null,2.0,2.0,2.5,2.0,2.5,2.5,1.5,null,null,null,2.0,null,3.0,1.0,0.0,3.0,null,2.5,1.0,1.0,3.0,0.0,1.0,4.0,2.0,2.0,null,3.0,null,3.0,0.0,1.5,0.0,null,2.0,null,null,0.5,3.0,0.0,1.5,1.0,1.0,0.5,4.0,1.0,1.0,0.0,null,null,null,null,null,null,null,0.0,2.0,2.5,null,null,4.0,2.0,0.0,null,3.5,1.5,3.0,0.0,null,0.0,1.5,1.5,null,2.0,0.0,null,null,null,0.0,2.0,null,null,4.0,null,null,2.0,null,2.0,null,1.5,0.0,1.5,2.5,2.0,null,0.5,1.5,null,null,3.0,null,3.0,null,null,1.5,0.0,null,null,0.8,2.0,1.0,2.0,0.0,null,0.0,2.0,0.0,1.0,0.5,0.0,null,1.7,1.5,1.0,null,0.0,0.0,1.5,0.0,1.0,3.0,1.5,0.0,0.0,0.0,null,0.0,null,0.0,0.0,3.0,2.0,0.0,0.0,0.0,2.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,3.0,0.0,2.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,3.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,2.0,2.0,2.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,3.0,0.0,0.0,1.0,1.0,2.0,1.0,0.0,0.0,0.0,1.0,1.0,3.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,3.0,1.0,2.0,3.0,0.0,0.0,1.0,0.0,2.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,3.0,0.0,2.0,2.0,1.0,0.0,3.0,0.0,0.0,2.0,0.0,null,1.0,0.0,0.0,1.0,0.0,0.0,0.0,2.0,1.0,3.0,1.0,1.0,3.0,0.0,2.0,2.0,0.0,0.0,2.0,0.0,3.0,1.0,3.0,0.0,3.0,null,3.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,3.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,2.0,2.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,3.0,1.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,1.0,1.0,2.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,2.0,0.0,2.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,3.0,2.0,null,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,2.0,0.0,0.0,2.0,1.0,1.0,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0.0,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0.0,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0.0,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,1.0,null,null,null,null,null,null,null,null,null,null,null,null,null,2.0,1.0,null,null,null,null,null,null,null,null,null,null,null,null,null,null,2.0,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,2.0,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0.0,null,null,null,null,null,null,null,null,null,null,0.0,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0.0,2.0,1.0,0.0,0.0,0.0,3.0,0.0,2.0,1.0,0.0,0.0,2.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,3.0,4.0,0.0,0.0,0.0,0.0,3.0,0.0,2.0,1.0,0.0,0.0,0.0,3.0,1.0,3.0,0.0,4.0,0.0,0.0,0.0,1.0,4.0,0.0,4.0,0.0,0.0,0.0,0.0,2.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,2.0,0.0,1.0,0.0,2.0,2.0,1.0,0.0,2.0,1.0,0.0,3.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,3.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,1.0,2.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,2.0,1.0,2.0,3.0,1.0,1.0,0.0,2.0,2.0,0.0,0.0,0.0,3.0,2.0,3.0,4.0,0.0,3.0,1.0,0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,3.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,3.0,1.0,1.0,1.0,2.0,0.0,0.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,3.0,0.0,1.0,0.0,4.0,1.0,0.0,1.0,0.0,0.0,3.0,2.0,0.0,0.0,1.0,0.0,0.0,2.0,1.0,2.0,0.0,3.0,1.0,2.0,0.0,3.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,3.0,3.0,3.0,0.0,1.0,0.0,4.0,0.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,1.0,0.0,0.0,0.0,3.0,2.0,0.0,2.0,1.0,0.0,0.0,3.0,2.0,1.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,2.0,2.0,1.0,3.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,3.0,0.0,0.0,4.0,2.0,2.0,2.0,1.0,0.0,1.0,0.0,2.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,2.0,0.0,3.0,0.0,2.0,4.0,2.0,0.0,0.0,0.0,1.0,0.0,2.0,2.0,1.0,0.0,3.0,1.0,1.0,2.0,3.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,3.0,1.0,2.0,1.0,0.0,1.0,2.0,1.0,2.0,2.0,1.0,2.0,3.0,4.0,2.0,0.0,1.0,1.0,1.0,3.0,2.0,1.0,2.0,1.0,3.0,3.0,1.0,2.0,4.0,3.0,2.0,1.0,2.0,2.0,2.0,2.0,2.0,2.0,3.0,0.0,1.0,1.0,1.0,4.0,2.0,0.0,2.0,3.0,1.0,0.0,1.0,4.0,2.0,0.0,2.0,2.0,1.0,2.0,2.0,3.0,1.0,1.0,3.0,2.0,3.0,2.0,1.0,1.0,4.0,1.0,1.0,3.0,2.0,1.0,1.0,1.0,3.0,1.0,3.0,3.0,1.0,3.0,1.0,1.0,3.0,3.0,3.0,1.0,2.0,2.0,2.0,1.0,1.0,3.0,2.0,1.0,3.0,1.0,2.0,3.0,3.0,1.0,3.0,1.0,1.0,1.0,3.0,0.0,3.0,1.0,3.0,2.0,3.0,1.0,1.0,3.0,1.0,3.0,0.0,1.0,1.0,2.0,0.0,2.0,1.0,0.0,0.0,3.0,0.0,3.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,4.0,1.0,0.0,0.0,2.0,1.0,3.0,0.0,2.0,2.0,2.0,1.0,1.0,3.0,3.0,3.0,3.0,3.0,2.0,3.0,2.0,0.0,1.0,0.0,3.0,1.0,3.0,0.0,4.0,2.0,1.0,0.0,2.0,0.0,1.0,0.0,2.0,0.0,1.0,2.0,2.0,3.0,0.0,1.0,0.0,2.0,1.0,4.0,2.0,2.0,3.0,1.0,1.0,1.0,0.0,2.0,0.0,3.0,3.0,1.0,2.0,3.0,2.0,1.0,0.0,2.0,2.0,2.0,4.0,2.0,3.0,0.0,1.0,1.0,3.0,0.0,3.0,1.0,0.0,3.0,0.0,1.0,1.0,0.0,1.0,3.0,1.0,1.0,0.0,3.0,1.0,1.0,0.0,0.0,3.0,0.0,3.0,1.0,1.0,3.0,2.0,2.0,3.0,1.0,2.0,3.0,1.0,0.0,1.0,4.0,1.0,2.0,0.0,0.0,1.0,3.0,1.0,0.0,3.0,0.0,3.0,3.0,0.0,2.0,0.0,4.0,2.0,4.0,0.0,0.0,0.0,1.0,3.0,3.0,0.0,3.0,1.0,1.0,2.0,2.0,2.0,2.0,2.0,1.0,4.0,3.0,3.0,3.0,1.0,3.0,2.0,0.0,2.0,1.0,1.0,0.0,3.0,1.0,0.0,0.0,4.0,2.0,3.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,3.0,2.0,2.0,0.0,0.0,4.0,3.0,1.0,2.0,1.0,0.0,2.0,0.0,1.0],\"y0\":\" \",\"yaxis\":\"y\",\"type\":\"box\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"variable\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"value\"}},\"legend\":{\"tracegroupgap\":0},\"margin\":{\"t\":60},\"boxmode\":\"group\",\"title\":{\"text\":\"Box Plots for Numeric Columns\"},\"height\":1000,\"width\":1000},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('9c6448d3-fd57-40bb-bd6a-b1156c0cef0f');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# there is an outlier in trestbps\n",
        "\n",
        "df[df['trestbps'] == 0]\n",
        "\n",
        "# Lets remove this row\n",
        "df = df[df['trestbps'] != 0]"
      ],
      "metadata": {
        "id": "m1VChF8Wz4dd"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Checking Missing Values"
      ],
      "metadata": {
        "id": "lgQfSHFKz4dd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create a heatmap for df to check missing values\n",
        "plt.figure(figsize=(16, 6))\n",
        "sns.heatmap(df.isnull(), cbar=False)\n",
        "plt.title('Missing values after imputation')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "z-4Skpo3z4dd",
        "outputId": "2ea82ad4-7118-460d-bd37-79f0881e951e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(df.isnull().sum() / len(df) * 100).sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQxnhgXGz4dd",
        "outputId": "9066f0bc-b697-4460-8473-7549557e3795"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ca          66.376496\n",
              "thal        52.774755\n",
              "slope       33.623504\n",
              "fbs          9.793254\n",
              "oldpeak      6.746464\n",
              "trestbps     6.420022\n",
              "thalch       5.984766\n",
              "exang        5.984766\n",
              "chol         3.264418\n",
              "restecg      0.217628\n",
              "age          0.000000\n",
              "sex          0.000000\n",
              "dataset      0.000000\n",
              "cp           0.000000\n",
              "num          0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ReQZGRCSz4dd",
        "outputId": "1aaa8196-a772-417c-ee9a-0e24985421dd"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 919 entries, 0 to 919\n",
            "Data columns (total 15 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       919 non-null    int64  \n",
            " 1   sex       919 non-null    object \n",
            " 2   dataset   919 non-null    object \n",
            " 3   cp        919 non-null    object \n",
            " 4   trestbps  860 non-null    float64\n",
            " 5   chol      889 non-null    float64\n",
            " 6   fbs       829 non-null    object \n",
            " 7   restecg   917 non-null    object \n",
            " 8   thalch    864 non-null    float64\n",
            " 9   exang     864 non-null    object \n",
            " 10  oldpeak   857 non-null    float64\n",
            " 11  slope     610 non-null    object \n",
            " 12  ca        309 non-null    float64\n",
            " 13  thal      434 non-null    object \n",
            " 14  num       919 non-null    int64  \n",
            "dtypes: float64(5), int64(2), object(8)\n",
            "memory usage: 114.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Impute Missing values through simple imputer\n",
        "\n",
        "cat_imputer = SimpleImputer(strategy='most_frequent')\n",
        "cat_columns = ['fbs', 'exang', 'restecg']\n",
        "\n",
        "df[cat_columns] = cat_imputer.fit_transform(df[cat_columns])\n",
        "\n",
        "num_imputer = IterativeImputer(max_iter=10, random_state=42)\n",
        "num_columns = ['oldpeak', 'trestbps', 'thalch', 'chol']\n",
        "\n",
        "df[num_columns] = num_imputer.fit_transform(df[num_columns])"
      ],
      "metadata": {
        "id": "qnklw3d3z4dd"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(df.isnull().sum() / len(df) * 100).sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F4KVYSw_z4di",
        "outputId": "f0681ff3-1e04-4ac4-eb09-b3dcc5e3b82c"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ca          66.376496\n",
              "thal        52.774755\n",
              "slope       33.623504\n",
              "age          0.000000\n",
              "sex          0.000000\n",
              "dataset      0.000000\n",
              "cp           0.000000\n",
              "trestbps     0.000000\n",
              "chol         0.000000\n",
              "fbs          0.000000\n",
              "restecg      0.000000\n",
              "thalch       0.000000\n",
              "exang        0.000000\n",
              "oldpeak      0.000000\n",
              "num          0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_clean = df.dropna()\n",
        "df_missing = df[df.isna().any(axis=1)]"
      ],
      "metadata": {
        "id": "kGv28mcWz4di"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "percentage_distribution = df_clean['slope'].value_counts(normalize=True) * 100\n",
        "percentage_distribution = percentage_distribution.round(2)\n",
        "print(percentage_distribution)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZEXyKoxz4dj",
        "outputId": "d3992704-40c8-4e09-b942-3fb8ef322928"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "flat           46.49\n",
            "upsloping      46.49\n",
            "downsloping     7.02\n",
            "Name: slope, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_clean.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PIbeUPIPz4dj",
        "outputId": "ec52658f-590f-4c6b-8f5c-c159af2d61ed"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age     sex    dataset               cp  trestbps   chol    fbs  \\\n",
              "0   63    Male  Cleveland   typical angina     145.0  233.0   True   \n",
              "1   67    Male  Cleveland     asymptomatic     160.0  286.0  False   \n",
              "2   67    Male  Cleveland     asymptomatic     120.0  229.0  False   \n",
              "3   37    Male  Cleveland      non-anginal     130.0  250.0  False   \n",
              "4   41  Female  Cleveland  atypical angina     130.0  204.0  False   \n",
              "\n",
              "          restecg  thalch  exang  oldpeak        slope   ca  \\\n",
              "0  lv hypertrophy   150.0  False      2.3  downsloping  0.0   \n",
              "1  lv hypertrophy   108.0   True      1.5         flat  3.0   \n",
              "2  lv hypertrophy   129.0   True      2.6         flat  2.0   \n",
              "3          normal   187.0  False      3.5  downsloping  0.0   \n",
              "4  lv hypertrophy   172.0  False      1.4    upsloping  0.0   \n",
              "\n",
              "                thal  num  \n",
              "0       fixed defect    0  \n",
              "1             normal    2  \n",
              "2  reversable defect    1  \n",
              "3             normal    0  \n",
              "4             normal    0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2d424603-fe65-46e5-a1c4-c569b72f570a\" 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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>dataset</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalch</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>Male</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>typical angina</td>\n",
              "      <td>145.0</td>\n",
              "      <td>233.0</td>\n",
              "      <td>True</td>\n",
              "      <td>lv hypertrophy</td>\n",
              "      <td>150.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2.3</td>\n",
              "      <td>downsloping</td>\n",
              "      <td>0.0</td>\n",
              "      <td>fixed defect</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>67</td>\n",
              "      <td>Male</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>asymptomatic</td>\n",
              "      <td>160.0</td>\n",
              "      <td>286.0</td>\n",
              "      <td>False</td>\n",
              "      <td>lv hypertrophy</td>\n",
              "      <td>108.0</td>\n",
              "      <td>True</td>\n",
              "      <td>1.5</td>\n",
              "      <td>flat</td>\n",
              "      <td>3.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>67</td>\n",
              "      <td>Male</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>asymptomatic</td>\n",
              "      <td>120.0</td>\n",
              "      <td>229.0</td>\n",
              "      <td>False</td>\n",
              "      <td>lv hypertrophy</td>\n",
              "      <td>129.0</td>\n",
              "      <td>True</td>\n",
              "      <td>2.6</td>\n",
              "      <td>flat</td>\n",
              "      <td>2.0</td>\n",
              "      <td>reversable defect</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37</td>\n",
              "      <td>Male</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>non-anginal</td>\n",
              "      <td>130.0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>False</td>\n",
              "      <td>normal</td>\n",
              "      <td>187.0</td>\n",
              "      <td>False</td>\n",
              "      <td>3.5</td>\n",
              "      <td>downsloping</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41</td>\n",
              "      <td>Female</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>atypical angina</td>\n",
              "      <td>130.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>False</td>\n",
              "      <td>lv hypertrophy</td>\n",
              "      <td>172.0</td>\n",
              "      <td>False</td>\n",
              "      <td>1.4</td>\n",
              "      <td>upsloping</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>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-2d424603-fe65-46e5-a1c4-c569b72f570a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2d424603-fe65-46e5-a1c4-c569b72f570a 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-2d424603-fe65-46e5-a1c4-c569b72f570a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-106eb638-b6c5-4266-a575-651ea141a245\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-106eb638-b6c5-4266-a575-651ea141a245')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-106eb638-b6c5-4266-a575-651ea141a245 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "While training the model, we need to remove the columns with missing values in df_missing dataframe.\n",
        "This is because when we will predict the missing column the model will not ask for the missing value columns.\n",
        "\n",
        "If we train the model with columns that have missing values in df_missing, when we will try to predict, the model will ask for those columns."
