a-study-based-on-the-probability-of-heart-disease.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "<h1 style='text-align:center'>Predicting the probability of heart disease: A study based on personal keys<h1>\n",
    "\n",
    "## Table of Contents\n",
    "* [Introduction](#chapter1)\n",
    "    * [Where is this data set coming from?](#section_1_1)\n",
    "    * [Dataset Link](#section_1_2)\n",
    "* [Objective](#chapter2)\n",
    "* [Import Libraries & Data Collection](#chapter3)\n",
    "* [Split Data](#chapter4)    \n",
    "* [Data Exploration](#chapter5)    \n",
    "    * [Missing Data](#section_5_1)\n",
    "    * [Explanation of column name](#section_5_2)\n",
    "    * [Numerical Features Analysis](#section_5_3)\n",
    "    * [Categorical Features Analysis](#section_5_4)\n",
    "* [Data Preparation](#chapter6)    \n",
    "    * [Remove Outliers](#section_6_1)\n",
    "    * [Transform Numerical Data](#section_6_2)\n",
    "    * [Transform Categorical Data](#section_6_3)\n",
    "* [Model Train & Select](#chapter7)                    \n",
    "    * [Cross-Validation on Selected model](#section_8_1)\n",
    "    * [Tune Hyperparameters for Model](#section_8_2)\n",
    "* [Model Save](#chapter9)                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction  <a class=\"anchor\" id=\"chapter1\"></a>\n",
    "<p>According to the CDC, heart disease is one of the leading causes of death for people of most races in the US (African Americans, American Indians and Alaska Natives, and white people). About half of all Americans (47%) have at least 1 of 3 key risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicator include diabetic status, obesity (high BMI), not getting enough physical activity or drinking too much alcohol. Detecting and preventing the factors that have the greatest impact on heart disease is very important in healthcare. Computational developments, in turn, allow the application of machine learning methods to detect \"patterns\" from the data that can predict a patient's condition.</p>\n",
    "\n",
    "#### Where is this data set coming from? <a class=\"anchor\" id=\"section_1_1\"></a>\n",
    "<p>Originally, the dataset come from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to gather data on the health status of U.S. residents. As the CDC describes: \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.\". The most recent dataset (as of February 15, 2022) includes data from 2020. It consists of 401,958 rows and 279 columns. The vast majority of columns are questions asked to respondents about their health status, such as \"Do you have serious difficulty walking or climbing stairs?\" or \"Have you smoked at least 100 cigarettes in your entire life? [Note: 5 packs = 100 cigarettes]\".</p>\n",
    "\n",
    "#### Dataset link <a class=\"anchor\" id=\"section_1_2\"></a> \n",
    "<a href='https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease'>https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective <a class=\"anchor\" id=\"chapter2\"></a> \n",
    "\n",
    "<p>To build a system using a machine learning model where any user gets the probabilities for heart disease itself by providing their personal keys.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries & Data Collection  <a class=\"anchor\" id=\"chapter3\"></a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
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    "execution": {
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, cross_validate, RepeatedStratifiedKFold\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler, KBinsDiscretizer, MinMaxScaler\n",
    "from sklearn.feature_selection import SelectKBest, chi2\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "# import xgboost as x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T16:12:22.705507Z",
     "iopub.status.busy": "2022-05-30T16:12:22.705253Z",
     "iopub.status.idle": "2022-05-30T16:12:23.219929Z",
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    }
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This dataset have 18 columns\n",
      "and 319795 rows\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('heart_2020_cleaned.csv')\n",
    "print(f'This dataset have {data.shape[1]} columns\\nand {data.shape[0]} rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
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    {
     "data": {
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       "<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>HeartDisease</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>AlcoholDrinking</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>PhysicalHealth</th>\n",
       "      <th>MentalHealth</th>\n",
       "      <th>DiffWalking</th>\n",
       "      <th>Sex</th>\n",
       "      <th>AgeCategory</th>\n",
       "      <th>Race</th>\n",
       "      <th>Diabetic</th>\n",
       "      <th>PhysicalActivity</th>\n",
       "      <th>GenHealth</th>\n",
       "      <th>SleepTime</th>\n",
       "      <th>Asthma</th>\n",
       "      <th>KidneyDisease</th>\n",
       "      <th>SkinCancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>16.60</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>55-59</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>20.34</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>80 or older</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>7.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>26.58</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Male</td>\n",
       "      <td>65-69</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fair</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>24.21</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>75-79</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Good</td>\n",
       "      <td>6.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>23.71</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Female</td>\n",
       "      <td>40-44</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>8.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  HeartDisease    BMI Smoking AlcoholDrinking Stroke  PhysicalHealth  \\\n",
       "0           No  16.60     Yes              No     No             3.0   \n",
       "1           No  20.34      No              No    Yes             0.0   \n",
       "2           No  26.58     Yes              No     No            20.0   \n",
       "3           No  24.21      No              No     No             0.0   \n",
       "4           No  23.71      No              No     No            28.0   \n",
       "\n",
       "   MentalHealth DiffWalking     Sex  AgeCategory   Race Diabetic  \\\n",
       "0          30.0          No  Female        55-59  White      Yes   \n",
       "1           0.0          No  Female  80 or older  White       No   \n",
       "2          30.0          No    Male        65-69  White      Yes   \n",
       "3           0.0          No  Female        75-79  White       No   \n",
       "4           0.0         Yes  Female        40-44  White       No   \n",
       "\n",
       "  PhysicalActivity  GenHealth  SleepTime Asthma KidneyDisease SkinCancer  \n",
       "0              Yes  Very good        5.0    Yes            No        Yes  \n",
       "1              Yes  Very good        7.0     No            No         No  \n",
       "2              Yes       Fair        8.0    Yes            No         No  \n",
       "3               No       Good        6.0     No            No        Yes  \n",
