final_project.ipynb

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  "metadata": {
    "colab": {
      "name": "final_project.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "suyt8Dq7fIRl",
        "Q4D6TAKLfK20",
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        "A27t628r_bNi",
        "CejGAnhTOUoU"
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      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/george-zakharov/ml_notebooks/blob/master/final_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "suyt8Dq7fIRl",
        "colab_type": "text"
      },
      "source": [
        "# Load libs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I2zVAM1fVsDq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Normalization tests\n",
        "from scipy.stats import kurtosis\n",
        "from scipy.stats import skew\n",
        "from scipy.stats import shapiro\n",
        "from scipy.stats import normaltest\n",
        "\n",
        "# Data normalization / standartization\n",
        "from sklearn.preprocessing import PowerTransformer, MinMaxScaler, StandardScaler\n",
        "\n",
        "# libs for train tuning\n",
        "from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split\n",
        "\n",
        "# Boosting\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from xgboost.sklearn import XGBClassifier\n",
        "\n",
        "# Classification\n",
        "from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier\n",
        "from sklearn.naive_bayes import BernoulliNB, ComplementNB, GaussianNB, MultinomialNB\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "# Model parameters fitting\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Metrics\n",
        "from sklearn.metrics import accuracy_score,auc, f1_score, confusion_matrix,precision_score, recall_score, roc_auc_score, roc_curve\n",
        "\n",
        "# Google files lib\n",
        "from google.colab import files\n",
        "\n",
        "# Serialize data\n",
        "import pickle\n",
        "\n",
        "import random"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q4D6TAKLfK20",
        "colab_type": "text"
      },
      "source": [
        "# Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gIQsdrclfYqt",
        "colab_type": "code",
        "outputId": "2bb5ad13-e488-463f-88be-4fbf97521915",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Get Kaggle libs\n",
        "!pip install kaggle"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.6/dist-packages (1.5.6)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.12.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.6.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.21.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle) (2019.11.28)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.0.0)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.28.1)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (2.8)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle) (1.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3XhNkOi-g_gA",
        "colab_type": "code",
        "outputId": "c708b31c-0f6c-4f58-e9d2-b2d80e110761",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "# Upload kaggle user-token\n",
        "file = files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-75a01969-1a00-4e6d-bf20-af6c41bbb2fd\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-75a01969-1a00-4e6d-bf20-af6c41bbb2fd\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tlHpBE5WhCdy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Move it to the root\n",
        "!mkdir ~/.kaggle\n",
        "!mv kaggle.json ~/.kaggle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHoxbWCDhlj0",
        "colab_type": "code",
        "outputId": "89b240ea-50d5-447c-a211-a485a3557f9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "# Download dataset\n",
        "!kaggle datasets download -d ronitf/heart-disease-uci"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "Downloading heart-disease-uci.zip to /content\n",
            "  0% 0.00/3.40k [00:00<?, ?B/s]\n",
            "100% 3.40k/3.40k [00:00<00:00, 5.62MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yJ9AiVgh1J_",
        "colab_type": "code",
        "outputId": "8486eac5-ae4c-4bcc-ec62-639001272a52",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Unzip file\n",
        "!unzip heart-disease-uci.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  heart-disease-uci.zip\n",
            "  inflating: heart.csv               \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vKZei2n2fSIl",
        "colab_type": "text"
      },
      "source": [
        "# User defined functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eWHdpHkwfWIN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Fit models on Grid SearchCV\n",
        "def grid_search_fitting(models, parameters, X_train, y_train, X_test, y_test): \n",
        "  scores = ['accuracy', 'precision', 'recall']\n",
        "  \n",
        "  for score in scores:\n",
        "    clf = GridSearchCV(estimator = models, scoring = score, param_grid = parameters, cv = 5, iid = False)\n",
        "\n",
        "    clf.fit(X_train, y_train)\n",
        "\n",
        "    score_test = clf.best_estimator_.score(X_test, y_test) \n",
        "    y_pred = clf.predict(X_test)\n",
        "\n",
        "    print(score)\n",
        "    print(clf.best_estimator_)\n",
        "    print(clf.best_score_)\n",
        "    print(\"\\n\")\n",
        "\n",
        "    print(\"Confusion matrix:\")\n",
        "    print(confusion_matrix(y_test, y_pred))\n",
        "\n",
        "    print('accuracy_on_test: ', score_test)\n",
        "    print(\"\\n\")\n",
        "  \n",
        "  return clf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIx_xUVS85if",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# Data standartization\n",
        "def scale_std_minmax(df):\n",
        "  print(df.shape)\n",
        "\n",
        "  scaler = StandardScaler()\n",
        "  print(scaler.fit(df.copy()))\n",
        "  StandardScaler()\n",
        "  result = scaler.transform(df.copy())\n",
        "  result = pd.DataFrame(result, columns = df.columns)\n",
        "\n",
        "\n",
        "  scaler = MinMaxScaler()\n",
        "  print(scaler.fit(result))\n",
        "  MinMaxScaler(copy = True, feature_range = (0, 1))\n",
        "  print(scaler.data_max_)\n",
        "  result = scaler.transform(result)\n",
        "  result = pd.DataFrame(result, columns = df.columns)\n",
        "  \n",
        "  return result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9AePzcYP88Bo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Data preprocessing according to skew metrics\n",
        "def data_preprocessing(df):\n",
        "  n = 1\n",
        "  for feature_name in df.columns:\n",
        "    print(f'\\n\\n{n}. {feature_name}')\n",
        "    feature = df[feature_name]\n",
        "\n",
        "    print_stats(feature)\n",
        "    \n",
        "    skew_ = np.abs(skew(feature))\n",
        "\n",
        "    if (skew_ >= 0.75) and (skew_ < 1.0):\n",
        "      print('\\nДанные рекомендуется логарифмировать\\n')\n",
        "      feature = np.log(feature)\n",
        "    elif skew_ >= 1:\n",
        "      if feature.min() > 0:\n",
        "        print('\\nДанные рекомендуется нормализовать методом boxcox\\n')\n",
        "        box_cox_transform = PowerTransformer(method = 'box-cox', standardize = False)\n",
        "        feature = box_cox_transform.fit_transform(feature.values.reshape(df.shape[0], -1))\n",
        "      else:\n",
        "        print('\\nДанные рекомендуется нормализовать методом yeo-johnson\\n')\n",
        "        yeo_johnson_transform = PowerTransformer(method = 'yeo-johnson', standardize = False)\n",
