LogisticRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import preprocessing, metrics\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "# import tabpy_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <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>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",
       "    </tr>\n",
       "    <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",
       "</div>"
      ],
      "text/plain": [
       "         id  gender   age  hypertension  heart_disease ever_married  \\\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",
       "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",
       "5105       0  \n",
       "5106       0  \n",
       "5107       0  \n",
       "5108       0  \n",
       "5109       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read the original Kaggle CSV\n",
    "stroke_df_initial = pd.read_csv(os.path.join('Resources/healthcare-dataset-stroke-data.csv'))\n",
    "stroke_df_initial.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
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       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>202.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>5105</th>\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>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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5109</th>\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",
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       "<p>5110 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "      gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "0       Male  67.0             0              1          Yes        Private   \n",
       "1     Female  61.0             0              0          Yes  Self-employed   \n",
       "2       Male  80.0             0              1          Yes        Private   \n",
       "3     Female  49.0             0              0          Yes        Private   \n",
       "4     Female  79.0             1              0          Yes  Self-employed   \n",
       "...      ...   ...           ...            ...          ...            ...   \n",
       "5105  Female  80.0             1              0          Yes        Private   \n",
       "5106  Female  81.0             0              0          Yes  Self-employed   \n",
       "5107  Female  35.0             0              0          Yes  Self-employed   \n",
       "5108    Male  51.0             0              0          Yes        Private   \n",
       "5109  Female  44.0             0              0          Yes       Govt_job   \n",
       "\n",
       "     Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "0             Urban             228.69  36.6  formerly smoked       1  \n",
       "1             Rural             202.21   NaN     never smoked       1  \n",
       "2             Rural             105.92  32.5     never smoked       1  \n",
       "3             Urban             171.23  34.4           smokes       1  \n",
       "4             Rural             174.12  24.0     never smoked       1  \n",
       "...             ...                ...   ...              ...     ...  \n",
       "5105          Urban              83.75   NaN     never smoked       0  \n",
       "5106          Urban             125.20  40.0     never smoked       0  \n",
       "5107          Rural              82.99  30.6     never smoked       0  \n",
       "5108          Rural             166.29  25.6  formerly smoked       0  \n",
       "5109          Urban              85.28  26.2          Unknown       0  \n",
       "\n",
       "[5110 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ID column is not necessary. Drop\n",
    "stroke_df = stroke_df_initial.drop(['id'], axis = 1)\n",
    "stroke_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender                 0\n",
      "age                    0\n",
      "hypertension           0\n",
      "heart_disease          0\n",
      "ever_married           0\n",
      "work_type              0\n",
      "Residence_type         0\n",
      "avg_glucose_level      0\n",
      "bmi                  201\n",
      "smoking_status         0\n",
      "stroke                 0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Checking for nulls\n",
    "print(stroke_df.isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "201 rows of NA for bmi column only. Consider Drop NA but do complete data exploration first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5110 entries, 0 to 5109\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   gender             5110 non-null   object \n",
      " 1   age                5110 non-null   float64\n",
      " 2   hypertension       5110 non-null   int64  \n",
      " 3   heart_disease      5110 non-null   int64  \n",
      " 4   ever_married       5110 non-null   object \n",
      " 5   work_type          5110 non-null   object \n",
      " 6   Residence_type     5110 non-null   object \n",
      " 7   avg_glucose_level  5110 non-null   float64\n",
      " 8   bmi                4909 non-null   float64\n",
      " 9   smoking_status     5110 non-null   object \n",
      " 10  stroke             5110 non-null   int64  \n",
      "dtypes: float64(3), int64(3), object(5)\n",
      "memory usage: 439.3+ KB\n"
     ]
    }
   ],
   "source": [
    "#Checking number of entries, data types\n",
    "stroke_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4612\n",
       "1     498\n",
       "Name: hypertension, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Value counts for hypertension column to ensure only two choices\n",
    "stroke_df[\"hypertension\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4834\n",
       "1     276\n",
       "Name: heart_disease, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Value counts for heart diseease column to ensure only two choices\n",
    "stroke_df[\"heart_disease\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4861\n",
       "1     249\n",
       "Name: stroke, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Value counts for stroke column to ensure only two choices\n",