       "4              Yes  Very good        8.0     No            No         No  "
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T15:38:11.564448Z",
     "iopub.status.busy": "2022-05-30T15:38:11.564188Z",
     "iopub.status.idle": "2022-05-30T15:38:11.984856Z",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 319795 entries, 0 to 319794\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count   Dtype  \n",
      "---  ------            --------------   -----  \n",
      " 0   HeartDisease      319795 non-null  object \n",
      " 1   BMI               319795 non-null  float64\n",
      " 2   Smoking           319795 non-null  object \n",
      " 3   AlcoholDrinking   319795 non-null  object \n",
      " 4   Stroke            319795 non-null  object \n",
      " 5   PhysicalHealth    319795 non-null  float64\n",
      " 6   MentalHealth      319795 non-null  float64\n",
      " 7   DiffWalking       319795 non-null  object \n",
      " 8   Sex               319795 non-null  object \n",
      " 9   AgeCategory       319795 non-null  object \n",
      " 10  Race              319795 non-null  object \n",
      " 11  Diabetic          319795 non-null  object \n",
      " 12  PhysicalActivity  319795 non-null  object \n",
      " 13  GenHealth         319795 non-null  object \n",
      " 14  SleepTime         319795 non-null  float64\n",
      " 15  Asthma            319795 non-null  object \n",
      " 16  KidneyDisease     319795 non-null  object \n",
      " 17  SkinCancer        319795 non-null  object \n",
      "dtypes: float64(4), object(14)\n",
      "memory usage: 43.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter4\"></a> <h2 style='text-align: center'>Split Data</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T16:09:13.314082Z",
     "iopub.status.busy": "2022-05-30T16:09:13.313538Z",
     "iopub.status.idle": "2022-05-30T16:09:13.674149Z",
     "shell.execute_reply": "2022-05-30T16:09:13.673399Z",
     "shell.execute_reply.started": "2022-05-30T16:09:13.314044Z"
    }
   },
   "outputs": [],
   "source": [
    "# Remove duplicates from dataset\n",
    "data = data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's dataset split train, validation, and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T16:12:30.241288Z",
     "iopub.status.busy": "2022-05-30T16:12:30.241034Z",
     "iopub.status.idle": "2022-05-30T16:12:30.83686Z",
     "shell.execute_reply": "2022-05-30T16:12:30.83609Z",
     "shell.execute_reply.started": "2022-05-30T16:12:30.241261Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Before split: (301717, 18)\n",
      "\n",
      "After split train: (241373, 18)\n",
      "\n",
      "After split test: (60344, 18)\n",
      "\n",
      "\n",
      "Now, let's split the train set into train and validation.\n",
      "\n",
      "After split train: (193098, 18)\n",
      "\n",
      "After split validation: (48275, 18)\n"
     ]
    }
   ],
   "source": [
    "print(f\"\\nBefore split: {data.shape}\")\n",
    "\n",
    "train, test = train_test_split(data, test_size = 0.2, stratify=data[\"HeartDisease\"])\n",
    "\n",
    "print(f\"\\nAfter split train: {train.shape}\")\n",
    "print(f\"\\nAfter split test: {test.shape}\")\n",
    "\n",
    "print(\"\\n\\nNow, let's split the train set into train and validation.\")\n",
    "train, validation = train_test_split(train, test_size = 0.2)\n",
    "print(f\"\\nAfter split train: {train.shape}\")\n",
    "print(f\"\\nAfter split validation: {validation.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter5\"></a>  <h2 style='text-align: center'>Data Exploration</h2> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Missing Data <a class=\"anchor\" id=\"section_5_1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of data that are missing from the dataset: 0\n"
     ]
    }
   ],
   "source": [
    "print('The number of data that are missing from the dataset:',data.isnull().sum().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explanation of column name <a class=\"anchor\" id=\"section_5_2\"></a>\n",
    "\n",
    "1. HeartDisease : Respondents that have ever reported having coronary heart disease (CHD) or myocardial infarction (MI).\n",
    "2. BMI : Body Mass Index (BMI).\n",
    "3. Smoking : Have you smoked at least 100 cigarettes in your entire life? ( The answer Yes or No ).\n",
    "4. AlcoholDrinking : Heavy drinkers (adult men having more than 14 drinks per week and adult women having more than 7 drinks per week\n",
    "5. Stroke : (Ever told) (you had) a stroke?\n",
    "6. PhysicalHealth : Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good? (0-30 days).\n",
    "7. MentalHealth : Thinking about your mental health, for how many days during the past 30 days was your mental health not good? (0-30 days).\n",
    "8. DiffWalking : Do you have serious difficulty walking or climbing stairs?\n",
    "9. Sex : Are you male or female?\n",
    "10. AgeCategory: Fourteen-level age category.\n",
    "11. Race : Imputed race/ethnicity value.\n",
    "12. Diabetic : (Ever told) (you had) diabetes?\n",
    "13. PhysicalActivity : Adults who reported doing physical activity or exercise during the past 30 days other than their regular job.\n",
    "14. GenHealth : Would you say that in general your health is...\n",
    "15. SleepTime : On average, how many hours of sleep do you get in a 24-hour period?\n",
    "16. Asthma : (Ever told) (you had) asthma?\n",
    "17. KidneyDisease : Not including kidney stones, bladder infection or incontinence, were you ever told you had kidney disease?\n",
    "18. SkinCancer : (Ever told) (you had) skin cancer?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of each unique values in columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HeartDisease           2\n",
       "BMI                 3302\n",
       "Smoking                2\n",
       "AlcoholDrinking        2\n",
       "Stroke                 2\n",
       "PhysicalHealth        31\n",
       "MentalHealth          31\n",
       "DiffWalking            2\n",
       "Sex                    2\n",
       "AgeCategory           13\n",
       "Race                   6\n",
       "Diabetic               4\n",
       "PhysicalActivity       2\n",
       "GenHealth              5\n",
       "SleepTime             24\n",
       "Asthma                 2\n",
       "KidneyDisease          2\n",
       "SkinCancer             2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numerical Features Analysis.  <a class=\"anchor\" id=\"section_5_3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  color: #000000;\n",
       "}\n",
       "#T_57610_row0_col1 {\n",
       "  background-color: #fff5b5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_57610_row0_col2 {\n",
       "  background-color: #ffeda0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_57610_row0_col3, #T_57610_row3_col6 {\n",
       "  background-color: #fed976;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_57610_row0_col4 {\n",
       "  background-color: #fecc68;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_57610_row0_col5 {\n",
       "  background-color: #febf5a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_57610_row0_col6 {\n",
       "  background-color: #800026;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_57610_row1_col0 {\n",
       "  background-color: #fffac0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_57610_row1_col1, #T_57610_row2_col1 {\n",
