stroke_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stroke Prediction\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project Description\n",
    "\n",
    "### 1. Data Set\n",
    "The data set is downloaded [here](https://www.kaggle.com/fedesoriano/stroke-prediction-dataset)\\.  \n",
    "According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths.  \n",
    "The data set provides information about the patients like gender, age, various diseases, smoking status, and whether the patient have got stroke.\n",
    "\n",
    "### 2. Goal\n",
    "- Use the information of patients to predict whether a patient is likely to get stroke.\n",
    "- Find which parameter contributes to stroke, so that we can adjust our lifestyle to avoid getting stroke."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Content\n",
    "1. [Importing Packages and Data](#1.-Importing-Packages-and-Data)\n",
    "2. [Exploratory Data Analysis](#2.-Exploratory-Data-Analysis)\n",
    "3. [Data Preprocessing](#3.-Data-Preprocessing)\n",
    "4. [Unsupervised Learning](#4.-Unsupervised-Learning)\n",
    "5. [Supervised Learning](#5.-Supervised-Learning)\n",
    "6. [Summary and Conclusions](#6.-Summary-and-Conclusions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Importing Packages and Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the packages used in the analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sampling\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "# preprocessing\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# cross validation\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# unsupervised learning\n",
    "from sklearn.decomposition import PCA, KernelPCA\n",
    "# supervised learning\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "# neural network\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
    "# model evaluation\n",
    "from sklearn.metrics import classification_report, plot_roc_curve, matthews_corrcoef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the data"
   ]
  },
  {
   "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 = pd.read_csv('healthcare-dataset-stroke-data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the data size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5110, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if there are empty values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   False\n",
       "gender               False\n",
       "age                  False\n",
       "hypertension         False\n",
       "heart_disease        False\n",
       "ever_married         False\n",
       "work_type            False\n",
       "Residence_type       False\n",
       "avg_glucose_level    False\n",
       "bmi                   True\n",
       "smoking_status       False\n",
       "stroke               False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Exploratory Data Analysis\n",
    "In this part, I will visualize the correlation between each paramater with the target column, in order to find some insights.  \n",
    "ID is a unique identity of each record, but contains no information. Therefore we will not use it in prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Stroke (the Output)\n",
    "First, let's check distribution of the output column \"stroke\".  \n",
    "From the plot, we can see that the stroke distribution is imbalanced, only about 5% of the data is stroke.\n",
    "We need to deal with the imbalanced data before training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x21bb5d65e20>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='stroke', kind='count', palette='pastel', edgecolor='.6', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The proportion of stroke:  4.87%\n"
     ]
    }
   ],
   "source": [
    "stroke_counts = df['stroke'].value_counts()\n",
    "print(f'The proportion of stroke: {stroke_counts[1]/stroke_counts.sum(): .2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is 1 record with \"Other\" gender. Consider to drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    2994\n",
       "Male      2115\n",
       "Other        1\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['gender'] != 'Other'].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now check the correlation between gender and stroke. Use confusion matrix to get the proportion of stroke in each gender.  \n",
    "There is no significant difference between male and female in getting stroke."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "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>stroke</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "stroke     0     1\n",
       "gender            \n",
       "Female  0.95  0.05\n",
       "Male    0.95  0.05"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.loc[df['gender'] != 'Other', 'gender'], \n",
    "            df.loc[df['gender'] != 'Other', 'stroke'], normalize='index').round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of age is roughly even. Age of 40-60 has more records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x='age', data=df, bins=20, kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the proportion of people getting stroke at each age.  \n",
    "We can find that starting from age of 40, older people have higher probability of getting stroke."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Proportion of Stroke'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('age').mean()['stroke'].plot(ylabel='Proportion of Stroke')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Hypertension"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "About 10% of records have hypertension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The proportion of hypertension: 9.75%\n"
     ]
    }
   ],
   "source": [
    "hyp_ten_counts = df['hypertension'].value_counts()\n",
    "print(f'The proportion of hypertension: {hyp_ten_counts[1]/hyp_ten_counts.sum():.2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the confusion matrix, we can tell that people with hypertension are more likely to get a stroke."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>stroke</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypertension</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.96</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.87</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "stroke           0     1\n",
       "hypertension            \n",
       "0             0.96  0.04\n",
       "1             0.87  0.13"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['hypertension'], df['stroke'], normalize='index').round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Heart Disease"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "About 5% of records have heart disease."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The proportion of heart disease: 5.40%\n"
     ]
    }
   ],
   "source": [
    "hrt_dis_counts = df['heart_disease'].value_counts()\n",
    "print(f'The proportion of heart disease: {hrt_dis_counts[1]/hrt_dis_counts.sum():.2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also find that people with heart disease are more likely to get a stroke."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>stroke</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heart_disease</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.96</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "stroke            0     1\n",
       "heart_disease            \n",
       "0              0.96  0.04\n",
       "1              0.83  0.17"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['heart_disease'], df['stroke'], normalize='index').round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6 Ever Married"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Ever Married\" column is correlated with age, because older people are more likely to be married."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    3353\n",
       "No     1756\n",
       "Name: ever_married, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ever_married'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['age', 'ever_married']).size().unstack(fill_value=0).plot(ylabel='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given that older people are more likely to get stroke, married people also have a higher proportion of getting stroke."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>stroke</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ever_married</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>0.98</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.93</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "stroke           0     1\n",
       "ever_married            \n",
       "No            0.98  0.02\n",
       "Yes           0.93  0.07"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['ever_married'], df['stroke'], normalize='index').round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.7 Work Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Private          2924\n",
       "Self-employed     819\n",
       "children          687\n",
       "Govt_job          657\n",
       "Never_worked       22\n",
       "Name: work_type, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['work_type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the cross table, we can find that children and people never worked have lower stroke rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>stroke</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>work_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Govt_job</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never_worked</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Self-employed</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "stroke            0     1\n",