        "        feature = yeo_johnson_transform.fit_transform(feature.values.reshape(df.shape[0], -1))\n",
        "    else:\n",
        "      print('\\nДанные достаточно стандартизировать\\n')\n",
        "\n",
        "    print_stats(pd.DataFrame(feature, columns = [feature_name]))\n",
        "    chech_skew(feature)\n",
        "\n",
        "    df[feature_name] = feature\n",
        "\n",
        "    n += 1\n",
        "\n",
        "  return df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SlS60H488_ND",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Functions for normalization test\n",
        "def draw_hist_for_feature(data):\n",
        "  plt.style.use('ggplot')\n",
        "  data.hist(bins = 60)\n",
        "  plt.show()\n",
        "\n",
        "\n",
        "def chech_skew(feature):\n",
        "  method_name = '\\nSKEW TEST: '\n",
        "  skew_ = np.abs(skew(feature))\n",
        "  if (skew_ >= 0.75) and (skew_ < 1.0):\n",
        "    print(method_name + 'Данные рекомендуется логарифмировать\\n')\n",
        "  elif skew_ >= 1:\n",
        "    print(method_name + 'Данные рекомендуется нормализовать\\n')\n",
        "  else:\n",
        "    print(method_name + 'Данные достаточно стандартизировать\\n')\n",
        "\n",
        "\n",
        "def check_shapiro(feature):\n",
        "  method_name = '\\nSHAPIRO TEST: '\n",
        "  shapiro_ = np.abs(shapiro(feature))\n",
        "  if (shapiro_[1] < 0.50):\n",
        "    print(method_name + 'Данные рекомендуется нормализовать\\n')\n",
        "  else:\n",
        "    print(method_name + 'Данные достаточно стандартизировать\\n')\n",
        "\n",
        "\n",
        "def print_stats(data, need_hist = True):\n",
        "  if (need_hist == True):\n",
        "    draw_hist_for_feature(data)\n",
        "\n",
        "  print(\"mean : \", np.mean(data))\n",
        "  print(\"var  : \", np.var(data))\n",
        "  print(\"skew : \", skew(data))\n",
        "  print(\"kurt : \", kurtosis(data))\n",
        "  print(\"shapiro : \", shapiro(data))\n",
        "  print(\"normaltest : \", normaltest(data))\n",
        "\n",
        "\n",
        "def print_stats_all(df, need_hist = True):\n",
        "  n = 1\n",
        "  for feature_name in df.columns:\n",
        "    print(f'\\n\\n{n}. {feature_name}')\n",
        "    print_stats(df[feature_name], need_hist)\n",
        "    chech_skew(df[feature_name])\n",
        "    check_shapiro(df[feature_name])\n",
        "    n += 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t1hojqc1fM3f",
        "colab_type": "text"
      },
      "source": [
        "# Dataset exploration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fn_9b17yfWud",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('heart.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q22hVgYUic8p",
        "colab_type": "code",
        "outputId": "6d11250b-bc18-46ba-96db-6cfe09e560bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            "age         303 non-null int64\n",
            "sex         303 non-null int64\n",
            "cp          303 non-null int64\n",
            "trestbps    303 non-null int64\n",
            "chol        303 non-null int64\n",
            "fbs         303 non-null int64\n",
            "restecg     303 non-null int64\n",
            "thalach     303 non-null int64\n",
            "exang       303 non-null int64\n",
            "oldpeak     303 non-null float64\n",
            "slope       303 non-null int64\n",
            "ca          303 non-null int64\n",
            "thal        303 non-null int64\n",
            "target      303 non-null int64\n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 33.3 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0j_c2WD1jCoy",
        "colab_type": "text"
      },
      "source": [
        "*It's a clean, easy to understand set of data. However, the meaning of some of the column headers are not obvious. Here's what they mean:*\n",
        "\n",
        "* age: The person's age in years\n",
        "* sex: The person's sex (1 = male, 0 = female)\n",
        "* cp: The chest pain experienced (Value 1: typical angina, Value 2: atypical angina, Value 3: non-anginal pain, Value 4: asymptomatic)\n",
        "trestbps: The person's resting blood pressure (mm Hg on admission to the hospital)\n",
        "* chol: The person's cholesterol measurement in mg/dl\n",
        "* fbs: The person's fasting blood sugar (> 120 mg/dl, 1 = true; 0 = false)\n",
        "* restecg: Resting electrocardiographic measurement (0 = normal, 1 = having ST-T wave abnormality, 2 = showing probable or definite left ventricular hypertrophy by Estes' criteria)\n",
        "* thalach: The person's maximum heart rate achieved\n",
        "* exang: Exercise induced angina (1 = yes; 0 = no)\n",
        "* oldpeak: ST depression induced by exercise relative to rest ('ST' relates to * positions on the ECG plot. See more here)\n",
        "* slope: the slope of the peak exercise ST segment (Value 1: upsloping, Value 2: flat, Value 3: downsloping)\n",
        "* ca: The number of major vessels (0-3)\n",
        "* thal: A blood disorder called thalassemia (3 = normal; 6 = fixed defect; 7 = reversable defect)\n",
        "* target: Heart disease (0 = no, 1 = yes)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ag_-ApTNiiNu",
        "colab_type": "code",
        "outputId": "38c06dda-ba42-47b8-9763-00271f0337cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age         sex          cp  ...          ca        thal      target\n",
              "count  303.000000  303.000000  303.000000  ...  303.000000  303.000000  303.000000\n",
              "mean    54.366337    0.683168    0.966997  ...    0.729373    2.313531    0.544554\n",
              "std      9.082101    0.466011    1.032052  ...    1.022606    0.612277    0.498835\n",
              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     47.500000    0.000000    0.000000  ...    0.000000    2.000000    0.000000\n",
              "50%     55.000000    1.000000    1.000000  ...    0.000000    2.000000    1.000000\n",
              "75%     61.000000    1.000000    2.000000  ...    1.000000    3.000000    1.000000\n",
              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000    1.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J-VbzdEfiBUq",
        "colab_type": "code",
        "outputId": "7d0edeb7-4ab6-4f0f-c4f1-6c53b6dfb2b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
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              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4   57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ScWxLTGF6It4",
        "colab_type": "code",
        "outputId": "d4c8cb53-a896-49fc-f90d-423fda59ea5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "# Check classes balance\n",
        "df['target'].value_counts(normalize = True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    0.544554\n",
              "0    0.455446\n",
              "Name: target, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KxsaUHuO9xRZ",
        "colab_type": "text"
      },
      "source": [
        "Classes are well-balanced.\n",
        "No need in balancing."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqzU5kjR6lPs",
        "colab_type": "code",
        "outputId": "e095b5a5-11e8-4586-a5d4-8e34fdc0620f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "# Check nulls\n",
        "df.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "cp          0\n",
              "trestbps    0\n",
              "chol        0\n",
              "fbs         0\n",
              "restecg     0\n",
              "thalach     0\n",
              "exang       0\n",
              "oldpeak     0\n",
              "slope       0\n",
              "ca          0\n",
              "thal        0\n",