    "stroke_df[\"stroke\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Only 249 patients with stroke. How many will we lose if we Drop NA for the bmi column?\n",
    "bmi_null_stroke = stroke_df.loc[(stroke_df['bmi'].isna() == True) & (stroke_df['stroke'] == 1)]\n",
    "len(bmi_null_stroke)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "40 is too many rows of valuable data to lose here. Consider other options. Insert mean into BMI? Other ideas?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Urban</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">ever_married</th>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>2994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">smoking_status</th>\n",
       "      <th>smokes</th>\n",
       "      <td>789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>formerly smoked</th>\n",
       "      <td>885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>1544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>never smoked</th>\n",
       "      <td>1892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">work_type</th>\n",
       "      <th>Never_worked</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Govt_job</th>\n",
       "      <td>657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Self-employed</th>\n",
       "      <td>819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private</th>\n",
       "      <td>2925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                counts\n",
       "column         value                  \n",
       "Residence_type Rural              2514\n",
       "               Urban              2596\n",
       "ever_married   No                 1757\n",
       "               Yes                3353\n",
       "gender         Other                 1\n",
       "               Male               2115\n",
       "               Female             2994\n",
       "smoking_status smokes              789\n",
       "               formerly smoked     885\n",
       "               Unknown            1544\n",
       "               never smoked       1892\n",
       "work_type      Never_worked         22\n",
       "               Govt_job            657\n",
       "               children            687\n",
       "               Self-employed       819\n",
       "               Private            2925"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the value counts of all columns with datatype 'object' for a guide to encoding needs\n",
    "counts = stroke_df.select_dtypes(include=object).columns.tolist()\n",
    "(pd.DataFrame(\n",
    "    stroke_df[counts]\n",
    "    .melt(var_name='column', value_name='value')\n",
    "    .value_counts())\n",
    ".rename(columns={0: 'counts'})\n",
    ".sort_values(by=['column', 'counts']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that gender only has one 'other'; probably easier to remove that point for binary encoding. Note that one of the work_types is \"children,\" meaning the dataset includes children. Look at age more closely.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3295</th>\n",
       "      <td>Male</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Rural</td>\n",
       "      <td>70.33</td>\n",
       "      <td>16.9</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1614</th>\n",
       "      <td>Female</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Urban</td>\n",
       "      <td>139.67</td>\n",
       "      <td>14.1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3618</th>\n",
       "      <td>Male</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Urban</td>\n",
       "      <td>114.71</td>\n",
       "      <td>17.4</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4021</th>\n",
       "      <td>Male</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Urban</td>\n",
       "      <td>109.52</td>\n",
       "      <td>13.9</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3968</th>\n",
       "      <td>Male</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Rural</td>\n",
       "      <td>69.79</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender   age  hypertension  heart_disease ever_married work_type  \\\n",
       "3295    Male  0.08             0              0           No  children   \n",
       "1614  Female  0.08             0              0           No  children   \n",
       "3618    Male  0.16             0              0           No  children   \n",
       "4021    Male  0.16             0              0           No  children   \n",
       "3968    Male  0.16             0              0           No  children   \n",
       "\n",
       "     Residence_type  avg_glucose_level   bmi smoking_status  stroke  \n",
       "3295          Rural              70.33  16.9        Unknown       0  \n",
       "1614          Urban             139.67  14.1        Unknown       0  \n",
       "3618          Urban             114.71  17.4        Unknown       0  \n",
       "4021          Urban             109.52  13.9        Unknown       0  \n",
       "3968          Rural              69.79  13.0        Unknown       0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Examining age column\n",
    "stroke_df.sort_values(by=['age']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset even contains babies. Children are likely less useful in predicting stroke, although these particular children may have some other health issues, which is why they were included. How many children actually are labeled with stroke?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Children stroke count\n",
    "children_stroke = stroke_df.loc[(stroke_df['age'] < 17) & (stroke_df['stroke'] == 1)]\n",
    "len(children_stroke)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove children from the dataset in this instance and compare model performance with the children data left in vs deleted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
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       "      <th>heart_disease</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>Female</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>87.52</td>\n",