       "work_type                \n",
       "Govt_job       0.95  0.05\n",
       "Never_worked   1.00  0.00\n",
       "Private        0.95  0.05\n",
       "Self-employed  0.92  0.08\n",
       "children       1.00  0.00"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['work_type'], df['stroke'], normalize='index').round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is also because of the age. Children and people never worked are all young people with maximum age of 23."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max age of \"children\": 16.0\n",
      "Max age of \"Never_worked\": 23.0\n"
     ]
    }
   ],
   "source": [
    "print(f'Max age of \"children\": {max(df.loc[df[\"work_type\"] == \"children\", \"age\"])}')\n",
    "print(f'Max age of \"Never_worked\": {max(df.loc[df[\"work_type\"] == \"Never_worked\", \"age\"])}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.8 Residence Type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no significant difference between urban and rural residence to get stroke."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Urban    2596\n",
       "Rural    2513\n",
       "Name: Residence_type, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Residence_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>stroke</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residence_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "stroke             0     1\n",
       "Residence_type            \n",
       "Rural           0.95  0.05\n",
       "Urban           0.95  0.05"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['Residence_type'], df['stroke'], normalize='index').round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.9 Average Glucose Level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the histogram of glucose level, we can find that the majority people have an average glucose level lower than 150. But some people have high glucose level up to about 250."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='avg_glucose_level', ylabel='Count'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x='avg_glucose_level', data=df, bins=20, kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "People with higher average glucose level are more likely to get stroke."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='avg_glucose_level', ylabel='Proportion of Stroke'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(pd.cut(df['avg_glucose_level'], bins=20)).mean()['stroke']\\\n",
    "    .plot.bar(rot=45, ylabel='Proportion of Stroke')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.10 BMI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is 201 instances have empty value in BMI column. We need to fill these numbers later in data preprocessing part."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of empty values in BMI: 201\n",
      "The proportion of empty value is 3.93%\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of empty values in BMI: {df[df[\"bmi\"].isnull()].shape[0]}')\n",
    "print(f'The proportion of empty value is {df[df[\"bmi\"].isnull()].shape[0] / df.shape[0]:.2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority of BMI ranges between 10-60."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bmi', ylabel='Count'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x='bmi', data=df, bins=20, kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The proportion of getting stroke varies between BMI, but no clearly positive or negative correlation was found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bmi', ylabel='Proportion of Stroke'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(pd.cut(df['bmi'], bins=20)).mean()['stroke']\\\n",
    "    .plot.bar(rot=45, ylabel='Proportion of Stroke')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.11 Smoking Status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "30% of the smoking status is unknown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "never smoked       1892\n",
       "Unknown            1544\n",
       "formerly smoked     884\n",
       "smokes              789\n",
       "Name: smoking_status, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smoking_counts = df['smoking_status'].value_counts()\n",
    "smoking_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The proportion of unknown smoking status: 30.22%\n"
     ]
    }
   ],
   "source": [
    "print('The proportion of unknown smoking status: '\n",
    "      f'{smoking_counts[\"Unknown\"]/smoking_counts.sum():.2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Formerly smoked people have a higher chance of getting stroke."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>stroke</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoking_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>formerly smoked</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>never smoked</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smokes</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "stroke              0     1\n",
       "smoking_status             \n",
       "Unknown          0.97  0.03\n",
       "formerly smoked  0.92  0.08\n",
       "never smoked     0.95  0.05\n",
       "smokes           0.95  0.05"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['smoking_status'], df['stroke'], normalize='index').round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Convert the categorical variables to numerical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the binary variables, such as gender, married and residence type, we just need to encode it as 0/1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['gender'] = df['gender'].map({'Female': 0, 'Male': 1}).astype(np.uint8)\n",
    "df['ever_married'] = df['ever_married'].map({'No': 0, 'Yes': 1}).astype(np.uint8)\n",
    "df['Residence_type'] = df['Residence_type'].map({'Rural': 0, 'Urban': 1}).astype(np.uint8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the other categorical variables, work type and smoking status, we need to use one-hot encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, columns=['work_type', 'smoking_status'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Smoking status contains unknown values, so we can drop the one-hot column representing the unknown value.  \n",
    "As a result, those records with unknown smoking status will be encoded as all 0 in smoking status one-hot columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('smoking_status_Unknown', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the input variables are all numerical.  \n",
    "We can set the input column names and target column name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_col = 'stroke'\n",
    "\n",
    "feature_cols = list(df.columns)\n",
    "feature_cols.remove('id')\n",
    "feature_cols.remove(target_col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Train Test Split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select 20% of the data as test set. Use stratified split to make the proportion of target class same in training and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test \\\n",
    "    = train_test_split(df[feature_cols], df[target_col], test_size = 0.2, stratify=df[target_col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size: 4087\n",
      "Test set size: 1022\n",
      "Number of features: 16\n"
     ]
    }
   ],
   "source": [
    "print(f'Training set size: {X_train.shape[0]}')\n",
    "print(f'Test set size: {X_test.shape[0]}')\n",
    "print(f'Number of features: {X_train.shape[1]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Process the Empty Values in BMI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider to fill the empty BMI with median value.  \n",
    "To avoid leakage of information from test set, we should compute the median value only based on training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median value of BMI used to fill empty value: 28.1\n"
     ]
    }
   ],
   "source": [
    "bmi_median = X_train['bmi'].median(skipna=True)\n",
    "print(f'Median value of BMI used to fill empty value: {bmi_median}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.fillna({'bmi': bmi_median})\n",
    "X_test = X_test.fillna({'bmi': bmi_median})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, all the input variables are numerical. We can compute the correlation matrix of all the features.  \n",
    "From the matrix, we can see that age and ever married have a strong positive correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the correlation matrix with pearson\n",
    "corr = X_train.corr(method='pearson')\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "# mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, cmap=cmap, vmax=1.0, vmin=-1.0, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to normalize the data for PCA and model training. Here I will use min-max scaler.  \n",
    "In order to prevent the information leakage from test set, min and max values should be computed only based on training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Unsupervised Learning\n",
    "In this part, I will use PCA and kernel PCA to perform dimension reduction in input variables. And also find if the dimension reduced data set will perform better in supervised learning task.  \n",