              "target      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nTHb67ps9Woq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get features and targets\n",
        "X = df.drop(['target'], axis = 1)\n",
        "y = df.target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JUNN0Cb2BB7Q",
        "colab_type": "code",
        "outputId": "7d128f98-e2a2-44ba-b1a9-82f0e1c21b10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        }
      },
      "source": [
        "# Check feature importance\n",
        "model = ExtraTreesClassifier()\n",
        "model.fit(X, y)\n",
        "\n",
        "feat_importances = pd.Series(model.feature_importances_, index = X.columns)\n",
        "\n",
        "main_features = feat_importances.nlargest(10).index\n",
        "\n",
        "feat_importances.nlargest(10).plot(kind = 'bar')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RuEZBL2mBpBskrZe0bILtu0g6r95+haQ5Xds+WK+/QdLL+1d6REQ0MWnQS5oBrACOBuYB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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4QuAwpeB5gU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get 10 main features\n",
        "X_main = X[main_features]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FlexC7ICzgO",
        "colab_type": "code",
        "outputId": "65209f9f-16e7-43f3-db30-802d3378fdf8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Let's see what we got\n",
        "X_main.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>ca</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>cp</th>\n",
              "      <th>thal</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>age</th>\n",
              "      <th>chol</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>slope</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2.3</td>\n",
              "      <td>63</td>\n",
              "      <td>233</td>\n",
              "      <td>145</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3.5</td>\n",
              "      <td>37</td>\n",
              "      <td>250</td>\n",
              "      <td>130</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.4</td>\n",
              "      <td>41</td>\n",
              "      <td>204</td>\n",
              "      <td>130</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.8</td>\n",
              "      <td>56</td>\n",
              "      <td>236</td>\n",
              "      <td>120</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.6</td>\n",
              "      <td>57</td>\n",
              "      <td>354</td>\n",
              "      <td>120</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ca  thalach  exang  cp  thal  oldpeak  age  chol  trestbps  slope\n",
              "0   0      150      0   3     1      2.3   63   233       145      0\n",
              "1   0      187      0   2     2      3.5   37   250       130      0\n",
              "2   0      172      0   1     2      1.4   41   204       130      2\n",
              "3   0      178      0   1     2      0.8   56   236       120      2\n",
              "4   0      163      1   0     2      0.6   57   354       120      2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bEv9HC4TFaiv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Divide numerical and categorical data\n",
        "X_num = X_main.drop(['thal', 'cp', 'ca', 'exang', 'slope'], axis = 1)\n",
        "X_cat = X_main[['thal', 'cp', 'ca', 'exang', 'slope']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "icT2RHVt9fQ_",
        "colab_type": "code",
        "outputId": "498d7940-c66e-4079-ecdc-7e74ab454626",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Check normalization\n",
        "print_stats_all(X_num)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "1. thalach\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  149.64686468646866\n",
            "var  :  522.9148994107329\n",
            "skew :  -0.5347455419075503\n",
            "kurt :  -0.08068888039194055\n",
            "shapiro :  (0.9763151407241821, 6.620309432037175e-05)\n",
            "normaltest :  NormaltestResult(statistic=13.46672289054534, pvalue=0.0011905243421222172)\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "SHAPIRO TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "\n",
            "2. oldpeak\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  1.0396039603960396\n",
            "var  :  1.3436460477730934\n",
            "skew :  1.2634255245891595\n",
            "kurt :  1.5301925815010025\n",
            "shapiro :  (0.8441835045814514, 8.183467206576554e-17)\n",
            "normaltest :  NormaltestResult(statistic=66.6201580770229, pvalue=3.4167757949760942e-15)\n",
            "\n",
            "SKEW TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "SHAPIRO TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "\n",
            "3. age\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  54.366336633663366\n",
            "var  :  82.21233212430155\n",
            "skew :  -0.2014596901125439\n",
            "kurt :  -0.55299857127345\n",
            "shapiro :  (0.9863710999488831, 0.005800189916044474)\n",
            "normaltest :  NormaltestResult(statistic=8.74798581312778, pvalue=0.012600826063683705)\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "SHAPIRO TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "\n",
            "4. chol\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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x6aefqqCgQOFwWNu2bVNxcfFd30/H5080BDxJBgYGVF9fr8WLF2v+/PmSJLfbrVAopPz8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\nAg4ARhFwADDq/wAa9BBdoC/+lAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  246.26402640264027\n",
            "var  :  2677.5606530950135\n",
            "skew :  1.1377326187082244\n",
            "kurt :  4.411682119729873\n",
            "shapiro :  (0.9468814134597778, 5.364669153351542e-09)\n",
            "normaltest :  NormaltestResult(statistic=83.50370500964404, pvalue=7.368869203474054e-19)\n",
            "\n",
            "SKEW TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "SHAPIRO TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "\n",
            "5. trestbps\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LNTw8rBdeeEGlpaVasGDBpPu4XK5Jwe71eqOZMmbiUUc0zxnreu6kWqI13Xpifb+ZSMRr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\nfBWwAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  131.62376237623764\n",
            "var  :  306.5713165375941\n",
            "skew :  0.7102300604861443\n",
            "kurt :  0.8940569555069118\n",
            "shapiro :  (0.965917706489563, 1.4579997014152468e-06)\n",
            "normaltest :  NormaltestResult(statistic=28.567604502994023, pvalue=6.260708761946876e-07)\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "SHAPIRO TEST: Данные рекомендуется нормализовать\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XVNd8dJRfPRg",
        "colab_type": "text"
      },
      "source": [
        "# Dataset transformation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vo433zBC_Izk",
        "colab_type": "text"
      },
      "source": [
        "## Normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2eYnTb8aDlLo",
        "colab_type": "code",
        "outputId": "664067b3-6c54-4a4a-a7ea-382691c4db31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "X_num.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 5 columns):\n",
            "thalach     303 non-null int64\n",
            "oldpeak     303 non-null float64\n",
            "age         303 non-null int64\n",
            "chol        303 non-null int64\n",
            "trestbps    303 non-null int64\n",
            "dtypes: float64(1), int64(4)\n",
            "memory usage: 12.0 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PiwmviRYfXW0",
        "colab_type": "code",