       "  background-color: #fff3ae;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_57610_row1_col2, #T_57610_row1_col3, #T_57610_row1_col4, #T_57610_row2_col2, #T_57610_row2_col3, #T_57610_row2_col4 {\n",
       "  background-color: #ffffcc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_57610_row1_col5 {\n",
       "  background-color: #fffcc5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_57610_row1_col6, #T_57610_row2_col6 {\n",
       "  background-color: #fec45f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_57610_row2_col0 {\n",
       "  background-color: #fff9bd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_57610_row2_col5 {\n",
       "  background-color: #fff9be;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_57610_row3_col0 {\n",
       "  background-color: #fff4b2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_57610_row3_col1 {\n",
       "  background-color: #fffdc8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_57610_row3_col2 {\n",
       "  background-color: #fffec9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_57610_row3_col3 {\n",
       "  background-color: #fff6b6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_57610_row3_col4 {\n",
       "  background-color: #fff5b3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_57610_row3_col5 {\n",
       "  background-color: #fff3af;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_57610\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_57610_level0_col0\" class=\"col_heading level0 col0\" >mean</th>\n",
       "      <th id=\"T_57610_level0_col1\" class=\"col_heading level0 col1\" >std</th>\n",
       "      <th id=\"T_57610_level0_col2\" class=\"col_heading level0 col2\" >min</th>\n",
       "      <th id=\"T_57610_level0_col3\" class=\"col_heading level0 col3\" >25%</th>\n",
       "      <th id=\"T_57610_level0_col4\" class=\"col_heading level0 col4\" >50%</th>\n",
       "      <th id=\"T_57610_level0_col5\" class=\"col_heading level0 col5\" >75%</th>\n",
       "      <th id=\"T_57610_level0_col6\" class=\"col_heading level0 col6\" >max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_57610_level0_row0\" class=\"row_heading level0 row0\" >BMI</th>\n",
       "      <td id=\"T_57610_row0_col0\" class=\"data row0 col0\" >28.444494</td>\n",
       "      <td id=\"T_57610_row0_col1\" class=\"data row0 col1\" >6.468668</td>\n",
       "      <td id=\"T_57610_row0_col2\" class=\"data row0 col2\" >12.020000</td>\n",
       "      <td id=\"T_57610_row0_col3\" class=\"data row0 col3\" >24.030000</td>\n",
       "      <td id=\"T_57610_row0_col4\" class=\"data row0 col4\" >27.410000</td>\n",
       "      <td id=\"T_57610_row0_col5\" class=\"data row0 col5\" >31.650000</td>\n",
       "      <td id=\"T_57610_row0_col6\" class=\"data row0 col6\" >94.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_57610_level0_row1\" class=\"row_heading level0 row1\" >PhysicalHealth</th>\n",
       "      <td id=\"T_57610_row1_col0\" class=\"data row1 col0\" >3.575345</td>\n",
       "      <td id=\"T_57610_row1_col1\" class=\"data row1 col1\" >8.141597</td>\n",
       "      <td id=\"T_57610_row1_col2\" class=\"data row1 col2\" >0.000000</td>\n",
       "      <td id=\"T_57610_row1_col3\" class=\"data row1 col3\" >0.000000</td>\n",
       "      <td id=\"T_57610_row1_col4\" class=\"data row1 col4\" >0.000000</td>\n",
       "      <td id=\"T_57610_row1_col5\" class=\"data row1 col5\" >2.000000</td>\n",
       "      <td id=\"T_57610_row1_col6\" class=\"data row1 col6\" >30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_57610_level0_row2\" class=\"row_heading level0 row2\" >MentalHealth</th>\n",
       "      <td id=\"T_57610_row2_col0\" class=\"data row2 col0\" >4.125004</td>\n",
       "      <td id=\"T_57610_row2_col1\" class=\"data row2 col1\" >8.137727</td>\n",
       "      <td id=\"T_57610_row2_col2\" class=\"data row2 col2\" >0.000000</td>\n",
       "      <td id=\"T_57610_row2_col3\" class=\"data row2 col3\" >0.000000</td>\n",
       "      <td id=\"T_57610_row2_col4\" class=\"data row2 col4\" >0.000000</td>\n",
       "      <td id=\"T_57610_row2_col5\" class=\"data row2 col5\" >4.000000</td>\n",
       "      <td id=\"T_57610_row2_col6\" class=\"data row2 col6\" >30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_57610_level0_row3\" class=\"row_heading level0 row3\" >SleepTime</th>\n",
       "      <td id=\"T_57610_row3_col0\" class=\"data row3 col0\" >7.082000</td>\n",
       "      <td id=\"T_57610_row3_col1\" class=\"data row3 col1\" >1.472415</td>\n",
       "      <td id=\"T_57610_row3_col2\" class=\"data row3 col2\" >1.000000</td>\n",
       "      <td id=\"T_57610_row3_col3\" class=\"data row3 col3\" >6.000000</td>\n",
       "      <td id=\"T_57610_row3_col4\" class=\"data row3 col4\" >7.000000</td>\n",
       "      <td id=\"T_57610_row3_col5\" class=\"data row3 col5\" >8.000000</td>\n",
       "      <td id=\"T_57610_row3_col6\" class=\"data row3 col6\" >24.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1602b19e280>"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()[1:].T.style.background_gradient(cmap='YlOrRd', axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oxPx+AK06JDjW19entmJjsuee+6Rfv16ZO3dBtUsBgPe0yy6frHYJdGJ+P4BWXqoKAABAIcERAACAQoIjAAAAhQRHAAAACgmOAAAAFBIcAQAAKCQ4AgAAUEhwBAAAoJDgCAAAQCHBEQAAgEKCIwAAAIUERwAAAAoJjgAAABQSHAEAACgkOAIAAFBIcAQAAKCQ4AgAAEAhwREAAIBCgiMAAACFBEcAAAAKCY4AAAAUEhwBAAAoJDgCAABQSHAEAACgkOAIAABAIcERAACAQoIjAAAAhQRHAAAACgmOAAAAFBIcAQAAKCQ4AgAAUEhwBAAAoJDgCAAAQCHBEQAAgEKCIwAAAIUERwAAAAoJjgAAABSq76iGmpLc37Nnuxz79bq6bNAuRwYAVicvzl8zP5i60Urtl2Sl9l3WsTZZ5aMAdC4dEhy33PKDmTXrlay9dv92Of4GSYYP37Rdjg0ArB423njoSu/b//W5SZI11u63ynVssoq1AHRGHRIczz77u+nXr1fmzl3QEc0BADWooeHwapcA0GV5jyMAAACFBEcAAAAKCY4AAAAUEhwBAAAoJDgCAABQSHAEAACgkOAIAABAIcERAACAQoIjAAAAhQRHAAAACgmOAAAAFBIcAQAAKCQ4AgAAUEhwBAAAoJDgCAAAQCHBEQAAgEKCIwAAAIUERwAAAAqVyuVyudpFAAAA0HlZcQQAAKCQ4AgAAEAhwREAAIBCgiMAAACFBEcAAAAKCY4AAAAUqm/Pg7e0tGTcuHF54okn0qNHj0yYMCEbb7xxezbZaXz2s59N3759kyQbbrhhzj777CpX1H7+8Y9/5IILLsjkyZPz7LPPZsyYMSmVStlss83yrW99K3V1XfP5icX7PXXq1Bx99NHZZJNNkiRf/OIXs99++1W3wApqbGzMaaedlhdeeCGLFi3KMccck0033bTLn+tl9Xvdddft0ue6ubk5Z5xxRp555pl069YtZ599dsrlcpc/13SsWv2fUg2zZ8/O5z73uVx99dWpr683xu3gyiuvzN13353GxsZ88YtfzI477micK6ixsTFjxozJCy+8kLq6uowfP97vcpW0a3C88847s2jRovz85z/PQw89lHPOOSeTJk1qzyY7hbfffjtJMnny5CpX0v5+8IMf5NZbb03Pnj2TJGeffXZGjRqVj370oznzzDNz1113Zc8996xylZW3dL//+c9/5vDDD8/IkSOrXFn7uPXWW9OvX7+cf/75mTNnTg488MBsueWWXf5cL6vfxx13XJc+1/fcc0+S5LrrrsuUKVPagmNXP9d0rFr9n9LRGhsbc+aZZ2bNNddMUjtzdEeaMmVKHnzwwfzsZz/LwoULc/XVVxvnCvv973+fpqamXHfddfnTn/6Uiy66KI2Njca4Cto1mv/tb3/LLrvskiTZbrvt8uijj7Znc53G448/noULF2bkyJE57LDD8tBDD1W7pHaz0UYbZeLEiW3bU6dOzY477pgk2XXXXXP//fdXq7R2tXS/H3300dx777059NBDc9ppp2XevHlVrK7y9tlnn5x44olt2926dauJc72sfnf1c73HHntk/PjxSZKZM2dm0KBBNXGu6Vi1+j+lo5177rk55JBD8oEPfCBJ7czRHemPf/xjNt988xx33HE5+uij88lPftI4V9jQoUPT3NyclpaWzJs3L/X19ca4Sto1OM6bNy99+vRp2+7WrVuampras8lOYc0118wRRxyRH/7wh/n2t7+dk08+ucv2e++99059/f9buC6XyymVSkmS3r17580336xWae1q6X5vu+22+eY3v5lrr702Q4YMyWWXXVbF6iqvd+/e6dOnT+bNm5cTTjgho0aNqolzvax+d/VznST19fU55ZRTMn78+Oy99941ca7pWLX6P6Uj3XzzzRkwYEDbE/hJ7czRHWnOnDl59NFHc/HFF7dd8xnnyurVq1deeOGF7Lvvvhk7dmwaGhqMcZW0a3Ds06dP5s+f37bd0tKyxMV2VzV06NB85jOfSalUytChQ9OvX7+8+uqr1S6rQyz++vL58+dnrbXWqmI1HWfPPffMNtts03b7n//8Z5UrqrwXX3wxhx12WA444ICMGDGiZs710v2uhXOdvLNScfvtt2fs2LFtL79Puva5pmPV6v+UjnLTTTfl/vvvT0NDQx577LGccsopee2119ruN8aV0a9fv3ziE59Ijx49MmzYsKyxxhpLhBjjvOquueaafOITn8jtt9+eX/7ylxkzZkwaGxvb7jfGHaddg+P222+f++67L0ny0EMPZfPNN2/P5jqNG2+8Meecc06S5OWXX868efMyePDgKlfVMT74wQ9mypQpSZL77rsvO+ywQ5Ur6hhHHHFEHn744STJAw88kK233rrKFVXWrFmzMnLkyHzjG9/IF77whSS1ca6X1e+ufq5/8Ytf5Morr0yS9OzZM6VSKdtss02XP9d0rFr9n9KRrr322vzkJz/J5MmTs9VWW+Xcc8/Nrrvuaowr7CMf+Uj+8Ic/pFwu5+WXX87ChQvzsY99zDhX0FprrdX2gZNrr712mpqa/L+oklK5XC6318FbP1X1ySefTLlczn//939n+PDh7dVcp7Fo0aKceuqpmTlzZkqlUk4++eRsv/321S6r3Tz//PMZPXp0rr/++jzzzDMZO3ZsGhsbM2zYsEyYMCHdunWrdontYvF+T506NePHj0/37t0zaNCgjB8/fomXaa/uJkyYkNtuuy3Dhg1r+9npp5+eCRMmdOlzvax+jxo1Kueff36XPdcLFizIqaeemlmzZqWpqSlHHnlkhg8fXjN/13SMWv2fUi0NDQ0ZN25c6urq/C23g/POOy9TpkxJuVzOSSedlA033NA4V9D8+fNz2mmn5dVXX01jY2MOO+ywbLPNNsa4Cto1OAIAALD684UnAAAAFBIcAQAAKCQ4AgAAUEhwBAAAoJDgCAAAQCHBkS5hypQp+djHPpaGhoY0NDTk4IMPzuTJk9PQ0JDp06ev1DFfffXVjBs3boXrOOmkk5Iku++++xJfnD59+vQ0NDSscB2tfZg7d25+9atfJUnGjBnT9h2pAFCLFp/7v/zlL+eQQw7J9OnTM2bMmOywww5ZtGhR22OnTp2aLbbYIlOmTMnzzz+fgw8+uIqVw+qpvtoFQKXstNNO+d73vpfkne/S3Geffdq+MHZlDB48eIWDY3t64okncvfdd2fEiBHVLgUAOoXF5/4//vGPOe+889K/f/8MHjw49913X/bYY48kya9+9asMGTKkmqXCak9wpEuaN29e6urq0q1bt1x22WWZNWtWFi5cmAsvvDA33HBD1llnnRx66KF5/fXXc/jhh+eqq67KqFGjUi6X09jYmG9/+9vp3bt3Ro8eneuvvz733HNPLr300iTJBz/4wXz729/OHXfckWuvvbatzYsvvni56/vLX/6S733ve+nWrVuGDBmSs846K2+//XZOP/30vPnmm5kzZ04OOuigfOlLX2rb54orrsjjjz+en//850mSn//857nqqqsyb968jBs3Lttuu22FRg8AVj9vvPFGNthggyxYsCD7779/fv3rX2ePPfZIS0tLpk6dmg996EPVLhFWa4IjXcaf//znNDQ0pFQqpXv37hk7dmyuuuqq7LbbbjnggAMyceLE/Pa3v81BBx2U0aNH59BDD82vf/3rjBgxIg8//HD69u2b7373u5k2bVrmzZuX3r17J0mampoyfvz43HDDDRk4cGAuvfTSvPTSS5kxY0a+//3vp2fPnjnzzDPzxz/+Meuss84SNY0cOTJ1de+8InzhwoXp2bNnyuVyxo4dm5/+9KcZOHBgLrrootxyyy3Zeuuts//++2evvfbKyy+/nIaGhiWC49FHH53rrrsu//Ef/5EHH3wwW2+9dY499tjcfPPNufnmmwVHAGpO69y/aNGiPPHEE7nyyitzyy23ZNttt83vfve7LFiwIA899FA++tGPrvRbV4B3CI50GYu/XKXVVVddlW222SZJMmjQoMyaNStDhgxJ7969M23atPzqV7/K5Zdfnn79+mXGjBk59thjU19fn2OOOabtGHPmzMlaa62VgQMHJkmOP/74JMnAgQNzyimnpHfv3nn66aez3Xbbvaumq6++OmussUaSd97jOG7cuLz22mt55ZVXMmrUqCTJW2+9lZ133jm77bZbfvzjH+eOO+5Inz590tTUVNjfrbfeuq1fb7311ooPGACs5haf+59++ukccsgh+fjHP57knc8auOuuu3L//ffnmGOOedc1ArBiBEdq0sEHH5xJkyZlnXXWyYABA/LAAw/kAx/4QK6++uo8+OCDufDCC3P22WcneScgvvHGG5k7d2769euXCRMmZK+99soll1ySe++9N0ly+OGHp1wuL1fb/fv3z7rrrpvLL788ffv2zV133ZVevXrl6quvznbbbZcvfelL+fOf/5zf//73S+xXV1eXlpaWtu1SqVSZwQCALmDQoEFLbI8YMSLf+c53UiqVstFGG1WpKug6BEdq0h577JGzzjor559/fpJkyy23zEknnZQf//jHqaury3HHHdf22Lq6unzrW9/K1772tdTV1eWDH/xg/v3f/z3bb799DjzwwPTq1StrrbVWXnnllWy44Ybv23ZdXV1OP/30HHXUUSmXy+ndu3fOO++8lEqljBs3Lr/61a/Sr1+/dOvWbYlPhNtoo43y5JNP5pprrqn4eADA6qj1pap1dXWZP39+xowZk7/85S9JkmHDhmXOnDn5/Oc/X+UqoWsolZd3mQS6kIULF+bLX/5ybrjhhrb3IAIAAMvmipma8/e//z0HH3xwjj32WKERAACWgxVHAAAAClluAQAAoJDgCAAAQCHBEQAAgEKCIwAAAIUERwAAAAoJjgAAABQSHAEAACgkOAIAAFBIcAQAAKBQfdGdLS0taW4uV6Shbt1KFTvW6qIW+5zUZr9rsc9Jbfa7FvucVK7f3bt3q0A1tW3xublWfx8rxfitGuO3aozfqjF+q651DJd3bi4Mjs3N5cydu6AihfXr16tix1pd1GKfk9rsdy32OanNftdin5PK9Xvw4L4VqKa2LT431+rvY6UYv1Vj/FaN8Vs1xm/VtY7h8s7NXqoKAABAIcERAACAQoIjAAAAhQRHAAAACgmOAAAAFBIcAQAAKCQ4AgAAUEhwBAAAoJDgCAAAQCHBEQAAgEKCIwAAAIUERwAAAAoJjgAAABQSHAEAACgkOAIAAFBIcAQAAKCQ4AgAAECh+o5o5JvfHJXZs1/N2mv374jm2s3w4ZvmuONGVbsMAKDKLr30okyf/tRK7//aa7OTJAMGDFzpYwwfvlmOP37USu8PsCI6JDg+/vhjmbdgXhYNmtsRzbWL7nPnVbsEAKCTmD79qTw49dE09ll7pfbv/ubcJMm0Nxes3P7zXl+p/QBWVocExyQp13fL7N0/3FHNVdzAux+sdgkAQCfS2GftzP7IJ1dq34F/uzdJVnl/gI7iPY4AAAAUEhwBAAAoJDgCAABQSHAEAACgkOAIAABAIcERAACAQoIjAAAAhQRHAAAACgmOAAAAFBIcAQAAKCQ4AgAAUEhwBAAAoJDgCAAAQCHBEQAAgEKCIwAAAIUERwAAAAoJjgAAABQSHAEAACgkOAIAAFBIcAQAAKCQ4AgAAEAhwREAAIBCgiMAAACFBEcAAAAKCY4AAAAUEhwBAAAoJDgCAABQSHAEAACgkOAIAABAIcERAACAQoIjAAAAhQRHAAAACgmOAAAAFBIcAQAAKCQ4AgAAUEhwBAAAoFB9RzTS1NSYtJQ7oimq7I47bkuvXj3yiU98utqlANCJ3XHHbUmSvfbat8qVUA3OP6x+OiQ4NjY2plRu6YimqLLf/vZ/U19fJzgCUOi2236dRHCoVc4/rH68VBUAAIBCgiMAAACFBEcAAAAKCY4AAAAUEhwBAAAoJDgCAABQSHAEAACgkOAIAABAIcERAACAQoIjAAAAhQRHAAAACgmOAAAAFBIcAQAAKCQ4AgAAUEhwBAAAoJDgCAAAQCHBEQAAgEKCIwAAAIUERwAAAAoJjgAAABQSHAEAACgkOAIAAFBIcAQAAKCQ4AgAAEAhwREAAIBCgiMAAACFBEcAAAAKCY4AAAAUEhwBAAAoJDgCAABQSHAEAACgkOAIAABAIcERAACAQoIjAAAAhQRHAAAACtVXuwBq26c/vXPb7bvu+lPFH98RbXSFPnzta1/JtGnTsvnmW2bSpB92ipq6wrjWak2sHnbf/eNtt++++/4O3x9WRbV/f1f3/Y866iuZNu2pbLHFlpk06eoOb3/27FkZP/7MnHnm+AwYMHC1a/+qqyblpz+dnIaGr+bww49a4f2T5P/8n79kzJjROe+8i7L99jus8P4HHrhfXn99bvr375+bbvrflaphRVlxBDJt2rQkyZNPPl7lSgCA9jZt2lNJkieeqM68P3nyj/LII//I5Mk/Wi3b/+lPJ//f41yz0jWcddbYtLS0ZNy401dq/9dfn5skmTNnzkrXsKIER6pm8VWMZW2v6uM7oo2u0Ievfe0rS2wfc8wRVa+pK4xrrdbE6mHxZ+uXtd3e+8OqqPbv7+q+/1FHLT3vj+zQ9mfPnpXf/vZ/Uy6Xc9tt/5vXXpu9WrV/1VWTltj+0Y++v0L7J++sNs6b92aSZN68N/P3v/+fFdr/wAP3W2L785/ff4VrWBleqrqc6t5alGnTnsro0ccv9z719XVpamppx6o6n2nTnsrgwYOqXQYroHW1sZVVR6AjvPbaa3nttdk56aTjqlbDqszT06Y9lbpyhQtaAXWL3sq0aU+t1uO3Mi8RZNW1rja26uhVx8mTf5SWlnf+eFpaWjJ58o9y4oknrzbtt642/r/jXbPCL1c966yxS2yPG3d6br319uXev3W1sVVHrTpacQQAADrEnXfekaamxiRJU1Njfve75Q9MXaH9JG2rje+13VlZcVxOLWv2yKbrD82FF1663Pv069crc+cuaMeqOp/Ro49Pfb3nIwAoNmDAgAwYMCDf+95lVathVebpk046Ln959rkKV7T8WnqsmU03HrJajx+1aY899spvfvPrNDU1pr6+e/bcc++aaj9J+vTpu0RY7NOnb4fXsDJc4UON23TTTZfY3nzzLatUCQDQ3jbddLMltrfYomPn/YaGw1NXV0qS1NXVpaHh8NWq/S99qWGp4311hWs488zxS2yPG/edFdp/7bX7LbHdv3//Fa5hZQiOVM3SH+v/fh/zv6KP74g2ukIfrrzyx0tsL8/XcXS2ceqM41qrNbF6WPrj61f04+xXdX9YFdX+/V3d9//+95ee91fs6zhWtf2BAwdln332T6lUyr777r/C73Wtdvv/+Z/HLLG9Ml/HscMOO7atMvbp03eFv47jllt+s8S2r+MAOkzrqqPVRgDo+lpXHTt6tbFVQ8Ph+dCH/q3DVxsr1X7rquPKrDa2OvPM8amrq1vh1cZWrauOHbXamCSlcrn8np8J1tjYXJH36O255y5pLrfkxYM/tcrHqpaBdz+YHb3H8X21vsfxvPMuqXYpHaoWz3VSm/2uxT4nlev34MGrx/s4OrPF5+bV+fex9T1uq/N79P7y7HOZ/ZFPrtT+A/92b5Ks0v47doH3OFaz/mpbnf9+OwPjt+pax3B552YrjgAAABQSHAEAACgkOAIAAFBIcAQAAKCQ4AgAAEAhwREAAIBCgiMAAACFBEcAAAAKCY4AAAAUEhwBAAAoJDgCAABQSHAEAACgkOAIAABAIcERAACAQoIjAAAAhQRHAAAACgmOAAAAFBIcAQAAKCQ4AgAAUEhwBAAAoJDgCAAAQCHBEQAAgEKCIwAAAIUERwAAAAoJjgAAABQSHAEAACgkOAIAAFBIcAQAAKCQ4AgAAEAhwREAAIBCgiMAAACFBEcAAAAKCY4AAAAUqu+IRrp3756mpsaOaIoq22ef/dOrV49qlwFAJ7fvvv9ftUugipx/WP10SHCsr++etDR1RFNU2V577Zt+/Xpl7twF1S4FgE5sr732rXYJVJHzD6sfL1UFAACgkOAIAABAIcERAACAQoIjAAAAhQRHAAAACgmOAAAAFBIcAQAAKCQ4AgAAUEhwBAAAoJDgCAAAQCHBEQAAgEKCIwAAAIUERwAAAAoJjgAAABQSHAEAACgkOAIAAFBIcAQAAKCQ4AgAAEAhwREAAIBCgiMAAACFBEcAAAAKCY4AAAAUEhwBAAAoJDgCAABQSHAEAACgkOAIAABAIcERAACAQoIjAAAAhQRHAAAACgmOAAAAFBIcAQAAKCQ4AgAAUEhwBAAAoJDgCAAAQCHBEQAAgEKCIwAAAIUERwAAAArVd1RDpabmDLz7wY5qruK6z52XrF/tKgCAzqL7vNcz8G/3rty+b85NkpXff97rSYas1L4AK6NDguOWW26V2bNfzdpr9++I5trH+snw4ZtWuwoAoBMYPnyzVdr/tdd6JUkGDBi4kkcYsso1AKyIDgmO5513Ufr165W5cxd0RHMAAO3q+ONHVbsEgA7lPY4AAAAUEhwBAAAoJDgCAABQSHAEAACgkOAIAABAIcERAACAQoIjAAAAhQRHAAAACgmOAAAAFBIcAQAAKCQ4AgAAUEhwBAAAoJDgCAAAQCHBEQAAgEKCIwAAAIUERwAAAAoJjgAAABQSHAEAAChUKpfL5WoXAQAAQOdlxREAAIBCgiMAAACFBEcAAAAKCY4AAAAUEhwBAAAoJDgCAABQqL49D97S0pJx48bliSeeSI8ePTJhwoRsvPHG7dlkp/HZz342ffv2TZJsuOGGOfvss6tcUfv5xz/+kQsuuCCTJ0/Os88+mzFjxqRUKmWzzTbLt771rdTVdc3nJxbv99SpU3P00Udnk002SZJ88YtfzH777VfdAiuosbExp512Wl544YUsWrQoxxxzTDbddNMuf66X1e911123S5/r5ubmnHHGGXnmmWfSrVu3nH322SmXy13+XK9OanlurZRamqMrqVbn+0qqpWuHSqrV65BKqdT1TLsGxzvvvDOLFi3Kz3/+8zz00EM555xzMmnSpPZsslN4++23kySTJ0+uciXt7wc/+EFuvfXW9OzZM0ly9tlnZ9SoUfnoRz+aM888M3fddVf23HPPKldZeUv3+5///GcOP/zwjBw5ssqVtY9bb701/fr1y/nnn585c+bkwAMPzJZbbtnlz/Wy+n3cccd16XN9zz33JEmuu+66TJkypS04dvVzvTqp1bm1Umppjq6kWp3vK6nWrh0qqVavQyqlUtcz7RrL//a3v2WXXXZJkmy33XZ59NFH27O5TuPxxx/PwoULM3LkyBx22GF56KGHql1Su9loo40yceLEtu2pU6dmxx13TJLsuuuuuf/++6tVWrtaut+PPvpo7r333hx66KE57bTTMm/evCpWV3n77LNPTjzxxLbtbt261cS5Xla/u/q53mOPPTJ+/PgkycyZMzNo0KCaONerk1qdWyulluboSqrV+b6Sau3aoZJq9TqkUip1PdOuwXHevHnp06fPEkU2NTW1Z5OdwpprrpkjjjgiP/zhD/Ptb387J598cpft99577536+v+3cF0ul1MqlZIkvXv3zptvvlmt0trV0v3edttt881vfjPXXntthgwZkssuu6yK1VVe796906dPn8ybNy8nnHBCRo0aVRPneln97urnOknq6+tzyimnZPz48dl7771r4lyvTmp1bq2UWpqjK6lW5/tKqrVrh0qq1euQSqnU9Uy7Bsc+ffpk/vz5bdstLS1L/MF0VUOHDs1nPvOZlEqlDB06NP369curr75a7bI6xOKvLZ8/f37WWmutKlbTcfbcc89ss802bbf/+c9/VrmiynvxxRdz2GGH5YADDsiIESNq5lwv3e9aONdJcu655+b222/P2LFj217al3Ttc726qNW5tVJqeY6upFqZA9pTrcwnlVKr1yGVUonrmXYNjttvv