    "Given that the number of features is not large, using a complex neural network to train an autoencoder is not a good idea here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first find the best number of components we will use in PCA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA()"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA()\n",
    "pca.fit(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the below graph, we can see that the first 9 components explain most of the variance. So we are going to choose 9 components in PCA transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29ce7235f40>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pca.explained_variance_ratio_, '-o')\n",
    "plt.axvline(x=9, c='r', ls='--', label=r'$j=9$')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first 9 components can explain 92.12% of the variance\n"
     ]
    }
   ],
   "source": [
    "print(f'The first 9 components can explain {sum(pca.explained_variance_ratio_[:9]):.2%} '\n",
    "      'of the variance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also check the first 2 principle components. We can see that they all shows the correlation between age, ever married and work type, which is what we have found in EDA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1, figsize=(15, 12))\n",
    "sns.barplot(x=pca.components_[0], y=feature_cols, orient='h', ax=ax[0])\n",
    "sns.barplot(x=pca.components_[1], y=feature_cols, orient='h', ax=ax[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the 9 components to reduce the dimension of input variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=9)\n",
    "X_train_pca = pca.fit_transform(X_train)\n",
    "X_test_pca = pca.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Kernel PCA\n",
    "Also try the performance of non-linear kernel PCA.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Polynomial Kernel\n",
    "First try the polynomial kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KernelPCA(kernel='poly')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kpca_poly = KernelPCA(kernel='poly', degree=3)\n",
    "kpca_poly.fit(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still using first 9 components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29ce74a5820>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(kpca_poly.lambdas_[:30], '-o')\n",
    "plt.axvline(x=9, c='r', ls='--', label=r'$j=9$')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first 9 components can explain 84.88% of the variance with polynomial kernel\n"
     ]
    }
   ],
   "source": [
    "print(f'The first 9 components can explain {sum(kpca_poly.lambdas_[:9])/sum(kpca_poly.lambdas_):.2%} '\n",
    "      'of the variance with polynomial kernel')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RBF kernel\n",
    "Also check the performance of RBF kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KernelPCA(kernel='rbf')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kpca_rbf = KernelPCA(kernel='rbf')\n",
    "kpca_rbf.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29c80066fd0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(kpca_rbf.lambdas_[:30], '-o')\n",
    "plt.axvline(x=9, c='r', ls='--', label=r'$j=9$')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first 9 components can explain 83.93% of the variance with RBF kernel\n"
     ]
    }
   ],
   "source": [
    "print(f'The first 9 components can explain {sum(kpca_rbf.lambdas_[:9])/sum(kpca_rbf.lambdas_):.2%} '\n",
    "      'of the variance with RBF kernel')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### PCA transformation with polynomial kernel\n",
    "Polynomial kernel performs better than RBF kernel, so we will use polynomial kernel PCA to transform the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "kpca = KernelPCA(n_components=9, kernel='poly', degree=3)\n",
    "X_train_kpca = kpca.fit_transform(X_train)\n",
    "X_test_kpca = kpca.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Supervised Learning\n",
    "In this part, I will use the models list below to predict whether the patient will get a stroke:\n",
    "- Logistic Regression\n",
    "- Decision Tree\n",
    "- Random Forest\n",
    "- AdaBoost\n",
    "- Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we start training, we need to first balance our data.\n",
    "That's because only 5% of the data is stroke, which will influence our prediction.  \n",
    "Here I will use random duplication to over-sample the minority class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_smote, y_train_smote = RandomOverSampler().fit_resample(X_train, y_train)\n",
    "X_train_pca_smote, y_train_pca_smote = RandomOverSampler().fit_resample(X_train_pca, y_train)\n",
    "X_train_kpca_smote, y_train_kpca_smote = RandomOverSampler().fit_resample(X_train_kpca, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then define a function to train classification model.  \n",
    "Use GridSearchCV to tune the hyperparameter with 5-fold validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(clf, X_train, y_train, param_grid, scoring='accuracy', n_jobs=-1):\n",
    "    # Use GridSearchCV to tune the hyperparameter\n",
    "    grid = GridSearchCV(clf, param_grid, scoring=scoring, cv=5, n_jobs=n_jobs)\n",
    "    grid.fit(X_train, y_train)\n",
    "    \n",
    "    # Show the result\n",
    "    print(f'Best parameters: {grid.best_params_}')\n",
    "    print(f'Best accuracy: {grid.best_score_: .4f}')\n",
    "    \n",
    "    # Return the best classifier\n",
    "    return grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also define a function to show the classification result on test set.  \n",
    "Here I use Matthews correlation coefficient (MCC) to score the model on test set. It is a suitable choice for imbalanced data classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Record the score of each model\n",
    "model_scores = pd.DataFrame(columns=['Model Type', 'Data Set', 'MCC Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_model(clf, X_test, y_test, model_type, data_set_type, show_report=False):\n",
    "    global model_scores\n",
    "\n",
    "    y_pred = clf.predict(X_test)\n",
    "    \n",
    "    # Show the classification_report or not\n",
    "    if show_report:\n",
    "        print(classification_report(y_test, y_pred))\n",
    "        \n",
    "    mcc_score = matthews_corrcoef(y_test, y_pred)\n",
    "    print(f'MCC score: {mcc_score: .4f}')\n",
    "    model_scores = model_scores.append({'Model Type': model_type, 'Data Set': data_set_type, \n",
    "                                        'MCC Score': mcc_score}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting the hyperparameters for tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'C': [0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a) Using the original features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the training set to train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'C': 0.05}\n",
      "Best accuracy:  0.7922\n"
     ]
    }
   ],
   "source": [
    "clf_logis = train_model(LogisticRegression(tol=0.0001, max_iter=1000), \n",
    "                        X_train_smote, y_train_smote, params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test the model with test set.  \n",
    "The test set is imbalanced, so the precision of positive class is low. But both classes have recall higher than 70%, which is much better than random guess."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.73      0.84       972\n",
      "           1       0.13      0.80      0.23        50\n",
      "\n",
      "    accuracy                           0.73      1022\n",
      "   macro avg       0.56      0.77      0.53      1022\n",
      "weighted avg       0.94      0.73      0.81      1022\n",
      "\n",
      "MCC score:  0.2508\n"
     ]
    }
   ],
   "source": [
    "test_model(clf_logis, X_test, y_test, 'Logistic Regression', 'Original', show_report=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also check the coefficients in the logistic regression model to find some details.\n",
    "- Age is the dominant features here. Older people have higher chance of getting stroke.\n",
    "- The following positive features are glucose level, hypertension and heart disease, which means people with higher glucose level, hypertension or heart disease are more likely to get stroke.\n",
    "- The most negative feature is never smoked. People never smoked are slightly less likely to get stroke."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Coefficients in logistic regression model'}, xlabel='feature'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "coef_logis = pd.DataFrame({'feature': feature_cols, 'coef': clf_logis.coef_[0]})\\\n",
    "    .sort_values('coef', ascending=False).reset_index(drop=True)\n",
    "coef_logis.plot(kind='bar', x='feature', y='coef',\n",
    "                   title='Coefficients in logistic regression model', legend=False, \n",
    "                   figsize=(15,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b) Using PCA data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'C': 0.05}\n",
      "Best accuracy:  0.7252\n"
     ]
    }
   ],
   "source": [
    "clf_logis_pca = train_model(LogisticRegression(tol=0.0001, max_iter=1000), \n",
    "                            X_train_pca_smote, y_train_pca_smote, params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MCC score:  0.2036\n"
     ]
    }
   ],
   "source": [
    "test_model(clf_logis_pca, X_test_pca, y_test, 'Logistic Regression', 'PCA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### c) Using Kernel PCA data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'C': 0.1}\n",