        "outputId": "033bd129-8dde-4aac-f7b8-754709a6bbfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "X_num_normalized = data_preprocessing(X_num)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "1. thalach\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  149.64686468646866\n",
            "var  :  522.9148994107329\n",
            "skew :  -0.5347455419075503\n",
            "kurt :  -0.08068888039194055\n",
            "shapiro :  (0.9763151407241821, 6.620309432037175e-05)\n",
            "normaltest :  NormaltestResult(statistic=13.46672289054534, pvalue=0.0011905243421222172)\n",
            "\n",
            "Данные достаточно стандартизировать\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  thalach    149.646865\n",
            "dtype: float64\n",
            "var  :  thalach    522.914899\n",
            "dtype: float64\n",
            "skew :  [-0.53474554]\n",
            "kurt :  [-0.08068888]\n",
            "shapiro :  (0.9763151407241821, 6.620309432037175e-05)\n",
            "normaltest :  NormaltestResult(statistic=array([13.46672289]), pvalue=array([0.00119052]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "2. oldpeak\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  1.0396039603960396\n",
            "var  :  1.3436460477730934\n",
            "skew :  1.2634255245891595\n",
            "kurt :  1.5301925815010025\n",
            "shapiro :  (0.8441835045814514, 8.183467206576554e-17)\n",
            "normaltest :  NormaltestResult(statistic=66.6201580770229, pvalue=3.4167757949760942e-15)\n",
            "\n",
            "Данные рекомендуется нормализовать методом yeo-johnson\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  oldpeak    0.445135\n",
            "dtype: float64\n",
            "var  :  oldpeak    0.146941\n",
            "dtype: float64\n",
            "skew :  [0.12362192]\n",
            "kurt :  [-1.42218346]\n",
            "shapiro :  (0.878540575504303, 9.218566190895457e-15)\n",
            "normaltest :  NormaltestResult(statistic=array([3236.44009043]), pvalue=array([0.]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "3. age\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  54.366336633663366\n",
            "var  :  82.21233212430155\n",
            "skew :  -0.2014596901125439\n",
            "kurt :  -0.55299857127345\n",
            "shapiro :  (0.9863710999488831, 0.005800189916044474)\n",
            "normaltest :  NormaltestResult(statistic=8.74798581312778, pvalue=0.012600826063683705)\n",
            "\n",
            "Данные достаточно стандартизировать\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  age    54.366337\n",
            "dtype: float64\n",
            "var  :  age    82.212332\n",
            "dtype: float64\n",
            "skew :  [-0.20145969]\n",
            "kurt :  [-0.55299857]\n",
            "shapiro :  (0.9863710999488831, 0.005800189916044474)\n",
            "normaltest :  NormaltestResult(statistic=array([8.74798581]), pvalue=array([0.01260083]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "4. chol\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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x6aefqqCgQOFwWNu2bVNxcfFd30/H5080BDxJBgYGVF9fr8WLF2v+/PmSJLfbrVAopPz8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\nAg4ARhFwADDq/wAa9BBdoC/+lAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  246.26402640264027\n",
            "var  :  2677.5606530950135\n",
            "skew :  1.1377326187082244\n",
            "kurt :  4.411682119729873\n",
            "shapiro :  (0.9468814134597778, 5.364669153351542e-09)\n",
            "normaltest :  NormaltestResult(statistic=83.50370500964404, pvalue=7.368869203474054e-19)\n",
            "\n",
            "Данные рекомендуется нормализовать методом boxcox\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZbHGtw+12y+12B1+3tLTEW1aQy+WydH2msqJP8Sw/Er9HJvaL91t0rO5TdnZ2n/Nivuom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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  chol    4.159172\n",
            "dtype: float64\n",
            "var  :  chol    0.012939\n",
            "dtype: float64\n",
            "skew :  [-0.00594408]\n",
            "kurt :  [0.81329289]\n",
            "shapiro :  (0.9928715825080872, 0.15816503763198853)\n",
            "normaltest :  NormaltestResult(statistic=array([5.72760579]), pvalue=array([0.05705139]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "5. trestbps\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  131.62376237623764\n",
            "var  :  306.5713165375941\n",
            "skew :  0.7102300604861443\n",
            "kurt :  0.8940569555069118\n",
            "shapiro :  (0.965917706489563, 1.4579997014152468e-06)\n",
            "normaltest :  NormaltestResult(statistic=28.567604502994023, pvalue=6.260708761946876e-07)\n",
            "\n",
            "Данные достаточно стандартизировать\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HIJHwPTbo7zgCBwBLEeAAYCkCHAAsRYADgKUIcACwFAEOAJYiwAHAUgHXgS9btkzbt29X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\nPz3lvVxLdaqFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  trestbps    131.623762\n",
            "dtype: float64\n",
            "var  :  trestbps    306.571317\n",
            "dtype: float64\n",
            "skew :  [0.71023006]\n",
            "kurt :  [0.89405696]\n",
            "shapiro :  (0.965917706489563, 1.4579997014152468e-06)\n",
            "normaltest :  NormaltestResult(statistic=array([28.5676045]), pvalue=array([6.26070876e-07]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UdoxgyP4_PRc",
        "colab_type": "text"
      },
      "source": [
        "## Standartization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ja7nGaza_P3g",
        "colab_type": "code",
        "outputId": "e65223c5-8ec5-49aa-b43d-1016fd14b4c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "X_num_scaled = scale_std_minmax(X_num_normalized)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(303, 5)\n",
            "StandardScaler(copy=True, with_mean=True, with_std=True)\n",
            "MinMaxScaler(copy=True, feature_range=(0, 1))\n",
            "[2.28942904 2.105124   2.49624017 4.0055063  3.90516547]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "djs9xb8c_UAZ",
        "colab_type": "code",
        "outputId": "0474f285-20fe-4871-de8e-f6632dd1ec80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "X_num_scaled.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>thalach</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>age</th>\n",
              "      <th>chol</th>\n",
              "      <th>trestbps</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.603053</td>\n",
              "      <td>0.717078</td>\n",
              "      <td>0.708333</td>\n",
              "      <td>0.429459</td>\n",
              "      <td>0.481132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.885496</td>\n",
              "      <td>0.842972</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.476894</td>\n",
              "      <td>0.339623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.770992</td>\n",
              "      <td>0.565692</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.338959</td>\n",
              "      <td>0.339623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.816794</td>\n",
              "      <td>0.406471</td>\n",
              "      <td>0.562500</td>\n",
              "      <td>0.438103</td>\n",
              "      <td>0.245283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.702290</td>\n",
              "      <td>0.334344</td>\n",
              "      <td>0.583333</td>\n",
              "      <td>0.706065</td>\n",
              "      <td>0.245283</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    thalach   oldpeak       age      chol  trestbps\n",
