src/task-two-sample.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This week's Competition involves creating classification models using a relatively simple data set.\n",
    "\n",
    "First, we should import the packages we'll need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score, roc_auc_score, confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to retrieve the data and store it in a pandas DataFrame variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>gender</th>\n",
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       "      <th>hypertension</th>\n",
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       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
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       "  </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",
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       "    <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",
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       "      <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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>5105</th>\n",
       "      <td>18234</td>\n",
       "      <td>Female</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>83.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5106</th>\n",
       "      <td>44873</td>\n",
       "      <td>Female</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>125.20</td>\n",
       "      <td>40.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5107</th>\n",
       "      <td>19723</td>\n",
       "      <td>Female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>82.99</td>\n",
       "      <td>30.6</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5108</th>\n",
       "      <td>37544</td>\n",
       "      <td>Male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>166.29</td>\n",
       "      <td>25.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5109</th>\n",
       "      <td>44679</td>\n",
       "      <td>Female</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>85.28</td>\n",
       "      <td>26.2</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5110 rows × 12 columns</p>\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",
       "5105  18234  Female  80.0             1              0          Yes   \n",
       "5106  44873  Female  81.0             0              0          Yes   \n",
       "5107  19723  Female  35.0             0              0          Yes   \n",
       "5108  37544    Male  51.0             0              0          Yes   \n",
       "5109  44679  Female  44.0             0              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",
       "5105        Private          Urban              83.75   NaN     never smoked   \n",
       "5106  Self-employed          Urban             125.20  40.0     never smoked   \n",
       "5107  Self-employed          Rural              82.99  30.6     never smoked   \n",
       "5108        Private          Rural             166.29  25.6  formerly smoked   \n",
       "5109       Govt_job          Urban              85.28  26.2          Unknown   \n",
       "\n",
       "      stroke  \n",
       "0          1  \n",
       "1          1  \n",
       "2          1  \n",
       "3          1  \n",
       "4          1  \n",
       "...      ...  \n",
       "5105       0  \n",
       "5106       0  \n",
       "5107       0  \n",
       "5108       0  \n",
       "5109       0  \n",
       "\n",
       "[5110 rows x 12 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r'healthcare-dataset-stroke-data.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we should see if there are any NaN values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>anyNaN</th>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <th>heart_disease</th>\n",
       "      <td>False</td>\n",
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       "      <th>ever_married</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <th>avg_glucose_level</th>\n",
       "      <td>False</td>\n",
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       "      <th>bmi</th>\n",
       "      <td>True</td>\n",
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       "      <th>smoking_status</th>\n",
       "      <td>False</td>\n",
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       "      <th>stroke</th>\n",
       "      <td>False</td>\n",
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      "text/plain": [
       "                   anyNaN\n",
       "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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict(data={\n",
    "    'anyNaN' : data.isna().any()\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like bmi is the only attribute with NaN values. Let's see how many such values there are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "201"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data.bmi[data.bmi.isna()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are only 201 NaN values in the bmi attribute. Therefore, we can drop these records, as we'd still have enough data left over."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>60182</td>\n",
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       "      <td>49.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
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       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</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",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56669</td>\n",
       "      <td>Male</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>4904</th>\n",
       "      <td>14180</td>\n",
       "      <td>Female</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Rural</td>\n",
       "      <td>103.08</td>\n",
       "      <td>18.6</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
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       "      <th>4905</th>\n",
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       "      <td>Female</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>125.20</td>\n",
       "      <td>40.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4906</th>\n",
       "      <td>19723</td>\n",
       "      <td>Female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>82.99</td>\n",
       "      <td>30.6</td>\n",
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       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>166.29</td>\n",
       "      <td>25.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4908</th>\n",
       "      <td>44679</td>\n",
       "      <td>Female</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Urban</td>\n",
       "      <td>85.28</td>\n",
       "      <td>26.2</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4909 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "0      9046    Male  67.0             0              1          Yes   \n",
       "1     31112    Male  80.0             0              1          Yes   \n",
       "2     60182  Female  49.0             0              0          Yes   \n",
       "3      1665  Female  79.0             1              0          Yes   \n",
       "4     56669    Male  81.0             0              0          Yes   \n",
       "...     ...     ...   ...           ...            ...          ...   \n",
       "4904  14180  Female  13.0             0              0           No   \n",
       "4905  44873  Female  81.0             0              0          Yes   \n",
       "4906  19723  Female  35.0             0              0          Yes   \n",
       "4907  37544    Male  51.0             0              0          Yes   \n",