      "Best accuracy:  0.7296\n"
     ]
    }
   ],
   "source": [
    "clf_logis_kpca = train_model(LogisticRegression(tol=0.0001, max_iter=1000), \n",
    "                            X_train_kpca_smote, y_train_kpca_smote, params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MCC score:  0.1998\n"
     ]
    }
   ],
   "source": [
    "test_model(clf_logis_kpca, X_test_kpca, y_test, 'Logistic Regression', 'Kernel PCA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'max_depth': [3, 4, 5, 6, 7, 8]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a) Using the original features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'criterion': 'gini', 'max_depth': 8}\n",
      "Best accuracy:  0.8789\n"
     ]
    }
   ],
   "source": [
    "clf_dectree = train_model(DecisionTreeClassifier(), \n",
    "                        X_train_smote, y_train_smote, params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.81      0.88       972\n",
      "           1       0.13      0.56      0.21        50\n",
      "\n",
      "    accuracy                           0.79      1022\n",
      "   macro avg       0.55      0.68      0.55      1022\n",
      "weighted avg       0.93      0.79      0.85      1022\n",
      "\n",
      "MCC score:  0.1928\n"
     ]
    }
   ],
   "source": [
    "test_model(clf_dectree, X_test, y_test, 'Decision Tree', 'Original', show_report=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b) Using PCA data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'criterion': 'gini', 'max_depth': 8}\n",
      "Best accuracy:  0.8561\n"
     ]
    }
   ],
   "source": [
    "clf_dectree_pca = train_model(DecisionTreeClassifier(), \n",
    "                            X_train_pca_smote, y_train_pca_smote, params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MCC score:  0.1468\n"
     ]
    }
   ],
   "source": [
    "test_model(clf_dectree_pca, X_test_pca, y_test, 'Decision Tree', 'PCA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### c) Using Kernel PCA data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'criterion': 'gini', 'max_depth': 8}\n",
      "Best accuracy:  0.8799\n"
     ]
    }
   ],
   "source": [
    "clf_dectree_kpca = train_model(DecisionTreeClassifier(), \n",
    "                            X_train_kpca_smote, y_train_kpca_smote, params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MCC score:  0.1970\n"
     ]
    }
   ],
   "source": [
    "test_model(clf_dectree_kpca, X_test_kpca, y_test, 'Decision Tree', 'Kernel PCA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'max_depth': [3, 4, 5, 6],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a) Using the original features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'criterion': 'gini', 'max_depth': 6, 'n_estimators': 300}\n",
      "Best accuracy:  0.8385\n"
     ]
    }
   ],
   "source": [
    "clf_rf = train_model(RandomForestClassifier(), \n",
    "                        X_train_smote, y_train_smote, params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.72      0.84       972\n",
      "           1       0.13      0.82      0.23        50\n",
      "\n",
      "    accuracy                           0.73      1022\n",
      "   macro avg       0.56      0.77      0.53      1022\n",
      "weighted avg       0.95      0.73      0.81      1022\n",
      "\n",
      "MCC score:  0.2556\n"
     ]
    }
   ],
   "source": [
    "test_model(clf_rf, X_test, y_test, 'Random Forest', 'Original', show_report=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get the feature importance in random forest. From the graph below, we have:\n",
    "- Age is the most important feature. We can conclude that age is the main driver to stroke.\n",
    "- Glucose level are followed, which is align with what we got from logistic regression model.\n",
    "- BMI is the third important feature, but from the logistic regression model we cannot see a significant positive or negative contribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Feature Importances in Random Forest Model'}, xlabel='feature'>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the feature importance from random forest\n",
    "importances_rf = pd.DataFrame({'feature': feature_cols, 'importance': clf_rf.feature_importances_})\\\n",
    "    .sort_values('importance', ascending=False).reset_index(drop=True)\n",
    "importances_rf.iloc[0:7, :].plot(kind='bar', x='feature', y='importance', rot=45,\n",
    "                                       title='Feature Importances in Random Forest Model', legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b) Using PCA data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'criterion': 'gini', 'max_depth': 6, 'n_estimators': 200}\n",
      "Best accuracy:  0.8654\n"
     ]
    }
   ],
   "source": [
    "clf_rf_pca = train_model(RandomForestClassifier(), \n",
    "                            X_train_pca_smote, y_train_pca_smote, params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MCC score:  0.2071\n"
     ]
    }
   ],
   "source": [
    "test_model(clf_rf_pca, X_test_pca, y_test, 'Random Forest', 'PCA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### c) Using Kernel PCA data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'criterion': 'gini', 'max_depth': 6, 'n_estimators': 200}\n",
      "Best accuracy:  0.8621\n"
     ]
    }
   ],
   "source": [
    "clf_rf_kpca = train_model(RandomForestClassifier(), \n",
    "                            X_train_kpca_smote, y_train_kpca_smote, params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MCC score:  0.1843\n"
     ]
    }
   ],
   "source": [
    "test_model(clf_rf_kpca, X_test_kpca, y_test, 'Random Forest', 'Kernel PCA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'n_estimators': [100, 200, 300, 500],\n",
    "    'learning_rate': [0.1, 0.5, 1.0, 5.0, 10.0],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a) Using the original features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'learning_rate': 1.0, 'n_estimators': 500}\n",
      "Best accuracy:  0.8738\n"
     ]
    }
   ],
   "source": [
    "clf_ada = train_model(AdaBoostClassifier(), \n",
    "                        X_train_smote, y_train_smote, params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.81      0.89       972\n",
      "           1       0.15      0.62      0.24        50\n",
      "\n",
      "    accuracy                           0.80      1022\n",
      "   macro avg       0.56      0.72      0.56      1022\n",
      "weighted avg       0.94      0.80      0.86      1022\n",
      "\n",
      "MCC score:  0.2308\n"
     ]
    }
   ],
   "source": [
    "test_model(clf_ada, X_test, y_test, 'AdaBoost', 'Original', show_report=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b) Using PCA data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'learning_rate': 1.0, 'n_estimators': 500}\n",
      "Best accuracy:  0.9173\n"
     ]
    }
   ],
   "source": [
    "clf_ada_pca = train_model(AdaBoostClassifier(), \n",
    "                            X_train_pca_smote, y_train_pca_smote, params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MCC score:  0.1747\n"
     ]
    }
   ],
   "source": [
    "test_model(clf_ada_pca, X_test_pca, y_test, 'AdaBoost', 'PCA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### c) Using Kernel PCA data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'learning_rate': 1.0, 'n_estimators': 500}\n",
      "Best accuracy:  0.9204\n"
     ]
    }
   ],
   "source": [
    "clf_ada_kpca = train_model(AdaBoostClassifier(), \n",
    "                            X_train_kpca_smote, y_train_kpca_smote, params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MCC score:  0.0857\n"
     ]
    }
   ],
   "source": [
    "test_model(clf_ada_kpca, X_test_kpca, y_test, 'AdaBoost', 'Kernel PCA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.5 Neural Network\n",
    "Use Keras to train a shallow neural network for prediction.  \n",
    "Because the training time for neural network is long, I will only do the training on original feature data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we define the network in a function. Set hidden layer size and learning rate as hyperparameters.  \n",
    "Here I use SGD for optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def create_model(hidden_size=10, learning_rate=0.01):\n",
    "    # Model structure\n",
    "    model = keras.Sequential([\n",
    "        layers.Input(shape=(16, )),\n",
    "        layers.Dense(hidden_size, activation='relu'),\n",
    "        layers.Dense(1, activation='sigmoid')\n",
    "    ])\n",
    "    # Model training\n",
    "    model.compile(\n",
    "        loss='binary_crossentropy',\n",
    "        optimizer=keras.optimizers.SGD(learning_rate),\n",
    "        metrics=['accuracy']\n",
    "    )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the training of neural network takes a long time, I just set the hyperparameter list simple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'hidden_size': [5, 10, 20],\n",
    "    'learning_rate': [0.01],\n",
    "    'batch_size': [64, 128],\n",
    "    'epochs': [50, 100]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the wrapper to make the neural network as a scikit-learn classifier, so that I can use the same function as the other models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\caibo\\AppData\\Local\\Temp/ipykernel_7628/1414649317.py:1: DeprecationWarning: KerasClassifier is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead.\n",
