model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PART 1: STROKE PREDICTION MODEL WITH DECISON TREES ALGORITHM USING SICKIT LEARN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from xgboost import XGBClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "RANDOM_STATE = 55 ## We will pass it to every sklearn call so we ensure reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset using pandas\n",
    "df = pd.read_csv(\"stroke.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9046</td>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51676</td>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>202.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31112</td>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60182</td>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1665</td>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "0   9046    Male  67.0             0              1          Yes   \n",
       "1  51676  Female  61.0             0              0          Yes   \n",
       "2  31112    Male  80.0             0              1          Yes   \n",
       "3  60182  Female  49.0             0              0          Yes   \n",
       "4   1665  Female  79.0             1              0          Yes   \n",
       "\n",
       "       work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "0        Private          Urban             228.69  36.6  formerly smoked   \n",
       "1  Self-employed          Rural             202.21   NaN     never smoked   \n",
       "2        Private          Rural             105.92  32.5     never smoked   \n",
       "3        Private          Urban             171.23  34.4           smokes   \n",
       "4  Self-employed          Rural             174.12  24.0     never smoked   \n",
       "\n",
       "   stroke  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=['id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>202.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "0    Male  67.0             0              1          Yes        Private   \n",
       "1  Female  61.0             0              0          Yes  Self-employed   \n",
       "2    Male  80.0             0              1          Yes        Private   \n",
       "3  Female  49.0             0              0          Yes        Private   \n",
       "4  Female  79.0             1              0          Yes  Self-employed   \n",
       "\n",
       "  Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "0          Urban             228.69  36.6  formerly smoked       1  \n",
       "1          Rural             202.21   NaN     never smoked       1  \n",
       "2          Rural             105.92  32.5     never smoked       1  \n",
       "3          Urban             171.23  34.4           smokes       1  \n",
       "4          Rural             174.12  24.0     never smoked       1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Performing one-hot encoding on the categorical variables\n",
    "cat_variables = ['gender',\n",
    "'ever_married',\n",
    "'work_type',\n",
    "'Residence_type',\n",
    "'smoking_status']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This will replace the columns with the one-hot encoded ones and keep the columns outside 'columns' argument as it is.\n",
    "df = pd.get_dummies(data = df, prefix = cat_variables, columns = cat_variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "      <th>ever_married_No</th>\n",
       "      <th>...</th>\n",
       "      <th>work_type_Never_worked</th>\n",
       "      <th>work_type_Private</th>\n",
       "      <th>work_type_Self-employed</th>\n",
       "      <th>work_type_children</th>\n",
       "      <th>Residence_type_Rural</th>\n",
       "      <th>Residence_type_Urban</th>\n",
       "      <th>smoking_status_Unknown</th>\n",
       "      <th>smoking_status_formerly smoked</th>\n",
       "      <th>smoking_status_never smoked</th>\n",
       "      <th>smoking_status_smokes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  hypertension  heart_disease  avg_glucose_level   bmi  stroke  \\\n",
       "0  67.0             0              1             228.69  36.6       1   \n",
       "1  61.0             0              0             202.21   NaN       1   \n",
       "2  80.0             0              1             105.92  32.5       1   \n",
       "3  49.0             0              0             171.23  34.4       1   \n",
       "4  79.0             1              0             174.12  24.0       1   \n",
       "\n",
       "   gender_Female  gender_Male  gender_Other  ever_married_No  ...  \\\n",
       "0          False         True         False            False  ...   \n",
       "1           True        False         False            False  ...   \n",
       "2          False         True         False            False  ...   \n",
       "3           True        False         False            False  ...   \n",
       "4           True        False         False            False  ...   \n",
       "\n",
       "   work_type_Never_worked  work_type_Private  work_type_Self-employed  \\\n",
       "0                   False               True                    False   \n",
       "1                   False              False                     True   \n",
       "2                   False               True                    False   \n",
       "3                   False               True                    False   \n",