       "      <td>39.2</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
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       "      <th>3777</th>\n",
       "      <td>Female</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>68.66</td>\n",
       "      <td>35.1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4611</th>\n",
       "      <td>Female</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Never_worked</td>\n",
       "      <td>Urban</td>\n",
       "      <td>78.08</td>\n",
       "      <td>44.9</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3256</th>\n",
       "      <td>Female</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>89.58</td>\n",
       "      <td>22.8</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2128</th>\n",
       "      <td>Male</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>61.67</td>\n",
       "      <td>97.6</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4452</th>\n",
       "      <td>Female</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>115.71</td>\n",
       "      <td>31.1</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Male</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>144.90</td>\n",
       "      <td>26.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2314</th>\n",
       "      <td>Male</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>89.83</td>\n",
       "      <td>24.7</td>\n",
       "      <td>smokes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Female</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Urban</td>\n",
       "      <td>196.92</td>\n",
       "      <td>22.2</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5062</th>\n",
       "      <td>Female</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>211.58</td>\n",
       "      <td>36.9</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4314 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "395   Female  17.0             0              0           No        Private   \n",
       "3777  Female  17.0             0              0           No        Private   \n",
       "4611  Female  17.0             0              0           No   Never_worked   \n",
       "3256  Female  17.0             0              0           No        Private   \n",
       "2128    Male  17.0             1              0           No        Private   \n",
       "...      ...   ...           ...            ...          ...            ...   \n",
       "4452  Female  82.0             1              0          Yes        Private   \n",
       "42      Male  82.0             0              1          Yes        Private   \n",
       "2314    Male  82.0             0              0          Yes        Private   \n",
       "32    Female  82.0             1              0          Yes  Self-employed   \n",
       "5062  Female  82.0             0              0          Yes  Self-employed   \n",
       "\n",
       "     Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "395           Urban              87.52  39.2     never smoked       0  \n",
       "3777          Rural              68.66  35.1     never smoked       0  \n",
       "4611          Urban              78.08  44.9     never smoked       0  \n",
       "3256          Urban              89.58  22.8          Unknown       0  \n",
       "2128          Rural              61.67  97.6          Unknown       0  \n",
       "...             ...                ...   ...              ...     ...  \n",
       "4452          Urban             115.71  31.1  formerly smoked       0  \n",
       "42            Urban             144.90  26.4           smokes       1  \n",
       "2314          Urban              89.83  24.7           smokes       0  \n",
       "32            Urban             196.92  22.2     never smoked       1  \n",
       "5062          Rural             211.58  36.9     never smoked       0  \n",
       "\n",
       "[4314 rows x 11 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removing children from the dataset\n",
    "\n",
    "no_children_df = stroke_df[stroke_df.age > 16]\n",
    "no_children_df.sort_values(by=['age'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Private          2840\n",
       "Self-employed     810\n",
       "Govt_job          654\n",
       "Never_worked       10\n",
       "Name: work_type, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ensure children is no longer listed as a work type\n",
    "no_children_df.sort_values(by=['age'])\n",
    "no_children_df['work_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5105</th>\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>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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5109</th>\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>4313 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "0       Male  67.0             0              1          Yes        Private   \n",
       "1     Female  61.0             0              0          Yes  Self-employed   \n",
       "2       Male  80.0             0              1          Yes        Private   \n",
       "3     Female  49.0             0              0          Yes        Private   \n",
       "4     Female  79.0             1              0          Yes  Self-employed   \n",
       "...      ...   ...           ...            ...          ...            ...   \n",
       "5105  Female  80.0             1              0          Yes        Private   \n",
       "5106  Female  81.0             0              0          Yes  Self-employed   \n",
       "5107  Female  35.0             0              0          Yes  Self-employed   \n",
       "5108    Male  51.0             0              0          Yes        Private   \n",
       "5109  Female  44.0             0              0          Yes       Govt_job   \n",
       "\n",
       "     Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "0             Urban             228.69  36.6  formerly smoked       1  \n",