      ],
      "metadata": {
        "id": "7m-6nEpXz4dj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def predictions(target, drop_c):\n",
        "    X = df_clean.drop(drop_c, axis = 1)\n",
        "    y = df_clean[target]\n",
        "\n",
        "    le = LabelEncoder()\n",
        "\n",
        "    for col in X.columns:\n",
        "        if X[col].dtype == 'object':\n",
        "            X[col] = le.fit_transform(X[col])\n",
        "\n",
        "    model = RandomForestClassifier()\n",
        "\n",
        "    model.fit(X, y)\n",
        "\n",
        "    # Perform k-fold cross-validation with k=5\n",
        "    kf = StratifiedKFold(n_splits=6, shuffle=True, random_state=42)\n",
        "    scores = cross_val_score(model, X, y, cv=kf, scoring='accuracy')\n",
        "\n",
        "    print(\"Scores for each fold:\", scores)\n",
        "    print(\"Mean score:\", scores.mean())\n",
        "\n",
        "    # Plot the results\n",
        "    plt.figure(figsize=(8,6))\n",
        "    plt.plot(range(1,7), scores, color='blue', linestyle='dashed', marker='o', markerfacecolor='red', markersize=12)\n",
        "    plt.title('K-Fold Cross-Validation Results')\n",
        "    plt.xlabel('Fold Number')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.ylim([0.5, 1.0])\n",
        "    plt.show()\n",
        "\n",
        "    return model"
      ],
      "metadata": {
        "id": "hYxQ7E1Ez4dj"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_slope = predictions('slope', ['slope', 'thal', 'ca'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "fXLoHf2zz4dj",
        "outputId": "82713f07-3231-45f7-b0df-03b51ba3b2ae"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scores for each fold: [0.64       0.78       0.66       0.78       0.7        0.67346939]\n",
            "Mean score: 0.7055782312925171\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIjCAYAAAAQgZNYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABi1UlEQVR4nO3dd3gUVfvG8XvTA2n0Xi0UpZeIIEXpiIDSlFeKgoigIjZQqYL4KiIWQEAQy0tRUERFqhQVpErV0BGE0ATSFLLJzu+P8yMQQ0lCkskk38917UV22Nl9NptN7p055zkuy7IsAQAAAA7kZXcBAAAAQHoRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgFkmpkzZ8rlcunQoUPXvW3ZsmXVs2fPTK8pp1q1apVcLpdWrVqVtK1nz54qW7bsdfc9dOiQXC6XZs6cmaE18Zpencvl0ogRI+wuA8gRCLNADnQxRG7atCnZ9qioKNWtW1cBAQFavHjxNfe90mXw4MFZUf51nT9/Xm+//bbCw8MVGhqqgIAA3XrrrRowYID27Nljd3nXVbVqVZUuXVrXWk28fv36KlKkiBISErKwsrRbu3atRowYoXPnztldSpJ//wz7+PioRIkS6tmzp44ePWp3eVeUHb+PgFP42F0AgKwRHR2t5s2ba/v27frqq6/UsmXLa95+1KhRKleuXLJtt99+e2aWmCqnT59Wy5YttXnzZt1777166KGHFBQUpN27d2vOnDmaOnWq4uPj7S7zmrp166bBgwfrxx9/VMOGDVP8/6FDh7Ru3ToNGDBAPj7p/zU9bdo0eTyeGyn1utauXauRI0eqZ8+eCgsLS/Z/u3fvlpeXfcdMLv4Mnz9/Xr/88otmzpypn376STt37lRAQIBtdV3Jtb6PAK6NMAvkAjExMWrRooW2bt2qL7/8Uq1atbruPq1atVLt2rWzoLq06dmzp3799VfNmzdPDzzwQLL/e/XVV/Xyyy9fc/+4uDjlzZs3M0u8roceekhDhgzRrFmzrhhmZ8+eLcuy1K1btxt6HF9f3xva/0b5+/vb+viX/wz37t1bBQsW1H//+18tXLhQnTt3trU2ABmHYQZADhcbG6uWLVtqy5Ytmj9/vtq0aZMh9/vDDz/orrvuUt68eRUWFqZ27drp999/v+5+lmVp9OjRKlmypPLkyaMmTZpo165dqXrM9evX67vvvtOjjz6aIshKJjyNGzcu6XrPnj0VFBSk/fv3q3Xr1goODk4KiHFxcXr22WdVqlQp+fv7q0KFCho3blyKU//Lli1TgwYNFBYWpqCgIFWoUEEvvfRSstu89957uu2225QnTx7ly5dPtWvX1qxZs676PEqVKqWGDRtq3rx5crvdKf5/1qxZuummmxQeHq4//vhDTzzxhCpUqKDAwEAVKFBAnTp1StU45CuNmT137px69uyp0NBQhYWFqUePHlc8tb19+3b17NlT5cuXV0BAgIoWLapHHnlEf/31V9JtRowYoeeff16SVK5cuaTT+hdru9KY2QMHDqhTp07Knz+/8uTJozvuuEPfffddsttcHP/7+eefa8yYMSpZsqQCAgJ0zz33aN++fdd93ldz1113SZL279+fbHtERIQ6duyo/PnzKyAgQLVr19bChQuT3cbtdmvkyJG65ZZbFBAQoAIFCqhBgwZatmxZ0m0aN26sxo0bp3jc641dvt73MTU/g0BuxpFZIAeLi4tTq1attHHjRs2bN0/33ntvqveNiorS6dOnk20rWLCgJGn58uVq1aqVypcvrxEjRuiff/7Re++9p/r162vLli3X/MM9bNgwjR49Wq1bt1br1q21ZcsWNW/ePFVDAy4GjIcffjjVzyMhIUEtWrRQgwYNNG7cOOXJk0eWZem+++7TypUr9eijj6p69epasmSJnn/+eR09elRvv/22JGnXrl269957VbVqVY0aNUr+/v7at2+ffv7556T7nzZtmp566il17NhRTz/9tM6fP6/t27dr/fr1euihh65aV7du3fTYY49pyZIlyV6XHTt2aOfOnRo2bJgkaePGjVq7dq26du2qkiVL6tChQ5o8ebIaN26s3377TXny5En198KyLLVr104//fSTHn/8cVWqVElfffWVevTokeK2y5Yt04EDB9SrVy8VLVpUu3bt0tSpU7Vr1y798ssvcrlcuv/++7Vnzx7Nnj1bb7/9dtLPR6FCha74+CdOnNCdd96pv//+W0899ZQKFCigjz/+WPfdd5/mzZunDh06JLv966+/Li8vLz333HOKiorSG2+8oW7dumn9+vWpfs6XuxgO8+XLl7Rt165dql+/vkqUKKHBgwcrb968+vzzz9W+fXvNnz8/qaYRI0Zo7Nix6t27t+rWravo6Ght2rRJW7ZsUbNmzdJVz0XX+j6m5mcQyPUsADnORx99ZEmyypQpY/n6+loLFixI875XulxUvXp1q3DhwtZff/2VtG3btm2Wl5eX1b179xT3dfDgQcuyLOvkyZOWn5+f1aZNG8vj8STd7qWXXrIkWT169LhmbR06dLAkWWfPnk3Vc+nRo4clyRo8eHCy7QsWLLAkWaNHj062vWPHjpbL5bL27dtnWZZlvf3225Yk69SpU1d9jHbt2lm33XZbquq53JkzZyx/f3/rwQcfTLZ98ODBliRr9+7dlmVZ1t9//51i33Xr1lmSrE8++SRp28qVKy1J1sqVK5O29ejRwypTpkzS9YvP+4033kjalpCQYN11112WJOujjz5K2n6lx509e7YlyVqzZk3StjfffDPZa3y5MmXKJHtNBw4caEmyfvzxx6RtMTExVrly5ayyZctaiYmJyZ5LpUqVrAsXLiTd9p133rEkWTt27EjxWJe7+HO3fPly69SpU9aRI0esefPmWYUKFbL8/f2tI0eOJN32nnvusapUqWKdP38+aZvH47HuvPNO65ZbbknaVq1aNatNmzbXfNxGjRpZjRo1SrH936+DZVmWJGv48OFJ16/2fUzNzyCQ2zHMAMjBTpw4oYCAAJUqVSrN+06cOFHLli1LdpGkyMhIbd26VT179lT+/PmTbl+1alU1a9ZMixYtuup9Ll++XPHx8XryySflcrmStg8cODBVNUVHR0uSgoOD0/Rc+vXrl+z6okWL5O3traeeeirZ9meffVaWZen777+XpKSJOF9//fVVJ1KFhYXpzz//1MaNG9NUU758+dS6dWstXLhQcXFxksyR0zlz5qh27dq69dZbJUmBgYFJ+7jdbv3111+6+eabFRYWpi1btqTpMRctWiQfH59k3w9vb289+eSTKW57+eOeP39ep0+f1h133CFJaX7cyx+/bt26atCgQdK2oKAgPfbYYzp06JB+++23ZLfv1auX/Pz8kq5fHCZw4MCBVD1e06ZNVahQIZUqVUodO3ZU3rx5tXDhQpUsWVKSdObMGf3www/q3LmzYmJidPr0aZ0+fVp//fWXWrRoob179yZ1PwgLC9OuXbu0d+/edD339ErNzyCQ2xFmgRxsypQp8vPzU8uWLbV79+6k7YmJiTp+/Hiyy79P89etW1dNmzZNdpGkP/74Q5JUoUKFFI9XqVIlnT59Oimc/dvFfW+55ZZk2wsVKpTs1O/VhISESDIT2lLLx8cnKbxcXkfx4sVThOJKlSolq7NLly6qX7++evfurSJFiqhr1676/PPPk4WKF198UUFBQapbt65uueUW9e/fP9kp4Pj4+BTf68TERElmqEFcXJy+/vprSWZG+6FDh5JN/Prnn380bNiwpLG9BQsWVKFChXTu3DlFRUWl+vtw8XkVK1ZMQUFBybZf6bU8c+aMnn76aRUpUkSBgYEqVKhQUneLtD7u5Y9/tZ+bi/9/udKlSye7fvFn5OzZs6l6vIsfyObNm6fWrVvr9OnTySal7du3T5ZlaejQoSpUqFCyy/DhwyVJJ0+elGQ6I5w7d0633nqrqlSpoueff17bt29P5TNPv9T8DAK5HWEWyMEqV66sRYsW6Z9//lGzZs105MgRSdKRI0dUrFixZJe1a9faXO31VaxYUZIZV5pa/v7+6W4PFRgYqDVr1mj58uV6+OGHtX37dnXp0kXNmjVLCqSVKlVKagvWoEEDzZ8/Xw0aNEgKQ2vXrk3xvb74Otx7770KDQ1Nmiw2a9YseXt7q2vXrkk1PPnkkxozZow6d+6szz//XEuXLtWyZctUoECBTA00nTt31rRp0/T444/ryy+/1NKlS5N6E2dVkPL29r7idusa/Xkvd/ED2QMPPKCFCxfq9ttv10MPPaTY2FhJl57Hc889l+IsxMXLzTffLElq2LCh9u/frxkzZuj222/Xhx9+qJo1a+rDDz9MerzLzzZc7uLPSnqk5mcQyO2YAAbkcHXr1tWCBQvUpk0bNWvWTD/++KOKFi2abBa2JFWrVi1V91emTBlJSnak96KIiAgVLFjwqq2vLu67d+9elS9fPmn7qVOnUnW0rW3btho7dqw+++yzpFPO6VGmTBktX75cMTExyY7ORkREJKtTkry8vHTPPffonnvu0fjx4/Xaa6/p5Zdf1sqVK5OOVufNm1ddunRRly5dFB8fr/vvv19jxozRkCFDVK1atRTf66JFi0oyQbtjx4765JNPdOLECX3xxRe6++67k/5fkubNm6cePXrorbfeStp2/vz5dDXXL1OmjFasWKHY2NhkR2f//VqePXtWK1as0MiRI5Mmokm64in2qwW4qz3+1X5uLv5/ZvH29tbYsWPVpEkTvf/++xo8eHDSz6Cvr2/Sa3kt+fPnV69evdSrVy/FxsaqYcOGGjFihHr37i3JHDm+0hCIfx9xvpJrfR9T8zMI5GYcmQVygXvuuUezZ8/Wvn371LJlS8XHx6cYQpCa0/ySVKxYMVWvXl0ff/xxskC1c+dOLV26VK1bt77qvk2bNpWvr6/ee++9ZEfXJkyYkKrHrlevnlq2bKkPP/xQCxYsSPH/8fHxeu655657P61bt1ZiYqLef//9ZNvffvttuVyupD68Z86cSbFv9erVJUkXLlyQpGStqiTJz89PlStXlmVZcrvdypcvX4rv9eUN+7t16ya3262+ffvq1KlTKXrLent7pzgS+d5776XrqFzr1q2VkJCgyZMnJ21LTEzUe++9l+IxpZRHQK/0Ol384JKacN26dWtt2LBB69atS9oWFxenqVOnqmzZsqpcuXJqn0q6NG7cWHXr1tWECRN0/vx5FS5cWI0bN9aUKVMUGRmZ4vanTp1K+vrfr3NQUJBuvvnmpJ8DSbrpppsUERGRbL9t27alqvPA1b6PqfkZBHI7jswCuUSHDh00bdo0PfLII7rvvvu0ePHidK+C9Oabb6pVq1aqV6+eHn300aTWXKGhoddcb75QoUJ67rnnNHbsWN17771q3bq1fv31V33//fdJ7Yiu55NPPlHz5s11//33q23btrrnnnuUN29e7d27V3PmzFFkZGSyXrNX0rZtWzVp0kQvv/yyDh06pGrVqmnp0qX6+uuvNXDgQN10002SzDjJNWvWqE2bNipTpoxOnjypSZMmqWTJkkmTmJo3b66iRYsmLT/7+++/6/3331ebNm1SNVGtUaNGKlmypL7++msFBgbq/vvvT/b/9957rz799FOFhoaqcuXKWrdunZYvX64CBQqk6vv17+ddv359DR48WIcOHVLlypX15ZdfphgDGxISooYNG+qNN96Q2+1WiRIltHTpUh08eDDFfdaqVUuS9PLLL6tr167y9fVV27Ztr3h0fvDgwZo9e7ZatWqlp556Svnz59fHH3+sgwcPav78+VmyWtjzzz+vTp06aebMmXr88cc1ceJENWjQQFWqVFGfPn1Uvnx5nThxQuvWrdOff/6pbdu2STJDdho3bqxatWopf/782rRpk+bNm6cBAwYk3fcjjzyi8ePHq0WLFnr00Ud18uRJffDBB7rtttuSJi9ezdW+j6n5GQRyPfsaKQDILBdbE23cuDHF/40bN86SZN17772W2+1O076XW758uVW/fn0rMDDQCgkJsdq2bWv99ttvV7yvy9sNJSYmWiNHjrSKFStmBQYGWo0bN7Z27tyZoo3Ttfz999/WuHHjrDp16lhBQUGWn5+fdcstt1hPPvlkUlstyzItkfLmzXvF+4iJibGeeeYZq3jx4pavr691yy23WG+++WaylmErVqyw2rVrZxUvXtzy8/Ozihcvbj344IPWnj17km4zZcoUq2HDhlaBAgUsf39/66abbrKef/55KyoqKlXPxbIs6/nnn7ckWZ07d07xf2fPnrV69eplFSxY0AoKCrJatGhhRUREpPh+paY1l2VZ1l9//WU9/PDDVkhIiBUaGmo9/PDD1q+//pqiNdeff/5pdejQwQoLC7NCQ0OtTp06WceOHUvRUsqyLOvVV1+1SpQoYXl5eSV7va/0mu7fv9/q2LGjFRYWZgUEBFh169a1vv3222S3ufhcvvjii2TbDx48mKLOK7nWz3BiYqJ10003WTfddJOVkJCQVFP37t2tokWLWr6+vlaJEiWse++915o3b17SfqNHj7bq1q1rhYWFWYGBgVbFihWtMWPGWPHx8cnu/7PPPrPKly9v+fn5WdWrV7eWLFmSqtZcV/s+puZnEMjtXJaVypH0AAAAQDbDmFkAAAA4FmEWAAAAjkWYBQAAgGPZGmbXrFmjtm3bqnjx4nK5XFdstfNvq1atUs2aNeXv76+bb75ZM2fOzPQ6AQAAkD3ZGmbj4uJUrVo1TZw4MVW3P3jwoNq0aaMmTZpo69atGjhwoHr37q0lS5ZkcqUAAADIjrJNNwOXy6WvvvpK7du3v+ptXnzxRX333XfauXNn0rauXbvq3LlzScssAgAAIPdw1KIJ69atS7F0X4sWLTRw4MCr7nPhwoVkq6R4PB6dOXNGBQoUSNMyjAAAAMgalmUpJiZGxYsXv+6CKo4Ks8ePH1eRIkWSbStSpIiio6P1zz//KDAwMMU+Y8eO1ciRI7OqRAAAAGSQI0eOqGTJkte8jaPCbHoMGTJEgwYNSroeFRWl0qVL68iRIwoJCbGxMgAAAFxJdHS0SpUqlaplwR0VZosWLaoTJ04k23bixAmFhIRc8aisJPn7+8vf3z/F9pCQEMIsAABANpaaIaGO6jNbr149rVixItm2ZcuWqV69ejZVBAAAADvZGmZjY2O1detWbd26VZJpvbV161YdPnxYkhki0L1796TbP/744zpw4IBeeOEFRUREaNKkSfr888/1zDPP2FE+AAAAbGZrmN20aZNq1KihGjVqSJIGDRqkGjVqaNiwYZKkyMjIpGArSeXKldN3332nZcuWqVq1anrrrbf04YcfqkWLFrbUDwAAAHtlmz6zWSU6OlqhoaGKiopizCwAAEA2lJa85qgxswAAAMDlCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxbA+zEydOVNmyZRUQEKDw8HBt2LDhqrd1u90aNWqUbrrpJgUEBKhatWpavHhxFlYLAACA7MTWMDt37lwNGjRIw4cP15YtW1StWjW1aNFCJ0+evOLtX3nlFU2ZMkXvvfeefvvtNz3++OPq0KGDfv311yyuHAAAANmBy7Isy64HDw8PV506dfT+++9Lkjwej0qVKqUnn3xSgwcPTnH74sWL6+WXX1b//v2Ttj3wwAMKDAzUZ599lqrHjI6OVmhoqKKiohQSEpIxTwQAAAAZJi15zbYjs/Hx8dq8ebOaNm16qRgvLzVt2lTr1q274j4XLlxQQEBAsm2BgYH66aefrvo4Fy5cUHR0dLILAAAAcgbbwuzp06eVmJioIkWKJNtepEgRHT9+/Ir7tGjRQuPHj9fevXvl8Xi0bNkyffnll4qMjLzq44wdO1ahoaFJl1KlSmXo8wAAAIB9bJ8AlhbvvPOObrnlFlWsWFF+fn4aMGCAevXqJS+vqz+NIUOGKCoqKuly5MiRLKwYAAAAmcm2MFuwYEF5e3vrxIkTybafOHFCRYsWveI+hQoV0oIFCxQXF6c//vhDERERCgoKUvny5a/6OP7+/goJCUl2AQAAQM5gW5j18/NTrVq1tGLFiqRtHo9HK1asUL169a65b0BAgEqUKKGEhATNnz9f7dq1y+xyAQAAkA352PnggwYNUo8ePVS7dm3VrVtXEyZMUFxcnHr16iVJ6t69u0qUKKGxY8dKktavX6+jR4+qevXqOnr0qEaMGCGPx6MXXnjBzqcBAAAAm9gaZrt06aJTp05p2LBhOn78uKpXr67FixcnTQo7fPhwsvGw58+f1yuvvKIDBw4oKChIrVu31qeffqqwsDCbngEAAADsZGufWTvQZxYAACB7c0SfWQAAAOBGEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWIRZAAAAOBZhFgAAAI5FmAUAAIBjEWYBAADgWLaH2YkTJ6ps2bIKCAhQeHi4NmzYcM3bT5gwQRUqVFBgYKBKlSqlZ555RufPn8+iagEAAJCd2Bpm586dq0GDBmn48OHasmWLqlWrphYtWujkyZNXvP2sWbM0ePBgDR8+XL///rumT5+uuXPn6qWXXsriygEAAJAd2Bpmx48frz59+qhXr16qXLmyPvjgA+XJk0czZsy44u3Xrl2r+vXr66GHHlLZsmXVvHlzPfjgg9c9mgsAAICcybYwGx8fr82bN6tp06aXivHyUtOmTbVu3bor7nPnnXdq8+bNSeH1wIEDWrRokVq3bn3Vx7lw4YKio6OTXQAAAJAz+Nj1wKdPn1ZiYqKKFCmSbHuRIkUUERFxxX0eeughnT59Wg0aNJBlWUpISNDjjz9+zWEGY8eO1ciRIzO0dgAAAGQPtk8AS4tVq1bptdde06RJk7RlyxZ9+eWX+u677/Tqq69edZ8hQ4YoKioq6XLkyJEsrBgAAACZybYjswULFpS3t7dOnDiRbPuJEydUtGjRK+4zdOhQPfzww+rdu7ckqUqVKoqLi9Njjz2ml19+WV5eKbO5v7+//P39M/4JAAAAwHa2HZn18/NTrVq1tGLFiqRtHo9HK1asUL169a64z99//50isHp7e0uSLMvKvGIBAACQLdl2ZFaSBg0apB49eqh27dqqW7euJkyYoLi4OPXq1UuS1L17d5UoUUJjx46VJLVt21bjx49XjRo1FB4ern379mno0KFq27ZtUqgFAABA7mFrmO3SpYtOnTqlYcOG6fjx46pevboWL16cNCns8OHDyY7EvvLKK3K5XHrllVd09OhRFSpUSG3bttWYMWPsegoAAACwkcvKZefno6OjFRoaqqioKIWEhNhdDgAAAP4lLXnNUd0MAAAAgMsRZgEAAOBYto6ZBbIzt1v67jspIkKKjZWCgqSKFaU2bSRfX7urA3IH3ocArocwC/zLsWPSlCnStMluRZ7yVT6faAW7YhVjBelsQoiKFXKrTz9f9e0rFS9ud7VAzsT7EEBqMcwAuMyqVVLlCgkaP+ZvtT81TdtVRWcSQvWHu4TOJIRqu6qo/alpGj/mb1WukKBVq+yuGMh5eB8CSAu6GQD/b9UqqUUzjxp5Vmqup6Py6dxVb3tWYersNV9rvBpryTIvNW6cVVUCORvvQwBS2vIaYRaQOaVZuUKC6v69St96WstP7uvuEy9f3ev1vTbmbaRdET6c6gRuEO9DABfRmgtIoylTpMR/4jXX0ylVf0AlyU9uzfV0lDvOralTM7lAIBfgfQggPQizyPXcbjPJ5OHEmdc8pXkl+XROD3tmatpkt9yp+9sL4Ap4HwJIL8Iscr3vvpMiT/mqnyana/9+mqxjJ321aFEGFwbkIrwPAaRXmsNs2bJlNWrUKB0+fDgz6gGyXESElM8nWlW0M137V9UOhXnHKCIigwsDchHehwDSK81hduDAgfryyy9Vvnx5NWvWTHPmzNGFCxcyozYgS8TGSsGu2Bu6j2CvWMXEZFBBQC7E+xBAeqUrzG7dulUbNmxQpUqV9OSTT6pYsWIaMGCAtmzZkhk1ApkqKEiKsYJu6D5iPEEKDs6ggoBciPchgPRK95jZmjVr6t1339WxY8c0fPhwffjhh6pTp46qV6+uGTNmKJd1/IKDVawonU0I0Q7dnq79t6uKziUGq2LFDC4MyEV4HwJIr3SHWbfbrc8//1z33Xefnn32WdWuXVsffvihHnjgAb300kvq1q1bRtYJZJo2baRihdyarH7p2n+y+ql4Ybdat87gwoBchPchgPRKc5jdsmVLsqEFt912m3bu3KmffvpJvXr10tChQ7V8+XJ99dVXmVEvkOF8faU+/Xz1qXdPnVVYmvY9qzB96tVTffr5ytc3c+oDcoOL78NPXOl7H37i6qkqNXzlRY8eINdJ89u+Tp062rt3ryZPnqyjR49q3Lhxqviv8zrlypVT165dM6xIILP17St5B/qpi9c8xSt1qTRevnpA8+Xy99Vjj2VygUAu4HJJ5y0/dVTa3oedvObrguWrJUuku+4SHQ2AXCbNYfbAgQNavHixOnXqJN+rHIrKmzevPvrooxsuDsgqwcFSr94+Wu3VRPd6fX/dI0NnFaZWWqxVaqx/4n20aVPW1AnkVG++KY0cKSXKR2vS8D5s47VYP3o11sBnfRQcLK1bJ1WvLr3+upSQkCWlA7BZmsPsyZMntX79+hTb169fr038RYcDud1S587ShAlSuw5e2pi3kUp7H1U/TUoxGWW7qqifJqmU1zFtCW6oRo29lJgoPfCAxMgaIH3GjpVeeMF8PXy4tHR56t+Hm/I21NLlXho3Ttq1S2rVSrpwQRoyRLrjDmn7dhueEIAsleYw279/fx05ciTF9qNHj6p///4ZUhSQVSxLeuIJafFiKU8e6fnnpV0RPnr2lTz6ulBvVdUO5fOOVmnfY8rnHa1q2q6FhXvruaGB2hXho2XLpK5dzRGgqVPN/QFIvb17pWHDzNejRkkjRkhNmqTtfdiokdm/VCmzktjHH0v58kmbN5thB1FRtj09AFnAZaWxh1ZQUJC2b9+u8uXLJ9t+8OBBVa1aVTHZvGN1dHS0QkNDFRUVpZCQELvLgc1ee016+WXJy8scWb3vvkv/53ZLixaZ8XcxMWYoQsWKUuvWSjbZKyFBGj9eGjDABGIAafPVV9KePdKLL6b8v9S+D//t+HHzQfXOO6Xnnsu82gFkjrTktTSH2QIFCujbb79VvXr1km1fu3at2rRpo7Nnz6a94ixEmMVFs2ZJFzvIvf++lFEnFixL2rpVqlEjY+4PyGksSzp7VsqfP/Mfx7KU1OFg7Vrp66/N0d/AwMx9bAA3Ji15Lc3DDJo3b64hQ4Yo6rLzNufOndNLL72kZs2apb1awAarV0u9epmvn302Y4Ps0KFSrVrmVCeA5CzLHIGtWVP644/MfSyX61KQdbulRx6R3njDTBD7+efMfWwAWSfNYXbcuHE6cuSIypQpoyZNmqhJkyYqV66cjh8/rrfeeiszagQy3JEjkscjdexo/rhlpL/+Mn+we/WSpk/P2PsGnMyyzIfHN980QXblyqx7bF9f814vVswMabjrLunpp6XY2KyrAUDmSPMwA0mKi4vT//73P23btk2BgYGqWrWqHnzwwau26spOGGaAi9auNUMBMvp0o2VJTz1lhi5I0pQpog8tcj3LMuHxvffM9UmTpH7pW+zrhpw7ZwL1jBnmetmy0ocfSvfck/W1ALi6TB0z63SE2dwrLs5cChfO/MeyLOmZZ6R33jHXJ040k1GA3MjjMRMkJ082p/6nTJH69LG3pqVLTQ2HD5vrP/0k1a9vb00ALklLXvNJ74P89ttvOnz4sOLj45Ntv+/y6eBANpGYKD34oLRzp5kZ/a9F6zKcyyW9/bbk4yO99ZYZk5uQYI7YArmJxyM9/rg0bZp5X0yffmm8up2aNze/DwYPlg4eNF0PADhTmsPsgQMH1KFDB+3YsUMul0sXD+y6XC5JUmJiYsZWCNwgy5IGDpS++Uby95fOnMmax3W5zNhAX1+zGhGzp5EbxcSYVbm8vKSZM6WHH7a7okuCg81Zk4QE836VTE/aoUPN4g0FCthbH4DUSfMEsKefflrlypXTyZMnlSdPHu3atUtr1qxR7dq1tWrVqkwoEbgxb79txq+6XNJnn2XtERiXy/SyXb/e/tOqgB1CQ6UVK6Qvv8xeQfZyPpcd1nn+eTOut3Jlaf58+2oCkHppDrPr1q3TqFGjVLBgQXl5ecnLy0sNGjTQ2LFj9RTnUJHNzJt3qWH6m2+a7gVZzeWS6ta9dP3UKbNaGJBTJSSYAHtR4cJSu3b21ZMWjzwiVaoknTxpfl906iSdOGF3VQCuJc1hNjExUcHBwZKkggUL6tixY5KkMmXKaPfu3RlbHXAD1q6V/vMfM8xgwABp0CC7K5LOn5eaNpX69pVGjrS7GiDjJSSY913Tppc6BjjJHXdIv/5qVgb09jYfiCtXlv73P5arBrKrNIfZ22+/Xdu2bZMkhYeH64033tDPP/+sUaNGpVjiFrCLZUkvvCBduGCWqJ0w4dKYODsFBFxadWzECLMmPX8gkVO43VLXrtLcuWasuFPHnPr7S6NHSxs3StWqmXH2//mPWbYaQPaT5jD7yiuvyOPxSJJGjRqlgwcP6q677tKiRYv07rvvZniBQHq4XGbZyieeMMvWenvbXdElL7xgOhxI0quvSi+9RKCF88XHS507m3Gmfn5mjKxThhZcTY0aJtC++qpUsqTUo4fdFQG4kgzpM3vmzBnly5cvqaNBdkaf2ZzNsrLHEdjUePdd00ReMuN633jDObUDl7twwYwtvdgx5Msvpdat7a4qY/3zz6WOJJZlJnZ262YWXQCQ8dKS19J0ZNbtdsvHx0c7d+5Mtj1//vyOCLLI2Twe00t28mS7K0mdy1cJGzfO/HEEnMbtljp0MEE2IEBauDDnBVkpeWu9L76QXnlFuv1209rr/09WArBJmsKsr6+vSpcuTS9ZZEsvvGDG6j39tHTggN3VpE7//mY1pDJlLo2lBZzEx0eqXt2EvW+/NYsR5HQ1akgNG5oVBQcMkBo3lvbssbsqIPdK8zCD6dOn68svv9Snn36q/PnzZ1ZdmYZhBjnTxInmj4pkesk6LRjGxUl589pdBZA+liXt3y/dfLPdlWQdj8ecBXrxRfP+DQgwY2ufeSZ7jdEHnCoteS3NYbZGjRrat2+f3G63ypQpo7z/+gu8ZcuWtFechQizOc8330jt25s/LmPGmAlVTrZggbRkiQnoXmmeoglkvrg4E9yGDZPy5LG7GnsdOiQ99pi0bJm53qmT9PnntpYE5AhpyWtpXs62ffv26a0LyHCbNplWQB6P1Lu3NGSI3RXdmOPHzbjf8+fN5cMPOcqD7CUmRmrTRvrxR2nfPtOHNTcrW9Z8+PzoI+nZZ02wBZC1MqSbgZNwZDbnOH3aTMA4cUJq0cIcofX1tbuqGzdnjulpmZho/p05k0CL7CE62kzu+vlnKSREWrpUCg+3u6rsIyrKLN970bx5UvnyUs2a9tUEOFWmdTMAspMCBcyqXjVqmNnFOSHISuZI85w5ZmLNZ5+Z9ewTEuyuCrldVJT50Pjzz1JYmLR8OUH23y4Psn/8IfXqZZayfuUV074MQOZIc5j18vKSt7f3VS9AVnG5TAeDdeuk/19hOcfo2NGMu/PxkWbPlh56yLRAAuxw9qzUrJn0yy9S/vzSihVSnTp2V5W95cljjmInJpqx/DVqmO8fgIyX5mEGX3/9dbLrbrdbv/76qz7++GONHDlSjz76aIYWmNEYZuBslmWWlOzTx5zmzOkWLjTB1u2WPvhA6tvX7oqQGzVrZo7EFihggmy1anZX5BxffmlWIjxxwnwAf+YZM3kut0+cA64nU7sZXM2sWbM0d+7cFGE3uyHMOtsrr5ijHLVrm6McueFkwKJFJtROmkR3A9hj0yZzdmD+fKlKFburcZ4zZ0yI/eQTc71CBWnrVtPOC8CV2RJmDxw4oKpVqyo2NjYj7i7TEGada9q0SzOFZ8ww49FyI7fbdG/w97e7EuRk/14aOiHBDHtB+i1aZM6udOsmvf663dUA2VuWTwD7559/9O6776pEiRIZcXdACosXS/36ma+HDs29QTYhwXQ4aN/erBUPZIYTJ6T69aX16y9tI8jeuNatpV27pBEjLm377TfT2gtA+qX511O+fPnkuuzjumVZiomJUZ48efTZZ59laHGAZE7HdepkJlI8/LA0cqTdFdnnt9/MkqF//y21a2cWWGDsHTJSZKR0991SRIT06KPStm25YzhPVrn8AFNCgtSzp7Rxo/mA/tZbUr58tpUGOFaahxnMnDkzWZj18vJSoUKFFB4ernwOeBcyzMBZjhyR7rhDOnbM/IH9/nvJz8/uquy1erVpWh8XZ74nCxeyFC4yxtGj5mdqzx6pVClp5UrpppvsrirnOn/eLIf73ntmWEexYmai53332V0ZYD9bxsw6BWHWWXbsML0t8+eXfvrJ9LeE+V60aiXFxkqNGpmjtUFBdlcFJztyRGrSRNq/XypTxgTZcuXsrip3+Pln6ZFHzIcIyawC+M47UqFC9tYF2ClTx8x+9NFH+uKLL1Js/+KLL/Txxx+n9e6Aa6pSxYzbW7SIIHu5Bg3M6kshIeZIbatWZplRID3++MN8KNq/3wTY1asJslmpfn0znOrFF03HktmzpcqVpd9/t7sywBnSHGbHjh2rggULptheuHBhvfbaaxlSFHI3y5L27r10vVQpqXRp++rJrurVk5YtM6sObdok7dxpd0VwqtGjpYMHzZCC1avNkVlkrcBA0+Fg/XrzIb5cOemWW+yuCnCGNIfZw4cPq9wVPrKXKVNGhw8fzpCikLuNHm1+mc+bZ3cl2V/duqaJ/ddfm3ALpMd775m2d6tXmw+PsE/t2ubD6YIFlzpInD9vVgTMXYMCgdRLc5gtXLiwtm/fnmL7tm3bVKBAgQwpCrnXp59Kw4aZdcxPn7a7GmeoVUtq3vzS9T17TJN24FpOnrwUjgICpClTJLorZg9+flLx4peujxwpdeliWntxzAhIKc1h9sEHH9RTTz2llStXKjExUYmJifrhhx/09NNPq2vXrplRI3KJH34wrYAk6YUXpMcft7ceJ9q924x9bNpU+usvu6tBdhURYZakHTyYo31OUKCAWSRl8WLptttMxwOPx+6qgOwjzWH21VdfVXh4uO655x4FBgYqMDBQzZs31913382YWaTbrl3S/feb1a26dJHGjrW7ImdKSDB/5H791bRYOnXK7oqQ3ezaJTVuLB0/blrdxcXZXRGu57nnzASxevVMB5N+/cwH1v377a4MyB7S3Zpr79692rp1qwIDA1WlShWVcciMAVpzZT+RkaaX7OHDZpb+smWsWX4jfv/dBNnjx6XbbzdjagsXtrsqZAc7dkj33GM+5FSvbt5rV5jPi2wqMVF6/33ppZfMwimBgdKsWWZFQCCnoc/sNRBms5+XXjJHYm+9VVq71pxSw43Zvdv0DI2MlCpVMkM4iha1uyrYads2E2T/+kuqWdME2fz57a4K6XHggNS7t1k5bOdOuk8gZ8rUPrMPPPCA/vvf/6bY/sYbb6hTp05pvTtAr74qDR1qTnkSZDNGhQpmZnqJEuZIbePGZhU15E5btpij9X/9JdWpIy1fTpB1svLlzWu4fn3yIPv992aoEZDbpDnMrlmzRq1bt06xvVWrVlqzZk2GFIWcz7IuTTzx9pZGjTK/oJFxbrnFBNrSpc3iCix5m3v9/rt09qwZzrNsmeSAlcdxHV5eZmGFi5YvN90O7rhDukLDISBHS3OYjY2NlZ+fX4rtvr6+io6OzpCikPO9+ab08MOmBRcyz003SWvWSEuWmMUVkDt162b6lvJzkHNFR5tVEjdvNu36hg+X4uPtrgrIGmkOs1WqVNHcuXNTbJ8zZ44qX/4xEbiKuXPNso3/+59p9o/MVaZM8iNxU6dKhw7ZVg6yyPr1ZhLgRffdZ47QI2e6/37pt9+kDh3MUINRo0yo3bjR7sqAzOeT1h2GDh2q+++/X/v379fdd98tSVqxYoVmzZqleSzZhOv46Sepe3fz9VNPSZ0721tPbjNjhtS3rxl6sHIlQztyqjVrzCnnMmWkVaukQoXsrghZoVgxaf586YsvpAEDzOSwO+4wqyoOGWJ3dUDmSfOR2bZt22rBggXat2+fnnjiCT377LM6evSofvjhB918882ZUSNyiN27pXbtzKmv9u2l8ePtrij3adHCdI04fNgsrrBvn90VIaOtWiW1amX6xxYvzljp3MblMgcJfvtNeugh03eaD63I6W64NVd0dLRmz56t6dOna/PmzUpMTMyo2jIFrbnscfKkafh94IAUHm5aReXJY3dVuVNkpGnR9PvvJuysXGkCLpxv+XIznOCff8wHl6++Mr1IkXv98ov5netymeubN0sVK/IhB9lfprbmumjNmjXq0aOHihcvrrfeekt33323fvnll/TeHXIwy5I6djRBtnx5aeFCgqydihUzAfa220y7rkaNTLCFsy1ZIrVta4Js69ZmwhdBFnfccSnInj5tjtpXqWIWUwFyijSF2ePHj+v111/XLbfcok6dOikkJEQXLlzQggUL9Prrr6tOnTqZVScczOWSRo40M+sXLWI1quygSBETaKtWNZOEGjeWzpyxuyqk1/LlZgjP+fPmyOyXX7KKHlI6csR8wDl40CyH27evFBVld1XAjUt1mG3btq0qVKig7du3a8KECTp27Jjee++9zKwNOUiTJlJEhGnmj+yhUCEz3KNGDWnQIJroO1mFCmbISIcOZvKPv7/dFSE7qlHDTArr399cnzrVnKH57jt76wJuVKrHzPr4+Oipp55Sv379dMsttyRt9/X11bZt2xzTlosxs1nno4/MWC2H/GjkWufPcxQvJ4iMlAoWlHx97a4ETrBmjfToo5cmgT78sPmd7e1tb13ARZkyZvann35STEyMatWqpfDwcL3//vs6ffr0DReLnOmrr8wvyvr1pT/+sLsaXMvlQTY6WmrTxkwSQfY2b545CntRsWIEWaRew4bStm3Ss8+a1cT8/QmycK5Uh9k77rhD06ZNU2RkpPr27as5c+aoePHi8ng8WrZsmWJiYjKzTjjI+vWmJYxlSV26mJ6mcIZXXjHjmu+5R9qwwe5qcDVz50pdu0oPPmjeb0B65MkjjRsnrV1rVmW86Ngx6cQJ++oC0uqGWnPt3r1b06dP16effqpz586pWbNmWrhwYUbWl+EYZpC59u83LbhOnTIzqr/+WvJJ89IcsEtMjHndfvrJrBa1eLF5PZF9zJplTgl7PGYBkhkzOKKGjGNZpuPBxo3Su++aAxMXuyEAWSlLWnNJUoUKFfTGG2/ozz//1OzZs2/krpAD/PWXCUKnTkk1a5qjRwRZZwkOlr7/3rTrio6Wmjc3wRbZwyefXAqyjzxCkEXGO3PGHJU9c0b6z39Md4yjR+2uCri2GwqzF3l7e6t9+/bZ/qgsMs/582ZVrz17zLCCb7+VgoLsrgrpERRkZjfffbcUGyu1bGkmi8BeM2ZIPXuaIPvYY9K0aQRZZLwCBcwQo9GjJT8/87u8cmVp+nRz1BbIjjIkzAIXLpg/rKGhZsxlsWJ2V4QbkTev9M03UrNmZlnUnj0lt9vuqnKvn34yEyotS3riCWnyZDNpB8gMvr7Syy9LW7ZIdeuaszS9e5szNSdP2l0dkNINL2frNIyZzTwXLki7d5tG/MgZzp+X+vSRBg82/ShhD49H6tfPdJ6YMIExjMg6iYnSO++YcFumjPTrr6wsh6yRlrxGmMUN2bWLkJPbREWZI/DIfJZ1Kbh6POZrgizssHevmSBas6a5npho2i6WL29vXci5smwCGHK3774zR2Gffdb8oUXOt3KlVK4cKwZlhbffljp2vDS8w8uLIAv73HLLpSArmTMEt91mWnslJl59P7dbWrBAev110/rv9dfNdYYtISMx1xzpsnmz6SHr8ZhZr/yRzR0++kg6e9YsmzpvnpnpjIz35pvSCy+Yr+fPNz1lgezCsqQffzTDkJ5/3izeMWNG8rN0x45JU6ZI0ya7FXnKV/l8ohXsilWMFaSzCSEqVsitPv181bevWYoZuBEcmUWa/fGHdO+9ZmJQ06ZmfW/CbO4wfbrUubM5qvLAA2alN2SssWMvBdlhw8yHRiA7cbnMe3/6dDPkaMMGqUYN6dVXze+GVaukyhUSNH7M32p/apq2q4rOJITqD3cJnUkI1XZVUftT0zR+zN+qXCFBq1bZ/YzgdIyZRZqcO2eWqP3tN6lKFfPpnPGTuUtCgmnWP3u26WAxe7bUqZPdVeUMo0ZJw4ebr0eONGEWyM6OHjWTE7/5xly/6SbpyB8eNfKs1FxPR+XTuavue1Zh6uw1X2u8GmvJMi81bpwlJcMhGDOLTBEfL91/vwmyxYubcZME2dzHx0f69FPTvD8x0SypypopN8ayTHC9GGRfe40gC2coUcKs9DhrlpQvn3Rof4Iaen7Qt55W1wyykpRP5/Sdp6UaeVaqw30JOnYsa2pGzpMtwuzEiRNVtmxZBQQEKDw8XBuusSh848aN5XK5UlzatGmThRXnTj/8YCYAXWyqX6qU3RXBLt7eZvxsz54m0C5YQEP1G3HokJlII0lvvCENGWJrOUCauFzmQ+0jj0gBXvH63NNJfkrdDC8/uTXX01HuOLemTs3kQpFj2R5m586dq0GDBmn48OHasmWLqlWrphYtWujkVTozf/nll4qMjEy67Ny5U97e3urEec5M17KlGej/xRdS9ep2VwO7eXubMXOTJ0uffca46RtRrpxZaemdd8yEGsBp3G5p1idudffMvO4R2X/Lp3N62DNT0ya76XKAdLF9zGx4eLjq1Kmj999/X5Lk8XhUqlQpPfnkkxo8ePB1958wYYKGDRumyMhI5c2b97q3Z8xs2nk8rDaE1PF4zGpVDRvaXUn2Z1lSZCQzuZEzLFhgupxsVxVV0c40779dVVRN27VggdSuXYaXBwdyzJjZ+Ph4bd68WU2bNk3a5uXlpaZNm2rdunWpuo/p06era9euVw2yFy5cUHR0dLILUm/ZMqlOHenPP+2uBNmdZUn9+0uNGpmWPLg6y5IGDjRnOH77ze5qgBsXESHl84lOV5CVpKraoTDvGEVEZHBhyBVsDbOnT59WYmKiihQpkmx7kSJFdPz48evuv2HDBu3cuVO9e/e+6m3Gjh2r0NDQpEspBnqm2vbtpv3Sli2m7yVwPQEB5t/HH5cmTrS3luzK45EGDJDefVc6dUrauNHuioAbFxsrBbtib+g+8ipWBw+yCA/SztEnj6dPn64qVaqobt26V73NkCFDFBUVlXQ5cuRIFlboXEePSq1bm+ULGzUyk1KAa3G5pPHjpeeeM9cHDDBjQHGJx2PaGE2aZL5f06dLPXrYXRVw44KCpBgr6IbuIyoxSFOmSPnzSy1amI4eixZJf/2VQUUix7I1zBYsWFDe3t46ceJEsu0nTpxQ0aJFr7lvXFyc5syZo0cfffSat/P391dISEiyC64tOlpq08YE2ooVTXNsf3+7q4ITuFzmg8/F4e4DB0pvvWVrSdmGxyP16WMWGfHykmbONLO/gZygYkXpbEKIduj2dO2/XVUUq2D5+UlRUdLSpWYRhjZtknf3iI+XNm0y/wIX2Rpm/fz8VKtWLa1YsSJpm8fj0YoVK1SvXr1r7vvFF1/owoUL+s9//pPZZeYqbrdZ4WnbNqlwYfOpOF8+u6uCk7hcpk/q0KHm+nPPMUwlMdEE1xkzTJD99FOz8ASQU7RpIxUr5NZk9UvX/pPVT8ULu3X2rBnaNnmyOWtRoYJ0eRzYts3M4wgNNQv4PPus6bBz5AjtAXMzH7sLGDRokHr06KHatWurbt26mjBhguLi4tSrVy9JUvfu3VWiRAmNHTs22X7Tp09X+/btVaBAATvKzrGGDpWWLJHy5DGtgsqVs7siOJHLZVaz8vEx/5Yvb3dF9jp/Xtqzx7Qz+9//WKIWOY+vr9Snn6/Gj+mpMYkvp6k911mF6VOvnnqun6/y5DFL49aoYcbeS8lD6rFj5gDL2bPS2rXmclGxYtJ775m5HshdbA+zXbp00alTpzRs2DAdP35c1atX1+LFi5MmhR0+fFhe/+oLtXv3bv30009aunSpHSXnaAMGmNM7I0aYT7/AjRg2zCx1W6mS3ZXYK29eafFi6ZdfpObN7a4GyBx9+0rvjPdTl7/n6VtPq1QtnBAvX3X2mi/fvL567LEr3+byHtbt2pkxtHv3SuvXm/fUL7+YI7aRkdLlx7fmzZNGj5buuMNcwsPNkV5aTeY8tveZzWr0mb2+xERzBAnIaH/8Ic2fLz3zTM5fZMHtNmc3OnSwuxIg66xaJbVo5lEjz0rN9XS85hHaswpTZ6/5WuPVWEuXe6lRo/Q/7t9/m+EJNWuaM4uS+T0zYULy24WGSnXrmnDbr585movsKS15jTALrV4tnTxpjqABmSUuTqpaVTpwwEwQe+21nBto4+PNUIIFC8wytc8+a3dFQNZZtUrqcF+CEv6O138SP9YTmpSs/+x2VdFk9dOnXj3lm9dXC77xuaEgezWRkdK6dZeO3m7aJP3zz6X/P3z40rLs33xjrt9xh/k95eub8fUgbQiz10CYTe7336U775TOnZO+/lq67z67K0JO9s47psOBZCaGvfFGzgu0Fy6YD4bffGO6gHz1ldSqld1VAVnr2DHTuWPqJLciT/kqzDtGwV6xivEE6VxisIoXdqtPPzO0IKtWwXO7pZ07TbD97TfT6/ni75927aSFC83XAQFSrVqXhibccYdUsmTO+12V3RFmr4Ewe8mJE+ZNeuiQmS26YoUUGGh3VcjpJk40Y7MlE2zHj885fyTOnzeTTxYtMn8Qv/6aMbLI3dxu836IiDB9y4ODTRuv1q2z19HPCRPM5Of1683ksssFBpp2YRfr3bvXBPCrLDyKDEKYvQbCrBEXJzVubE673HyzORVTsKDdVSG3mDLl0kzli6thOT3Q/vOPGR+7ZIn54/fNN9I999hdFYC0sCwTVi8OTVi/3oyz/eGHS7e5/XYTzqtUSX709tZbmVyWkQiz10CYNRO8OnQwf2wLFDBv2Jtvtrsq5DbTp5tFBCxLGjNGeukluytKv8REM5Rg2TIz+eS778yHRQDO5/FcCqkXLpjQevhwytuFhZk+7VOmZGl5OVZa8prtrbmQtSzLnNq9OJ5v4UKCLOzx6KOmD+24ceZrJ/P2NmF23TpzSvWuu+yuCEBGufxoq7+/6cry55+XWoOtX2/Ocp47Z4YaXZSQINWufekILpPLMg9HZnMZy5JefNEEiM8/lzp2tLsi5Hbx8ZKf36XrluXcIQeRkbT6AXIjt1vascP8Lrv9/1f03bZNql49+e0un1zWvr3UoEFWV+ocaclrjO7IZVwuM4P8118JssgeLg+yH30k9eplTttnd9HRpk/luXOXthFkgdzJ19f0uL0YZCVz1vP776Xhw6WWLc3KZefPSz//LL31VvJxuCdPmmW/16wxc1qQNgwzyCV+/90sKervb65Xq2ZvPcC/HTliJoXFx5ujHB9/bIYhZEdRUVKLFub04qFD5g8WAFwub14TYlu2NNf/PbmsWbNLt/35Z+mFF8zX3t5mOMLFiWXh4fZOLnO7zTyAiAgpNlYKCjIdKdq0yT5DJhhmkAvs22dab1WqZFoF5ctnd0XAlX35pVlsICHBTKT47LPs88vyorNnTbutTZuk/PnNpK+aNe2uCoCTrVljurqsW2d69P7b//4nPfSQ+frsWTMp7fKlezPDsWNmMtu0yaZXcD6faAW7YhVjBelsQoiKFTK9gvv2zZxewQwzQJLTp83ElNOnTeugy0/pAtnN/feb9dR9fc2Y7q5dzZHa7OKvv0y7rU2bTCu7H34gyAK4cQ0bmt99R4+as1Tz5pmFZRo0MK3+wsMv3Xb6dPP759Zbpe7dpUmTpM2bzRHUjLJqlVS5QoLGj/lb7U9N03ZV0ZmEUP3hLqEzCaHaripqf2qaxo/5W5UrJGjVqox77PTgyGwO9s8/5g/vunVSmTLmtEbRonZXBVzft9+axQfi483KPHPnXhoiY5fTp6WmTc2kjkKFTJC9fHwcAGQGt9sMubo4MfbJJ6X33095u4AA0z1h1qxLy/Smx6pVUotmHjXyrNRcT0fl07mr3vaswtTZa77WeDXWkmVeGdqSkD6z15BbwqzHY07Tzp9vet+tXWuGGQBOsXixme174YIZP9u9u731tGxpFkQoUsQE2cqV7a0HQO515oy0YUPyxR3OnTNntaKjTbCVpGHDpF27Lo29rVXr2iuXHTtmjsjW/XuVvvW0lp+uf7g3Xr661+t7bczbSLsifDJsyAFh9hpyS5h97jkzW9LX14zpa9TI7oqAtFu2zBwlGD3a/nZdERFSt25m7FrFivbWAgCX83jM5LI9e6S2bS9tr1nTdC+66N+Ty7p3T/67dfhwafyYv3U4scQ1j8j+21mFqZTXMT03NFAjRtzw05FEmL2m3BBmIyOl224zg8Q/+8z8AQZygn/+MTOC8+TJmsdLTDS//C9ycg9cALnPzz+bM7MXj+BePrmsXDnpwIFL12fMkF4Y5FbnqGmapP5pfqx+mqSFhXvr0J++GTJxlzB7DbkhzErmKNIPP0hPPGF3JUDGOH/eDDtwu83Kddc6VZYRjh6VWrc2C4xc3kIHAJzqzz8vDUsIDjbDECRzZDc4WPr7b2m7qqiKdqb5vreriqppuxYsMHMdbhRh9hpycpj991EkICfZts3M7I2NNcNmvv3W9DvMDEeOSE2aSPv3myEFO3Zk3563AHCjoqOlxo2l/b9GK0qh6b6ffN7RGjwmWC++mBE10Zor1zl0yAwtWLHC7kqAzFGtmrR0qRQSIq1ebVrOxcRk/OMcOmTC8v795jTc4sUEWQA5W0iIORMV5ht7Q/cT7BWbKb+Xr4cwmwOcPWt+CHfvloYMMacLgJyoXj0zKSw0VPrpJ7MKV3R0xt3/gQMmyB48KN10kwnNZcpk3P0DQHYVFCTFWDd2uivGE6Tg4AwqKA0Isw534YLUoYNZrrZkSemrr+xb8g7ICnXrmjMQ+fKZHsrNm5uWNDdq3z5zmu3wYdOMfPXqG+vVCABOUrGidDYhRDuUvgba21VF5xKDben2QuxxMMuSHnnE/NENDjZrJ5coYXdVQOarVcsE2vz5zQe5Q4du/D7ff9+Mla1Y0bQD470EIDdp00YqVsityeqXrv0nq5+KF3ardesMLiwVGAnmYK+8Ylb68PExiyNUrWp3RUDWqVHDdOz45x+pevUr38btNh/yIiLMxLGgIBNW27RRitYx48aZRuPPPGMWRgCA3MTXV+rTz1fjx/TUmMSX09xn9lOvnnquX8a05Uoruhk41DffSPfdZ76eMUPq1cveeoDs4NdfzXAbt1uaMkWaNtmtyFO+yucTrWBXrGKsIJ1NCFGxQm716eere+81R3kZmgMAl68AtlrfelqlegWwNl6LtSlvQ1YAyyo5JczGx0t9+pjJKaNG2V0NYL8tW6R77jHLN585lSDP+Xg9nDhT/TQ5Wc/EHbpdk9VPn3r11HmPn5q39tE33xBoAUAyw6xaNPOokWel5no6XvMI7VmFqbPXfK3xaqyly70ydLVRwuw15JQwK5kxsxIrEgGS6eZRv7505i+Pmmil5un6v4Qf0HytVmMt/NZLbdpkXa0AkJ2tWiV1uC9BCX/H6z+JH+sJTUp2UGC7qiQdFPDN66sF3/hkaJCV6DObY/35pzRihFkcQTIhliALGMHBkvufBN2tH/S9Wl13vFc+ndNitdQ9Xiv1nwcTki3zCAC5WePG0q4IHz37Sh59Xai3qmqH8nlHq7TvMeXzjlY1bdfCwr313NBA7YrI+CCbVkwAc4ioKNNLdscOM5Fl3Di7KwKylylTJM+FeH2hTqka5yVJfnJrrqejSsUd09SpPhoxInNrBACnKF7cHEB7+WVfLVokRUQEKyYmWMHBZiJt69b2TPa6EoYZOIDbbWZfL1smFS1q1lWmkTtwidstlSnhVvtT0zRJ/dO8fz9N0sLCvXXoz+zzyxkAcjOGGeQgliX17WuCbN68ps0QQRZI7rvvpMhTvuqnyenav58m69hJc/QBAOAshNlsbvRo6aOPzEzrzz+Xata0uyIg+4mIkPL5RCeboJAWVbVDYd4xiojI4MIAAJmOMJuNffqpNGyY+XriRNmyqgbgBLGxUrAr9obuI9grVjExGVQQACDLMAEsGwsONisSPfWU9PjjdlcDZF9BQVKMFXRD9xHjCVJwcAYVBADIMoTZbKx9e7Oi0a232l0JkL1VrCidTQjRDt2erqEG21VF5xKDVbFiJhQHAMhUDDPIZiIjpcOHL12vWJGViYDradNGKlbIrcnql679J6ufihd2M5QHAByImJSNxMZK994rhYdLW7faXQ3gHL6+Up9+vvrUu6fOKixN+55VmD716qk+/WjLBQBORJjNJhISpC5dzPryiYmSQ1rgAtlG376Sd6CfunjNU7xSl0rj5avOXvPlm9dXjz2WyQUCADIFYTYbsCzpySelRYvMhK+FC6Xy5e2uCnCW4sWlBd/4aLVXE93r9f11j9CeVZjaeC3WGq/GWvCNj4oXz5o6AQAZizCbDbz5pvTBB5LLJc2aJd1xh90VAc7UuLG0ZJmXNuZtpNLeR9VPk7RDtye7zXZVUT9NUimvY9qUt6GWLveyfV1xAED6sZytzebOlbp2NV+//bY0cKCt5QA5wrFj0tSp0tRJbkWe8lWYd4zpI+sJ0rnEYBUv7FaffmZoAUdkASD7SUteI8xmErfbLLEZEWEmdgUFmc4EbdooaZKJxyM1aCCtW2d6yb7zTqaVA+RKbrcZvhMRIcXEmN7NFSuaBUiY7AUA2Rdh9hoyO8weOyZNmSJNm2yOCOXziVawK1YxVpDOJoSoWCFzRKhvX3NEKDZWev996fnnJW/vDC8HAADAcdKS1xgzm4FWrZIqV0jQ+DF/q/2padquKjqTEKo/3CV0JiFU21VF7U9N0/gxf6tyhQStWmWO2A4eTJAFAABID8JsBlm1SmrRzKO6f6/W4cQSmqT+KVYiqqKdmqT+OpxYQrXi1qhFM49WrbKlXAAAgByBMJsBjh2T2rdNUCPPD/rW00r5dO6at8+nc/reaqmGnpXqcF+Cjh3LmjoBAAByGsJsBpgyRUr8J15zPZ3kJ3eq9vGTW597Osod59bUqZlcIAAAQA5FmL1BbreZ7PVw4szrHpH9t3w6p4c9MzVtslvu1GVgAAAAXIYwe4O++06KPOWrfpqcrv37abKOnfTVokUZXBgAAEAuQJi9QRERUj6f6BSTvVKrqnYozDtGEREZXBgAAEAuQJi9QbGxUrAr9obuI9grVjExGVQQAABALkKYvUFBQVKMFXRD9xHjCVJwcAYVBAAAkIsQZm9QxYrS2YQQ7dDt6dp/u6roXGKwKlbM4MIAAAByAcLsDWrTRipWyK3J6peu/Sern4oXdqt16wwuDAAAIBcgzN4gX1+pTz9fferdU2cVlqZ9zypMn3r1VJ9+vvL1zZz6AAAAcjLCbAbo21fyDvRTF695ilfqUmm8fNXZa7588/rqsccyuUAAAIAcijCbAYoXlxZ846PVXk10r9f31z1Ce1ZhauO1WGu8GmvBNz4qXjxr6gQAAMhpCLMZpHFjackyL23M20ilvY+qnyalmBS2XVXUT5NUyuuYNuVtqKXLvdSokT31AgAA5AQuy7Isu4vIStHR0QoNDVVUVJRCQkIy/P6PHZOmTpWmTnIr8pSvwrxjTB9ZT5DOJQareGG3+vQzQws4IgsAAJBSWvIaYTaTuN3SokVmhbCYGCk42LTxat1aTPYCAAC4hrTkNZ8sqinX8fWV2rUzFwAAAGQOxswCAADAsQizAAAAcCzCLAAAAByLMAsAAADHIswCAADAsQizAAAAcCzCLAAAAByLMAsAAADHIswCAADAsQizAAAAcCzCLAAAAByLMAsAAADHIswCAADAsQizAAAAcCzCLAAAAByLMAsAAADHIswCAADAsQizAAAAcCzCLAAAAByLMAsAAADHIswCAADAsQizAAAAcCzbw+zEiRNVtmxZBQQEKDw8XBs2bLjm7c+dO6f+/furWLFi8vf316233qpFixZlUbUAAADITnzsfPC5c+dq0KBB+uCDDxQeHq4JEyaoRYsW2r17twoXLpzi9vHx8WrWrJkKFy6sefPmqUSJEvrjjz8UFhaW9cUDAADAdi7Lsiy7Hjw8PFx16tTR+++/L0nyeDwqVaqUnnzySQ0ePDjF7T/44AO9+eabioiIkK+vb7oeMzo6WqGhoYqKilJISMgN1Q8AAICMl5a8Ztswg/j4eG3evFlNmza9VIyXl5o2bap169ZdcZ+FCxeqXr166t+/v4oUKaLbb79dr732mhITE6/6OBcuXFB0dHSyCwAAAHIG28Ls6dOnlZiYqCJFiiTbXqRIER0/fvyK+xw4cEDz5s1TYmKiFi1apKFDh+qtt97S6NGjr/o4Y8eOVWhoaNKlVKlSGfo8AAAAYB/bJ4ClhcfjUeHChTV16lTVqlVLXbp00csvv6wPPvjgqvsMGTJEUVFRSZcjR45kYcUAAADITLZNACtYsKC8vb114sSJZNtPnDihokWLXnGfYsWKydfXV97e3knbKlWqpOPHjys+Pl5+fn4p9vH395e/v3/GFg8AAIBswbYjs35+fqpVq5ZWrFiRtM3j8WjFihWqV6/eFfepX7++9u3bJ4/Hk7Rtz549Klas2BWDLAAAAHI2W4cZDBo0SNOmTdPHH3+s33//Xf369VNcXJx69eolSerevbuGDBmSdPt+/frpzJkzevrpp7Vnzx599913eu2119S/f3+7ngIAAABsZGuf2S5duujUqVMaNmyYjh8/rurVq2vx4sVJk8IOHz4sL69LebtUqVJasmSJnnnmGVWtWlUlSpTQ008/rRdffNGupwAAAAAb2dpn1g70mQUAAMjeHNFnFgAAALhRhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4FmEWAAAAjkWYBQAAgGMRZgEAAOBYhFkAAAA4VrYIsxMnTlTZsmUVEBCg8PBwbdiw4aq3nTlzplwuV7JLQEBAFlYLAACA7ML2MDt37lwNGjRIw4cP15YtW1StWjW1aNFCJ0+evOo+ISEhioyMTLr88ccfWVgxAAAAsgvbw+z48ePVp08f9erVS5UrV9YHH3ygPHnyaMaMGVfdx+VyqWjRokmXIkWKZGHFAAAAyC587Hzw+Ph4bd68WUOGDEna5uXlpaZNm2rdunVX3S82NlZlypSRx+NRzZo19dprr+m222674m0vXLigCxcuJF2PioqSJEVHR2fQswAAAEBGupjTLMu67m1tDbOnT59WYmJiiiOrRYoUUURExBX3qVChgmbMmKGqVasqKipK48aN05133qldu3apZMmSKW4/duxYjRw5MsX2UqVKZcyTAAAAQKaIiYlRaGjoNW9ja5hNj3r16qlevXpJ1++8805VqlRJU6ZM0auvvpri9kOGDNGgQYOSrns8Hp05c0YFChSQy+XK9Hqjo6NVqlQpHTlyRCEhIZn+eMh4vIbOx2vofLyGzsbr53xZ/RpalqWYmBgVL178ure1NcwWLFhQ3t7eOnHiRLLtJ06cUNGiRVN1H76+vqpRo4b27dt3xf/39/eXv79/sm1hYWHpqvdGhISE8AZ2OF5D5+M1dD5eQ2fj9XO+rHwNr3dE9iJbJ4D5+fmpVq1aWrFiRdI2j8ejFStWJDv6ei2JiYnasWOHihUrllllAgAAIJuyfZjBoEGD1KNHD9WuXVt169bVhAkTFBcXp169ekmSunfvrhIlSmjs2LGSpFGjRumOO+7QzTffrHPnzunNN9/UH3/8od69e9v5NAAAAGAD28Nsly5ddOrUKQ0bNkzHjx9X9erVtXjx4qRJYYcPH5aX16UDyGfPnlWfPn10/Phx5cuXT7Vq1dLatWtVuXJlu57CNfn7+2v48OEphjrAOXgNnY/X0Pl4DZ2N18/5svNr6LJS0/MAAAAAyIZsXzQBAAAASC/CLAAAAByLMAsAAADHIswCAADAsQizmWjNmjVq27atihcvLpfLpQULFthdEtJg7NixqlOnjoKDg1W4cGG1b99eu3fvtrsspMHkyZNVtWrVpCbf9erV0/fff293WUin119/XS6XSwMHDrS7FKTSiBEj5HK5kl0qVqxod1lIo6NHj+o///mPChQooMDAQFWpUkWbNm2yu6wkhNlMFBcXp2rVqmnixIl2l4J0WL16tfr3769ffvlFy5Ytk9vtVvPmzRUXF2d3aUilkiVL6vXXX9fmzZu1adMm3X333WrXrp127dpld2lIo40bN2rKlCmqWrWq3aUgjW677TZFRkYmXX766Se7S0IanD17VvXr15evr6++//57/fbbb3rrrbeUL18+u0tLYnuf2ZysVatWatWqld1lIJ0WL16c7PrMmTNVuHBhbd68WQ0bNrSpKqRF27Ztk10fM2aMJk+erF9++UW33XabTVUhrWJjY9WtWzdNmzZNo0ePtrscpJGPj0+ql6hH9vPf//5XpUqV0kcffZS0rVy5cjZWlBJHZoFUioqKkiTlz5/f5kqQHomJiZozZ47i4uJSvVw2sof+/furTZs2atq0qd2lIB327t2r4sWLq3z58urWrZsOHz5sd0lIg4ULF6p27drq1KmTChcurBo1amjatGl2l5UMR2aBVPB4PBo4cKDq16+v22+/3e5ykAY7duxQvXr1dP78eQUFBemrr77KtisGIqU5c+Zoy5Yt2rhxo92lIB3Cw8M1c+ZMVahQQZGRkRo5cqTuuusu7dy5U8HBwXaXh1Q4cOCAJk+erEGDBumll17Sxo0b9dRTT8nPz089evSwuzxJhFkgVfr376+dO3cy1suBKlSooK1btyoqKkrz5s1Tjx49tHr1agKtAxw5ckRPP/20li1bpoCAALvLQTpcPtSuatWqCg8PV5kyZfT555/r0UcftbEypJbH41Ht2rX12muvSZJq1KihnTt36oMPPsg2YZZhBsB1DBgwQN9++61WrlypkiVL2l0O0sjPz08333yzatWqpbFjx6patWp655137C4LqbB582adPHlSNWvWlI+Pj3x8fLR69Wq9++678vHxUWJiot0lIo3CwsJ06623at++fXaXglQqVqxYig//lSpVylbDRTgyC1yFZVl68skn9dVXX2nVqlXZbsA70sfj8ejChQt2l4FUuOeee7Rjx45k23r16qWKFSvqxRdflLe3t02VIb1iY2O1f/9+Pfzww3aXglSqX79+iraUe/bsUZkyZWyqKCXCbCaKjY1N9unz4MGD2rp1q/Lnz6/SpUvbWBlSo3///po1a5a+/vprBQcH6/jx45Kk0NBQBQYG2lwdUmPIkCFq1aqVSpcurZiYGM2aNUurVq3SkiVL7C4NqRAcHJxijHrevHlVoEABxq47xHPPPae2bduqTJkyOnbsmIYPHy5vb289+OCDdpeGVHrmmWd055136rXXXlPnzp21YcMGTZ06VVOnTrW7tCSE2Uy0adMmNWnSJOn6oEGDJEk9evTQzJkzbaoKqTV58mRJUuPGjZNt/+ijj9SzZ8+sLwhpdvLkSXXv3l2RkZEKDQ1V1apVtWTJEjVr1szu0oBc4c8//9SDDz6ov/76S4UKFVKDBg30yy+/qFChQnaXhlSqU6eOvvrqKw0ZMkSjRo1SuXLlNGHCBHXr1s3u0pK4LMuy7C4CAAAASA8mgAEAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAIAAMCxCLMAAABwLMIsAAAAHIswCwAAAMcizAJAJmncuLEGDhx4zduULVtWEyZMyJJ60svlcmnBggV2lwEAV0SYBYCr6Nmzp1wuV4rLvn37sqyGESNGyOVy6fHHH0+2fevWrXK5XDp06FCW1QIA2RFhFgCuoWXLloqMjEx2KVeuXJbWEBAQoOnTp2vv3r1Z+riZKT4+3u4SAOQQhFkAuAZ/f38VLVo02cXb21uStHr1atWtW1f+/v4qVqyYBg8erISEhKve18mTJ9W2bVsFBgaqXLly+t///peqGipUqKAmTZro5ZdfvuptZs6cqbCwsGTbFixYIJfLlXR9xIgRql69umbMmKHSpUsrKChITzzxhBITE/XGG2+oaNGiKly4sMaMGZPi/iMjI9WqVSsFBgaqfPnymjdvXrL/P3LkiDp37qywsDDlz59f7dq1S3bUuGfPnmrfvr3GjBmj4sWLq0KFCql67gBwPYRZAEiHo0ePqnXr1qpTp462bdumyZMna/r06Ro9evRV9+nZs6eOHDmilStXat68eZo0aZJOnjyZqsd7/fXXNX/+fG3atOmG6t6/f7++//57LV68WLNnz9b06dPVpk0b/fnnn1q9erX++9//6pVXXtH69euT7Td06FA98MAD2rZtm7p166auXbvq999/lyS53W61aNFCwcHB+vHHH/Xzzz8rKChILVu2THYEdsWKFdq9e7eWLVumb7/99oaeBwBc5GN3AQCQnX377bcKCgpKut6qVSt98cUXmjRpkkqVKqX3339fLpdLFStW1LFjx/Tiiy9q2LBh8vJKfqxgz549+v7777VhwwbVqVNHkjR9+nRVqlQpVXXUrFlTnTt31osvvqgVK1ak+/l4PB7NmDFDwcHBqly5spo0aaLdu3dr0aJF8vLyUoUKFfTf//5XK1euVHh4eNJ+nTp1Uu/evSVJr776qpYtW6b33ntPkyZN0ty5c+XxePThhx8mHQn+6KOPFBYWplWrVql58+aSpLx58+rDDz+Un59fuusHgH8jzALANTRp0kSTJ09Oup43b15J0u+//6569eolO41fv359xcbG6s8//1Tp0qWT3c/vv/8uHx8f1apVK2lbxYoVUwwNuJbRo0erUqVKWrp0qQoXLpyu51O2bFkFBwcnXS9SpIi8vb2The8iRYqkOGJcr169FNe3bt0qSdq2bZv27duX7H4l6fz589q/f3/S9SpVqhBkAWQ4wiwAXEPevHl18803212GJOmmm25Snz59NHjwYE2fPj3Z/3l5ecmyrGTb3G53ivvw9fVNdt3lcl1xm8fjSXVdsbGxqlWr1hXHABcqVCjp64sfBAAgIzFmFgDSoVKlSlq3bl2yAPnzzz8rODhYJUuWTHH7ihUrKiEhQZs3b07atnv3bp07dy5Njzts2DDt2bNHc+bMSba9UKFCiomJUVxcXNK2i0dOM8Ivv/yS4vrFIRI1a9bU3r17VbhwYd18883JLqGhoRlWAwBcCWEWANLhiSee0JEjR/Tkk08qIiJCX3/9tYYPH65BgwalGC8rmY4ELVu2VN++fbV+/Xpt3rxZvXv3VmBgYJoet0iRIho0aJDefffdZNvDw8OVJ08evfTSS9q/f79mzZqlmTNn3shTTOaLL77QjBkztGfPHg0fPlwbNmzQgAEDJEndunVTwYIF1a5dO/344486ePCgVq1apaeeekp//vlnhtUAAFdCmAWAdChRooQWLVqkDRs2qFq1anr88cf16KOP6pVXXrnqPh999JGKFy+uRo0a6f7779djjz2WrrGvzz33XLJJaZKUP39+ffbZZ1q0aJGqVKmi2bNna8SIEWm+76sZOXKk5syZo6pVq+qTTz7R7NmzVblyZUlSnjx5tGbNGpUuXVr333+/KlWqpEcffVTnz59XSEhIhtUAAFfisv49yAoAAABwCI7MAgAAwLEIswAAAHAswiwAAAAcizALAAAAxyLMAgAAwLEIswAAAHAswiwAAAAcizALAAAAxyLMAgAAwLEIswAAAHAswiwAAAAc6/8AcvuQamTi/ckAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_thal = predictions('thal', ['slope', 'thal', 'ca'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "_KNWu9-Bz4dj",
        "outputId": "38a91ffc-f930-4a37-e550-dfc47c405c3e"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scores for each fold: [0.64       0.72       0.74       0.72       0.76       0.67346939]\n",
            "Mean score: 0.7089115646258501\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_ca = predictions('ca', ['slope', 'thal', 'ca'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "z-XNcT05z4dk",
        "outputId": "e4fa3893-cbe3-4aff-bf11-b64ce7cabc4e"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scores for each fold: [0.6        0.64       0.68       0.64       0.62       0.63265306]\n",