33uu+++JMlDDz2UzTffvD2b6zRuvPHGnHPOOUmSl19+OfPmzcvgwYOrXFXH+OAHP5gpU6YkSe67777ssMMOVa6oYxxxxBF5+OGHkyQPPPBAtt566ypXVFmzZs3KyJEj841vfCNf+MIXktTGuV5Wv7v6uf7FL36RK6+8MknSs2fPlEqlbLPNNl3+XK9OanVurZRanqMrqRbmgPbW1eeTSqrV65BKqdT1TKlcLpfbq8jWT3578sknUy6X89///d8ZPnx4ezXXaSxatCinnnpqZs6cmVKplJNPPjnbb799tctqN88//3xGjx6d66+/Ps8880zGjh2bxsbGDBs2LBMmTEi3bt2qXWK7WLzfU6dOzfjx49O9e/cMGjQo48ePX+KlZKu7CRMm5LbbbsuwYcPafnb66adnwoQJXfpcL6vfo0aNyvnnn99lz/WCBQty6qmnZtasWWlqasqRRx6Z4cOH18zf9eqgVufWSqm1ObqSanW+r6RaunaopFq9DqmUSl3PtGtwBAAAYPXny04AAAAoJDgCAABQSHAEAACgkOAIAABAIcERAACAQoIjXc6UKVOyxRZb5De/+c0SPx8xYkTGjBmzQsf661//mscff/w977/55ptzwQUXJEl23nnnJe677777Vri9JNl9993z9ttvZ+bMmbn77ruTJA0NDZk+ffoKHwsAOrvvf//7+epXv5qRI0fmiCOOyKOPPlqxee9Pf/pTGhoa0tDQkG222abt9qOPPppLL720AtVD7aivdgHQHoYNG5Zf//rX2W+//ZIkTzzxRBYuXLjCx7npppuy3377Zcstt6x0ie/rz3/+c55++unsvvvuHd42AHSEadOm5e67787PfvazlEqlPPbYYznllFOy9tprV+T4O++8c9sTuzvvvHMmT57cdt8222xTkTagVgiOdElbbrllZsyYkTfeeCNrrbVWbr311owYMSIvvvhibrvttlxzzTWpq6vLRz7ykZx88smZOHFinn/++cyePTszZ87Mqaeemv79++cPf/hDpk6dmk033TR333137rjjjjQ1NaVv376ZOHHictezrDZfeumljBs3Lm+//Xbmzp2b4447LnvssUeSpLm5Od///vfz1ltv5cMf/nCS5LLLLsusWbOycOHCXHjhhRkyZEi7jB0AdJQBAwZk5syZufHGG7Prrrtmq622yo033pgjjjgiSfLmm2/m9NNPz5w5c5IkZ5xxRrbYYov3nMuffvrpzJ49O2+88UbOOOOM7LDDDstsd8qUKbnuuuvyve99L3vuuWc+/OEP59lnn81OO+2UN998Mw8//HCGDh2a888/Py+++GLGjh2bt99+O2ussUbGjx+f9dZbr8PGCDoLwZEua88998zvfve7fO5zn8vDDz+cI488Mo899lgmTpyYm266KT179sw3vvGN/OlPf0qS9OjRI1dddVX+9Kc/5eqrr84Pf/jD7LLLLtlvv/2y7rrrZu7cuW2T1BFHHJFHHnlkifZef/31NDQ0tG3PnTs3W2+9debOnbvMNkulUg4//PB89KMfzd///vdMnDixLTh269YtRx11VJ5++ul8+tOfzjXXXJPddtstBxxwQCZOnJjf/va3OfLIIztuMAGgHQwYMCCTJk3KT37yk1x22WVZc801c9JJJ7Xdf8UVV2SnnXbKl770pcyYMSOnnnpqJk2a9J5z+Zprrpn/+Z//yVNPPZWvf/3rufXWW9+3hhdeeCE//vGPM3jw4Oy444654YYbMnbs2Hz605/OG2+8kXPPPTcNDQ3Zbbfd8sADD+SCCy7Id7/73XYbE+isBEe6rBEjRmTcuHEZMmRI2zOOzc3Nee2113LUUUclSebPn5/nnnsuSbLVVlslSdZdd90sWrRoiWPV1dWle/fuGT16dHr16pWXXnopTU1NSzxm7bXXXuIlMPfdd19+85vf5F//+tcy2/zIRz6SSZMm5cYbb0ypVHrX8ZbW+pKaQYMGZdasWSs7LADQaTz77LPp06dPzj777CTJI488kqOOOiqDBg1Kkjz55JP585//nNtuuy1J8sYbb7znvJokO+20U5Jks802W+65sl+/fll//fWTJL169cqmm26aJOnbt2/efvvtPPnkk7nyyitz1VVXpVwup3v37hXqPaxeBEe6rCFDhmTBggWZPHlyRo8eneeeey6lUinrrbderr766nTv3j0333xzttpqq9x5550plUrvOkapVEq5XM7jjz+eO++8MzfccEMWLlyYz33ucymXy8tVx4YbbrjMNi+++OIcdNBB2W233XLTTTfllltuWWK/urq6tLS0VGQsAKAzeuKJJ/Kzn/0sV1xxRdZYY40MHTo0ffv2Tbdu3ZK885kFn/nMZzJixIjMnj07N9xww3vOq3feeWemTp2aAw44IE8++WTWWWed5aphWfP/4oYNG5aRI0dm++23z/Tp0/PXv/51lfsNqyPBkS5tv/32yy9/+csMHTo0zz33XAYMGJD9998/DQ0NaW5uzgYbbJB99933Pff/t3/7t1xwwQW58MIL07Nnz3zuc59Ljx49Mnjw4LzyyivLVcOAAQPy1a9+9V1t7rPPPvnOd76TK6+8Muutt17b+zdabb755pk0aVK23nrrVRoDAOis9tprr0yfPj0HHXRQevXqlXK5nG9+85v58Y9/nCQ5+uijc/rpp+f666/PvHnzcvzxx7/nvJokjz32WL7yla9k4cKFGT9+fEVqPOWUU9o+k+Ctt97K6aefXpHjwuqmVF7eZRMAAOikJk6cmEGDBuWLX/xitUuBLsn3OAIAAFDIiiMAAACFrDgCAABQSHAEAACgkOAIAABAIcERAACAQoIjAAAAhQRHAAAACv3/Wlo1TMD8KwUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(2, 2, figsize=(16,8))\n",
    "sns.boxplot(x=train[\"PhysicalHealth\"],  ax=axes[0,0], palette=[\"#8c1515\"])\n",
    "sns.boxplot(x=train[\"BMI\"],   ax=axes[0,1], palette=[\"#E98300\"])\n",
    "sns.boxplot(x=train[\"MentalHealth\"],   ax=axes[1,0], palette=[\"#009779\"])\n",
    "sns.boxplot(x=train[\"SleepTime\"],   ax=axes[1,1], palette=[\"#007C92\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of Numerical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = ['SleepTime', 'BMI', 'PhysicalHealth', 'MentalHealth']\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(16,8))\n",
    "\n",
    "for i,t in zip(names, axes.flatten()):\n",
    "    sns.kdeplot(x=i, data=train, ax=t, multiple=\"stack\",hue=\"HeartDisease\")\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Categorical Features Analysis.  <a class=\"anchor\" id=\"section_5_4\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[37;m\u001b[30;1mHeartDisease\t \u001b[34;1mNo: \u001b[35;1m91% \u001b[34;1mYes: \u001b[35;1m9%\n",
      "\u001b[48;5;254m\u001b[30;1mSmoking\t \u001b[34;1mNo: \u001b[35;1m58% \u001b[34;1mYes: \u001b[35;1m42%\n",
      "\u001b[37;m\u001b[30;1mAlcoholDrinking\t \u001b[34;1mNo: \u001b[35;1m93% \u001b[34;1mYes: \u001b[35;1m7%\n",
      "\u001b[48;5;254m\u001b[30;1mStroke\t \u001b[34;1mNo: \u001b[35;1m96% \u001b[34;1mYes: \u001b[35;1m4%\n",
      "\u001b[37;m\u001b[30;1mDiffWalking\t \u001b[34;1mNo: \u001b[35;1m85% \u001b[34;1mYes: \u001b[35;1m15%\n",
      "\u001b[48;5;254m\u001b[30;1mSex\t \u001b[34;1mFemale: \u001b[35;1m53% \u001b[34;1mMale: \u001b[35;1m47%\n",
      "\u001b[37;m\u001b[30;1mAgeCategory\t \u001b[34;1m65-69: \u001b[35;1m11% \u001b[34;1m60-64: \u001b[35;1m10% \u001b[34;1m70-74: \u001b[35;1m10% \u001b[34;1m55-59: \u001b[35;1m9% \u001b[34;1m50-54: \u001b[35;1m8% \u001b[34;1m80 or older: \u001b[35;1m8% \u001b[34;1m75-79: \u001b[35;1m7% \u001b[34;1m45-49: \u001b[35;1m7% \u001b[34;1m18-24: \u001b[35;1m7% \u001b[34;1m40-44: \u001b[35;1m7% \u001b[34;1m35-39: \u001b[35;1m6% \u001b[34;1m30-34: \u001b[35;1m6% \u001b[34;1m25-29: \u001b[35;1m5%\n",
      "\u001b[48;5;254m\u001b[30;1mRace\t \u001b[34;1mWhite: \u001b[35;1m75% \u001b[34;1mHispanic: \u001b[35;1m9% \u001b[34;1mBlack: \u001b[35;1m8% \u001b[34;1mOther: \u001b[35;1m4% \u001b[34;1mAsian: \u001b[35;1m3% \u001b[34;1mAmerican Indian/Alaskan Native: \u001b[35;1m2%\n",
      "\u001b[37;m\u001b[30;1mDiabetic\t \u001b[34;1mNo: \u001b[35;1m83% \u001b[34;1mYes: \u001b[35;1m13% \u001b[34;1mNo, borderline diabetes: \u001b[35;1m2% \u001b[34;1mYes (during pregnancy): \u001b[35;1m1%\n",
      "\u001b[48;5;254m\u001b[30;1mPhysicalActivity\t \u001b[34;1mYes: \u001b[35;1m76% \u001b[34;1mNo: \u001b[35;1m24%\n",
      "\u001b[37;m\u001b[30;1mGenHealth\t \u001b[34;1mVery good: \u001b[35;1m35% \u001b[34;1mGood: \u001b[35;1m30% \u001b[34;1mExcellent: \u001b[35;1m20% \u001b[34;1mFair: \u001b[35;1m12% \u001b[34;1mPoor: \u001b[35;1m4%\n",
      "\u001b[48;5;254m\u001b[30;1mAsthma\t \u001b[34;1mNo: \u001b[35;1m86% \u001b[34;1mYes: \u001b[35;1m14%\n",
      "\u001b[37;m\u001b[30;1mKidneyDisease\t \u001b[34;1mNo: \u001b[35;1m96% \u001b[34;1mYes: \u001b[35;1m4%\n",
      "\u001b[48;5;254m\u001b[30;1mSkinCancer\t \u001b[34;1mNo: \u001b[35;1m90% \u001b[34;1mYes: \u001b[35;1m10%\n"
     ]
    }
   ],
   "source": [
    "for c, i in enumerate(train.select_dtypes(exclude=['float64'])):    \n",
    "    color = '\\u001b[48;5;254m' if c%2 != 0 else '\\u001b[37;m'\n",
    "    s = color+'\\u001b[30;1m'+str(i) + '\\t'\n",
    "    for j in range(len(train[i].value_counts().keys())):                \n",
    "        s = s +' \\u001b[34;1m'+ str(train[i].value_counts().keys()[j]) +': \\u001b[35;1m'+ str(round(train[i].value_counts(normalize=True)[j]*100)) + '%'\n",
    "    print(s)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (13,6))\n",
    "sns.countplot(x = 'AgeCategory', hue = 'HeartDisease', data = train, order = train['AgeCategory'].value_counts().index, palette = [\"#FF604D\",\"#009ABB\"])\n",
    "plt.title('Distribution of heart disease cases by Yes/No based on gender', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (13,6))\n",
    "sns.countplot(x = 'Race', hue = 'HeartDisease', data = train, palette = [\"#7ADFA9\",\"#5E0229\"])\n",
    "plt.title('Distribution of heart disease cases by Yes/No based on Race', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (13,6))\n",
    "sns.countplot(x = 'Diabetic', hue = 'HeartDisease', data = train, palette = [\"#FFBF00\",\"#009ABB\"])\n",
    "plt.title('Distribution of heart disease cases by Yes/No based on Diabetic', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (13,6))\n",