       "4                   False              False                     True   \n",
       "\n",
       "   work_type_children  Residence_type_Rural  Residence_type_Urban  \\\n",
       "0               False                 False                  True   \n",
       "1               False                  True                 False   \n",
       "2               False                  True                 False   \n",
       "3               False                 False                  True   \n",
       "4               False                  True                 False   \n",
       "\n",
       "   smoking_status_Unknown  smoking_status_formerly smoked  \\\n",
       "0                   False                            True   \n",
       "1                   False                           False   \n",
       "2                   False                           False   \n",
       "3                   False                           False   \n",
       "4                   False                           False   \n",
       "\n",
       "   smoking_status_never smoked  smoking_status_smokes  \n",
       "0                        False                  False  \n",
       "1                         True                  False  \n",
       "2                         True                  False  \n",
       "3                        False                   True  \n",
       "4                         True                  False  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Choosing the target variable and the features\n",
    "features = [x for x in df.columns if x not in 'stroke'] ## Removing our target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n"
     ]
    }
   ],
   "source": [
    "print(len(features))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the data into training and testing sets\n",
    "X_train, X_val, y_train, y_val = train_test_split(df[features], df['stroke'], train_size=0.8, random_state=RANDOM_STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train samples: 4088 validation samples: 1022\n",
      "target proportion: 0.0492\n"
     ]
    }
   ],
   "source": [
    "print(f'train samples: {len(X_train)} validation samples: {len(X_val)}')\n",
    "print(f'target proportion: {sum(y_train)/len(y_train):.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define the Hyperparameters for the Decision Tree model\n",
    "min_samples_split_list = [2, 5, 10, 80, 250, 800]\n",
    "max_depth_list = [2, 5, 10, 20, 50, 100, None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24ffe0c4620>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Testing out the Decision Tree model using different values in the sample minimum split list hyperparameters\n",
    "\n",
    "accuracy_list_train = []\n",
    "accuracy_list_val = []\n",
    "for min_samples_split in min_samples_split_list:\n",
    "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
    "    model = DecisionTreeClassifier(min_samples_split = min_samples_split,\n",
    "                                   random_state = RANDOM_STATE).fit(X_train,y_train) \n",
    "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
    "    predictions_val = model.predict(X_val) ## The predicted values for the test dataset\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_val = accuracy_score(predictions_val,y_val)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_val.append(accuracy_val)\n",
    "\n",
    "plt.title('Train x Validation metrics')\n",
    "plt.xlabel('min_samples_split')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xticks(ticks = range(len(min_samples_split_list )),labels=min_samples_split_list)\n",
    "plt.plot(accuracy_list_train)\n",
    "plt.plot(accuracy_list_val)\n",
    "plt.legend(['Train','Validation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24ffe6755e0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Testing out the Decision Tree model using different values in the max depth list hyperparameters\n",
    "\n",
    "accuracy_list_train = []\n",
    "accuracy_list_val = []\n",
    "for max_depth in max_depth_list:\n",
    "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
    "    model = DecisionTreeClassifier(max_depth = max_depth,\n",
    "                                   random_state = RANDOM_STATE).fit(X_train,y_train) \n",
    "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
    "    predictions_val = model.predict(X_val) ## The predicted values for the test dataset\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_val = accuracy_score(predictions_val,y_val)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_val.append(accuracy_val)\n",
    "\n",
    "plt.title('Train x Validation metrics')\n",
    "plt.xlabel('max_depth')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xticks(ticks = range(len(max_depth_list )),labels=max_depth_list)\n",
    "plt.plot(accuracy_list_train)\n",
    "plt.plot(accuracy_list_val)\n",
    "plt.legend(['Train','Validation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using the best hyperparameters define the model\n",
    "\n",
    "decision_tree_model = DecisionTreeClassifier(min_samples_split = 150,\n",
    "                                             max_depth = 2,\n",
    "                                             random_state = RANDOM_STATE).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metrics train:\n",
      "\tAccuracy score: 0.9508\n",
      "Metrics validation:\n",
      "\tAccuracy score: 0.9530\n"
     ]