            "Mean score: 0.6354421768707483\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Our Models are ready, lets use them to predict the missing values"
      ],
      "metadata": {
        "id": "8arCAI6Wz4dk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_missing.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4yV_4M6xz4dk",
        "outputId": "966463f0-1017-4e07-ef38-f175f53b5a42"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age           0\n",
              "sex           0\n",
              "dataset       0\n",
              "cp            0\n",
              "trestbps      0\n",
              "chol          0\n",
              "fbs           0\n",
              "restecg       0\n",
              "thalch        0\n",
              "exang         0\n",
              "oldpeak       0\n",
              "slope       309\n",
              "ca          610\n",
              "thal        485\n",
              "num           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "def predict_missing_values(df_missing, model, target_column, exclude_columns=[]):\n",
        "    # Prepare encoded DataFrame\n",
        "    df_encoded = df_missing.drop(exclude_columns + [target_column], axis=1, errors='ignore')\n",
        "\n",
        "    # Label encode categorical columns\n",
        "    le = LabelEncoder()\n",
        "    for col in df_encoded.columns:\n",
        "        if df_encoded[col].dtype == 'object':\n",
        "            df_encoded[col] = le.fit_transform(df_encoded[col])\n",
        "\n",
        "    # Predict missing values\n",
        "    features_to_predict = df_encoded.drop([target_column], axis=1, errors='ignore')\n",
        "    y_pred = model.predict(features_to_predict)\n",
        "\n",
        "    # Replace missing values in the original DataFrame\n",
        "    df_missing[target_column] = y_pred\n",
        "\n",
        "# Usage:\n",
        "predict_missing_values(df_missing, model_slope, 'slope', exclude_columns=['thal', 'ca'])\n"
      ],
      "metadata": {
        "id": "FYCy-L9Vz4dk"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predict_missing_values(df_missing, model_thal, 'thal', exclude_columns=['ca', 'slope'])\n",
        "predict_missing_values(df_missing, model_ca, 'ca', exclude_columns=['thal', 'slope'])"
      ],
      "metadata": {
        "id": "KhBmCjqMz4dk"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_missing.isnull().sum() / len(df) * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ZvDeM8Vz4dk",
        "outputId": "e93deca1-7879-453a-faa8-d63141e6dc2e"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0.0\n",
              "sex         0.0\n",
              "dataset     0.0\n",
              "cp          0.0\n",
              "trestbps    0.0\n",
              "chol        0.0\n",
              "fbs         0.0\n",
              "restecg     0.0\n",
              "thalch      0.0\n",
              "exang       0.0\n",
              "oldpeak     0.0\n",
              "slope       0.0\n",
              "ca          0.0\n",
              "thal        0.0\n",
              "num         0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create a heatmap for df to check missing values\n",
        "plt.figure(figsize=(16, 6))\n",
        "sns.heatmap(df_missing.isnull(), cbar=False)\n",
        "plt.title('Missing values after imputation')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "yDDHxJ5Iz4dl",
        "outputId": "86c9e43d-869c-444c-b777-da266a8f2e89"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Concatenate the two dataframes\n",
        "df_complete = pd.concat([df_missing, df_clean], axis=0)\n",
        "\n",
        "# Shape of the Dataframe\n",
        "print(\"The shape of the complete dataframe is: \", df_complete.shape)\n",
        "\n",
        "df_complete.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "44kRjzFWz4dl",
        "outputId": "d54ccfa1-ec87-466e-dc20-a5b762e7a7b2"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The shape of the complete dataframe is:  (919, 15)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age     sex    dataset               cp  trestbps   chol    fbs  \\\n",
              "87    53  Female  Cleveland      non-anginal     128.0  216.0  False   \n",
              "166   52    Male  Cleveland      non-anginal     138.0  223.0  False   \n",
              "192   43    Male  Cleveland     asymptomatic     132.0  247.0   True   \n",
              "266   52    Male  Cleveland     asymptomatic     128.0  204.0   True   \n",
              "287   58    Male  Cleveland  atypical angina     125.0  220.0  False   \n",
              "\n",
              "            restecg  thalch  exang  oldpeak      slope   ca  \\\n",
              "87   lv hypertrophy   115.0  False      0.0  upsloping  0.0   \n",
              "166          normal   169.0  False      0.0  upsloping  0.0   \n",
              "192  lv hypertrophy   143.0   True      0.1  upsloping  0.0   \n",
              "266          normal   156.0   True      1.0  upsloping  2.0   \n",
              "287          normal   144.0  False      0.4  upsloping  0.0   \n",
              "\n",
              "                  thal  num  \n",
              "87              normal    0  \n",
              "166             normal    0  \n",
              "192  reversable defect    1  \n",
              "266  reversable defect    2  \n",
              "287  reversable defect    0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-11490fac-076e-4fe0-8243-4e00e5069e9e\" 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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>dataset</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalch</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>53</td>\n",
              "      <td>Female</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>non-anginal</td>\n",
              "      <td>128.0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>False</td>\n",
              "      <td>lv hypertrophy</td>\n",
              "      <td>115.0</td>\n",
              "      <td>False</td>\n",
              "      <td>0.0</td>\n",
              "      <td>upsloping</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>52</td>\n",
              "      <td>Male</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>non-anginal</td>\n",
              "      <td>138.0</td>\n",
              "      <td>223.0</td>\n",
              "      <td>False</td>\n",
              "      <td>normal</td>\n",
              "      <td>169.0</td>\n",
              "      <td>False</td>\n",
              "      <td>0.0</td>\n",
              "      <td>upsloping</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>43</td>\n",
              "      <td>Male</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>asymptomatic</td>\n",
              "      <td>132.0</td>\n",
              "      <td>247.0</td>\n",
              "      <td>True</td>\n",
              "      <td>lv hypertrophy</td>\n",
              "      <td>143.0</td>\n",
              "      <td>True</td>\n",
              "      <td>0.1</td>\n",
              "      <td>upsloping</td>\n",
              "      <td>0.0</td>\n",
              "      <td>reversable defect</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>266</th>\n",
              "      <td>52</td>\n",
              "      <td>Male</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>asymptomatic</td>\n",
              "      <td>128.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>True</td>\n",
              "      <td>normal</td>\n",
              "      <td>156.0</td>\n",
              "      <td>True</td>\n",
              "      <td>1.0</td>\n",
              "      <td>upsloping</td>\n",
              "      <td>2.0</td>\n",
              "      <td>reversable defect</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>287</th>\n",
              "      <td>58</td>\n",
              "      <td>Male</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>atypical angina</td>\n",
              "      <td>125.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>False</td>\n",
              "      <td>normal</td>\n",
              "      <td>144.0</td>\n",
              "      <td>False</td>\n",
              "      <td>0.4</td>\n",
              "      <td>upsloping</td>\n",
              "      <td>0.0</td>\n",
              "      <td>reversable defect</td>\n",