      "  clf_nn = train_model(KerasClassifier(build_fn=create_model, verbose=0),\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'batch_size': 64, 'epochs': 100, 'hidden_size': 10, 'learning_rate': 0.01}\n",
      "Best accuracy:  0.7085\n"
     ]
    }
   ],
   "source": [
    "clf_nn = train_model(KerasClassifier(build_fn=create_model, verbose=0), \n",
    "                        X_train_smote, y_train_smote, params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.72      0.83       972\n",
      "           1       0.13      0.80      0.22        50\n",
      "\n",
      "    accuracy                           0.72      1022\n",
      "   macro avg       0.56      0.76      0.52      1022\n",
      "weighted avg       0.94      0.72      0.80      1022\n",
      "\n",
      "MCC score:  0.2416\n"
     ]
    }
   ],
   "source": [
    "test_model(clf_nn, X_test, y_test, 'Neural Network', 'Original', show_report=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.6 Summary of Supervised Learning\n",
    "Let's compare the performance of each models by MCC score and ROC curve."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a) Performance of models by MCC score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the table below, we can conclude that:\n",
    "- Except for decision tree, data set with original features performs better than PCA data sets. That's because the original dimension is small enough, while PCA may lose some information in the original data set.\n",
    "- Neural network, random forest and logistic regression perform similarly with original features. According to the report above, they all have an accuracy of about 73% and recall higher than 70% in both classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_scores = model_scores.sort_values('MCC Score', ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>Model Type</th>\n",
       "      <th>Data Set</th>\n",
       "      <th>MCC Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>Original</td>\n",
       "      <td>0.255633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>Original</td>\n",
       "      <td>0.250781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Neural Network</td>\n",
       "      <td>Original</td>\n",
       "      <td>0.241551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>Original</td>\n",
       "      <td>0.230772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>PCA</td>\n",
       "      <td>0.207051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>PCA</td>\n",
       "      <td>0.203593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>Kernel PCA</td>\n",
       "      <td>0.199795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>Kernel PCA</td>\n",
       "      <td>0.196968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>Original</td>\n",
       "      <td>0.192816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>Kernel PCA</td>\n",
       "      <td>0.184290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>PCA</td>\n",
       "      <td>0.174665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>PCA</td>\n",
       "      <td>0.146774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>Kernel PCA</td>\n",
       "      <td>0.085677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Model Type    Data Set  MCC Score\n",
       "0         Random Forest    Original   0.255633\n",
       "1   Logistic Regression    Original   0.250781\n",
       "2        Neural Network    Original   0.241551\n",
       "3              AdaBoost    Original   0.230772\n",
       "4         Random Forest         PCA   0.207051\n",
       "5   Logistic Regression         PCA   0.203593\n",
       "6   Logistic Regression  Kernel PCA   0.199795\n",
       "7         Decision Tree  Kernel PCA   0.196968\n",
       "8         Decision Tree    Original   0.192816\n",
       "9         Random Forest  Kernel PCA   0.184290\n",
       "10             AdaBoost         PCA   0.174665\n",
       "11        Decision Tree         PCA   0.146774\n",
       "12             AdaBoost  Kernel PCA   0.085677"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b) Performance of models on original features by ROC Curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the ROC Curve, we can also find that neural network, random forest and logistic regression perform similarly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ar6S1ng5MBwgNDc3amLid/GbcjFzmT4UjftS7oTovvXKb/Q2KGckWcpKUy7D2Y9j8NXj7Qd/x0OEx8PR2dWSiiOTaECilGgKvA7cCh4EYwAdorJRKAr4Bfszhds42IEgpVR84A9wH3J9lnVNAL2CdUqo60AQ4lv/TEUI4LCMD9swzHgYnnoM2D0Kvd6B8VVdHJoqYvSuC94GvgSdM39otlFLVMD7YhwM/Zt1Qa52mlHoWWI6RPvqD1nqvUupJ0/JpwHvATKXUHoxbSa9rrWMLeE5CCHvO7jR6BZ/eCjXbwX1zoFY7V0clXCTXhkBrPSyXZeeBSXa2XwoszTJvmtXrs0AfRwIVQhSCK7Gwahzs+AnKVYHBX0HrYeAhRQbcWb6zhpRSvbXWfxdmMCJn5mwhRzNu9q47w6GtmR/jNInrbZR+CMxho2Ika00hyRYqoPQ0CP/eGDD+2hXo/Ax0fw18AlwdmSgGCpI++j1Gj2BRBKwbAUcybg5tPWdzWMXkgDgad2jprDALTdZUUckWKoDja41ewef3QYOexljBVZu4OipRjNh7WLw4p0VA5cIPR+Qmr9lCVWqV585X2lqmRy77EoDRN2d9Zl88SZZQAV2KghVvwr7foWIdGDobmg6QXsEiG3tXBDcDDwKJWeYrjJ7DQojiJjUZNpp6BQP0fAO6PAfevq6NSxRb9hqCzUCS1vrfrAuUUrnXIhZCFC2t4cASWP4/uHQKgu+APu8ZVwNC5MJe1lCOvY601t0KPxwhRL7EHDSeAxxbDVWbwUOLoUF3V0clSggZj6AE+GXLKbYcj6Nj/ZzTfbJmCVk/KDaPKnYw7iBNAovvQ0LrTCHJEnJQSjz8+zFsmQZlyhnloUMfAU/5ry0cJ/9aSgBzkbncsoWyZglVqVWexh2McWOtG4H+Dfo7P+B8ss4UkiwhOzIyYNccWPkOXImBtg9Br7eNvgFC5JE0BCVEx/qB3N8x93u9WbOErNkbVay4kEwhB5zZbvQKPhMOtdrD/WFQ0/bfXQhHSEMgREmReB5WvQs7Z0H56nDHNGg1VHoFiwJz+F+QUuqd3KaFEE6SngqbvoIv28GuuUYq6LPhECKlIUThyMsVwXY700KIwnZsjZENFHMAGvaCfhOgav4HFhLCFocbAq31H7lNC+fILWPIOlMo9nQiunIyI5eNzLZeccoWylpDyJpkClm5eBJWvAH7/4BK9YzqoE1uk17BwinslZj4kuyjillorZ8v9IhEJrllDFlnClWpVZ6N5dbb/NAvTtlCuQ03KZlCGL2C10+CDZNAecAtb0Ln58Dbx9WRiVLM3hVBeJFEIXKVW8aQdabQ4mVf0oTinx0kmUE2aA37F8PyNyH+FDS/y+gVHFDL1ZEJN2CvZ3GmAWeUUuW01lecG5IQbub8fuM5wPF/oVpzGPEn1Ovq6qiEG3F0zOLOGGWnywN1lFKtMUYte9qZwQlRqiVfgn8/gi3fQNkK0H8itBspvYJFkXP0X9wkoC+wGEBrvUspJbWGnGzvujO0OnwVgIWf7si23Px8oLiXkJDSEVlkZEDELFj5LiRdgHYj4Ja3oJxUdheukZesoSiVOWMhvfDDEdYObT1H+eQMEn1t54qby0hMPPZlsS4hIaUjrJwOh6WvGmMG1+4Ew3+DGq1dHZVwc442BFFKqS6AVkqVAZ4H9jsvLGGW6OvB7qCyvPFELiUElhX/EhJu/4A44ZzRKzhiNpS/Ae76FlreK+mgolhwtCF4EvgCqAmcAZYDzzgrKCFKjbRrsPUbWPMRpKXATS9Ct1eNZwJCFBMONQRa61jgASfHIkTpcmQVLBsNsYcgqI/RK7hyQ1dHJUQ2DhUqUUo1UEr9oZSKUUqdV0otUko1cHZwQpRIccdh7gMw6y7ISIP758ED86UREMWWo7eGfgGmAneapu8D5gAdnRGUO/plyylLL+IbYtOodjGd8skZJHlkAGUtmUG2FJdsoZzKR7hNptC1JFj/OWz4Ajy8oNdY6PwMeJV1dWRC5MrR0oVKa/2z1jrN9DOLXEpPiLxbFHGGfdGXASyNQKKvB4k3lGVwSE1LeqgtxSVbyJwdlFWpzxTSGvYuhCntYe3HEDwInguHm1+WRkCUCPZqDZkrna1WSo0G5mI0AEOBP50cm9sJruFP2BOdLX0GrAeZ+bsEZAaBG2YHndtr9Ao+sQ6qt4S7v4W6XVwdlRB5Yu/W0HaMD35zjtsTVss08J4zghKi2Eu+CKvHw7bvwMcfBnxmdAzz8HR1ZELkmb1aQ/WLKhAhSoSMdNj5M6waZzQGof8HPd8Av+xlwoUoKRzuWayUagEEA5Z6uFprN7oHINzeqS3w12sQHQF1usBtH0GNVq6OSogCc7To3FigB0ZDsBS4DVgPSENQSMyZQgs/3WGpIWQ2/9B8ws+FE1o91CWx5TaYjLVSmx2U8B/8PRZ2z4UKN8Ld30OLu6VXsCg1HL0iuAdoDezUWo9USlUHvnNeWO7HnCkE12sImZnTRl2VGZTbYDLWSl12UNo12PI1/PsxpF+Dm1+Bri9D2fL2txWiBHG0IUjWWmcopdKUUv7AeUA6lBWyRF+PTJlC1kKrh3Jv43uLOKLr3C4b6PBKWPY6XDgCjW+Dvh9IhzBRajnaEIQrpSoC32JkEiUCW50VlBAuE3cMlr8BB5dCYEN44FcI6u3qqIRwKkdrDZkHoJmmlFoG+GutdzsvLCGK2LUrsO5T2PgleJaBW9+FTk+DVxlXRyaE09nrUJZj7WOlVFutdfbRUoQoSbSGyAWw4i1IOAut7oNb3wH/Gq6OTIgiY++K4NNclmnglkKMxW3tXXeGiokZXCqfveJHYWYMOZr9k1WpzQb6b4/RK/jkBrihFdw7A+p0cnVUQhQ5ex3KehZVIO7s0NZzAJyvlL1XamFmDDma/ZNVqcsGSoqD1R9A+A/gUxFunwRtH5JewcJtySjZxcSl8h78V8X2n6MwM4bcLvvHWkY6bJ8J/7wHKfHQ/jHoOQZ8K7k6MiFcShoC4R5OboK/Rhm3g+p2hf4fQ/Xmro5KiGJBGgJRul0+a/QK3jMP/GvCPTOg+Z3SK1gIK46WmFAYQ1U20FqPU0rVAW7QWktfAlE8pV2FTVNh7URjlLBuo6DrS1CmnKsjE6LYcfSK4CsgAyNLaByQACwA2jspLrdyPiGFy8mpgDGIifVoZAUdfcw6U6jUZv9kdWi5MVZw3DFoMsDoFRwohXSFyImjI5R11Fo/A6QAaK0vAtLTppDEJl4DYHBITYBMo5EVdPQx61HDSl32T1YXjsLsIfDLEFCe8OACGPaLNAJC2OHoFUGqUsoT0/CUSqmqGFcIopD4+3pzf8c6lunCHI2s1GcKXU2EdRONW0GeZaHP+9DhCekVLISDHG0IJgMLgWpKqQ8wqpG+6bSohHCE1rDnV/j7LUiIhtb3w61jocINro5MiBLF0VpDs5VS24FeGMNW3qG13u/UyITITfQuWPoaRG2GG9vAkJ+htjyyEiI/HM0a+gII01pPdXI8QuQuKc7oELZ9JvgGwqAvIeRB8HD0cZcQIitHbw3tAN5USjXGuEUUprUOd15YpdvedWcsZSUAyidnkOhrfJA5UlsoLzWDSk2mUHoabJ8B/7wPVxOMZwA9RoNvRVdHJkSJ59DXKK31j1rr/kAH4BDwkVLqsFMjK8UObT1H7OlEy3Sir4elzpAjtYWsM4HsKRWZQic2wPTusPRVY4zgpzbAbROkERCikOS1Z3EjoClQD9hX6NG4kSq1yltGI5v7zaZMyxypLVTqM4EA4s8YD4IjF0BAbRjyEzQbJL2ChShkjj4j+Ai4CzgKzAPe01pfcmJcwp2lpsCmKcZAMToDuo+Gm16AMn6ujkyIUsnRK4LjQGetdWxedq6U6gd8AXgC32mtJ9hYpwcwCfAGYrXW3fNyDFGKaA2HlsGyMXDxODQbCH0+gEp1XR2ZEKWavRHKmmqtD2CMT1zHVGPIIrcRykwd0KYCvYHTwDal1GKt9T6rdSpilK/op7U+pZSqlu8zESVb7GGjLMSRlVClCQxfCA1l3CMhioK9K4KXgcexPVKZvRHKOgBHtNbHAJRSc4HBZH62cD/wm9b6FIDW+ryDcZcoWbOEYk8nkhHgxVDTs4F90ZcJruHvcMZQ0rZt+LUvJTnzVxPg349h89fg7Qt9x0OHx8DT29WRCeE27I1Q9rjp5W1a6xTrZUopHzv7rglEWU2fBjpmWacx4K2UWgNUAL7QWmd7AqqUehyjQaJOnTpZFxd75iyhKrXKA8aD4jUpV9gXnURwDX+Ca/gzOKQmS49NB+xnDAElPxMoI8MoDf3325B4zugLcOtYKC8XhUIUNUefEWwEsg5kb2ueNVupHdrG8dth9Fj2BTYppTZrrQ9l2kjr6cB0gNDQ0Kz7KBGss4TAyBQKxp+wJzpb5v29zLGMIb/27ak0dIjTYnW6szuNXsGnt0LNdnDfHKjVztVRCeG27D0juAHjm72vUqoN1z/c/QF7KRyngdpW07WAszbWidVaXwGuKKXWAq0x+iqI0uZKLKwaBzt+gnJVYPBUoz6Q9AoWwqXsXRH0BUZgfIh/ZjU/AfifnW23AUFKqfrAGeA+jGcC1hYBU5RSXhhlrTsCnzsUuSg50tMg/HtjwPhrV6DT09DjdfAJcHVkQgjsPyP4EfhRKXW31npBXnastU5TSj0LLMdIH/1Ba71XKfWkafk0rfV+pdQyYDdGWevvtNaR+ToTUTwdXwd/vQbn90GDHtDvI6hWCkpeCFGK2Ls19KDWehZQTyn1ctblWuvPbGxmvXwpsDTLvGlZpj8BPnE44lLgly2n2HI8jo71Ay3zHMkYKlEuRRm9gvcuhIp1YOgsaHq79AoWohiyd2vIPMBreWcH4k4WRZwBro9IBo7VGCoRUlNg42RY9xmgocf/4KbnjdRQIUSxZO/W0Dem3+8WTTjuo2P9wEwjkoFjGUPFltZw4E9Y/j+4dBKCBxsjhVUseem+Qrgbh9I1lFIfK6X8lVLeSqlVSqlYpdSDzg5OlBAxh2DWXRD2AHj7wUOLjQJx0ggIUSI4mrfXR2t9GbgdI+WzMTDKaVGJkiHlMix/A77uDKe3Gw+Cn1wHDaRclBAliaMdysz9/fsDc7TWcUoe+rmvjAzYNQdWvgNXYqDtcOg11ugbIIQocRxtCP5QSh0AkoGnlVJVgRQ72wgr5xNSrtcWSlyBX+BuRi7ztyw/GHeQJoFNCudgKZdhzjBIvlg4+8vqWgJcOgW12