              "0  0.603053  0.717078  0.708333  0.429459  0.481132\n",
              "1  0.885496  0.842972  0.166667  0.476894  0.339623\n",
              "2  0.770992  0.565692  0.250000  0.338959  0.339623\n",
              "3  0.816794  0.406471  0.562500  0.438103  0.245283\n",
              "4  0.702290  0.334344  0.583333  0.706065  0.245283"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dssdjkQq_Vbh",
        "colab_type": "code",
        "outputId": "fc2dcb8e-e5f7-4ab9-876f-7729e067b9c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "X_num_scaled.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>thalach</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>age</th>\n",
              "      <th>chol</th>\n",
              "      <th>trestbps</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.600358</td>\n",
              "      <td>0.355514</td>\n",
              "      <td>0.528465</td>\n",
              "      <td>0.451248</td>\n",
              "      <td>0.354941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.174849</td>\n",
              "      <td>0.306658</td>\n",
              "      <td>0.189210</td>\n",
              "      <td>0.137226</td>\n",
              "      <td>0.165454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.477099</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.385417</td>\n",
              "      <td>0.362051</td>\n",
              "      <td>0.245283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.625954</td>\n",
              "      <td>0.406471</td>\n",
              "      <td>0.541667</td>\n",
              "      <td>0.449440</td>\n",
              "      <td>0.339623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.725191</td>\n",
              "      <td>0.606051</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.539321</td>\n",
              "      <td>0.433962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          thalach     oldpeak         age        chol    trestbps\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000\n",
              "mean     0.600358    0.355514    0.528465    0.451248    0.354941\n",
              "std      0.174849    0.306658    0.189210    0.137226    0.165454\n",
              "min      0.000000    0.000000    0.000000    0.000000    0.000000\n",
              "25%      0.477099    0.000000    0.385417    0.362051    0.245283\n",
              "50%      0.625954    0.406471    0.541667    0.449440    0.339623\n",
              "75%      0.725191    0.606051    0.666667    0.539321    0.433962\n",
              "max      1.000000    1.000000    1.000000    1.000000    1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A27t628r_bNi",
        "colab_type": "text"
      },
      "source": [
        "## Prepare train/test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JGruJmBVH65F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_processed = pd.concat([X_num_scaled, X_cat], axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_-TjnPvSILTD",
        "colab_type": "code",
        "outputId": "fc5d8db4-34fc-4202-9e53-f05ea92b82a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "X_processed.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>thalach</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>age</th>\n",
              "      <th>chol</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>thal</th>\n",
              "      <th>cp</th>\n",
              "      <th>ca</th>\n",
              "      <th>exang</th>\n",
              "      <th>slope</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.603053</td>\n",
              "      <td>0.717078</td>\n",
              "      <td>0.708333</td>\n",
              "      <td>0.429459</td>\n",
              "      <td>0.481132</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.885496</td>\n",
              "      <td>0.842972</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.476894</td>\n",
              "      <td>0.339623</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.770992</td>\n",
              "      <td>0.565692</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.338959</td>\n",
              "      <td>0.339623</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.816794</td>\n",
              "      <td>0.406471</td>\n",
              "      <td>0.562500</td>\n",
              "      <td>0.438103</td>\n",
              "      <td>0.245283</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.702290</td>\n",
              "      <td>0.334344</td>\n",
              "      <td>0.583333</td>\n",
              "      <td>0.706065</td>\n",
              "      <td>0.245283</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    thalach   oldpeak       age      chol  trestbps  thal  cp  ca  exang  slope\n",
              "0  0.603053  0.717078  0.708333  0.429459  0.481132     1   3   0      0      0\n",
              "1  0.885496  0.842972  0.166667  0.476894  0.339623     2   2   0      0      0\n",
              "2  0.770992  0.565692  0.250000  0.338959  0.339623     2   1   0      0      2\n",
              "3  0.816794  0.406471  0.562500  0.438103  0.245283     2   1   0      0      2\n",
              "4  0.702290  0.334344  0.583333  0.706065  0.245283     2   0   0      1      2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEGy0KVFIjyk",
        "colab_type": "code",
        "outputId": "e24c392c-1c1c-4c22-c50b-a8415628265d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "X_processed.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>thalach</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>age</th>\n",
              "      <th>chol</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>thal</th>\n",
              "      <th>cp</th>\n",
              "      <th>ca</th>\n",
              "      <th>exang</th>\n",
              "      <th>slope</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.600358</td>\n",
              "      <td>0.355514</td>\n",
              "      <td>0.528465</td>\n",
              "      <td>0.451248</td>\n",
              "      <td>0.354941</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.399340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.174849</td>\n",
              "      <td>0.306658</td>\n",
              "      <td>0.189210</td>\n",
              "      <td>0.137226</td>\n",
              "      <td>0.165454</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>0.616226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.477099</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.385417</td>\n",
              "      <td>0.362051</td>\n",
              "      <td>0.245283</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.625954</td>\n",
              "      <td>0.406471</td>\n",
              "      <td>0.541667</td>\n",
              "      <td>0.449440</td>\n",
              "      <td>0.339623</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.725191</td>\n",
              "      <td>0.606051</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.539321</td>\n",
              "      <td>0.433962</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          thalach     oldpeak         age  ...          ca       exang       slope\n",
              "count  303.000000  303.000000  303.000000  ...  303.000000  303.000000  303.000000\n",
              "mean     0.600358    0.355514    0.528465  ...    0.729373    0.326733    1.399340\n",
              "std      0.174849    0.306658    0.189210  ...    1.022606    0.469794    0.616226\n",
              "min      0.000000    0.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "25%      0.477099    0.000000    0.385417  ...    0.000000    0.000000    1.000000\n",
              "50%      0.625954    0.406471    0.541667  ...    0.000000    0.000000    1.000000\n",
              "75%      0.725191    0.606051    0.666667  ...    1.000000    1.000000    2.000000\n",