              "      <td>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-11490fac-076e-4fe0-8243-4e00e5069e9e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-11490fac-076e-4fe0-8243-4e00e5069e9e 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-11490fac-076e-4fe0-8243-4e00e5069e9e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c3b18241-03f4-4102-91b5-60a82b2a2620\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c3b18241-03f4-4102-91b5-60a82b2a2620')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c3b18241-03f4-4102-91b5-60a82b2a2620 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_complete.isnull().sum() / len(df) * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WR9nodO3z4dl",
        "outputId": "49ea17e9-9820-4c05-abfa-f4fa7994e488"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0.0\n",
              "sex         0.0\n",
              "dataset     0.0\n",
              "cp          0.0\n",
              "trestbps    0.0\n",
              "chol        0.0\n",
              "fbs         0.0\n",
              "restecg     0.0\n",
              "thalch      0.0\n",
              "exang       0.0\n",
              "oldpeak     0.0\n",
              "slope       0.0\n",
              "ca          0.0\n",
              "thal        0.0\n",
              "num         0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df_complete"
      ],
      "metadata": {
        "id": "Pyfi5dEPz4dl"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(columns=['dataset'], axis =1)"
      ],
      "metadata": {
        "id": "MMqBiEhKz4dl"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nQcD4sMuz4dl",
        "outputId": "3c7f7777-0f90-4f8a-fe93-9e938555ed1a"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(919, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "------\n",
        "\n",
        "<span style=\"color: teal; font-size: 300%; font-weight: bold;\">Machine Learning</span>"
      ],
      "metadata": {
        "id": "VtSCvN0Bz4dm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Log Transformation"
      ],
      "metadata": {
        "id": "Ca159XlOz4dm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['age'] = np.log(df['age'])\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wTFyAxgLz4dm",
        "outputId": "be94d3c2-ea1f-4a89-c899-d790f4e21941"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          age     sex               cp  trestbps   chol    fbs  \\\n",
              "87   3.970292  Female      non-anginal     128.0  216.0  False   \n",
              "166  3.951244    Male      non-anginal     138.0  223.0  False   \n",
              "192  3.761200    Male     asymptomatic     132.0  247.0   True   \n",
              "266  3.951244    Male     asymptomatic     128.0  204.0   True   \n",
              "287  4.060443    Male  atypical angina     125.0  220.0  False   \n",
              "\n",
              "            restecg  thalch  exang  oldpeak      slope   ca  \\\n",
              "87   lv hypertrophy   115.0  False      0.0  upsloping  0.0   \n",
              "166          normal   169.0  False      0.0  upsloping  0.0   \n",
              "192  lv hypertrophy   143.0   True      0.1  upsloping  0.0   \n",
              "266          normal   156.0   True      1.0  upsloping  2.0   \n",
              "287          normal   144.0  False      0.4  upsloping  0.0   \n",
              "\n",
              "                  thal  num  \n",
              "87              normal    0  \n",
              "166             normal    0  \n",
              "192  reversable defect    1  \n",
              "266  reversable defect    2  \n",
              "287  reversable defect    0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-29799746-064f-45f3-87f7-3861f1adbe1c\" 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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalch</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>3.970292</td>\n",
              "      <td>Female</td>\n",
              "      <td>non-anginal</td>\n",
              "      <td>128.0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>False</td>\n",
              "      <td>lv hypertrophy</td>\n",
              "      <td>115.0</td>\n",
              "      <td>False</td>\n",
              "      <td>0.0</td>\n",
              "      <td>upsloping</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>3.951244</td>\n",
              "      <td>Male</td>\n",
              "      <td>non-anginal</td>\n",
              "      <td>138.0</td>\n",
              "      <td>223.0</td>\n",
              "      <td>False</td>\n",
              "      <td>normal</td>\n",
              "      <td>169.0</td>\n",
              "      <td>False</td>\n",
              "      <td>0.0</td>\n",
              "      <td>upsloping</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>3.761200</td>\n",
              "      <td>Male</td>\n",
              "      <td>asymptomatic</td>\n",
              "      <td>132.0</td>\n",
              "      <td>247.0</td>\n",
              "      <td>True</td>\n",
              "      <td>lv hypertrophy</td>\n",
              "      <td>143.0</td>\n",
              "      <td>True</td>\n",
              "      <td>0.1</td>\n",
              "      <td>upsloping</td>\n",
              "      <td>0.0</td>\n",
              "      <td>reversable defect</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>266</th>\n",
              "      <td>3.951244</td>\n",
              "      <td>Male</td>\n",
              "      <td>asymptomatic</td>\n",
              "      <td>128.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>True</td>\n",
              "      <td>normal</td>\n",
              "      <td>156.0</td>\n",
              "      <td>True</td>\n",
              "      <td>1.0</td>\n",
              "      <td>upsloping</td>\n",
              "      <td>2.0</td>\n",
              "      <td>reversable defect</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>287</th>\n",
              "      <td>4.060443</td>\n",
              "      <td>Male</td>\n",
              "      <td>atypical angina</td>\n",
              "      <td>125.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>False</td>\n",
              "      <td>normal</td>\n",
              "      <td>144.0</td>\n",
              "      <td>False</td>\n",
              "      <td>0.4</td>\n",
              "      <td>upsloping</td>\n",
              "      <td>0.0</td>\n",
              "      <td>reversable defect</td>\n",
              "      <td>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-29799746-064f-45f3-87f7-3861f1adbe1c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-29799746-064f-45f3-87f7-3861f1adbe1c 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-29799746-064f-45f3-87f7-3861f1adbe1c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b958c0b4-d24b-4e98-9f1d-15edeecb85f3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b958c0b4-d24b-4e98-9f1d-15edeecb85f3')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b958c0b4-d24b-4e98-9f1d-15edeecb85f3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SgILemvOz4dm",
        "outputId": "c343c72a-7185-494e-f0e4-ea67689aba6c"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalch',\n",
              "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'num'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Spliting Data\n",
        "\n",
        "X = df.drop('num', axis= 1)\n",
        "y = df['num']\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "columns_to_scale = ['trestbps', 'thalch', 'chol']\n",
        "X[columns_to_scale] = scaler.fit_transform(X[columns_to_scale])\n",
        "\n",
        "\n",
        "sex_encoder = LabelEncoder()\n",
        "dataset_encoder = LabelEncoder()\n",
        "cp_encoder = LabelEncoder()\n",
        "fbs_encoder = LabelEncoder()\n",
        "restecg_encoder = LabelEncoder()\n",
        "exang_encoder = LabelEncoder()\n",
        "slope_encoder = LabelEncoder()\n",
        "thal_encoder = LabelEncoder()\n",
        "\n",
        "\n",
        "X['sex'] = sex_encoder.fit_transform(X['sex'])\n",
        "X['cp'] = cp_encoder.fit_transform(X['cp'])\n",
        "X['fbs'] = fbs_encoder.fit_transform(X['fbs'])\n",
        "X['restecg'] = restecg_encoder.fit_transform(X['restecg'])\n",
        "X['exang'] = exang_encoder.fit_transform(X['exang'])\n",
        "X['slope'] = slope_encoder.fit_transform(X['slope'])\n",
        "X['thal'] = thal_encoder.fit_transform(X['thal'])"
      ],
      "metadata": {
        "id": "pGRYDitzz4dm"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "0oBKL179z4dm",