sP9YVAztw7mQojiztHB60ebxiS4rLVOV0pdwSggJxwUm3iNfdFXCa7hj1/gbtK9zmB00DY0CWxSeBlDJ9bDyfVQOQiqFlLjYk0pIxuo1VDpFSxEKeDowDTewHCgm+mW0L/AtFw3EtkE1zBqCxlXAv7M6DfDOQeK2gwe3sb9eknbFELY4eitoa8xnhN8ZZoebpr3qDOCEgUUtRVqtJZGQAjhEEcbgvZa69ZW0/8opXY5IyBRQGlX4cwOo6a/EEI4wNEbvOlKqYbmCaVUAyDdOSGJAoneDelXoXYHV0cihCghHL0iGAWsVkodwyhFXRcY6bSoSqBftpyylI7IqlX0VZLS4zhRZjYjl/nnOUPoYtg8y4A0KQcO4NM0l6JtUZuN37WzjgEkhBC22W0ITKmi8RhDT1bDaAgOaK2vOjm2EmVRxBnLkJO2eHhdsWQK5TVD6PKSJZYGwKdp09xHJ4vaAhXrQoUb8ngGQgh3Za/66KPAh8BRoD7wuNZ6cVEEVhKZs4KyWvjpDg7GedKyarN8Zwr5NG1K3Z+zjeKZmdZwags07JmvYwgh3JO9K4IXgeZa6xjTc4HZgDQExdXF43DlvNwWEkLkib2Hxde01jEAWutjQFnnhyTyLWqr8VsaAiFEHti7IqillJqc07TW+nnnhCXy5dRmKOsP1Zq5OhIhRAliryHIWmF0u7MCKUlsZQjl9KB477oznD18CSo7vn/rLCFwIFPILGor1AoFD0/HDyaEcHuOjFkssrCVIRRcwz/TiGNmh7aeA+BC7RMO7986SwiwnykEkHzJGBc4WEpACSHyxl7W0HRgsq0B5ZVS5YChwFWt9WwnxVds5ZQhZMuNQRUJr3c4T/t3KEvI2ulwQEMdeT4ghMgbe7eGvgLeVkq1BCKBGMAHCMIonfkDRiaRcLWoLaA8oGY7V0cihChh7N0aigCGKKXKA6FADYwxCfZrrQ86PzzhsKjNUL0FlK3g6kiEECWMo+MRJAJrnBuKyLf0NGOoyJD7XR2JEKIEklFF8uiXLafYcjzO7nrzD81n5LKRHIw7YPpx4gVUzH5IvSL9B4QQ+SINQR6Z00ZtZQhZW3psaaYP/0IdgSyrS6eM31UaOWf/QohSzdHqo4CRKaS1vuKsYEqKjvUDub9jHbvrNQlsQpNAIwV0dD8n3ra5fNb4XeFG5x1DCFFqOXRFoJTqopTaB+w3TbdWSn1lZzNRVBKiwcMLylV1dSRCiBLI0VtDnwN9gQsAWutdQDdnBSXy6HI0lL9BBpIXQuSLw58cWuuoLLNkhLLiIuEs+NdwdRRCiBLK0WcEUUqpLoBWSpUBnsd0m8idmDOGOtYPdGj9KieCOHv4EjcGVcx1vXzXFjK7fBaq5mF9IYSw4ugVwZPAM0BN4DQQAjztpJiKLUczhswqR9UDoHGH6rmuZ64tZOZQbaFMO4gGf3lQLITIH0evCJporR+wnqGUugnYUPghFW+OZgyZ3RhUkeY322848lxbyOxqAlxLgApya0gIkT+OXhF86eA8UdQuRxu/5YpACJFP9qqPdga6AFWVUi9bLfIHpOh9cZBg7kMgVwRCiPyxd2uoDFDetJ51NbPLwD3OCkqYXDoF1+z03zv6j/FbrgiEEPlkr/rov8C/SqmZWuuTRRRTiTX/0HyWHltKlRNB1D7eFd8rgWCVYJQ1O8jMZpbQ6XD47lZA2z+w8pQrAiFEvjn6sDhJKfUJ0BxjPAIAtNa3OCWqEspcX6hJVG98rwTie4PKlDGUdeQxM5tZQus/B58AuP1zUCr3AwfUhjJ+hXUaQgg342hDMBsIA27HSCV9GGOQGpGFpb5QINz5Sttsyx3KDoo5BAf+hG6joMVdTopUCCEMjmYNVdZafw+kaq3/1Vr/H9DJiXG5t01fgldZ6PC4qyMRQrgBR68IUk2/o5VSA4CzQC3nhOTmEv6DXXOhzXAoL0XkhBDO52hD8L5SKgB4BaP/gD/worOCcmubv4aMNOjyrKsjEUK4CYduDWmtl2it47XWkVrrnlrrdoD9YbrckLm+UL6kXIbwHyB4MAQ2KNS4hBAiJ/Y6lHkCQzBqDC3TWkcqpW4H/gf4Am2cH2LJ4mh9IZu2z4Crl6HL84UblBBC5MLeraHvgdrAVmCyUuok0BkYrbX+3cmxlViO1hfKJO2qcVuofjeomT3bSAghnMVeQxAKtNJaZyilfIBYoJHW+j/nh+Zm9sw3RhobPNXVkQgh3Iy9ZwTXtNYZAFrrFOCQNAJOkJEBG76A6i2hofTRE0IULXtXBE2VUrtNrxXQ0DStAK21buXU6EqYKieCqHDhhkxlJeB6aYkcB5w5tAxiD8Fd39nvRSyEEIXMXkPQrEiiKCVyelBs3QjYHHBmwxcQUAea31kEUQohRGb2is5Jobk8Sqj8H81vzn57J8fSEqc2Q9RmuO1j8HS0W4cQQhQehwevF06y4QvwDYQ2D7o6EiGEm3JqQ6CU6qeUOqiUOqKUGp3Leu2VUulKKfca4yDmIBxcatQUKlPO1dEIIdyUww2BUspXKdUkD+t7AlOB24BgYJhSKjiH9T4Clju671Jj42Tw8oUOj7k6EiGEG3OoIVBKDQQigGWm6RCl1GI7m3UAjmitj2mtrwFzgcE21nsOWACcdzTo4mb+ofmMmvahkTGUxcWweSRt25Z9o8tnYVeYcUuoXJUiiFIIIWxz9IrgHYwP9ksAWusIoJ6dbWoCUVbTp03zLJRSNYE7gWm57Ugp9bhSKlwpFR4TU/yGQVh6bCm+x41MoRvblM+0zDwiWbZsoc1fg06X4nJCCJdztCFI01rH53HfthLis467OAl4XWudntuOtNbTtdahWuvQqlWLZ2lmPy9fbgyqyEP3DMq+rH17Kg0dcn1GSjyEzzDSRSvVK7oghRDCBkfzFSOVUvcDnkqpIOB5YKOdbU5j1Ckyq4UxjoG1UGCuMjpRVQH6K6XSSn0do/Af4FoC3PSCqyMRQgiHrwiewxiv+CrwCxCP/fEItgFBSqn6SqkywH1ApucKWuv6Wut6Wut6wK/A06W+ETAXl2vQE2q0dnU0Qgjh8BVBE631G8Abju5Ya52mlHoWIxvIE/hBa71XKfWkaXmuzwVKrd1hkHgO7vzG1ZEIIQTgeEPwmVKqBjAfmKu13uvIRlrrpcDSLPNsNgBa6xEOxuJy8w/NZ+mx66eVvreCzRpDnNwIl88Yrzd+afze9j3c0Aoa9CiSWIUQwh6HGgKtdU+l1A0Yg9RMV0r5A2Fa6/edGl0xtfTYUg7GHaRJoNGtIjiuE2BjMJqFT0DcFeP1ijevzx86S4rLCSGKDYeL25jKT09WSq0GXgPeBtyyIQBoEtiEGf1mALBw7w4IJPNgNNeS4NIpqBgCAbVgjOlCSHlCGb+iD1gIIXLgUEOglGoGDAXuAS5gdA57xYlxlXwXjhi/vcsZH/5lK7g2HiGEyIGjVwQzgDlAH6111hRQYcuFw8Zvb1/XxiGEEHY4+oygk7MDKXVijwBKGgIhRLGXa0OglJqntR6ilNpD5l7Bbj9CWZUTQSz8dAcAsacTqVIrc2kJYg9BQG1QUulbCFG82bsiMHd9tTGslnurHFWP2CSjAahSq3z2jKELh6FKI9cEJ4QQeWBvhLJo08untdavWy9TSn0EvJ59K/dRpVZ57nylbfYFWhu3htp2BqKzLxdCiGLE0fsWvW3Mu60wAylVLp+F1CtQWa4IhBDFX64NgVLqKdPzgSZKqd1WP8eB3UUTYglkzhiqEuTaOIQQwgH2nhH8AvwFjAesh5pM0FrHOS2qki7W3BA0dm0cQgjhAHsNgdZan1BKPZN1gVIq0F0ag1+2nGLmnjlE6434lfXCMy6aJjbvlpnEHubiiUpcfmY0KQcO4NO0adEFK4QQeeTIFcHtwHaM9FHrAjkaaOCkuIqVRRFniNYb8fCJpopfI6r5NyHQt3LOG1w4zOXT/qTEG41AttHJhBCiGLGXNXS76Xf9ogmn+PIr60XwDc0z1xfKSewR8PbDp2kT6v78UxFFKIQQ+ePo4PU3KaXKmV4/qJT6TClVx7mhlVDXkiD+FHhJj2IhRMngaPro10CSUqo1RuXRk8DPTouqJIs7avyW0hJCiBIiL4PXa2Aw8IXW+gtAymnaYs4Y8pZS00KIksHR6qMJSqkxwHDgZqWUJ+DtvLCKj1+2nGLHxb/wqXEICM113Yth87g86yu4VJmUlFP4NG1WNEEKIUQBOHpFMBRj4Pr/Mw1QUxP4xGlRFSOLIs7g5R8BQP8G/XNd9/KSJaRExYJXWXyaNpNsISFEieBoGer/lFKzgfZKqduBrVprt0mH8ff1Jrh6KPc2vtfuuj5VPKj7cBAMd5u3RwhRwjmaNTQE2ArcizFu8Ral1D3ODKzESk2GylJaQghRcjj6jOANoL3W+jyAUqoqsBL41VmBlUjp1yAjXWoMCSFKFEefEXiYGwGTC3nY1n2kJhm/pSEQQpQgjl4RLFNKLccYtxiMh8dLnRNS8fDLllMsijjDvujL+NW1v/7FsHkk7TmMX1Xk1pAQokRx9GHxKKXUXUBXjHpD07XWC50amYuZG4HgGv4klitrd/3LS5YA4N8gHfxvdHZ4QghRaOyNRxCklFqklIrEeFD8qdb6pdLeCJgF1/An7InOVPO33xAA+NXxo1KXuqCU/ZWFEKKYsHef/wdgCXA3RgXSL50eUUmWmiS3hYQQJY69W0MVtNbfml4fVErlUnLTzekMSLsqD4qFECWOvYbARynVhuvjEPhaT2utpWEwS002fktDIIQoYew1BNHAZ1bT/1lNa+AWZwRVnMw/NJ/wc+GEVr9eZ2jvujOcPXyJG4MqAqaMoV37JGNICFEi2RuYpmdRBVJcLT1mZMla1xk6tPUcAI07VAesMobqJkPlhkUcoRBCFIyj/QjcWqiNOkM3BlWk+c01LdN+9f2p1M4TypQr6vCEEKJApHdwYUlNhsqNXB2FEELkmTQEhSU1WR4UCyFKJEerjyrTWMVvm6brKKU6ODe0EsRSbK6xqyMRQog8c/SK4CugMzDMNJ0ATHVKRMXERc+1nCgzkYNxBzPNN2cMWdYLm0fSjl3GhNwaEkKUQI42BB211s8AKQBa64tAGadFVQzEe24lRUXRJLCJ4xlDcmtICFECOZo1lGoap1iDZTyCDKdFVUz46NrM6Dcj2/xsGUMNK1OpWTxUkGJzQoiSx9ErgsnAQqCaUuoDYD3wodOiKmlSTf0HPOTZuxCi5HG0DPVspdR2oBdGeYk7tNb7nRpZSZKaBFVauzoKIYTIF4caAqVUHSAJ+MN6ntb6lLMCKzEsxeYkY0gIUTI5+ozgT4znAwrwAeoDB4HmTorLpX7ZcorLyan4+3pnmm9dY+hi2DwuL1lCyoH9+PgiGUNCiBLL0VtDLa2nlVJtgSecElExsCjiDABVsoxMZp0xdHn6JFIOHMCnbnX8fc9KxpAQosTK19NNU/np9oUcS7Hi7+ttc2Qy64whn6ZNqft8Tyo1SpIrAiFEieXoM4KXrSY9gLZAjFMiKmkuHAb/WlJsTghRYjn6jKCC1es0jGcGCwo/nBIo9hBUkasBW1JTUzl9+jQpKSmuDkUIt+Hj40OtWrXw9va2v7KJ3YbA1JGsvNZ6VEGCK7Vij0DIMPvruaHTp09ToUIF6tWrh1LK/gZCiALRWnPhwgVOnz5N/fr1Hd4u14ZAKeWltU4zPRwuteYfmm8ZgAbA+1Itbo29jQpH/Fi49/ponDHH46hw9Twnh5seFAc1gGsJMipZDlJSUqQREKIIKaWoXLkyMTF5u3Nv72HxVtPvCKXUYqXUcKXUXeaffEVaDC09tjRTcbn6sY2pklSTQN/KmdarcPU81U5vBIwHxf43tTAWyK2hHEkjIETRys//OUezhgKBCxhjFN8ODDT9LjWaBDZhRr8ZzOg3Ax9dmyRfXx5/sy93vtLW8tMlZRmNqlym7s8/Uffnn6jUoYaxsXQmK7bKly9f4H2Eh4fz/PPP57j8xIkT/PLLLw6vD1CvXj1atmxJq1at6N69OydPnixwnIVl2rRp/PTTT4Wyr+joaG6/PfNHxQsvvEDNmjXJyLheruydd95h4sSJmdarV68esbGxAPz333/cd999NGzYkODgYPr378+hQ4cKFNvVq1cZOnQojRo1omPHjpw4ccLmenPmzLH8rfr162eJyezXX39FKUV4eDgAMTEx9OvXr0CxFTV7DUE1U8ZQJLDH9Huv6XekvZ0rpfoppQ4qpY4opUbbWP6AUmq36WejUqpk1Wm4cAS8/aTYXCkXGhrK5MmTc1yetSGwt77Z6tWr2b17Nz169OD9998vcJxa60wfrvn15JNP8tBDDxV4PwCfffYZjz32mGU6IyODhQsXUrt2bdauXevQPrTW3HnnnfTo0YOjR4+yb98+PvzwQ86dO1eg2L7//nsqVarEkSNHeOmll3j99dezrZOWlsYLL7xg+Vu1atWKKVOmWJYnJCQwefJkOnbsaJlXtWpVatSowYYNGwoUX1Gy1xB4AuVNPxWsXpt/cmR6yDwVuA0IBoYppYKzrHYc6K61bgW8B0zP6wm4VOwhKTZXAkVERNCpUydatWrFnXfeycWLFwHYtm0brVq1onPnzowaNYoWLYxbf2vWrLF8q/33338JCQkhJCSENm3akJCQwOjRo1m3bh0hISF8/vnnmdZPTExk5MiRlm+UCxZkT7br3LkzZ84YnRhjYmK4++67ad++Pe3bt7d8mMTExNC7d2/atm3LE088Qd26dYmNjeXEiRM0a9aMp59+mrZt2xIVFcUnn3xC+/btadWqFWPHjgXgypUrDBgwgNatW9OiRQvCwsIAGD16NMHBwbRq1YpXX30VyPztPKf3qkePHrz++ut06NCBxo0bs27dOpvv9YIFCzJ9O169ejUtWrTgqaeeYs6cOQ79vVavXo23tzdPPvmkZV5ISAg333yzQ9vnZNGiRTz88MMA3HPPPaxatQqtdaZ1tNZorbly5Qpaay5fvsyNN17/4vfWW2/x2muv4ePjk2m7O+64g9mzZxcovqJkL2soWms9Lp/77gAc0VofA1BKzQUGA/vMK2itN1qtvxmolc9juUbsYagV6uooSoR3/9jLvrOXC3WfwTf6M3Zg3qucPPTQQ3z55Zd0796dt99+m3fffZdJkyYxcuRIpk+fTpcuXRg9OtsFLAATJ05k6tSp3HTTTSQmJuLj48OECROYOHEiS0xjU6xZs8ay/nvvvUdAQAB79uwBsHyQWlu2bBl33HEHYNw2eemll+jatSunTp2ib9++7N+/n3fffZdbbrmFMWPGsGzZMqZPv/6d6eDBg8yYMYOvvvqKFStWcPjwYbZu3YrWmkGDBrF27VpiYmK48cYb+fPPPwGIj48nLi6OhQsXcuDAAZRSXLp0yeH3Coxvy1u3bmXp0qW8++67rFy5MtO2x48fp1KlSpQte71j5pw5cxg2bBiDBw/mf//7H6mpqXbTHCMjI2nXrl2u65jdfPPNJCQkZJs/ceJEbr311kzzzpw5Q+3atQHw8vIiICCACxcuUKVKFcs63t7efP3117Rs2ZJy5coRFBTE1KnGmFw7d+4kKiqK22+/PdttrdDQUN58802HYi4O7H2VLciTvppAlNX0adO8nDwC/FWA4+XL/EPzCT8Xbpneu+4MFRMzX15fDJvHyeEPkXLgwPWZqSlw6ZRkDJUw8fHxXLp0ie7duwPw8MMPs3btWi5dukRCQgJdunQB4P7777e5/U033cTLL7/M5MmTuXTpEl5euX+XWrlyJc8884xlulKlSpbXPXv2pFq1aqxcudJyvJUrV/Lss88SEhLCoEGDuHz5MgkJCaxfv5777rsPgH79+mXaT926denUqRMAK1asYMWKFbRp04a2bdty4MABDh8+TMuWLVm5ciWvv/4669atIyAgAH9/f3x8fHj00Uf57bff8PPzc+i9MrvrLiNfpF27djbvr0dHR1O1alXL9LVr11i6dCl33HEH/v7+dOzYkRUrVgA5P+DM64PPdevWERERke0nayMAZPv2b+t4qampfP311+zcuZOzZ8/SqlUrxo8fT0ZGBi+99BKffvqpzTiqVavG2bNn8xS7K9m7IuhVgH3b+gtmf+cBpVRPjIagaw7LHwceB6hTp04BQsrOnDZqHoXMXE/ofCVPyzpGcbkDRqaQ+cFX3FFAS40hB+Xnm3tRsvWhYMvo0aMZMGAAS5cupVOnTtm+Bdvab04fZqtXr6ZcuXKMGDGCt99+m88++4yMjAw2bdqEr6+vw/GVK1cu03pjxozhiSeylwLbvn07S5cuZcyYMfTp04e3336brVu3smrVKubOncuUKVP4559/cj0fa+Zv+p6enqSlpWVb7uvrm6kz4bJly4iPj6dlS6N0WVJSEn5+fgwYMIDKlSsTHR2dafuEhAQqVqxI8+bN+fXXXx2KKS9XBLVq1SIqKopatWqRlpZGfHw8gYGBmdaJiIgAoGHDhgAMGTKECRMmkJCQQGRkJD169ACMh9mDBg1i8eLFhIaGkpKSku1vWJzlekWgtY4rwL5PA7WtpmsB2ZpIpVQr4DtgsNb6Qg5xTNdah2qtQ62/YRSW0Oqh3Nv4Xsv0pfIe/Fclcxvp07SpkSk0dIgxI/aw8VsaghIlICCASpUqWe5p//zzz3Tv3p1KlSpRoUIFNm/eDMDcuXNtbn/06FFatmzJ66+/TmhoKAcOHKBChQo2P3wA+vTpk+nhYtZbQ76+vkyaNImffvqJuLi4bOubP4i6du3KvHnzAONbv61bTAB9+/blhx9+IDExETBuf5w/f56zZ8/i5+fHgw8+yKuvvsqOHTtITEwkPj6e/v37M2nSJMux7L1XjmrcuHGmK4U5c+bw3XffceLECU6cOMHx48dZsWIFSUlJdOvWjcWLF1vex99++43WrVvj6enJLbfcwtWrV/n2228t+9q2bRv//vtvtmPm5Ypg0KBB/Pjjj4CR+XPLLbdka7Rr1qzJvn37LHn5f//9N82aNSMgIMDyjObEiRN06tTJ0ggAHDp0yPKMqSRwtMREfmwDgpRS9YEzwH1Aputt0zgHvwHDtdYFywUrahdMDYEUmyvWkpKSqFXr+qOnl19+mR9//JEnn3ySpKQkGjRowIwZxnCk33//PY899hjlypWjR48eBAQEZNvfpEmTWL16NZ6engQHB3Pbbbfh4eGBl5cXrVu3ZsSIEbRp08ay/ptvvskzzzxDixYt8PT0ZOzYsZZbKmY1atRg2LBhTJ06lcmTJ/PMM8/QqlUr0tLS6NatG9OmTWPs2LEMGzaMsLAwunfvTo0aNahQoYLlA9+sT58+7N+/n86dOwNG+uysWbM4cuQIo0aNwsPDw3LfOyEhgcGDB5OSkoLWms8//zzb+eb0XjmiXLlyNGzYkCNHjnDjjTeyfPlyvvnmm0zLu3btyh9//MHQoUN59tln6dq1K0opqlWrxnfffQcYt2sWLlzIiy++yIQJE/Dx8aFevXqWZxX59cgjjzB8+HAaNWpEYGBgpsY/JCSEiIgIbrzxRsaOHUu3bt3w9vambt26zJw50+6+V69ezYABAwoUX5EyPxV3xg/QHzgEHAXeMM17EnjS9Po74CIQYfoJt7fPdu3a6cI04q8ResRfIyzTv03crt9/ZbUeMm2jZd6JB4frEw8Oz7zhgse0/rRZocZS2uzbt8