       "1             Rural             202.21   NaN     never smoked       1  \n",
       "2             Rural             105.92  32.5     never smoked       1  \n",
       "3             Urban             171.23  34.4           smokes       1  \n",
       "4             Rural             174.12  24.0     never smoked       1  \n",
       "...             ...                ...   ...              ...     ...  \n",
       "5105          Urban              83.75   NaN     never smoked       0  \n",
       "5106          Urban             125.20  40.0     never smoked       0  \n",
       "5107          Rural              82.99  30.6     never smoked       0  \n",
       "5108          Rural             166.29  25.6  formerly smoked       0  \n",
       "5109          Urban              85.28  26.2          Unknown       0  \n",
       "\n",
       "[4313 rows x 11 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop the 'other' row in gender column\n",
    "cleaned_stroke_df = no_children_df[no_children_df.gender != 'Other']\n",
    "cleaned_stroke_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
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       "      <th>0</th>\n",
       "      <td>67.0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Private</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Private</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
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       "      <td>Self-employed</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Private</td>\n",
       "      <td>83.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>5106</th>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>125.20</td>\n",
       "      <td>40.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>5107</th>\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",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>5108</th>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Private</td>\n",
       "      <td>166.29</td>\n",
       "      <td>25.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>5109</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>85.28</td>\n",
       "      <td>26.2</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4313 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  hypertension  heart_disease      work_type  avg_glucose_level  \\\n",
       "0     67.0             0              1        Private             228.69   \n",
       "1     61.0             0              0  Self-employed             202.21   \n",
       "2     80.0             0              1        Private             105.92   \n",
       "3     49.0             0              0        Private             171.23   \n",
       "4     79.0             1              0  Self-employed             174.12   \n",
       "...    ...           ...            ...            ...                ...   \n",
       "5105  80.0             1              0        Private              83.75   \n",
       "5106  81.0             0              0  Self-employed             125.20   \n",
       "5107  35.0             0              0  Self-employed              82.99   \n",
       "5108  51.0             0              0        Private             166.29   \n",
       "5109  44.0             0              0       Govt_job              85.28   \n",
       "\n",
       "       bmi   smoking_status  stroke  gender_Female  gender_Male  \\\n",
       "0     36.6  formerly smoked       1              0            1   \n",
       "1      NaN     never smoked       1              1            0   \n",
       "2     32.5     never smoked       1              0            1   \n",
       "3     34.4           smokes       1              1            0   \n",
       "4     24.0     never smoked       1              1            0   \n",
       "...    ...              ...     ...            ...          ...   \n",
       "5105   NaN     never smoked       0              1            0   \n",
       "5106  40.0     never smoked       0              1            0   \n",
       "5107  30.6     never smoked       0              1            0   \n",
       "5108  25.6  formerly smoked       0              0            1   \n",
       "5109  26.2          Unknown       0              1            0   \n",
       "\n",
       "      ever_married_No  ever_married_Yes  Residence_type_Rural  \\\n",
       "0                   0                 1                     0   \n",
       "1                   0                 1                     1   \n",
       "2                   0                 1                     1   \n",
       "3                   0                 1                     0   \n",
       "4                   0                 1                     1   \n",
       "...               ...               ...                   ...   \n",
       "5105                0                 1                     0   \n",
       "5106                0                 1                     0   \n",
       "5107                0                 1                     1   \n",
       "5108                0                 1                     1   \n",
       "5109                0                 1                     0   \n",
       "\n",
       "      Residence_type_Urban  \n",
       "0                        1  \n",
       "1                        0  \n",
       "2                        0  \n",
       "3                        1  \n",
       "4                        0  \n",
       "...                    ...  \n",
       "5105                     1  \n",
       "5106                     1  \n",
       "5107                     0  \n",
       "5108                     0  \n",
       "5109                     1  \n",
       "\n",
       "[4313 rows x 14 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Pandas get_dummies to convert categorical data\n",
    "\n",
    "encoded_df = pd.get_dummies(cleaned_stroke_df, columns=['gender','ever_married', 'Residence_type'])\n",
    "encoded_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>work_type</th>\n",