    "sns.countplot(x = 'Race', hue = 'HeartDisease', data = train, palette = [\"#FF604D\",\"#009ABB\"])\n",
    "plt.title('Distribution of heart disease cases by Yes/No based on Race', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HeartDisease',\n",
       " 'Smoking',\n",
       " 'AlcoholDrinking',\n",
       " 'Stroke',\n",
       " 'DiffWalking',\n",
       " 'Sex',\n",
       " 'PhysicalActivity',\n",
       " 'Asthma',\n",
       " 'KidneyDisease',\n",
       " 'SkinCancer']"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = [i for i in train.select_dtypes(exclude=['float64']) if len(train[i].value_counts().keys()) <= 2]\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1800 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4,3, figsize=(16, 25))\n",
    "\n",
    "for i,t in zip(labels, axes.flatten()):\n",
    "    sns.histplot(x=i, data=train, ax=t, hue=\"HeartDisease\", multiple=\"stack\")\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter6\"></a>  <h2 style='text-align: center'>Data Preparation</h2> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove Outliers <a class=\"anchor\" id=\"section_6_1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T16:12:45.775163Z",
     "iopub.status.busy": "2022-05-30T16:12:45.774518Z",
     "iopub.status.idle": "2022-05-30T16:12:45.781187Z",
     "shell.execute_reply": "2022-05-30T16:12:45.780545Z",
     "shell.execute_reply.started": "2022-05-30T16:12:45.775126Z"
    }
   },
   "outputs": [],
   "source": [
    "def remove_outliers(data):\n",
    "    df = data.copy()\n",
    "    # BMI \n",
    "#     lower_limit = abs(df.BMI.mean() - 2* df.BMI.std())\n",
    "#     upper_limit = abs(df.BMI.mean() + 2 * df.BMI.std())\n",
    "#     df = df[(df.BMI >= lower_limit) & (df.BMI < upper_limit)]\n",
    "    \n",
    "    # PHysical Health         \n",
    "    upper_limit = abs(df.PhysicalHealth.mean() +  2 * df.PhysicalHealth.std())\n",
    "    df = df[(df.PhysicalHealth >= 0) & (df.PhysicalHealth <= upper_limit)]\n",
    "    \n",
    "    # Mental Health         \n",
    "    upper_limit = abs(df.MentalHealth.mean() +  2 * df.MentalHealth.std())\n",
    "    df = df[(df.MentalHealth >= 0) & (df.MentalHealth <= upper_limit)]\n",
    "    \n",
    "#     # Sleep TIme\n",
    "#     lower_limit = abs(df.SleepTime.mean() - 3 * df.SleepTime.std())\n",
    "#     upper_limit = abs(df.SleepTime.mean() + 3 * df.SleepTime.std())\n",
    "#     df = df[(df.SleepTime > lower_limit) & (df.SleepTime <= upper_limit)]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transform Numerical Data <a class=\"anchor\" id=\"section_6_2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T16:12:48.011817Z",
     "iopub.status.busy": "2022-05-30T16:12:48.011078Z",
     "iopub.status.idle": "2022-05-30T16:12:48.01807Z",
     "shell.execute_reply": "2022-05-30T16:12:48.017086Z",
     "shell.execute_reply.started": "2022-05-30T16:12:48.011769Z"
    }
   },
   "outputs": [],
   "source": [
    "def trainform_numerical_data(data):\n",
    "    df = data.copy()\n",
    "    est = KBinsDiscretizer(n_bins=4, encode='ordinal', strategy='uniform')\n",
    "    scaler = StandardScaler()\n",
    "\n",
    "    for i in df.select_dtypes(include=['float64']):    \n",
    "        df[i] = est.fit_transform(df[i].values.reshape(-1, 1)).astype('int64')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transform Categorical Data <a class=\"anchor\" id=\"section_6_3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T16:12:50.028807Z",
     "iopub.status.busy": "2022-05-30T16:12:50.026896Z",
     "iopub.status.idle": "2022-05-30T16:12:50.034506Z",
     "shell.execute_reply": "2022-05-30T16:12:50.03343Z",
     "shell.execute_reply.started": "2022-05-30T16:12:50.028757Z"
    }
   },
   "outputs": [],
   "source": [
    "def trainform_categorical_data(data):\n",
    "    df = data.copy()\n",
    "    for i in list(df.select_dtypes(include=['object']).columns):\n",
    "        le = LabelEncoder()\n",
    "        df[i] =  le.fit_transform(df[i]).astype('int64')\n",
    "    \n",
    "    more_two_cols = ['AgeCategory', 'Race', 'Diabetic']  \n",
    "    df = pd.get_dummies(df, columns=more_two_cols, drop_first=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T16:12:51.134586Z",
     "iopub.status.busy": "2022-05-30T16:12:51.134315Z",
     "iopub.status.idle": "2022-05-30T16:12:51.139335Z",
     "shell.execute_reply": "2022-05-30T16:12:51.138612Z",
     "shell.execute_reply.started": "2022-05-30T16:12:51.134557Z"
    }
   },
   "outputs": [],
   "source": [
    "def data_preparation(data_frame):        \n",
    "    df = 0\n",
    "    df = data_frame.copy()\n",
    "\n",
    "    # remove outliers\n",
    "    df = remove_outliers(df)\n",
    "    \n",
    "    # Transform numarical data\n",
    "    df = trainform_numerical_data(df)\n",
    "    # Transform categorical data\n",
    "    df = trainform_categorical_data(df)    \n",
    "            \n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's make prepare data for ML model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T16:12:52.931052Z",
     "iopub.status.busy": "2022-05-30T16:12:52.930402Z",
     "iopub.status.idle": "2022-05-30T16:12:55.85202Z",
     "shell.execute_reply": "2022-05-30T16:12:55.851123Z",
     "shell.execute_reply.started": "2022-05-30T16:12:52.931009Z"
    }
   },
   "outputs": [],
   "source": [
    "data = data_preparation(data)\n",
    "train = data_preparation(train)\n",
    "test = data_preparation(test)\n",
    "validation = data_preparation(validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T16:12:55.853946Z",
     "iopub.status.busy": "2022-05-30T16:12:55.853703Z",
     "iopub.status.idle": "2022-05-30T16:12:55.882455Z",
     "shell.execute_reply": "2022-05-30T16:12:55.881767Z",
     "shell.execute_reply.started": "2022-05-30T16:12:55.853913Z"
    }
   },
   "outputs": [],
   "source": [
    "X, y = data.loc[:, data.columns != 'HeartDisease'], data['HeartDisease']\n",
    "\n",
    "X_train, y_train = train.loc[:, train.columns != 'HeartDisease'], train['HeartDisease']\n",
    "X_test, y_test = test.loc[:, test.columns != 'HeartDisease'], test['HeartDisease']\n",
    "X_val, y_val = validation.loc[:, validation.columns != 'HeartDisease'], validation['HeartDisease']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <a class=\"anchor\" id=\"chapter7\"></a>  <h2 style='text-align: center'>Model Train & Select</h2> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T16:12:55.91449Z",
     "iopub.status.busy": "2022-05-30T16:12:55.914281Z",
     "iopub.status.idle": "2022-05-30T16:12:55.91922Z",
     "shell.execute_reply": "2022-05-30T16:12:55.918433Z",
     "shell.execute_reply.started": "2022-05-30T16:12:55.914465Z"
    }
   },
   "outputs": [],
   "source": [
    "def model_pipe(model, x, y, x_test, y_test):\n",
    "    \n",
    "    pipeline = Pipeline(steps = [            \n",
    "#             ('selector', SelectKBest(chi2, k=34)),\n",
    "            ('classifier', model)              \n",
    "           ])\n",
    "\n",
    "    model = pipeline.fit(x, y)\n",
    "    return model.score(x_test, y_test)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select five models for training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T16:13:51.458361Z",
     "iopub.status.busy": "2022-05-30T16:13:51.458085Z",
     "iopub.status.idle": "2022-05-30T16:13:51.466459Z",
     "shell.execute_reply": "2022-05-30T16:13:51.465368Z",
     "shell.execute_reply.started": "2022-05-30T16:13:51.458324Z"