/VIeRJQkKC5fX48eP1888/78JoMktJSdGpqalaa603btyoW7du7dqAHPTbb7/pN954w9VhFLmbb75Zx8XFuez4tv7v5fb56swrArTWS4GlWeZNs3r9KPCoM2NwmtjDcluolPnzzz8ZP348aWlpDn/zKyqnTp1iyJAhZGRkUKZMmUy3SYqzO++8kwsXbN7xLbViYmJ4+eWXMz3QL+6c2hAUd+aModDq11NAzyek4H3pAiPm/8zJ9f4AlgfFFlobnclaDS3qkIUTDR06lKFDi+ffNCgoiJ07d7o6jHx59NGS+V0vv6pWrWpJBy4p3LonVNaMIYDYxGsEpCRSK+60ZV6mbCGAxHNw9bJcEQghSgW3viKA7BlDAF6eCv/mwdT9OYd6K5IxJIQoRdz6iiDfLBlD0hAIIUo+aQjyI/YwePmCf24dpYUQomSQhiA/Yg8bYxBIsTkhRCkgn2RWLobN44aYU5S5djX3Fc/vlzEISghPT09CQkJo3rw5rVu3tpRxyI+3334715IS+a3jv3z5cktF0/Lly9OkSRNCQkIKpRT0xIkTadq0KS1atKB169aW+Hr06GGpn19Q1uMvXL16lVtvvZWQkBDCwsJ49NFH2bdvn5095M7c89osLS2NKlWqMGbMmEzrWY9fAJmrxgL89ddfhIaG0qxZM5o2bWqptloQ27dvp2XLljRq1Ijnn38+x1Igu3fvpnPnzjRv3pyWLVuSkpJCQkKC5e8eEhJClSpVePHFFwGYMmVKnjrvFVhOHQyK609hdijL2pnsxIPD9U/3TdffPfSjjpsbZnujiye1Huuv9aavCy2O0qo4dCgrV66c5fW5c+d0r1699Ntvv+3CiHLXvXt3vW3btmzz09LS8ryvr7/+Wvfp00fHx8drrbW+dOmSnjlzZq7HKahNmzbpbt265Xv7rOeZmpqqW7ZsaelMp7XWf/75p+7SpYtu0KCBzsjIsMyvW7eujomJsUyvXr1aDxgwQGut9Z49e3SDBg30/v37LfudOnVqvuM0a9++vd64caPOyMjQ/fr100uXLs22jvkcIiIitNZax8bG2vx7tm3bVv/7779aa62vXLmiQ0JC8h1XXjuUyRVBFtfKlOW/qnWu1xTK6uQm43e9m4ouqNLgr9EwY0Dh/vxlu1R0TqpVq8b06dOZMmUKWmvS09MZNWqUpXa/dfmDjz/+mJYtW9K6dWtLSeoRI0ZYip85u44/GN9wx40bR9euXZk/fz4rVqygc+fOtG3blnvvvddSXmL79u10796ddu3a0bdvX0vxtg8//JCvvvoKf3+jP0xAQICl/r61p556itDQUJo3b24ZvyCnc5w/f77l6qJbt27A9W/e58+f58EHHyQiIoKQkBCOHj2a6cojp/iznqe1f/75h7Zt22aq8jpnzhxeeOEF6tSpY6kNZc/HH3/MG2+8QVNTfyAvLy+efvpph7bNSXR0NJcvX6Zz584opXjooYf4/fffs623YsUKWrVqRevWxrhblStXxtPTM9M6hw8f5vz585YxFvz8/KhXrx5bt27Ntj9ncPv00Tw7uQF8AqBa1jF2REnQoEEDMjIyOH/+PIsWLSIgIIBt27Zx9epVbrrpJvr06cOBAwf4/fff2bJlC35+fsTFZa696Ow6/tZ8fHxYv349sbGx3HXXXaxcuZJy5crx0Ucf8dlnnzFmzBiee+45Fi1aRNWqVQkLC+ONN97giy++ICEhwVI1MzcffPABgYGBpKen06tXL3bv3k2tWrVsnuO4ceNYvnw5NWvWzHbe5vpA1mMzmMXGxvL+++9ni//tt9/OdJ5ZbdiwIdNYBMnJyaxatYpvvvmGS5cuMWfOHEtdpdxERkbyyiuv2F1v9erVvPTSS9nm+/n5sXHjxkzzzpw5k6mOVa1atSwDDFk7dOgQSin69u1LTEwM9913H6+99lqmdebMmcPQoUMzFb0LDQ1l3bp1dOjQwW7cBSUNQV6d3Ah1OoOHp/11xXW3TXB1BBbadB93xYoV7N692/ItPz4+nsOHD7Ny5UpGjhxpqc+ftTSxdR3/AQMGZBuT11Yd/3vvvd5XxV4df2vmns6bN29m37593HSTcSV67do1OnfuzMGDB4mMjKR3794ApKenU6NGjVzLX2c1b948pk+fTlpaGtHR0ezbt4/g4GCb53jTTTcxYsQIhgwZkq14Xm5yij/reWYVHR1Ns2bNLNNLliyhZ8+e+Pn5cffdd/Pee+/x+eef4+npafN88zqeQc+ePbNVYc2J+d+RveOlpaWxfv16tm3bhp+fH7169aJdu3b06nW9yv/cuXP5+eefM21XrVo1DliPgeJE0hCYXAybx+HTZUhpkssgaQnnjD4EbYcXXWCiUB07dgxPT0+qVauG1povv/ySvn37Zlpn2bJluX6AeHl5ObWOvzXzWANaa3r37p1teMc9e/bQvHlzNm3aZHPbY8eO0aBBgxz3f/z4cSZOnMi2bduoVKkSI0aMICUlJcdznDZtGlu2bOHPP/+0VOh0RE7xZz3PrLKOaTBnzhw2bNhAvXr1ALhw4QKrV6/m1ltvpXLlyly8eNEywlhcXJzldfPmzdm+fbvl9kxO8nJFUKtWLU6fvl6B4PTp05mGsbRer3v37pZY+vfvz44dOywNwa5du0hLS8s2CltRjmkgzwhMLi9ZwjlTzSHrQWkyOWX6h1BXng+URDExMTz55JM8++yzlkv1r7/+mtTUVMC4hL9y5Qp9+vThhx9+ICkpCSDbrSFn1/G3pVOnTmzYsIEjR44ARnntQ4cO0aRJE2JiYiwNQWpqKnv37gVgzJgxPPPMM1y+bAwRevny5UxDXJrnlStXjoCAAM6dO8dff/2V6zkePXqUjh07Mm7cOKpUqUJUVBSOyCl+e5o1a2bZ5vLly6xfv55Tp05ZxgGYOnWqpXHp0aOH5Vt1eno6s2bNomfPngCMGjWKDz/80HLMjIwMPvvss2zHM18RZP3J2ggAllLgmzdvRmvNTz/9xODBg7Ot17dvX3bv3k1SUhJpaWn8+++/BAdfv7VsHr4zq6Ic00CuCKx4VPC3OSiNxcmN4F0OauT+rUIUH8nJyYSEhJCamoqXlxfDhw/n5ZdfBoxiaCdOnKBt27ZoralatSq///47/fr1IyIigtDQUMqUKUP//v358MMPLft0dh1/W6pWrcrMmTMZNmwYV68a6c3vv/8+jRs35tdff+X5558nPj6etLQ0XnzxRZo3b85TTz1FYmIi7du3x9vbG29v72z3yVu3bk2bNm1o3rw5DRo0sNy6yekcR40axeHDh9Fa06tXL1q3bm1zgJi8xJ+b2267jeHDjSvw3377jVtuuSXTGMiDBw/mtdde4+rVq7z11ls89dRTtG7dGq01/fr148EHHwSgVatWTJo0iWHDhpGUlIRSqlDGC/j6668ZMWIEycnJ3Hbbbdx2220ALF68mPDwcMaNG0elSpV4+eWXad++PUop+vfvn+nY8+bNY+nSpdn2vWHDhkwP751J2brPVZyFhobqwsp/HrlsJAAz+s3g5PCH2OjTj2MBN7I7qCxhT9h4APX1TVCuKjz0e6Ecv7Tbv39/pvu7QuTHnXfeyccff0xQkPuUdNm5cyefffZZtucGjrL1f08ptV1rHWprfbk15KikODi3V24LCVHEJkyYkG0849IuNjaW9957r8iOJ7eGHBW1BdBQt4urIxHCrTRp0oQmTZq4OowiZc4CKypu2xBcDJvHfT/tJb5cB+Ys+IWMjG4kegTmvMGJ9eBZFmq2y3kdIYQogdz21tDlJUuodiaJBL8QLnsE4uHnR2AllXPG0MmNUCsUvH2KNlAhhHAyt70iADhf04+Ymn40CazGna/0A+DXb7LnY3M1AaJ3wc0vF3GEQgjhfG57RZAnUVtBp8vzASFEqSQNgSNObgTlCbWcX/NDFC5zGeoWLVowcOBAm3WB8mPmzJk8++yzhbKvevXq0bJlS0s5YludlwpDREREtnz1nEozWxfQKwxdulz/EjVq1CiaN2/OqFGj8l2629rOnTt59NFHM80bPHhwthpE1kUDzcqXL295fejQIfr370+jRo1o1qwZQ4YM4dy5cwWKLS4ujt69exMUFETv3r0txQez+vzzz2nevDktWrRg2LBhlt7Ub731Fq1atSIkJIQ+ffpw9uxZwOhRPmLEiALFZk0aAkec3Ag3hkDZ8nZXFcWLr68vERERREZGEhgYyNSpU10dkk2rV6+29GK1/tDMjb3yFFllbQgiIyN59tlnmTVrFvv37ycyMjLXchQFYd24ffPNN+zYsYNPPvmEJ598Mk/jLtg65w8//JDnnnvOMn3p0iV27NjBpUuXOH78uEP7TUlJYcCAATz11FMcOXKE/fv389RTTxETE+NwbLZMmDCBXr16cfjwYXr16sWECdlrbp05c4bJkycTHh5OZGQk6enpzJ07FzAazd27dxMREcHtt9/OuHHjAGjZsiWnT5/m1KlTBYrPzK2fEcT5tqfChRvYd/Uyc03PBvZFXya4hv/1lVKT4Uw4dHzSRVGWDh9t/YgDcYVbQKtpYFNe7/C6w+t37tyZ3bt3A7B161ZefPFFkpOT8fX1ZcaMGTRp0oSZM2eyePFikpKSOHr0qKUzE8CMGTMYP348NWrUoHHjxpYeridPnuT//u//iImJoWrVqsyYMYM6deowYsQIfH19OXDgACdPnmTGjBn8+OOPbNq0iY4dOzJz5swcY81tn4GBgezcuZO2bdvy9NNP88wzzxATE4Ofnx/ffvstTZs2Zf78+bz77rt4enoSEBDAypUrefvtt0lOTmb9+vWMGTOGP//806HSzN9++y3Tp0/n2rVrNGrUiJ9//hk/P79sx1i7di179+5l5MiRXLt2jYyMDBYsWEBQUBDly5cnMTGRQYMGceXKFTp27MiYMWPYv38/5cuX59VXX+Xo0aM2zyXrOX/66aeW2BISEti9e3emGkILFixg4MCBVK9enblz52YbwMaWX375hc6dOzNw4EDLPHN5ioJYtGgRa9asAYzigz169OCjjz7Ktl5aWhrJycl4e3uTlJRkqVlkLiEOcOXKlUw1sAYOHMjcuXOzVTLND7e+IkgsFwJkri0UXMOfwSFWYxGf2Q7p16QjWQmXnp7OqlWrGDRoEABNmzZl7dq17Ny5k3HjxvG///3Psm5ERARhYWHs2bOHsLAwoqKiiI6OZuzYsWzYsIG///4706hbzz77LA899BC7d+/mgQcesIzWBXDx4kX++ecfPv/8cwYOHMhLL73E3r172bNnT6YaRT179iQkJISOHTva3eehQ4dYuXIln376KY8//jhffvkl27dvZ+LEiZYPcnO56F27drF48WLKlCnDuHHjGDp0KBEREQwdOpTIyMhshc5sueuuu9i2bRu7du2iWbNmfP/99zaPAcYobS+88AIRERGEh4dnKtMMRukF81Va1oqjOZ1L1nO2Fh4enq0ej7l2z7Bhw3IscpeVo+9F1lHFrH9sjcR27tw5atSoARi1ic6fP59tnZo1a/Lqq69Sp04datSoQUBAAH369LEsf+ONN6hduzazZ8+2XBHA9TLVhcGtrwgA/qtwhi/eyKWa6MmNgII6HYssptIoL9/cC5O51tCJEydo166dpaNOfHw8Dz/8MIcPH0YpZSk8B9CrVy8CAgIACA4O5uTJk8TGxtKjRw+qVq0KGGWTzQXMNm3axG+//QbA8OHDM31DGzhwIEopWrZsSfXq1WnZsiVgVMM8ceIEISEhgHFryFyd0t4+7733Xjw9PUlMTGTjxo2ZSlyb6/jkt1y0LZGRkbz55ptcunSJxMRES7VWW8fo3LkzH3zwAadPn+auu+5yuCxEbudifc5ZRUdHW/4mYHzwHjlyhK5du6KUwsvLi8jISFq0aFEoZaorVKjgcMVVR128eJFFixZx/PhxKlasyL333susWbMsdZI++OADPvjgA8aPH8+UKVN49913AaNMtfmZQUG59RWBQ05ugOotwLeSqyMR+WD+9nny5EmuXbtmeUbw1ltv0bNnTyIjI/njjz8ylTq2LmpmXSra0Q8N6/XM+/Lw8Mi0Xw8Pjzzd47fep7lkc0ZGBhUrVsxUJXP//v2A8c38/fffJyoqipCQEC5cuJBtn+bSzPaMGDGCKVOmsGfPHsaOHWt5r2wd4/7777d86+/bt6/D5blzOxfrc84qa5nqsLAwLl68SP369alXrx4nTpyw3G83l6k2s1Wm2p68XhFUr17dUh4jOjqaatWqZVtn5cqV1K9fn6pVq+Lt7c1dd91lM2Hg/vvvZ8GCBZbpwixTLQ1BbtJTjdRRSRst8QICApg8eTITJ04kNTWV+Ph4atY0bgHmdq/erGPHjqxZs4YLFy6QmpqaaUjFLl26WD5sZs+eTdeuXQscryP79Pf3p379+pZYtNbs2rULsF0uukKFCiQkJFi2d7Q0c0JCAjVq1CA1NZXZs2db5ts6hnn8g+eff55BgwZZnsnYk9u55Ma6TDUYt4WWLVtmKVO9fft2y/vYo0cPwsLCuHbtGmD83c3PAe6//342btzIn3/+adnXsmXL2LNnT6bjma8IbP1Yl5Y2GzRoED/++CNgVKS1VabaPORmUlISWmtWrVplKRh3+PBhy3qLFy+2PM+Bwi1TLQ1BbqJ3QWqSjE9cSrRp04bWrVtbHrCNGTOGm266ifT0dLvb1qhRg3feeYfOnTtz66230rZtW8uyyZMnM2PGDFq1asXPP//MF198UeBYHd3n7Nmz+f7772ndujXNmzdn0aJFgPEh37JlS1q0aEG3bt1o3bo1PXv2ZN++fYSEhBAWFpapNHOzZs1o0aKFzeJu7733Hh07dqR3796ZPohsHSMsLIwWLVoQEhLCgQMH8pQRlNO55KZp06bEx8eTkJDAiRMnOHXqFJ06dbIsr1+/Pv7+/mzZsoXbb7+dm2++mXbt2hESEsKGDRssD259fX1ZsmQJX375JUFBQQQHBzNz5kyb3+DzYvTo0fz9998EBQXx999/W8a/Pnv2LP379weMLxn33HMPbdu2pWXLlmRkZPD4449btm/RogWtWrVixYoVmf4drF69ulBKaYMblqHeu+4M+/6M5NqJ48RXqEV0xYu890kOzwjWT4KVY+HVI1C+qu11RI6kDLUoCp9//jkVKlTI1pegNLt69Srdu3dn/fr1eHllf9QrZajtOLT1HHEXjcbvitcZvBrm8rz85Eao0lgaASGKsaeeeirT8xd3cOrUKSZMmGCzEcgPt8wa8s+Iw+fCt/zatx5bRi6wvVJGOpzaDC3uLNrghBB54uPjYxnFzF0EBQUV6kA9bndF4LBze+FqvPQfEEKUetIQ5OSkeaB6yRgSQpRu0hDk5OR6qFgXAmrZX1cIIUowt2sI0mJiyEi4TFpGLtlSWsPJTXJbSAjhFtyvITD1sFzXpAJVyuWQaRC1FZJioUH3IoxMOMvChQtRSnHgQM5F73r06IG9tOQePXrQpEkTQkJCaNasGdOnTy/UOGfOnJmpZEBqaiqjR48mKCiIFi1a0KFDB/766y/AKF0dGxtbKMddvHixpSpmTEwMHTt2pE2bNqxbt47+/fsXuHT3iy++yNq1ay3TMTExeHt7880332Raz7okNGQv9f3TTz/RokULmjdvTnBwcKGUyV62bBlNmjShUaNGNiuDglGOZODAgZb+DTNmzLC7/auvvupwr+riwO0aAoCUsn5saV+Fav45NAS7fgFvP2haOJ01hGvNmTOHrl27WnqYFsTs2bOJiIhgw4YNvP7665ZeqoUha0Pw1ltvER0dTWRkpKUUhnXP4MIyaNAgS0enVatW0bRpU3bu3MnNN9/M0qVLqVixosP7yto5Ly4ujs2bN9OtWzfLvPnz59OpUyeHC8KBMW7CpEmTWLFiBXv37mXHjh2WelD5lZ6ezjPPPMNff/3Fvn37mDNnjs0yEVOnTiU4OJhdu3axZs0aXnnlFa5du5br9s8991yODUtx5Jbpo7lKTYbIhdBsEJSt4OpoSo3/PvyQq/sLtwx12WZNucGqaqgtiYmJbNiwgdWrVzNo0CDeeecdwChGN3LkSPbt20ezZs1ITk62bPPUU0+xbds2kpOTueeeeyxFvrLut1y5cpZCaHPmzOHDDz9Ea82AAQMsPVZtzU9PT+eRRx4hPDwcpRT/93//R+3atQkPD+eBBx7A19eXDRs28O2333L8+HFLjnz16tUZMmRItljuuOMOoqKiSElJ4YUXXuDxxx+3eYyXXnqJyZMnM23aNLy8vAgODmbu3LnMnDmT8PBwHn30UV577TVLob5NmzbRrFkzwsPDqVKlCrNmzWLy5Mlcu3aNjh078tVXX+Hp6Un58uV5+eWXWb58OZ9++mmmchi//vor/fr1yxTvnDlz+PTTT7n//vs5c+aMpdRHbsaPH8/EiRMt5Zl9fHx47LHH7G6Xm61bt9KoUSPLGAz33XcfixYtylYqQilFQkICWmsSExMJDAzEy8uLLVu25Lh93bp1uXDhAv/99x833HBDgeIsCtIQZHXwLyNttPV9ro5EFILff/+dfv360bhxYwIDA9mxYwdt27bl66+/xs/Pj927d7N79+5MJSM++OADAgMDSU9Pp1evXuzevZtWrVoB8MADD1C2bFkOHz7MpEmT8PT05OzZs7z++uts376dSpUq0adPH37//Xc6dOhgc37t2rU5c+YMkZGRgDGQSsWKFZkyZQoTJ04kNDSU3bt3U6dOnUz16HPyww8/EBgYSHJyMu3bt+fuu+/mxIkT2Y4BxkAp5sYl6y2fkJAQxo0bR3h4OFOmTMm0bP/+/YSFhbFhwwa8vb15+umnmT17Ng899BBXrlyhRYsWmUokm23YsIF77rnHMh0VFcV///1Hhw4dGDJkCGFhYbz8sv2xwB0tEz179mw++eSTbPMbNWqUbXSyM2fOULt2bct0rVq12LJlS7Ztn332WQYNGsSNN95IQkICYWFheHh42N2+bdu2bNiwgbvvvttu3K4mDUFWu+aAf02o383+usJh9r65O8ucOXN48cUXAeMb25w5c2jbti1r16611Phv1aqV5YMeYN68eUyfPp20tDSio6PZt2+fZfns2bMJDQ0lJiaGLl260K9fPyIiIjKVqH7ggQdYu3YtSimb89966y2OHTvGc889x4ABAzLVns+PyZMns3DhQsD4oD18+DBNmjSxeYxWrVrxwAMPcMcdd3DHHXc4fIxVq1axfft22rdvDxhXVOY6PJ6enjl+2GUtEz137lzLVc19993HI488kmtDkNcy0Q888AAPPPCAQ+vaKq9j63jLly8nJCSEf/75h6NHj9K7d29uvvlmu9sXZploZ3ObhuCXLac4+dMs/D074a2PkqKigObGwgtHYd2nRrXRI6vgpufBI3vtc1GyXLhwgX/++YfIyEiUUqSnp6OUsow4Zus//fHjx5k4cSLbtm2jUqVKjBgxIlOZY7OqVavStm1btmzZQpkyZWweP6c6XpUqVWLXrl0sX76cqVOnMm/ePH744YdM6zRq1IhTp06RkJBAhQo536Jcs2YNK1euZNOmTfj5+dGjRw9SUlJyPMaff/7J2rVrWbx4Me+99x579+7Ncd9Zz+Xhhx9m/Pjx2Zb5+PjYHCsAspeJnjNnDufOnbNUMT179iyHDx8mKCgIX19frl27Znk/bZWJvuWWW3KNMy9XBLVq1