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       "      <th>1</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>202.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>Private</td>\n",
       "      <td>105.92</td>\n",
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       "      <td>never smoked</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Private</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5105</th>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Private</td>\n",
       "      <td>83.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5106</th>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>125.20</td>\n",
       "      <td>40.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5107</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>82.99</td>\n",
       "      <td>30.6</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5108</th>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Private</td>\n",
       "      <td>166.29</td>\n",
       "      <td>25.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5109</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>85.28</td>\n",
       "      <td>26.2</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4313 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  hypertension  heart_disease      work_type  avg_glucose_level  \\\n",
       "0     67.0             0              1        Private             228.69   \n",
       "1     61.0             0              0  Self-employed             202.21   \n",
       "2     80.0             0              1        Private             105.92   \n",
       "3     49.0             0              0        Private             171.23   \n",
       "4     79.0             1              0  Self-employed             174.12   \n",
       "...    ...           ...            ...            ...                ...   \n",
       "5105  80.0             1              0        Private              83.75   \n",
       "5106  81.0             0              0  Self-employed             125.20   \n",
       "5107  35.0             0              0  Self-employed              82.99   \n",
       "5108  51.0             0              0        Private             166.29   \n",
       "5109  44.0             0              0       Govt_job              85.28   \n",
       "\n",
       "       bmi   smoking_status  stroke  gender_Male  ever_married_Yes  \\\n",
       "0     36.6  formerly smoked       1            1                 1   \n",
       "1      NaN     never smoked       1            0                 1   \n",
       "2     32.5     never smoked       1            1                 1   \n",
       "3     34.4           smokes       1            0                 1   \n",
       "4     24.0     never smoked       1            0                 1   \n",
       "...    ...              ...     ...          ...               ...   \n",
       "5105   NaN     never smoked       0            0                 1   \n",
       "5106  40.0     never smoked       0            0                 1   \n",
       "5107  30.6     never smoked       0            0                 1   \n",
       "5108  25.6  formerly smoked       0            1                 1   \n",
       "5109  26.2          Unknown       0            0                 1   \n",
       "\n",
       "      Residence_type_Urban  \n",
       "0                        1  \n",
       "1                        0  \n",
       "2                        0  \n",
       "3                        1  \n",
       "4                        0  \n",
       "...                    ...  \n",
       "5105                     1  \n",
       "5106                     1  \n",
       "5107                     0  \n",
       "5108                     0  \n",
       "5109                     1  \n",
       "\n",
       "[4313 rows x 11 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop the extra dummies columns\n",
    "encoded_stroke_df = encoded_df.drop(columns=['gender_Female','ever_married_No','Residence_type_Rural'])\n",
    "encoded_stroke_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>work_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
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       "      <th>gender_Male</th>\n",
       "      <th>ever_married_Yes</th>\n",
       "      <th>Residence_type_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>202.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "    age  hypertension  heart_disease  work_type  avg_glucose_level   bmi  \\\n",
       "0  67.0             0              1          2             228.69  36.6   \n",
       "1  61.0             0              0          3             202.21   NaN   \n",
       "2  80.0             0              1          2             105.92  32.5   \n",
       "3  49.0             0              0          2             171.23  34.4   \n",
       "4  79.0             1              0          3             174.12  24.0   \n",
       "\n",
       "    smoking_status  stroke  gender_Male  ever_married_Yes  \\\n",
       "0  formerly smoked       1            1                 1   \n",
       "1     never smoked       1            0                 1   \n",
       "2     never smoked       1            1                 1   \n",
       "3           smokes       1            0                 1   \n",
       "4     never smoked       1            0                 1   \n",
       "\n",
       "   Residence_type_Urban  \n",
       "0                     1  \n",
       "1                     0  \n",
       "2                     0  \n",
       "3                     1  \n",
       "4                     0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Label encode the categorical columns that have more than two choices\n",
    "# label_encoder object knows how to understand word labels. \n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "# Encode labels in column 'Country'. \n",
    "encoded_stroke_df['work_type']= label_encoder.fit_transform(encoded_stroke_df['work_type']) \n",
    "encoded_stroke_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>work_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>ever_married_Yes</th>\n",