       "4908  44679  Female  44.0             0              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           Private          Rural             105.92  32.5     never smoked   \n",
       "2           Private          Urban             171.23  34.4           smokes   \n",
       "3     Self-employed          Rural             174.12  24.0     never smoked   \n",
       "4           Private          Urban             186.21  29.0  formerly smoked   \n",
       "...             ...            ...                ...   ...              ...   \n",
       "4904       children          Rural             103.08  18.6          Unknown   \n",
       "4905  Self-employed          Urban             125.20  40.0     never smoked   \n",
       "4906  Self-employed          Rural              82.99  30.6     never smoked   \n",
       "4907        Private          Rural             166.29  25.6  formerly smoked   \n",
       "4908       Govt_job          Urban              85.28  26.2          Unknown   \n",
       "\n",
       "      stroke  \n",
       "0          1  \n",
       "1          1  \n",
       "2          1  \n",
       "3          1  \n",
       "4          1  \n",
       "...      ...  \n",
       "4904       0  \n",
       "4905       0  \n",
       "4906       0  \n",
       "4907       0  \n",
       "4908       0  \n",
       "\n",
       "[4909 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(subset=['bmi'], inplace=True)\n",
    "data.reset_index(drop=True, inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's make sure there are no duplicates in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anyDuplicates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypertension</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heart_disease</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ever_married</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>work_type</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residence_type</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoking_status</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   anyDuplicates\n",
       "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                        False\n",
       "smoking_status             False\n",
       "stroke                     False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict(data={\n",
    "    'anyDuplicates' : data[data.duplicated()].any()\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems there are no duplicates in the data, so we can move on.\n",
    "\n",
    "Now, let's check the data type each column contains."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypertension</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heart_disease</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ever_married</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>work_type</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residence_type</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoking_status</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  dataType\n",
       "id                   int64\n",
       "gender              object\n",
       "age                float64\n",
       "hypertension         int64\n",
       "heart_disease        int64\n",
       "ever_married        object\n",
       "work_type           object\n",
       "Residence_type      object\n",
       "avg_glucose_level  float64\n",
       "bmi                float64\n",
       "smoking_status      object\n",
       "stroke               int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict(data={\n",
    "    'dataType' : data.dtypes\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our baseline model, we'll only use bmi for our independent/explanatory variable. In your model, you can use any combination of the features in this data set as you'd like. Lust make sure you don't overfit the model on the training data, or it'll perform very poorly on the testing data.\n",
    "\n",
    "**Note: If you want to use a categorical feature of the data in your model, such as gender, you need to encode the feature's values first (more on this [here](https://www.askpython.com/python/examples/label-encoding)).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4904</th>\n",
       "      <td>18.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4905</th>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4906</th>\n",
       "      <td>30.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4907</th>\n",
       "      <td>25.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4908</th>\n",
       "      <td>26.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4909 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bmi\n",
       "0     36.6\n",
       "1     32.5\n",
       "2     34.4\n",
       "3     24.0\n",
       "4     29.0\n",
       "...    ...\n",
       "4904  18.6\n",
       "4905  40.0\n",
       "4906  30.6\n",
       "4907  25.6\n",
       "4908  26.2\n",
       "\n",
       "[4909 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data[['bmi']]\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should also store the dependent/target variable in its own Python variable. In this case, the dependent/target variable is the stroke feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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       "      <th>4904</th>\n",
       "      <td>0</td>\n",
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       "      <th>4905</th>\n",
       "      <td>0</td>\n",
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       "      <th>4906</th>\n",
       "      <td>0</td>\n",
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       "      <th>4907</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4908</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4909 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      stroke\n",
       "0          1\n",
       "1          1\n",
       "2          1\n",
       "3          1\n",
       "4          1\n",
       "...      ...\n",
       "4904       0\n",
       "4905       0\n",
       "4906       0\n",
       "4907       0\n",
       "4908       0\n",
       "\n",
       "[4909 rows x 1 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data['stroke']\n",
    "pd.DataFrame(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before you do anything else, we need to split the data into training and testing sets.\n",
    "\n",
    "The training set will be used to \"train\" the model we use; this essentially means that the model will fit its parameters and such so that it performs better based on the training data. Think of it like buying a suit or dress that doesn't fit: for it to fit you better, you need to have it tailored to your specific body shape. This is a good analogy for how the training set is used to tailor the model to better fit the data.\n",
    "\n",
    "The testing set is used for evaluating the model after we train it using the training set. Therefore, your evaluation metrics should be calculated using the performance of the model on the testing set, **NOT** the training set. **Always make sure you train your model *before* testing it with the testing set, or else the model won't work correctly!**\n",
    "\n",
    "When splitting the data using the train_test_split function from scikit-learn, make sure to define the \"random_state\" parameter, or the data will be split differently each time you execute the code in the cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we'll create a baseline classification model using the Logistic Regression algorithm. Note: whenever you're initializing a model, make sure you define the \"random_state\" parameter, or your model will perform differently every time. \n",