              "max      1.000000    1.000000    1.000000  ...    4.000000    1.000000    2.000000\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yT2bI1Ml_iui",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Split test and train data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_processed, y, test_size = 0.2, random_state = 42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uQACJcpO6_SL",
        "colab_type": "text"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CejGAnhTOUoU",
        "colab_type": "text"
      },
      "source": [
        "## Find best classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LnCmzfUp7ArH",
        "colab_type": "code",
        "outputId": "b579d690-8942-4ee2-8022-4c3a94564840",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "source": [
        "GaussianNB_param = {'var_smoothing':  [1e-09, 1e-08, 1e-07, 1e-06, 1e-05, 1e-04, 1e-03, 0.01, 0.1, 0.2, 0.5, 0.75, 0.9, 1.0]}\n",
        "grid_search_fitting(GaussianNB(), GaussianNB_param, X_train, y_train, X_test, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy\n",
            "GaussianNB(priors=None, var_smoothing=0.2)\n",
            "0.8183383991894629\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[25  4]\n",
            " [ 4 28]]\n",
            "accuracy_on_test:  0.8688524590163934\n",
            "\n",
            "\n",
            "precision\n",
            "GaussianNB(priors=None, var_smoothing=0.1)\n",
            "0.7929629629629631\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[26  3]\n",
            " [ 4 28]]\n",
            "accuracy_on_test:  0.8852459016393442\n",
            "\n",
            "\n",
            "recall\n",
            "GaussianNB(priors=None, var_smoothing=0.9)\n",
            "0.9327635327635327\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[24  5]\n",
            " [ 3 29]]\n",
            "accuracy_on_test:  0.8688524590163934\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
              "             estimator=GaussianNB(priors=None, var_smoothing=1e-09), iid=False,\n",
              "             n_jobs=None,\n",
              "             param_grid={'var_smoothing': [1e-09, 1e-08, 1e-07, 1e-06, 1e-05,\n",
              "                                           0.0001, 0.001, 0.01, 0.1, 0.2, 0.5,\n",
              "                                           0.75, 0.9, 1.0]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='recall', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6LrsVfYD7DPX",
        "colab_type": "code",
        "outputId": "d87aeb64-bc35-483b-c720-0180a734db31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        }
      },
      "source": [
        "BernoulliNB_param = {'alpha': [1e-09, 1e-08, 1e-07, 1e-06, 1e-05, 1e-04, 1e-03, 0.01, 0.1, 0.2, 0.5, 0.75, 0.9, 1.0], \n",
        "                     'binarize': [1e-09, 1e-08, 1e-07, 1e-06, 1e-05, 1e-04, 1e-03, 0.01, 0.1, 0.2, 0.5, 0.75, 0.9, 1.0],\n",
        "                     'fit_prior': ['True', 'False']}\n",
        "grid_search_fitting(BernoulliNB(), BernoulliNB_param, X_train, y_train, X_test, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy\n",
            "BernoulliNB(alpha=0.1, binarize=0.75, class_prior=None, fit_prior='True')\n",
            "0.8139130120133158\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[22  7]\n",
            " [ 5 27]]\n",
            "accuracy_on_test:  0.8032786885245902\n",
            "\n",
            "\n",
            "precision\n",
            "BernoulliNB(alpha=0.1, binarize=0.75, class_prior=None, fit_prior='True')\n",
            "0.7983912165780909\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[22  7]\n",
            " [ 5 27]]\n",
            "accuracy_on_test:  0.8032786885245902\n",
            "\n",
            "\n",
            "recall\n",
            "BernoulliNB(alpha=1e-09, binarize=1e-09, class_prior=None, fit_prior='True')\n",
            "0.8951566951566953\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[22  7]\n",
            " [ 5 27]]\n",
            "accuracy_on_test:  0.8032786885245902\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
              "             estimator=BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None,\n",
              "                                   fit_prior=True),\n",
              "             iid=False, n_jobs=None,\n",
              "             param_grid={'alpha': [1e-09, 1e-08, 1e-07, 1e-06, 1e-05, 0.0001,\n",
              "                                   0.001, 0.01, 0.1, 0.2, 0.5, 0.75, 0.9, 1.0],\n",
              "                         'binarize': [1e-09, 1e-08, 1e-07, 1e-06, 1e-05, 0.0001,\n",
              "                                      0.001, 0.01, 0.1, 0.2, 0.5, 0.75, 0.9,\n",
              "                                      1.0],\n",
              "                         'fit_prior': ['True', 'False']},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='recall', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EY_LuI297Hgq",
        "colab_type": "code",
        "outputId": "3275f61a-34e2-4ded-bbb5-b27ca27fb2a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "SVM_param = {'C': [0.1, 1, 10]}\n",
        "grid_search_fitting(SVC(), SVM_param, X_train, y_train, X_test, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "accuracy\n",
            "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
            "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
            "    kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
            "    shrinking=True, tol=0.001, verbose=False)\n",
            "0.8062671877261544\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[24  5]\n",
            " [ 4 28]]\n",
            "accuracy_on_test:  0.8524590163934426\n",
            "\n",
            "\n",
            "precision\n",
            "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n",
            "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
            "    kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
            "    shrinking=True, tol=0.001, verbose=False)\n",
            "0.8012684110860828\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[26  3]\n",
            " [ 5 27]]\n",
            "accuracy_on_test:  0.8688524590163934\n",
            "\n",
            "\n",
            "recall\n",
            "SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,\n",
            "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
            "    kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
            "    shrinking=True, tol=0.001, verbose=False)\n",
            "0.9022792022792023\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[24  5]\n",
            " [ 5 27]]\n",
            "accuracy_on_test:  0.8360655737704918\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
              "             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
              "                           decision_function_shape='ovr', degree=3,\n",
              "                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,\n",
              "                           probability=False, random_state=None, shrinking=True,\n",
              "                           tol=0.001, verbose=False),\n",
              "             iid=False, n_jobs=None, param_grid={'C': [0.1, 1, 10]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='recall', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "inj3XfkbJ8zR",
        "colab_type": "code",
        "outputId": "55cc862f-7d78-47c2-ac92-03e518f20f12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "RF_param = {'n_estimators': [10, 20, 30, 50, 100], 'criterion': ['gini', 'entropy'], 'max_depth': [2, 3, 4, 5, None], 'min_samples_split': [2, 3, 4, 5], 'min_samples_split': [2, 3, 4, 5]}\n",