       "      <th>Residence_type_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67.0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>202.21</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>80.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  hypertension  heart_disease  work_type  avg_glucose_level   bmi  \\\n",
       "0  67.0             0              1          2             228.69  36.6   \n",
       "1  61.0             0              0          3             202.21   NaN   \n",
       "2  80.0             0              1          2             105.92  32.5   \n",
       "3  49.0             0              0          2             171.23  34.4   \n",
       "4  79.0             1              0          3             174.12  24.0   \n",
       "\n",
       "   smoking_status  stroke  gender_Male  ever_married_Yes  Residence_type_Urban  \n",
       "0               1       1            1                 1                     1  \n",
       "1               2       1            0                 1                     0  \n",
       "2               2       1            1                 1                     0  \n",
       "3               3       1            0                 1                     1  \n",
       "4               2       1            0                 1                     0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# label_encoder object knows how to understand word labels. \n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "# Encode labels in column 'Country'. \n",
    "encoded_stroke_df['smoking_status']= label_encoder.fit_transform(encoded_stroke_df['smoking_status']) \n",
    "encoded_stroke_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'bmi'}>]], dtype=object)"
      ]
     },
     "execution_count": 21,
     "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": [
    "#Histogram of bmi column to view outliers\n",
    "encoded_stroke_df.hist(column = 'bmi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>work_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>ever_married_Yes</th>\n",
       "      <th>Residence_type_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2128</th>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>61.67</td>\n",
       "      <td>97.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4209</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>56.90</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>928</th>\n",
       "      <td>23.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>70.03</td>\n",
       "      <td>78.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>210.48</td>\n",
       "      <td>71.9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1559</th>\n",
       "      <td>53.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>72.63</td>\n",
       "      <td>66.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  hypertension  heart_disease  work_type  avg_glucose_level   bmi  \\\n",
       "2128  17.0             1              0          2              61.67  97.6   \n",
       "4209  38.0             1              0          2              56.90  92.0   \n",
       "928   23.0             1              0          2              70.03  78.0   \n",
       "544   42.0             0              0          2             210.48  71.9   \n",
       "1559  53.0             0              0          2              72.63  66.8   \n",
       "\n",
       "      smoking_status  stroke  gender_Male  ever_married_Yes  \\\n",
       "2128               0       0            1                 0   \n",
       "4209               2       0            1                 1   \n",
       "928                3       0            0                 0   \n",
       "544                2       0            1                 1   \n",
       "1559               0       0            0                 1   \n",
       "\n",
       "      Residence_type_Urban  \n",
       "2128                     0  \n",
       "4209                     0  \n",
       "928                      1  \n",
       "544                      0  \n",
       "1559                     0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort by bmi to see highest values\n",
    "encoded_stroke_df.sort_values(by=['bmi'], ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.349999999999994\n",
      "47.150000000000006\n",
      "117\n"
     ]
    }
   ],
   "source": [
    "#Determine bmi outliers using upper and lower bounds\n",
    "quartiles = encoded_stroke_df['bmi'].quantile([.25,.5,.75])\n",
    "lowerq = quartiles[0.25]\n",
    "upperq = quartiles[0.75]\n",
    "iqr = upperq-lowerq\n",
    "lower_bound = lowerq - (1.5*iqr)\n",
    "upper_bound = upperq + (1.5*iqr)\n",
    "outliers = encoded_stroke_df.loc[(encoded_stroke_df['bmi'] < lower_bound) | (encoded_stroke_df['bmi'] > upper_bound)]\n",
    "        \n",
    "#Display upper & lower bounds and number of outliers\n",
    "       \n",
    "print(lower_bound)\n",
    "print(upper_bound)\n",
    "print(len(outliers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>work_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>ever_married_Yes</th>\n",
       "      <th>Residence_type_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4313.000000</td>\n",
       "      <td>4313.000000</td>\n",
       "      <td>4313.000000</td>\n",
       "      <td>4313.000000</td>\n",
       "      <td>4313.000000</td>\n",
       "      <td>4130.000000</td>\n",
       "      <td>4313.000000</td>\n",
       "      <td>4313.000000</td>\n",
       "      <td>4313.000000</td>\n",
       "      <td>4313.000000</td>\n",
       "      <td>4313.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>49.745885</td>\n",
       "      <td>0.115465</td>\n",
       "      <td>0.063761</td>\n",
       "      <td>1.882217</td>\n",
       "      <td>108.226388</td>\n",
       "      <td>30.421719</td>\n",
       "      <td>1.570369</td>\n",
       "      <td>0.057269</td>\n",
       "      <td>0.394157</td>\n",
       "      <td>0.777417</td>\n",
       "      <td>0.50939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.125129</td>\n",