    }
   ],
   "source": [
    "#Compute Accuracy Scores\n",
    "print(f\"Metrics train:\\n\\tAccuracy score: {accuracy_score(decision_tree_model.predict(X_train),y_train):.4f}\")\n",
    "print(f\"Metrics validation:\\n\\tAccuracy score: {accuracy_score(decision_tree_model.predict(X_val),y_val):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   stroke  stroke_probability\n",
      "0       1            0.079417\n",
      "1       1            0.079417\n",
      "2       1            0.157729\n",
      "3       1            0.010076\n",
      "4       1            0.157729\n"
     ]
    }
   ],
   "source": [
    "#Use the model to predict the probability of stroke\n",
    "df['stroke_probability'] = decision_tree_model.predict_proba(df[features])[:,1]\n",
    "print(df[['stroke','stroke_probability']].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PART 2: STROKE PREDICTION MODEL WITH DECISON TREES BUT USING RANDOM FOREST ALGORITHM FROM SICKIT LEARN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define the Hyperparameters for the Decision Tree model\n",
    "min_samples_split_list = [2, 5, 10, 80, 250, 800]\n",
    "max_depth_list = [2, 5, 10, 20, 50, 100, None]\n",
    "n_estimators_list = [10,50,100,500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24ffe573950>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_list_train = []\n",
    "accuracy_list_val = []\n",
    "for min_samples_split in min_samples_split_list:\n",
    "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
    "    model = RandomForestClassifier(min_samples_split = min_samples_split,\n",
    "                                   random_state = RANDOM_STATE).fit(X_train,y_train) \n",
    "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
    "    predictions_val = model.predict(X_val) ## The predicted values for the test dataset\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_val = accuracy_score(predictions_val,y_val)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_val.append(accuracy_val)\n",
    "\n",
    "plt.title('Train x Validation metrics')\n",
    "plt.xlabel('min_samples_split')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xticks(ticks = range(len(min_samples_split_list )),labels=min_samples_split_list) \n",
    "plt.plot(accuracy_list_train)\n",
    "plt.plot(accuracy_list_val)\n",
    "plt.legend(['Train','Validation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24ffe676d80>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_list_train = []\n",
    "accuracy_list_val = []\n",
    "for max_depth in max_depth_list:\n",
    "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
    "    model = RandomForestClassifier(max_depth = max_depth,\n",
    "                                   random_state = RANDOM_STATE).fit(X_train,y_train) \n",
    "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
    "    predictions_val = model.predict(X_val) ## The predicted values for the test dataset\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_val = accuracy_score(predictions_val,y_val)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_val.append(accuracy_val)\n",
    "\n",
    "plt.title('Train x Validation metrics')\n",
    "plt.xlabel('max_depth')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xticks(ticks = range(len(max_depth_list )),labels=max_depth_list)\n",
    "plt.plot(accuracy_list_train)\n",
    "plt.plot(accuracy_list_val)\n",
    "plt.legend(['Train','Validation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24fff6b0050>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_list_train = []\n",
    "accuracy_list_val = []\n",
    "for n_estimators in n_estimators_list:\n",
    "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
    "    model = RandomForestClassifier(n_estimators = n_estimators,\n",
    "                                   random_state = RANDOM_STATE).fit(X_train,y_train) \n",
    "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
    "    predictions_val = model.predict(X_val) ## The predicted values for the test dataset\n",
    "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
    "    accuracy_val = accuracy_score(predictions_val,y_val)\n",
    "    accuracy_list_train.append(accuracy_train)\n",
    "    accuracy_list_val.append(accuracy_val)\n",
    "\n",
    "plt.title('Train x Validation metrics')\n",
    "plt.xlabel('n_estimators')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xticks(ticks = range(len(n_estimators_list )),labels=n_estimators_list)\n",
    "plt.plot(accuracy_list_train)\n",
    "plt.plot(accuracy_list_val)\n",
    "plt.legend(['Train','Validation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using the best hyperparameters define the model using Random Forest\n",
    "random_forest_model = RandomForestClassifier(n_estimators = 50,\n",
    "                                             max_depth = 15,min_samples_split=10,\n",
    "                                             random_state = RANDOM_STATE).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metrics train:\n",
      "\tAccuracy score: 0.9540\n",
      "Metrics test:\n",
      "\tAccuracy score: 0.9540\n"
     ]
    }
   ],
   "source": [
    "#Compute Accuracy Scores\n",
    "print(f\"Metrics train:\\n\\tAccuracy score: {accuracy_score(random_forest_model.predict(X_train),y_train):.4f}\\nMetrics test:\\n\\tAccuracy score: {accuracy_score(random_forest_model.predict(X_val),y_val):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   stroke  stroke_probability\n",