    "\n",
    "**This baseline model is what you're trying to beat with your own models!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline = LogisticRegression(random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we're going to train the baseline model using the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=42)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baseline.fit(X=X_train, y=y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we'll use the independent/explanatory variable data in the testing set to generate predictions for the dependent/target variable values in the testing set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1227</th>\n",
       "      <td>0</td>\n",
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       "<p>1228 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      prediction\n",
       "0              0\n",
       "1              0\n",
       "2              0\n",
       "3              0\n",
       "4              0\n",
       "...          ...\n",
       "1223           0\n",
       "1224           0\n",
       "1225           0\n",
       "1226           0\n",
       "1227           0\n",
       "\n",
       "[1228 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = baseline.predict(X=X_test)\n",
    "pd.DataFrame.from_dict(data={\n",
    "    'prediction' : y_pred\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we evaluate our model using these generated predictions, we should make sure the predictions aren't one-sided, meaning all 0's or all 1's.\n",
    "\n",
    "If this is the case, then the data we trained on model using might've been massively unbalanced, meaning there were far more instances of one case type than the other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "pd.DataFrame.from_dict(data={\n",
    "    'count' : pd.Series(y_pred).value_counts()\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like our baseline model predicted all observations were negative, i.e., no stroke. Thus, our suspicion that our data is unbalanced may be true. Let's check how many values in the overall target data (training and testing) are positive/1 (stroke) versus negative/0 (no stroke)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   count\n",
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "pd.DataFrame.from_dict(data={\n",
    "    'count' : y.value_counts()\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is indeed massively unbalanced:\n",
    "  - 4,700 negative (non-stroke) cases.\n",
    "  - 209 positive (stroke) cases.\n",
    "\n",
    "We should try to balance out the data so that there's about as many positive cases as there are negative cases. We'll use a method called SMOTE to do this: SMOTE generates synthetic data so that a data set is more evenly balanced between positive and negative target variable cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_res, y_res = SMOTE(random_state=42).fit_resample(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's make sure the data has been balanced using the SMOTE resampling method.\n",
    "\n",
    "First, we'll view the resampled X data, and then we'll view the resampled y data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "            bmi\n",
       "0     36.600000\n",
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       "3     24.000000\n",
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       "...         ...\n",
       "9395  32.654579\n",
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       "\n",
       "[9400 rows x 1 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      stroke\n",
       "0          1\n",
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       "...      ...\n",
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(y_res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data was indeed balanced correctly, so now we can redefine the training and testing data sets using train_test_split (again, make sure to define the \"random_state\" parameter, preferrably using the same value as we did previously)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_res, y_res, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we should re-initialize the baseline Logistic Regression model (once again defining the \"random_state\" parameter, preferrably using the same value as we did previously). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline = LogisticRegression(random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we'll train our new baseline model using the SMOTE-resampled training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=42)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baseline.fit(X_train, y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll use the SMOTE-resampled testing data to generate predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>2350 rows × 1 columns</p>\n",
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      "text/plain": [
       "      prediction\n",
       "0              1\n",
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       "...          ...\n",
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     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = baseline.predict(X_test)\n",
    "pd.DataFrame.from_dict(data={\n",
    "    'prediction' : y_pred\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this competition, we'll be using the recall score as our evaluation metric. Recall intuitively measures the number of observations correctly classified as positive compared to the number of all the observations that actually are positivel it is calculated using the formula\n",
    "\n",
    "$$recall = \\frac{TP}{TP + FN},$$\n",
    "\n",
    "where\n",
    "  - $TP:$ the number of observations correctly classified as positive cases.\n",
    "  - $FN:$ the number of observations incorrectly classified as negative cases.\n",
    "\n",
    "For this specific data set, $TP$ is the number of times the classifier predicted a patient had a stroke when the patient actually had a stroke, while $FN$ is the number of times the classifier predicted a patient **dindn't** have a stroke when the patient did in fact have a stroke. Thus, we can see that $TP + FN$ would be the total number of patients who did have a stroke. \n",
    "\n",
    "We're using recall over the other metrics we could've chosen because when you're building a model meant to determine something as important as whether or not a patient suffered a stroke, the emphasis should be on trying to correctly classify actual positive instances so that these instances can be dealt with appropriately. In this case, it's much more important to correctly classify a stroke patient as such so that the side effects of the stroke can be treated as soon as possible, potentially fast enough to prevent any long-term brain damage the stroke might cause if left untreated for a time.\n",
    "\n",
    "**YOUR GOAL IS TO BEAT THE RECALL SCORE OF THIS BASELINE MODEL!!!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the recall score you're trying to beat: 0.5050590219224284\n"
     ]
    }
   ],
   "source": [
    "recall = recall_score(y_test, y_pred)\n",
    "print(f'This is the recall score you\\'re trying to beat: {recall}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Therefore,\n",
    "$$recall_{goal} > 0.5050590219224284$$ \n",
    "\n",
    "In other words, your goal is to obtain a recall score greater than the above value. \n",
    "\n",
    "**Please make sure you use the testing data when calculating your recall score, as testing data is solely meant for evaluation!**"
   ]
  }
 ],
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