       "      <td>0.319619</td>\n",
       "      <td>0.244355</td>\n",
       "      <td>0.884856</td>\n",
       "      <td>47.568050</td>\n",
       "      <td>7.323256</td>\n",
       "      <td>1.010025</td>\n",
       "      <td>0.232382</td>\n",
       "      <td>0.488726</td>\n",
       "      <td>0.416029</td>\n",
       "      <td>0.49997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.120000</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>77.450000</td>\n",
       "      <td>25.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>92.270000</td>\n",
       "      <td>29.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>115.920000</td>\n",
       "      <td>34.100000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>271.740000</td>\n",
       "      <td>97.600000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age  hypertension  heart_disease    work_type  \\\n",
       "count  4313.000000   4313.000000    4313.000000  4313.000000   \n",
       "mean     49.745885      0.115465       0.063761     1.882217   \n",
       "std      18.125129      0.319619       0.244355     0.884856   \n",
       "min      17.000000      0.000000       0.000000     0.000000   \n",
       "25%      35.000000      0.000000       0.000000     2.000000   \n",
       "50%      50.000000      0.000000       0.000000     2.000000   \n",
       "75%      64.000000      0.000000       0.000000     2.000000   \n",
       "max      82.000000      1.000000       1.000000     3.000000   \n",
       "\n",
       "       avg_glucose_level          bmi  smoking_status       stroke  \\\n",
       "count        4313.000000  4130.000000     4313.000000  4313.000000   \n",
       "mean          108.226388    30.421719        1.570369     0.057269   \n",
       "std            47.568050     7.323256        1.010025     0.232382   \n",
       "min            55.120000    11.300000        0.000000     0.000000   \n",
       "25%            77.450000    25.400000        1.000000     0.000000   \n",
       "50%            92.270000    29.200000        2.000000     0.000000   \n",
       "75%           115.920000    34.100000        2.000000     0.000000   \n",
       "max           271.740000    97.600000        3.000000     1.000000   \n",
       "\n",
       "       gender_Male  ever_married_Yes  Residence_type_Urban  \n",
       "count  4313.000000       4313.000000            4313.00000  \n",
       "mean      0.394157          0.777417               0.50939  \n",
       "std       0.488726          0.416029               0.49997  \n",
       "min       0.000000          0.000000               0.00000  \n",
       "25%       0.000000          1.000000               0.00000  \n",
       "50%       0.000000          1.000000               1.00000  \n",
       "75%       1.000000          1.000000               1.00000  \n",
       "max       1.000000          1.000000               1.00000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#quick look at the data\n",
    "encoded_stroke_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                     50.00\n",
       "hypertension             0.00\n",
       "heart_disease            0.00\n",
       "work_type                2.00\n",
       "avg_glucose_level       92.27\n",
       "bmi                     29.20\n",
       "smoking_status           2.00\n",
       "stroke                   0.00\n",
       "gender_Male              0.00\n",
       "ever_married_Yes         1.00\n",
       "Residence_type_Urban     1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the median values to compare mean and median in bmi\n",
    "encoded_stroke_df.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean and median are nearly identical. Either is an acceptable replacement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.421719128329308"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Set mean to a variable\n",
    "mean_bmi = encoded_stroke_df['bmi'].mean()\n",
    "mean_bmi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>work_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>ever_married_Yes</th>\n",
       "      <th>Residence_type_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5105</th>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>83.75</td>\n",
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       "    <tr>\n",
       "      <th>5106</th>\n",
       "      <td>81.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>125.20</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5107</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>82.99</td>\n",
       "      <td>30.600000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5108</th>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>166.29</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5109</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85.28</td>\n",
       "      <td>26.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  hypertension  heart_disease  work_type  avg_glucose_level  \\\n",
       "5105  80.0             1              0          2              83.75   \n",
       "5106  81.0             0              0          3             125.20   \n",
       "5107  35.0             0              0          3              82.99   \n",
       "5108  51.0             0              0          2             166.29   \n",
       "5109  44.0             0              0          0              85.28   \n",
       "\n",
       "            bmi  smoking_status  stroke  gender_Male  ever_married_Yes  \\\n",
       "5105  30.421719               2       0            0                 1   \n",
       "5106  40.000000               2       0            0                 1   \n",
       "5107  30.600000               2       0            0                 1   \n",
       "5108  25.600000               1       0            1                 1   \n",
       "5109  26.200000               0       0            0                 1   \n",
       "\n",
       "      Residence_type_Urban  \n",
       "5105                     1  \n",
       "5106                     1  \n",
       "5107                     0  \n",