        "grid_search_fitting(RandomForestClassifier(), RF_param, X_train, y_train, X_test, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy\n",
            "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
            "                       max_depth=2, max_features='auto', max_leaf_nodes=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=1, min_samples_split=2,\n",
            "                       min_weight_fraction_leaf=0.0, n_estimators=20,\n",
            "                       n_jobs=None, oob_score=False, random_state=None,\n",
            "                       verbose=0, warm_start=False)\n",
            "0.8393526559559994\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[24  5]\n",
            " [ 3 29]]\n",
            "accuracy_on_test:  0.8688524590163934\n",
            "\n",
            "\n",
            "precision\n",
            "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
            "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=1, min_samples_split=3,\n",
            "                       min_weight_fraction_leaf=0.0, n_estimators=20,\n",
            "                       n_jobs=None, oob_score=False, random_state=None,\n",
            "                       verbose=0, warm_start=False)\n",
            "0.8407593445980541\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[25  4]\n",
            " [ 6 26]]\n",
            "accuracy_on_test:  0.8360655737704918\n",
            "\n",
            "\n",
            "recall\n",
            "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
            "                       max_depth=2, max_features='auto', max_leaf_nodes=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=1, min_samples_split=3,\n",
            "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
            "                       n_jobs=None, oob_score=False, random_state=None,\n",
            "                       verbose=0, warm_start=False)\n",
            "0.9173789173789174\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[24  5]\n",
            " [ 4 28]]\n",
            "accuracy_on_test:  0.8524590163934426\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
              "             estimator=RandomForestClassifier(bootstrap=True, class_weight=None,\n",
              "                                              criterion='gini', max_depth=None,\n",
              "                                              max_features='auto',\n",
              "                                              max_leaf_nodes=None,\n",
              "                                              min_impurity_decrease=0.0,\n",
              "                                              min_impurity_split=None,\n",
              "                                              min_samples_leaf=1,\n",
              "                                              min_samples_split=2,\n",
              "                                              min_weight_fraction_leaf=0.0,\n",
              "                                              n_estimators='warn', n_jobs=None,\n",
              "                                              oob_score=False,\n",
              "                                              random_state=None, verbose=0,\n",
              "                                              warm_start=False),\n",
              "             iid=False, n_jobs=None,\n",
              "             param_grid={'criterion': ['gini', 'entropy'],\n",
              "                         'max_depth': [2, 3, 4, 5, None],\n",
              "                         'min_samples_split': [2, 3, 4, 5],\n",
              "                         'n_estimators': [10, 20, 30, 50, 100]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='recall', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o9jeLmq2OvGS",
        "colab_type": "text"
      },
      "source": [
        "## Train on best classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6h96GXLhO_iP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "clf = RandomForestClassifier(bootstrap = True, class_weight = None, criterion = 'gini', max_depth = 2, max_features = 'auto', max_leaf_nodes = None,\n",
        "                             min_impurity_decrease = 0.0, min_impurity_split = None, min_samples_leaf = 1, min_samples_split = 3,\n",
        "                             min_weight_fraction_leaf = 0.0, n_estimators = 100, n_jobs = None, oob_score = False, random_state = None,\n",
        "                             verbose = 0, warm_start = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v6OAGvgCPXYd",
        "colab_type": "code",
        "outputId": "875321c0-5042-4345-bc62-60b48aae14a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
              "                       max_depth=2, max_features='auto', max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=3,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AHpEQMteOzOK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = clf.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BxSLHOVPPqwv",
        "colab_type": "code",
        "outputId": "dfc4e047-2d0f-46f0-c808-8827733c3711",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "X_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>thalach</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>age</th>\n",
              "      <th>chol</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>thal</th>\n",
              "      <th>cp</th>\n",
              "      <th>ca</th>\n",
              "      <th>exang</th>\n",
              "      <th>slope</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>0.312977</td>\n",
              "      <td>0.334344</td>\n",
              "      <td>0.583333</td>\n",
              "      <td>0.542942</td>\n",
              "      <td>0.528302</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>228</th>\n",
              "      <td>0.671756</td>\n",
              "      <td>0.139143</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.571140</td>\n",
              "      <td>0.716981</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>0.778626</td>\n",
              "      <td>0.139143</td>\n",
              "      <td>0.583333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.528302</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>0.603053</td>\n",
              "      <td>0.658558</td>\n",
              "      <td>0.562500</td>\n",
              "      <td>0.798762</td>\n",
              "      <td>0.377358</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>0.450382</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.515875</td>\n",
              "      <td>0.150943</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>0.572519</td>\n",
              "      <td>0.674281</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.487537</td>\n",
              "      <td>0.433962</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>0.702290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.437500</td>\n",
              "      <td>0.311471</td>\n",
              "      <td>0.330189</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>0.534351</td>\n",
              "      <td>0.834495</td>\n",
              "      <td>0.812500</td>\n",
              "      <td>0.300841</td>\n",
              "      <td>0.471698</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>0.541985</td>\n",
              "      <td>0.776780</td>\n",
              "      <td>0.645833</td>\n",
              "      <td>0.557207</td>\n",
              "      <td>0.481132</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184</th>\n",
              "      <td>0.435115</td>\n",
              "      <td>0.754398</td>\n",
              "      <td>0.437500</td>\n",
              "      <td>0.457807</td>\n",