      "0       1            0.460374\n",
      "1       1            0.280161\n",
      "2       1            0.346945\n",
      "3       1            0.260232\n",
      "4       1            0.306028\n"
     ]
    }
   ],
   "source": [
    "#Use the model to predict the probability of stroke\n",
    "df['stroke_probability'] = random_forest_model.predict_proba(df[features])[:,1]\n",
    "print(df[['stroke','stroke_probability']].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PART 2: STROKE PREDICTION MODEL WITH DECISON TREES BUT USING XGBOOST."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define subset of training set to train the model\n",
    "n = int(len(X_train)*0.8) ## Let's use 80% to train and 20% to eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_fit, X_train_eval, y_train_fit, y_train_eval = X_train[:n], X_train[n:], y_train[:n], y_train[n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-logloss:0.23073\n",
      "[1]\tvalidation_0-logloss:0.22161\n",
      "[2]\tvalidation_0-logloss:0.21294\n",
      "[3]\tvalidation_0-logloss:0.20620\n",
      "[4]\tvalidation_0-logloss:0.20007\n",
      "[5]\tvalidation_0-logloss:0.19512\n",
      "[6]\tvalidation_0-logloss:0.19062\n",
      "[7]\tvalidation_0-logloss:0.18738\n",
      "[8]\tvalidation_0-logloss:0.18459\n",
      "[9]\tvalidation_0-logloss:0.18182\n",
      "[10]\tvalidation_0-logloss:0.17872\n",
      "[11]\tvalidation_0-logloss:0.17624\n",
      "[12]\tvalidation_0-logloss:0.17384\n",
      "[13]\tvalidation_0-logloss:0.17178\n",
      "[14]\tvalidation_0-logloss:0.16966\n",
      "[15]\tvalidation_0-logloss:0.16871\n",
      "[16]\tvalidation_0-logloss:0.16801\n",
      "[17]\tvalidation_0-logloss:0.16684\n",
      "[18]\tvalidation_0-logloss:0.16574\n",
      "[19]\tvalidation_0-logloss:0.16465\n",
      "[20]\tvalidation_0-logloss:0.16400\n",
      "[21]\tvalidation_0-logloss:0.16361\n",
      "[22]\tvalidation_0-logloss:0.16324\n",
      "[23]\tvalidation_0-logloss:0.16288\n",
      "[24]\tvalidation_0-logloss:0.16251\n",
      "[25]\tvalidation_0-logloss:0.16225\n",
      "[26]\tvalidation_0-logloss:0.16217\n",
      "[27]\tvalidation_0-logloss:0.16199\n",
      "[28]\tvalidation_0-logloss:0.16179\n",
      "[29]\tvalidation_0-logloss:0.16173\n",
      "[30]\tvalidation_0-logloss:0.16165\n",
      "[31]\tvalidation_0-logloss:0.16175\n",
      "[32]\tvalidation_0-logloss:0.16174\n",
      "[33]\tvalidation_0-logloss:0.16187\n",
      "[34]\tvalidation_0-logloss:0.16179\n",
      "[35]\tvalidation_0-logloss:0.16161\n",
      "[36]\tvalidation_0-logloss:0.16170\n",
      "[37]\tvalidation_0-logloss:0.16178\n",
      "[38]\tvalidation_0-logloss:0.16173\n",
      "[39]\tvalidation_0-logloss:0.16165\n",
      "[40]\tvalidation_0-logloss:0.16175\n",
      "[41]\tvalidation_0-logloss:0.16215\n",
      "[42]\tvalidation_0-logloss:0.16224\n",
      "[43]\tvalidation_0-logloss:0.16234\n",
      "[44]\tvalidation_0-logloss:0.16242\n",
      "[45]\tvalidation_0-logloss:0.16254\n",
      "[46]\tvalidation_0-logloss:0.16273\n",
      "[47]\tvalidation_0-logloss:0.16289\n",
      "[48]\tvalidation_0-logloss:0.16283\n",
      "[49]\tvalidation_0-logloss:0.16241\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=15,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=50, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=55, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=15,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=50, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=55, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=15,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=50, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=55, ...)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Define and Fit the model\n",
    "xgb_model = XGBClassifier(n_estimators = 50, learning_rate = 0.1, early_stopping_rounds=15, verbosity = 1, random_state = RANDOM_STATE)\n",
    "xgb_model.fit(X_train_fit, y_train_fit, eval_set=[(X_train_eval, y_train_eval)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best iteration: 35\n"
     ]
    }
   ],
   "source": [
    "#Check Best Iteration\n",
    "print(f\"Best iteration: {xgb_model.best_iteration}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metrics train:\n",
      "\tAccuracy score: 0.9555\n",
      "Metrics test:\n",
      "\tAccuracy score: 0.9521\n"
     ]
    }
   ],
   "source": [
    "#Compute Accuracy Scores\n",
    "print(f\"Metrics train:\\n\\tAccuracy score: {accuracy_score(xgb_model.predict(X_train),y_train):.4f}\\nMetrics test:\\n\\tAccuracy score: {accuracy_score(xgb_model.predict(X_val),y_val):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      stroke  stroke_probability\n",
      "5105       0            0.301030\n",
      "5106       0            0.096702\n",
      "5107       0            0.011853\n",
      "5108       0            0.033187\n",
      "5109       0            0.010012\n"
     ]
    }
   ],
   "source": [
    "#Use the model to predict the probability of stroke\n",
    "df['stroke_probability'] = xgb_model.predict_proba(df[features])[:,1]\n",
    "print(df[['stroke','stroke_probability']].tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FROM  THIS WE CAN CONCLUDE IN TERMS OF ACCURACY THE BEST ALGORITHMS FOR A DECISON TREE:\n",
    "\n",
    "1. XG BOOST\n",
    "2. RANDOM FOREST\n",
    "3. NORMAL DECISION TREE\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}