       "5108                     0  \n",
       "5109                     1  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#replace NaN with mean bmi\n",
    "replace_stroke_df = encoded_stroke_df.fillna(mean_bmi)\n",
    "replace_stroke_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Make a correlation heatmap of all of the features\n",
    "correlation = replace_stroke_df.corr()\n",
    "plt.figure (figsize = (10,8))\n",
    "sns.heatmap(correlation, annot = True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Marital status had a small correlation with age. Age makes more sense to keep in the features for a medical model, so try removing ever_married to see the effect.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove ever_married column and then train/test the model to see if it helps performance.\n",
    "final_stroke_df = replace_stroke_df.drop(['ever_married_Yes'], axis = 1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing the ever_married column does not make improvement to the model. Actually the F1 and precision drop one-tenth. Keep the ever_married column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model work starts here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4313, 10) (4313,)\n"
     ]
    }
   ],
   "source": [
    "# Assign X (data) and y (target)\n",
    "X = replace_stroke_df.drop([\"stroke\"], axis=1)\n",
    "y = replace_stroke_df[\"stroke\"]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split data into training and testing\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1008\n",
       "1      71\n",
       "Name: stroke, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the distribution of stroke/no stroke in the test data\n",
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic regression requires feature scaling, but with the small dataset this may not have much of an effect. Try running the model with and without scaling.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a StandardScater model and fit it to the training data\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X_scaler = StandardScaler().fit(X_train)\n",
    "\n",
    "\n",
    "# Transform the training and testing data using the X_scaler model. Y is the target so we don't scale the y.\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model performance is exactly the same with scaling. It never predicts a \"1\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "#Create a Logistic Regression Model\n",
    "\n",
    "classifier = LogisticRegression()\n",
    "\n",
    "\n",
    "#Fit (train) model using the training data\n",
    "classifier.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy does not improve with Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Make predictions\n",
    "predictions = classifier.predict(X_test_scaled)\n",
    "sum(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1008    0]\n",
      " [  71    0]]\n"
     ]
    }
   ],
   "source": [
    "#Confusion matrix to see correct and incorrect predictions\n",
    "\n",
    "confusion_matrix = confusion_matrix(y_test, predictions)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      1.00      0.97      1008\n",
      "           1       0.00      0.00      0.00        71\n",
      "\n",
      "    accuracy                           0.93      1079\n",
      "   macro avg       0.47      0.50      0.48      1079\n",
      "weighted avg       0.87      0.93      0.90      1079\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\betsy\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "#Compute precision, recall F1-score and support\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-39-e21149686de8>:18: RuntimeWarning: invalid value encountered in longlong_scalars\n",
      "  precision_stroke = confusion_matrix[1,1] / sum(confusion_matrix[:,1])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Logistic Regression Model')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create an informative confusion matrix using seaborn\n",
    "\n",
    "#Define variables for Information inside each box\n",
    "category = ['True Neg', 'False Pos', 'False Neg', 'True Pos']\n",
    "counts = ['{0:0.0f}'.format(value) for value in confusion_matrix.flatten()]\n",
    "percentages = ['{0:.1%}'.format(value) for value in confusion_matrix.flatten()/np.sum(confusion_matrix)]\n",
    "labels = [f'{v1}\\n{v2}\\n{v3}' for v1, v2, v3 in zip(category, counts, percentages)]\n",
    "\n",
    "#Convert all of the labels to an array and reshape\n",
    "labels = np.asarray(labels).reshape(2,2)\n",
    "\n",
    "#Change the 0,1 to no stroke and stroke for labels\n",
    "tick_labels = ['No Stroke', 'Stroke']\n",
    "\n",
    "#Calculate all of the values to be displayed underneath the matrix as xlabel. Calculate precision, recall, F1 for both\n",
    "#stroke and no stroke\n",
    "accuracy  = np.trace(confusion_matrix) / float(np.sum(confusion_matrix))\n",
    "precision_stroke = confusion_matrix[1,1] / sum(confusion_matrix[:,1])\n",
    "recall_stroke    = confusion_matrix[1,1] / sum(confusion_matrix[1,:])\n",
    "f1_score_stroke  = 2*precision_stroke*recall_stroke / (precision_stroke + recall_stroke)\n",
    "precision = confusion_matrix[0,0] / sum(confusion_matrix[:,0])\n",
    "recall    = confusion_matrix[0,0] / sum(confusion_matrix[0,:])\n",
    "f1_score  = 2*precision*recall / (precision + recall)\n",
    "\n",
    "#Set up the text for the x label display underneath the matrix\n",
    "stats_text = \"\\n\\nAccuracy={:0.2f}\\nPrecision (stroke)={:0.2f}, Precision (no stroke)={:0.2f}\\nRecall (stroke)={:0.2f}, Recall (no stroke)={:0.2f}\\nF1 Score (stroke)={:0.2f}, F1 Score (no stroke)={:0.2f} \".format(accuracy,precision_stroke, precision, recall_stroke, recall, f1_score_stroke, f1_score)\n",
    "\n",
    "#Make the seaborn heatmap\n",
    "sns.heatmap(confusion_matrix, annot = labels, fmt = '', xticklabels = tick_labels, yticklabels = tick_labels)\n",
    "plt.xlabel(stats_text, fontsize = 14)\n",
    "plt.title(\"Logistic Regression Model\", fontsize = 14)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}