    }
   },
   "outputs": [],
   "source": [
    "pipelines = []\n",
    "pipelines.append(('Gradient Boosting Classifier' , (Pipeline([('scaled' , StandardScaler()),('GBC' ,GradientBoostingClassifier())]))))\n",
    "# pipelines.append(('Extreme Gradient Boosting Classifier' , (Pipeline([('scaled' , StandardScaler()),('GBC' ,x.XGBClassifier())]))))\n",
    "pipelines.append(('Random Forest Classifier' , (Pipeline([('scaled' , StandardScaler()),('RFC' ,RandomForestClassifier())]))))\n",
    "pipelines.append(('Logistic Regression' , (Pipeline([('scaled' , StandardScaler()),('LR' ,LogisticRegression())]))))\n",
    "pipelines.append(('Decision Tree Classifier' , (Pipeline([('scaled' , StandardScaler()),('DTC' ,DecisionTreeClassifier())]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T16:13:53.90054Z",
     "iopub.status.busy": "2022-05-30T16:13:53.900275Z",
     "iopub.status.idle": "2022-05-30T16:14:45.481422Z",
     "shell.execute_reply": "2022-05-30T16:14:45.48069Z",
     "shell.execute_reply.started": "2022-05-30T16:13:53.90051Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test On validation set\n",
      "\n",
      "Gradient Boosting Classifier: 92.14\n",
      "Random Forest Classifier: 91.39\n",
      "Logistic Regression: 92.08\n",
      "Decision Tree Classifier: 90.23\n"
     ]
    }
   ],
   "source": [
    "model_name = []\n",
    "results = []\n",
    "print('Test On validation set\\n')\n",
    "for pipe ,model in pipelines:       \n",
    "    print(\"{}: {:.2f}\".format(pipe, model_pipe(model, X_train, y_train, X_val, y_val)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T16:16:23.575296Z",
     "iopub.status.busy": "2022-05-30T16:16:23.57457Z",
     "iopub.status.idle": "2022-05-30T16:17:14.994614Z",
     "shell.execute_reply": "2022-05-30T16:17:14.993088Z",
     "shell.execute_reply.started": "2022-05-30T16:16:23.575259Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on test set\n",
      "\n",
      "Gradient Boosting Classifier: 92.33\n",
      "Random Forest Classifier: 91.48\n",
      "Logistic Regression: 92.29\n",
      "Decision Tree Classifier: 90.16\n"
     ]
    }
   ],
   "source": [
    "model_name = []\n",
    "results = []\n",
    "print('Test on test set\\n')\n",
    "for pipe ,model in pipelines:       \n",
    "    print(\"{}: {:.2f}\".format(pipe, model_pipe(model, X_train, y_train, X_test, y_test)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross-Validation on Selected model <a class=\"anchor\" id=\"section_8_1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T12:40:25.51251Z",
     "iopub.status.busy": "2022-05-30T12:40:25.51191Z",
     "iopub.status.idle": "2022-05-30T12:45:24.036238Z",
     "shell.execute_reply": "2022-05-30T12:45:24.035417Z",
     "shell.execute_reply.started": "2022-05-30T12:40:25.512469Z"
    }
   },
   "outputs": [],
   "source": [
    "cv_results = {}\n",
    "for pipe ,model in pipelines: \n",
    "    cv = RepeatedStratifiedKFold(n_splits=4, n_repeats=3, random_state=1)\n",
    "    cv_results[pipe] = cross_val_score(model, X, y, cv=cv, scoring=('accuracy')) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T12:46:37.638322Z",
     "iopub.status.busy": "2022-05-30T12:46:37.638045Z",
     "iopub.status.idle": "2022-05-30T12:46:37.649732Z",
     "shell.execute_reply": "2022-05-30T12:46:37.648473Z",
     "shell.execute_reply.started": "2022-05-30T12:46:37.638294Z"
    }
   },
   "outputs": [
    {
     "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>Gradient Boosting Classifier</th>\n",
       "      <th>Random Forest Classifier</th>\n",
       "      <th>Logistic Regression</th>\n",
       "      <th>Decision Tree Classifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>92.295619</td>\n",
       "      <td>91.493792</td>\n",
       "      <td>92.217923</td>\n",
       "      <td>90.451106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>92.329806</td>\n",
       "      <td>91.527978</td>\n",
       "      <td>92.309605</td>\n",
       "      <td>90.423135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>92.301835</td>\n",
       "      <td>91.462713</td>\n",
       "      <td>92.241232</td>\n",
       "      <td>90.396718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>92.340564</td>\n",
       "      <td>91.502984</td>\n",
       "      <td>92.309485</td>\n",
       "      <td>90.348396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92.336022</td>\n",
       "      <td>91.614999</td>\n",
       "      <td>92.273864</td>\n",
       "      <td>90.426243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>92.283188</td>\n",
       "      <td>91.551287</td>\n",
       "      <td>92.221031</td>\n",
       "      <td>90.407596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>92.362438</td>\n",
       "      <td>91.431635</td>\n",
       "      <td>92.339129</td>\n",
       "      <td>90.371855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>92.345226</td>\n",
       "      <td>91.507645</td>\n",
       "      <td>92.261313</td>\n",
       "      <td>90.342181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>92.306497</td>\n",
       "      <td>91.451836</td>\n",
       "      <td>92.269203</td>\n",
       "      <td>90.395164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>92.348453</td>\n",
       "      <td>91.531086</td>\n",
       "      <td>92.284742</td>\n",
       "      <td>90.319022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>92.300281</td>\n",
       "      <td>91.566827</td>\n",
       "      <td>92.241232</td>\n",
       "      <td>90.447998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>92.329687</td>\n",
       "      <td>91.549602</td>\n",
       "      <td>92.298608</td>\n",
       "      <td>90.460281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gradient Boosting Classifier  Random Forest Classifier  \\\n",
       "0                      92.295619                 91.493792   \n",
       "1                      92.329806                 91.527978   \n",
       "2                      92.301835                 91.462713   \n",
       "3                      92.340564                 91.502984   \n",
       "4                      92.336022                 91.614999   \n",
       "5                      92.283188                 91.551287   \n",
       "6                      92.362438                 91.431635   \n",
       "7                      92.345226                 91.507645   \n",
       "8                      92.306497                 91.451836   \n",
       "9                      92.348453                 91.531086   \n",
       "10                     92.300281                 91.566827   \n",
       "11                     92.329687                 91.549602   \n",
       "\n",
       "    Logistic Regression  Decision Tree Classifier  \n",
       "0             92.217923                 90.451106  \n",
       "1             92.309605                 90.423135  \n",
       "2             92.241232                 90.396718  \n",
       "3             92.309485                 90.348396  \n",
       "4             92.273864                 90.426243  \n",
       "5             92.221031                 90.407596  \n",
       "6             92.339129                 90.371855  \n",
       "7             92.261313                 90.342181  \n",
       "8             92.269203                 90.395164  \n",
       "9             92.284742                 90.319022  \n",
       "10            92.241232                 90.447998  \n",