SIqKsoyffr0acutJ2szZsxg9OjRKKVo1KgR9evX58CBA3a3L8wy0c7mNg+LF0WcoXKC8c3kP/8D1PJrRP8GRvU/IhdAxGw4vRWqNoF2I1wXqCg0v/76Kw899BAnT57kxIkTREVFUb9+fdavX0+3bt0sH0aRkZGWcsmXL1+mXLlyBAQEcO7cOUuWTlZJSUns3LmThg0b0rFjR/79919iY2NJT09nzpw5dO/ePcf5sbGxZGRkcPfdd/Pee++xY8cOgExlov38/HjkkUd4/vnnLQ+ko6OjmTVrVqY44uPjqVSpEn5+fhw4cIDNmzcD2DxGRkYGUVFR9OzZk48//tgy0IwjevXqxa+//moZYSsuLo6TJ0/a3c66TPTBgwe5cuUKZ86csZSJHjNmjOUhfvfu3S3nl5yczLx58yxloseMGcNrr73Gf//9BxhF1yZPnpzteA888IDNEtFZGwGA9u3bc/jwYY4fP861a9eYO3euZQQ7a3Xq1GHVqlWAMfDNwYMHadCggd3tC7NMtLO5zRUBgFLgk3KMt2dmHu6Oy2fArwq8YL/+uSg55syZY8mGMbv77rv55Zdf+Oyzzxg5ciStWrUiJCSEDh06ANC6dWvatGlD8+bNadCgATfdlLkviflh7tWrVxkxYoTlvvX48ePp2bMnWmv69+9vqTtva/6uXbsYOXIkGRkZlnXAGADmySefxNfXl02bNvH+++/z5ptvEhwcjI+PD+XKlct2H75fv35MmzaNVq1a0aRJE0sJ5jNnzmQ7Rnp6Og8++CDx8fForXnppZcczggKDg7m/fffp0+fPmRkZODt7c3UqVOpW7durtsNGDCAb775hkcffZQ5c+Zw552Za3fdfffd3Hfffbz11lt88cUXPPHEE0yePBmtNQ899JAl26h///6cO3eOW2+9Fa215QF4QXh5eTFlyhT69u1Leno6//d//0fz5sZdgmnTpgHw5JNP8tZbbzFixAhatmyJ1pqPPvrIcqWS0/apqakcOXKE0FCbxT6LHbcpQz30m03cujESD6V4ZGaWcrWzh0DCWXhyfSFFKUDKUAtD165dWbJkSZ7SUEu6hQsXsmPHDt577z2XHF/KUOdHwlmokP3eoBCi4D799FNOnTrl6jCKVFpaGq+88oqrw3CYW90aytHlaKhpPzVNCJF3HTt2dHUIRe7ee+91dQh54jYNQcju1Vzz7YRPyjFIuQxLXoSrpl6aSbFyRSCEcFtuc2uoSqKRMRRQPxkOLTcyhS6fhSsxUKsDBN3q4giFEMI13OaKAKBM8lHuGfscLHoGfALgibXSX0AI4fbc5orAQms49q/Rc1gaASGEcG5DoJTqp5Q6qJQ6opQabWO5UkpNNi3frZRqa2s/hSruGMRHQX0pMe0OrEsbL126lKCgIKdmsPz111+EhobSrFkzmjZtyquvvgrAO++8Uyhlk826dLk+ct6oUaNo3rw5o0aNYtq0afz0008F2vfOnTt59NHMKdaDBw+mc+fOmeaNGDEiW0ct6/f70KFD9O/fn0aNGtGsWTOGDBnCuXPnChRbXFwcvXv3JigoiN69e3Px4kWb633++ec0b96cFi1aMGzYMEvv5rfeesvSd6RPnz6WEhB79uxhxIgRBYqtRNNaO+UH8ASOAg2AMsAuIDjLOv2BvwAFdAK22Ntvu3btdH5Mf2i6nv7QdK23fqf1WH+tYw7naz/Ccfv27XN1CLpcuXJaa61XrlypGzRooI8cOeLwtmlpaXk61p49e3SDBg30/v37tdZap6am6qlTp2qttR47dqz+5JNP8rQ/R1WoUEGnpKTka9vU1NRs8+655x4dERFhmb548aKuVauWbtq0qT527Jhl/sMPP6znz5+faVvz+52cnKwbNWqkFy9ebFn2zz//6D179uQrTrNRo0bp8ePHa621Hj9+vH7ttdeyrXP69Gldr149nZSUpLXW+t5779UzZszQWmsdHx9vWe+LL77QTzzxhGW6V69e+uTJkwWKr7iw9X8PCNc5fK468xlBB+CI1voYgFJqLjAYsC74PRj4yRTkZqVURaVUDa11dOGHo/FAw5+vGkXlKjcs/EOIHK2bd4jYKMfKGTiqSu3y3Dyksf1jr1vHY489xtKlS2nY0Pi7O1pS+Z9//uGPP/4gOTmZLl268M0336CUslnO+eOPP+aNN96gadOmgNFz9emnn84Wz7fffsv06dO5du0ajRo14ueff8bPz4/58+fz7rvv4unpSUBAAGvXrmXv3r2MHDmSa9eukZGRwYIFCwgKCqJ8+fIkJiYyaNAgrly5QseOHRkzZgz79++nfPnyvPrqqxw9epRnnnmGmJgY/Pz8+Pbbb2natCkjRowgMDCQnTt30rZtWz799HpP+4SEBHbv3k3r1q0t8xYsWMDAgQOpXr06c+fOZcyYMXbf819++YXOnTszcOBAyzxzuYiCWLRoEWvWrAHg4YcfpkePHpaS39bS0tJITk7G29ubpKQkSw0g62quV65cyVRvauDAgcydO5fXXnutwHGWNM68NVQTiLKaPm2al9d1UEo9rpQKV0qFx8TE5CsY7XUBD4+z0PAW6DHaqDchSr2rV68yePBgfv/9d8sHtHVJ5YiICDw9PS11h8wllbds2ULXrl159tln2bZtG5GRkSQnJ7NkyRLAKOe8c+dOdu/ebSlH4Gip5Lvuuott27axa9cumjVrxvfffw/AuHHjWL58Obt27WLx4sWAUerghRdeICIigvDwcGrVyjyG9uLFi/H19SUiIoKhQ4dmWvb444/z5Zdfsn37diZOnJipUTp06BArV67M1AgAhIeHZ6uPM2fOHIYNG8awYcMcHkzG0fciISGBkJAQmz+2Bok5d+4cNWrUAKBGjRqW2kfWatasyauvvkqdOnWoUaMGAQEBmSq8vvHGG9SuXZvZs2dnKtkRGhrKunXrHDq/0saZVwS2Pmmz1rNwZB201tOB6WCUmMhPMI9/b/9bjHAeR765O4O3tzddunTh+++/54svvgDyVlJ59erVfPzxxyQlJREXF0fz5s0ZOHBgvss5g/Eh+eabb1qKvvXt2xeAm266iREjRjBkyBDuuusuADp37swHH3zA6dOnueuuuwgKCnLoGImJiWzcuDFTx6arV69aXt977702K4ZmLRt97tw5jhw5QteuXVFK4eXlRWRkJC1atLBZvTWvZaMrVKhAREREnrax5+LFiyxatIjjx49TsWJF7r33XmbNmsWDDz4IGONNfPDBB4wfP54pU6ZYBh4qSWWjC5szrwhOA7WtpmsBWd9lR9YRIt88PDyYN28e27Zt48MPPwSul1Q2V6Y8ePCgZeQy65LKKSkpPP300/z666/s2bOHxx57zPLQ8c8//+SZZ55h+/bttGvXjrS0NEupZHtGjBjBlClT2LNnD2PHjrXsc9q0abz//vtERUUREhLChQsXuP/++y3f+vv27evwOLgZGRlUrFgxUwXO/fv3W5aXK1fO5nZZy0aHhYVx8eJF6tevT7169Thx4oSlWmjlypUzPay1VTbanrxeEVSvXp3oaOPOcXR0tKUBt7Zy5Urq169P1apV8fb25q677mLjxo3Z1rv//vtZsGCBZboklY0ubM5sCLYBQUqp+kqpMsB9wOIs6ywGHjJlD3UC4p3zfEC4Mz8/P5YsWcLs2bP5/vvvHS6pbP5ArFKlComJiZYMmZzKOY8aNYoPP/yQQ4cOWdb77LPPsu03ISGBGjVqkJqaarklBXD06FE6duzIuHHjqFKlClFRURw7dowGDRrw/PPPM2jQIEu5bHv8/f2pX78+8+fPB4zGb9cu+9V1rctGg3FbaNmyZZay0du3b7c0BD169CAsLMxSJnvmzJmW5wD3338/Gzdu5M8//7Tsa9myZezZsyfT8cxXBLZ+sg4ZCcb4yj/++CMAP/74o6XKq7U6deqwefNmkpKS0FqzatUqSwG2w4cPW9ZbvHix5XYhlKyy0YXNabeGtNZpSqlngeUYGUQ/aK33KqWeNC2fBizFyBw6AiQBI50Vj3BvgYGBLFu2jG7dujFp0iSHSipXrFiRxx57jJYtW1KvXj3LraScyjlXrFiRSZMmMWzYMJKSklBKMWDAgGyxvPfee3Ts2JG6devSsmVLyxgEo0aN4vDhw2it6dWrF61bt2bChAnMmjULb29vbrjhBt5++22Hz3n27Nk89dRTvP/++6SmpnLfffdleghsS9OmTYmPjychIYELFy5w6tQpS2lrgPr16+Pv78+WLVu4/fbbLVdEnp6eNGzY0PK8xNfXlyVLlvDiiy/y4osv4u3tTatWrSy35/Jr9OjRDBkyhO+//546depYGrqzZ8/y6KOPsnTpUjp27Mg999xD27Zt8fLyok2bNjz++OOW7Q8ePIiHhwd169a1xAvGbUBbfy934DZlqEXRkzLUJdPnn39OhQoVsvUlKM2uXr1K9+7dWb9+PV5eJb/ggpShFkIUyFNPPUXZsmVdHUaROnXqFBMmTCgVjUB+uOdZCyFy5OPjw/Dhw10dRpEKCgpyOCOrNJIrAuFUJe3WoxAlXX7+z0lDIJzGx8eHCxcuSGMgRBHRWnPhwgV8fHzytJ3cGhJOU6tWLU6fPk1+e4MLIfLOx8cnWw90e6QhEE7j7e1N/fr1XR2GEMIOuTUkhBBuThoCIYRwc9IQCCGEmytxPYuVUjFA9sIwjqkCxBZiOCWBnLN7kHN2DwU557pa66q2FpS4hqAglFLhOXWxLq3knN2DnLN7cNY5y60hIYRwc9IQCCGEm3O3hmC6qwNwATln9yDn7B6ccs5u9YxACCFEdu52RSCEECKLUtkQKKX6KaUOKqWOKKVG21iulFKTTct3K6XauiLOwuTAOT9gOtfdSqmNSqnch6oqAeyds9V67ZVS6Uqpe4oyPmdw5JyVUj2UUhFKqb1KqX+LOsbC5sC/7QCl1B9KqV2mcy7RIx0qpX5QSp1XSkXmsLzwP7+01qXqB2NYzKNAA6AMsAsIzrJOf+AvQAGdgC2ujrsIzrkLUMn0+jZ3OGer9f7BGBb1HlfHXQR/54rAPqCOabqaq+MugnP+H/CR6XVVIA4o4+rYC3DO3YC2QGQOywv986s0XhF0AI5orY9pra8Bc4GsI1wPBn7Shs1ARaVUjaIOtBDZPWet9Uat9UXT5GYgb+UJix9H/s4AzwELgPNFGZyTOHLO9wO/aa1PAWitS/p5O3LOGqiglFJAeYyGIK1owyw8Wuu1GOeQk0L//CqNDUFNIMpq+rRpXl7XKUnyej6PYHyjKMnsnrNSqiZwJzCN0sGRv3NjoJJSao1SartS6qEii845HDnnKUAz4CywB3hBa51RNOG5RKF/fpXGMtTKxrysqVGOrFOSOHw+SqmeGA1BV6dG5HyOnPMk4HWtdbrxZbHEc+ScvYB2QC/AF9iklNqstT7k7OCcxJFz7gtEALcADYG/lVLrtNaXnRybqxT651dpbAhOA7WtpmthfFPI6zoliUPno5RqBXwH3Ka1vlBEsTmLI+ccCsw1NQJVgP5KqTSt9e9FEmHhc/TfdqzW+gpwRSm1FmgNlNSGwJFzHglM0MYN9CNKqeNAU2Br0YRY5Ar986s03hraBgQppeorpcoA9wGLs6yzGHjI9PS9ExCvtY4u6kALkd1zVkrVAX4Dhpfgb4fW7J6z1rq+1rqe1roe8CvwdAluBMCxf9uLgJuVUl5KKT+gI7C/iOMsTI6c8ymMKyCUUtWBJsCxIo2yaBX651epuyLQWqcppZ4FlmNkHPygtd6rlHrStHwaRgZJf+AIkITxjaLEcvCc3wYqA1+ZviGn6RJcsMvBcy5VHDlnrfV+pdQyYDeQAXyntbaZhlgSOPh3fg+YqZTag3Hb5HWtdYmtSqqUmgP0AKoopU4DYwFvcN7nl/QsFkIIN1cabw0JIYTIA2kIhBDCzUlDIIQQbk4aAiGEcHPSEAghhJuThkAIIdycNARuwFSCOcLqp14u6yYWwvFmKqWOm461QynVOR/7+E4pFWx6/b8syzYWNEbTfszvS6SpjHFFO+uHKKX65+M4NZRSS0yveyil4pVSO5VS+5VSY/Oxv0HmcsxKqTvM75NpepxS6ta87tPGMWYqO2W7TfWMHO6LYjr3JQ6sZ7MMs1JqolLqFkePJxwnDYF7SNZah1j9nCiCY47SWocAo4Fv8rqx1vpRrfU+0+T/sizrUvDwgOvvSwuMao/P2Fk/BKMjT169DHxrNb1Oa90GowTGg0qpdnnZmdZ6sdZ6gmnyDiDYatnbWuuV+YixOJkJ9LMx/0uMf0+ikElD4IaUUuWVUqtM39b3KKWylW82fYtda/WN+WbT/D5KqU2mbecrpcrbOdxaoJFp25dN+4pUSr1omldOKfWnMgYViVRKDTXNX6OUClVKTQB8TXHMNi1LNP0Os/6GbvoWe7dSylMp9YlSapsyBu54woG3ZROmCo5KqQ7KGLxnp+l3E1N5g3HAUFMsQ02x/2A6zk5b76PJ3cCyrDNN9YC2Aw1NVxubTfEuVEpVMsXyvFJqn2n+XNO8EUqpKUqpLsAg4BNTTA3N3+SVUrcppeZZvTc9lFJ/mF7n6W+olHrbdI6RSqnpSmWq4Peg6T2KVEp1MK3v6PtiU05lmLXWJ4HKSqkb8rI/4YCiGmxBflz3A6RjVGeMABZilBbxNy2rgtFV3dzLPNH0+xXgDdNrT6CCad21QDnT/NeBt20cbyamQWCAe4EtGBUx9wDlMGrG7wXaYHxIfmu1bYDp9xog1Domq3XMMd4J/Gh6XQajNK8v8Djwpml+WSAcqG8jzkSr85sP9DNN+wNepte3AgtMr0cAU6y2/xB40PS6IkZht3JZjlEf2G413QNYYnpdGTgBNMcoCdHdNH8cMMn0+ixQ1nyMrHFYv9fW06a/8Smrv9XXwIP5/BsGWs3/GRho9Tf61vS6G6aBVHJ6X7KceyhG+Yuc/s3Ww8bALBhXVne7+v9UafspdbWGhE3J2rhNA4BSyhv4UCnVDaMeTU2gOvCf1TbbgB9M6/6utY5QSnXHuA2xwfSlsAzGN2lbPlFKvQnEYJS97gUs1Ma3YJRSvwE3Y3xTnqiU+gjjQ2JdHs7rL2CyUqosxq2EtVrrZKVUH6CV1T3uACAIOJ5le1+lVATGh8524G+r9X9USgVhlPf1zuH4fYBBSqlXTdM+QB0yF3mrYXoPrN2slNqJ8d5PwKgmWVFrbR5W8keMhgmMBmK2Uup34Pcc4shGGzV6lgEDlVK/AgOA14C8/A3NeiqlXgP8gECMRvwP07I5puOtVUr5K+M5S07vi3V84cCjjp6PlfPAjfnYTuRCGgL39ADGkH7ttNapSqkTGP9ZLUz/sbthfID8rJT6BLgI/K21HubAMUZprX81T6gcHmBqrQ+Z7pH3B8YrpVZorcc5chJa6xSl1BqMevRDMX0oYRQee05rvdzOLpK11iFKqQBgCcYzgskYRcxWa63vVMaD9TU5bK8wvp0ezO0YZHlvMZ4R3G7ZiXH8nAzA+LY9CHhLKdU8l3WzCsM4pzhgm9Y6wXRbx9G/IUopH+ArjKuzKKXUO2Q+n6zFyjQ5vC/KqAxaUD4Y76koRPKMwD0FAOdNjUBPoG7WFZRSdU3rfAt8jzGG6mbgJqWU+Z6/n1KqsYPHXAvcYdqmHMZtnXVKqRuBJK31LGCi6ThZpZquTGyZi1F98WaMCpWYfj9l3kYp1dh0TJu01vHA88Crpm0CgDOmxSOsVk3AuEVmthx4znzPXCnVxsbuD2FcceTIdPyLyvQcBhgO/KuU8gBqa61XY3ybr4hxW81a1pisrcF4Px/DaBQg739D84d+rOlZQtZMIvMzna4Y5ZDjcex9ya/GQImtplpcSUPgnmYDoUqpcIyrgwM21ukBRJhuYdwNfKG1jsH4YJyjlNqN8aHS1JEDaq13YNx33orxzOA7rfVOoCWw1XSL5g3gfRubTwd2K9PD4ixWYHxjXqmNMW3BGHxnH7BDGSmI32Dn6tcUyy6MevcfY1ydbMB4fmC2Ggg2PyzGuHLwNsUWaZrOut8rwFHzB28uHsa4nbYbIztpnOnYs5RRXnkn8LnW+lKW7eYCo0wPZRtmOXY6xpXObabf5PVvaDretxjPd37HuGVo7aIy0nmnYdwCBAfeF2UkAnxn65jKKMO8CWiilDqtlHrENN8bI/EgPKd4Rf5IGWohnEwpdSfGbbg3XR1LSWZ6H9tqrd9ydSyljTwjEMLJtNYLlVKVXR1HKeAFfOrqIEojuSIQQgg3J88IhBDCzUlDIIQQbk4aAiGEcHPSEAghhJuThkAIIdzc/wNeQ53qa13V/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = [clf_logis, clf_dectree, clf_rf, clf_ada, clf_nn]\n",
    "\n",
    "# Fix the error that neural network not showing\n",
    "clf_nn._estimator_type = 'classifier'\n",
    "\n",
    "f, ax = plt.subplots(figsize=(6, 6))\n",
    "for model in models:\n",
    "    plot_roc_curve(model, X_test, y_test, pos_label=1, ax=ax)\n",
    "    \n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Summary and Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above training, we can get a model to predict the stroke with about **73% accuracy** and **higher than 70% recall** on test set.\n",
    "\n",
    "Meanwhile, we also find some correlation between features and stroke:\n",
    "- **Age** is the main driver to stroke. **Older people** are more likely to get a stroke, so we should pay attention to the health status of old people.\n",
    "- People with **higher glucose level, hypertension and heart disease** are more likely to get a stroke. That means vascular disease can induce stroke.\n",
    "- People **never smoked** are slightly less likely to get stroke. It is always a good idea not to smoke."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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",
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 },
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}