              "      <td>0.528302</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      thalach   oldpeak       age      chol  ...  cp  ca  exang  slope\n",
              "179  0.312977  0.334344  0.583333  0.542942  ...   0   1      1      1\n",
              "228  0.671756  0.139143  0.625000  0.571140  ...   3   0      0      1\n",
              "111  0.778626  0.139143  0.583333  0.000000  ...   2   1      0      2\n",
              "246  0.603053  0.658558  0.562500  0.798762  ...   0   2      1      1\n",
              "60   0.450382  0.000000  0.875000  0.515875  ...   2   1      0      2\n",
              "..        ...       ...       ...       ...  ...  ..  ..    ...    ...\n",
              "249  0.572519  0.674281  0.833333  0.487537  ...   2   3      0      1\n",
              "104  0.702290  0.000000  0.437500  0.311471  ...   2   0      0      2\n",
              "300  0.534351  0.834495  0.812500  0.300841  ...   0   2      0      1\n",
              "193  0.541985  0.776780  0.645833  0.557207  ...   0   2      1      1\n",
              "184  0.435115  0.754398  0.437500  0.457807  ...   0   0      0      1\n",
              "\n",
              "[61 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMKRnwp3RJMR",
        "colab_type": "code",
        "outputId": "0aa2eab3-d32f-4503-de1a-fd5698a49cc7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "X_test.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\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>thalach</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>age</th>\n",
              "      <th>chol</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>thal</th>\n",
              "      <th>cp</th>\n",
              "      <th>ca</th>\n",
              "      <th>exang</th>\n",
              "      <th>slope</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>61.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.586159</td>\n",
              "      <td>0.358464</td>\n",
              "      <td>0.520492</td>\n",
              "      <td>0.445804</td>\n",
              "      <td>0.402258</td>\n",
              "      <td>2.360656</td>\n",
              "      <td>0.868852</td>\n",
              "      <td>0.918033</td>\n",
              "      <td>0.377049</td>\n",
              "      <td>1.311475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.191521</td>\n",
              "      <td>0.331778</td>\n",
              "      <td>0.180040</td>\n",
              "      <td>0.137235</td>\n",
              "      <td>0.183563</td>\n",
              "      <td>0.683930</td>\n",
              "      <td>1.071998</td>\n",
              "      <td>1.129824</td>\n",
              "      <td>0.488669</td>\n",
              "      <td>0.646555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.104167</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.075472</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.465649</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.354167</td>\n",
              "      <td>0.352260</td>\n",
              "      <td>0.283019</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.587786</td>\n",
              "      <td>0.334344</td>\n",
              "      <td>0.541667</td>\n",
              "      <td>0.463322</td>\n",
              "      <td>0.386792</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.740458</td>\n",
              "      <td>0.658558</td>\n",
              "      <td>0.645833</td>\n",
              "      <td>0.535680</td>\n",
              "      <td>0.528302</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.923664</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.798762</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         thalach    oldpeak        age  ...         ca      exang      slope\n",
              "count  61.000000  61.000000  61.000000  ...  61.000000  61.000000  61.000000\n",
              "mean    0.586159   0.358464   0.520492  ...   0.918033   0.377049   1.311475\n",
              "std     0.191521   0.331778   0.180040  ...   1.129824   0.488669   0.646555\n",
              "min     0.000000   0.000000   0.104167  ...   0.000000   0.000000   0.000000\n",
              "25%     0.465649   0.000000   0.354167  ...   0.000000   0.000000   1.000000\n",
              "50%     0.587786   0.334344   0.541667  ...   0.000000   0.000000   1.000000\n",
              "75%     0.740458   0.658558   0.645833  ...   2.000000   1.000000   2.000000\n",
              "max     0.923664   1.000000   0.875000  ...   4.000000   1.000000   2.000000\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jid-lWCIPnZd",
        "colab_type": "code",
        "outputId": "45c2fb95-1a30-40b5-8feb-2a4952002912",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "y_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
              "       1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3KmHYOMPwQN",
        "colab_type": "code",
        "outputId": "e5a9c665-8ee0-49b6-e7cb-56dafd1891af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(clf.predict([[0.6,\t0.1,\t0.4,\t0.7,\t0.8,\t3,\t3,\t0,\t0,\t1]]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h3PRruYRQD5H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pickle.dump(clf, open('model.pkl', 'wb'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GeQGtZlyRR0f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#model = pickle.load(open('model.pkl','rb'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ossuw6o0TcHB",
        "colab_type": "text"
      },
      "source": [
        "# App functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4MYikGnLV8S9",
        "colab_type": "code",
        "outputId": "bad49320-f70a-49d2-b16c-23e632c82ed4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "test_row"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>oldpeak</th>\n",
              "      <th>thalach</th>\n",
              "      <th>age</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>thal</th>\n",
              "      <th>cp</th>\n",
              "      <th>ca</th>\n",
              "      <th>exang</th>\n",
              "      <th>slope</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.586478</td>\n",
              "      <td>0.473282</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.216981</td>\n",
              "      <td>0.446626</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    oldpeak   thalach  age  trestbps      chol  thal  cp  ca  exang  slope\n",
              "0  0.586478  0.473282  0.0  0.216981  0.446626     0   3   0      1      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xBfG-OPCWtFw",
        "colab_type": "code",
        "outputId": "d69fce52-9b69-4dcf-f473-1efe7ee3167e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "test_row.values"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.46747093, 0.67175573, 0.33333333, 0.73584906, 0.48223853,\n",
              "        0.        , 2.        , 2.        , 0.        , 2.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uTQvqtAUWei-",
        "colab_type": "code",
        "outputId": "f33cbb4d-73fd-4277-c933-667848883f17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(clf.predict(test_row))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mxlkFHjEWOE_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pickle.dump(X_processed, open('X_processed.pkl', 'wb'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qSdDf07-gjQ0",
        "colab_type": "text"
      },
      "source": [
        "# Useful links"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9T8WSWQGgleE",
        "colab_type": "text"
      },
      "source": [
        "* https://www.kaggle.com/ronitf/heart-disease-uci (dataset page)\n",
        "* https://www.kaggle.com/tentotheminus9/what-causes-heart-disease-explaining-the-model (good dataset exploration)"
      ]
    }
  ]
}