       "11            92.298608                 90.460281  "
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(cv_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tune Hyperparameters for Model <a class=\"anchor\" id=\"section_8_2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T16:17:14.996792Z",
     "iopub.status.busy": "2022-05-30T16:17:14.996104Z",
     "iopub.status.idle": "2022-05-30T16:17:15.001825Z",
     "shell.execute_reply": "2022-05-30T16:17:15.001033Z",
     "shell.execute_reply.started": "2022-05-30T16:17:14.996754Z"
    }
   },
   "outputs": [],
   "source": [
    "parameters = {\n",
    "    \"n_estimators\":[50,250,32],\n",
    "    \"max_depth\":[1,3],\n",
    "    \"learning_rate\":[0.01,0.1,1,10]\n",
    "}\n",
    "\n",
    "gbc = GradientBoostingClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T16:17:15.003266Z",
     "iopub.status.busy": "2022-05-30T16:17:15.002971Z",
     "iopub.status.idle": "2022-05-30T16:23:37.589033Z",
     "shell.execute_reply": "2022-05-30T16:23:37.588371Z",
     "shell.execute_reply.started": "2022-05-30T16:17:15.00323Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=2, estimator=GradientBoostingClassifier(),\n",
       "             param_grid={'learning_rate': [0.01, 0.1, 1, 10],\n",
       "                         'max_depth': [1, 3], 'n_estimators': [50, 250, 32]})"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv = GridSearchCV(gbc,parameters,cv=2)\n",
    "cv.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T16:23:37.591165Z",
     "iopub.status.busy": "2022-05-30T16:23:37.590844Z",
     "iopub.status.idle": "2022-05-30T16:23:37.596494Z",
     "shell.execute_reply": "2022-05-30T16:23:37.595498Z",
     "shell.execute_reply.started": "2022-05-30T16:23:37.591127Z"
    }
   },
   "outputs": [],
   "source": [
    "def display(results):\n",
    "    print(f'Best parameters are: {results.best_params_}')\n",
    "    print(\"\\n\")\n",
    "    mean_score = results.cv_results_['mean_test_score']\n",
    "    std_score = results.cv_results_['std_test_score']\n",
    "    params = results.cv_results_['params']\n",
    "    for mean,std,params in zip(mean_score,std_score,params):\n",
    "        print(f'{round(mean,3)} + or -{round(std,3)} for the {params}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T16:23:37.59839Z",
     "iopub.status.busy": "2022-05-30T16:23:37.597915Z",
     "iopub.status.idle": "2022-05-30T16:23:37.611974Z",
     "shell.execute_reply": "2022-05-30T16:23:37.611299Z",
     "shell.execute_reply.started": "2022-05-30T16:23:37.598325Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters are: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 250}\n",
      "\n",
      "\n",
      "0.923 + or -0.0 for the {'learning_rate': 0.01, 'max_depth': 1, 'n_estimators': 50}\n",
      "0.923 + or -0.0 for the {'learning_rate': 0.01, 'max_depth': 1, 'n_estimators': 250}\n",
      "0.923 + or -0.0 for the {'learning_rate': 0.01, 'max_depth': 1, 'n_estimators': 32}\n",
      "0.923 + or -0.0 for the {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 50}\n",
      "0.923 + or -0.0 for the {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 250}\n",
      "0.923 + or -0.0 for the {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 32}\n",
      "0.923 + or -0.0 for the {'learning_rate': 0.1, 'max_depth': 1, 'n_estimators': 50}\n",
      "0.923 + or -0.0 for the {'learning_rate': 0.1, 'max_depth': 1, 'n_estimators': 250}\n",
      "0.923 + or -0.0 for the {'learning_rate': 0.1, 'max_depth': 1, 'n_estimators': 32}\n",
      "0.923 + or -0.0 for the {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 50}\n",
      "0.923 + or -0.0 for the {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 250}\n",
      "0.923 + or -0.0 for the {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 32}\n",
      "0.923 + or -0.0 for the {'learning_rate': 1, 'max_depth': 1, 'n_estimators': 50}\n",
      "0.923 + or -0.0 for the {'learning_rate': 1, 'max_depth': 1, 'n_estimators': 250}\n",
      "0.923 + or -0.0 for the {'learning_rate': 1, 'max_depth': 1, 'n_estimators': 32}\n",
      "0.922 + or -0.0 for the {'learning_rate': 1, 'max_depth': 3, 'n_estimators': 50}\n",
      "0.922 + or -0.0 for the {'learning_rate': 1, 'max_depth': 3, 'n_estimators': 250}\n",
      "0.922 + or -0.0 for the {'learning_rate': 1, 'max_depth': 3, 'n_estimators': 32}\n",
      "0.923 + or -0.0 for the {'learning_rate': 10, 'max_depth': 1, 'n_estimators': 50}\n",
      "0.923 + or -0.0 for the {'learning_rate': 10, 'max_depth': 1, 'n_estimators': 250}\n",
      "0.923 + or -0.0 for the {'learning_rate': 10, 'max_depth': 1, 'n_estimators': 32}\n",
      "0.199 + or -0.005 for the {'learning_rate': 10, 'max_depth': 3, 'n_estimators': 50}\n",
      "0.199 + or -0.005 for the {'learning_rate': 10, 'max_depth': 3, 'n_estimators': 250}\n",
      "0.199 + or -0.005 for the {'learning_rate': 10, 'max_depth': 3, 'n_estimators': 32}\n"
     ]
    }
   ],
   "source": [
    "display(cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T16:54:41.537921Z",
     "iopub.status.busy": "2022-05-30T16:54:41.537514Z",
     "iopub.status.idle": "2022-05-30T16:54:41.596102Z",
     "shell.execute_reply": "2022-05-30T16:54:41.595281Z",
     "shell.execute_reply.started": "2022-05-30T16:54:41.537887Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92.37841620339773"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.score(X_test, y_test) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Probability predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T16:23:37.785723Z",
     "iopub.status.busy": "2022-05-30T16:23:37.785465Z",
     "iopub.status.idle": "2022-05-30T16:23:37.837546Z",
     "shell.execute_reply": "2022-05-30T16:23:37.836789Z",
     "shell.execute_reply.started": "2022-05-30T16:23:37.785686Z"
    }
   },
   "outputs": [],
   "source": [
    "y_pred = cv.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T15:46:41.00189Z",
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     "shell.execute_reply.started": "2022-05-30T15:46:41.001856Z"
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       "            0          1\n",
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       "13  91.043002   8.956998\n",
       "14  88.995971  11.004029"
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     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(y_pred[:15]*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T15:46:50.25504Z",
     "iopub.status.busy": "2022-05-30T15:46:50.254491Z",
     "iopub.status.idle": "2022-05-30T15:46:50.261407Z",
     "shell.execute_reply": "2022-05-30T15:46:50.260655Z",
     "shell.execute_reply.started": "2022-05-30T15:46:50.255003Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "189681    0\n",
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       "102639    0\n",
       "Name: HeartDisease, dtype: int64"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter9\"></a>  <h2 style='text-align: center'>Model Save</h2> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'heart_disease_model.sav'\n",
    "pickle.dump(cv, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9237841620339774\n"
     ]
    }
   ],
   "source": [
    "loaded_model = pickle.load(open(filename, 'rb'))\n",
    "result = loaded_model.score(X_test, y_test)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = loaded_model.predict_proba(X_test[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  }
 ],
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