        "outputId": "e29763e7-7311-4357-8e1f-8f8fc13829b6"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          age  sex  cp  trestbps      chol  fbs  restecg    thalch  exang  \\\n",
              "87   3.970292    0   2  0.400000  0.358209    0        0  0.387324      0   \n",
              "166  3.951244    1   2  0.483333  0.369818    0        1  0.767606      0   \n",
              "192  3.761200    1   0  0.433333  0.409619    1        0  0.584507      1   \n",
              "266  3.951244    1   0  0.400000  0.338308    1        1  0.676056      1   \n",
              "287  4.060443    1   1  0.375000  0.364842    0        1  0.591549      0   \n",
              "\n",
              "     oldpeak  slope   ca  thal  \n",
              "87       0.0      2  0.0     1  \n",
              "166      0.0      2  0.0     1  \n",
              "192      0.1      2  0.0     2  \n",
              "266      1.0      2  2.0     2  \n",
              "287      0.4      2  0.0     2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f4335bca-0ed4-434e-8fd7-e3e7c0a0dfdb\" 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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalch</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>3.970292</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.358209</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.387324</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>3.951244</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.483333</td>\n",
              "      <td>0.369818</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.767606</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>3.761200</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.433333</td>\n",
              "      <td>0.409619</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.584507</td>\n",
              "      <td>1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>266</th>\n",
              "      <td>3.951244</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.338308</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.676056</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>287</th>\n",
              "      <td>4.060443</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.364842</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.591549</td>\n",
              "      <td>0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</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-f4335bca-0ed4-434e-8fd7-e3e7c0a0dfdb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f4335bca-0ed4-434e-8fd7-e3e7c0a0dfdb 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-f4335bca-0ed4-434e-8fd7-e3e7c0a0dfdb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-47ecd631-7872-47f4-b3f3-466b669507bf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-47ecd631-7872-47f4-b3f3-466b669507bf')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-47ecd631-7872-47f4-b3f3-466b669507bf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=31)\n"
      ],
      "metadata": {
        "id": "wl3uCjmOz4dn"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Following are the models that we will be using to for the classification task\n",
        "\n",
        "- Random Forest Classifier  \n",
        "- KNN\n",
        "- XGBoost\n",
        "- Adaboost\n",
        "- SVC\n",
        "- Gradient Boost Classifier"
      ],
      "metadata": {
        "id": "pkQ3-HUHz4dn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a list of models to evaluate\n",
        "models = [\n",
        "    ('Random Forest', RandomForestClassifier(random_state=42)),\n",
        "    ('Gradient Boosting', GradientBoostingClassifier(random_state=42)),\n",
        "    ('Support Vector Machine', SVC(random_state=42)),\n",
        "    ('K-Nearest Neighbors', KNeighborsClassifier()),\n",
        "    ('Ada Boost', AdaBoostClassifier(random_state=42)),\n",
        "    ('XG Boost', XGBClassifier(random_state=42)),\n",
        "]\n",
        "\n",
        "best_model = None\n",
        "best_accuracy = 0.0\n",
        "\n",
        "# Iterate over the models and evaluate their performance\n",
        "for name, model in models:\n",
        "\n",
        "    skf = StratifiedKFold(n_splits=6, shuffle=True, random_state=42)\n",
        "\n",
        "    # Perform cross-validation\n",
        "    scores = cross_val_score(model, X_train, y_train, cv=skf)\n",
        "\n",
        "    # Calculate mean accuracy\n",
        "    mean_accuracy = scores.mean()\n",
        "\n",
        "    # Fit the pipeline on the training data\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    # Make predictions on the test data\n",
        "    y_pred = model.predict(X_test)\n",
        "\n",
        "    # Calculate accuracy score\n",
        "    accuracy = accuracy_score(y_test, y_pred)\n",
        "    report = classification_report(y_test, y_pred)\n",
        "\n",
        "    # Print the performance metrics\n",
        "    print(\"Model:\", name)\n",
        "    print(\"Cross-validation Accuracy:\", mean_accuracy)\n",
        "    print(\"Test Accuracy:\", accuracy)\n",
        "    print()\n",
        "\n",
        "    # Check if the current model has the best accuracy\n",
        "    if accuracy > best_accuracy:\n",
        "        best_accuracy = accuracy\n",
        "        best_model = name\n",
        "        v = report\n",
        "# Retrieve the best model\n",
        "print(\"Best Model:\", best_model)\n",
        "print(\"Classification Report:\\n\", v)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EbRB4jvGz4dn",
        "outputId": "d4d787f3-f8cb-43b5-ad26-67b54a75f96d"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: Random Forest\n",
            "Cross-validation Accuracy: 0.6421986760851215\n",
            "Test Accuracy: 0.6793478260869565\n",
            "\n",
            "Model: Gradient Boosting\n",
            "Cross-validation Accuracy: 0.6612132924607935\n",
            "Test Accuracy: 0.6467391304347826\n",
            "\n",
            "Model: Support Vector Machine\n",
            "Cross-validation Accuracy: 0.6434315162823759\n",
            "Test Accuracy: 0.657608695652174\n",
            "\n",
            "Model: K-Nearest Neighbors\n",
            "Cross-validation Accuracy: 0.6258496601359457\n",
            "Test Accuracy: 0.6304347826086957\n",
            "\n",
            "Model: Ada Boost\n",
            "Cross-validation Accuracy: 0.6068017237549425\n",
            "Test Accuracy: 0.5978260869565217\n",
            "\n",
            "Model: XG Boost\n",
            "Cross-validation Accuracy: 0.6381225287662713\n",
            "Test Accuracy: 0.6467391304347826\n",
            "\n",
            "Best Model: Random Forest\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.92      0.89        84\n",
            "           1       0.63      0.70      0.66        53\n",
            "           2       0.32      0.42      0.36        19\n",
            "           3       0.33      0.14      0.20        21\n",
            "           4       0.00      0.00      0.00         7\n",
            "\n",
            "    accuracy                           0.68       184\n",
            "   macro avg       0.43      0.44      0.42       184\n",
            "weighted avg       0.64      0.68      0.65       184\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "------\n",
        "\n",
        "<span style=\"color: teal; font-size: 300%; font-weight: bold;\">Outputs</span>"
      ],
      "metadata": {
        "id": "5Iti3C6Qz4dn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Best Model`\n",
        "\n",
        "- After comparing different models the best model selected is Random Forest.\n",
        "- The overall accuracy of the model on the test data is 69%.\n",
        "\n",
        "*Class-wise Performance:*\n",
        "\n",
        "Class 0: High precision and recall (0.88 and 0.91), indicating good performance.\\\n",
        "Class 1: Moderate precision and recall (0.58 and 0.73), suggesting decent but not excellent performance.\\\n",
        "Class 2: Lower precision and recall (0.41 and 0.32), indicating challenges in correctly identifying this class.\\\n",
        "Class 3: Moderate precision and recall (0.60 and 0.46), similar to Class 1.\\\n",
        "Class 4: The model struggles to predict Class 4, as both precision and recall are 0.\n"
      ],
      "metadata": {
        "id": "XgTdMsVnz4dn"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZbPXmgi8z4do"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}