SupportVectorMachine.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, cross_val_predict \n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn import preprocessing, metrics, svm, ensemble\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "import math\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9046</td>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51676</td>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>202.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31112</td>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60182</td>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1665</td>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "0   9046    Male  67.0             0              1          Yes   \n",
       "1  51676  Female  61.0             0              0          Yes   \n",
       "2  31112    Male  80.0             0              1          Yes   \n",
       "3  60182  Female  49.0             0              0          Yes   \n",
       "4   1665  Female  79.0             1              0          Yes   \n",
       "\n",
       "       work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "0        Private          Urban             228.69  36.6  formerly smoked   \n",
       "1  Self-employed          Rural             202.21   NaN     never smoked   \n",
       "2        Private          Rural             105.92  32.5     never smoked   \n",
       "3        Private          Urban             171.23  34.4           smokes   \n",
       "4  Self-employed          Rural             174.12  24.0     never smoked   \n",
       "\n",
       "   stroke  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stroke_df_initial = pd.read_csv(os.path.join('Resources/healthcare-dataset-stroke-data.csv'))\n",
    "stroke_df_initial.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>202.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <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>5110 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",
       "[5110 rows x 11 columns]"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "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": 23,
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4612\n",
       "1     498\n",
       "Name: hypertension, dtype: int64"
      ]
     },
     "execution_count": 24,
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4834\n",
       "1     276\n",
       "Name: heart_disease, dtype: int64"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4861\n",
       "1     249\n",
       "Name: stroke, dtype: int64"
      ]
     },
     "execution_count": 26,
     "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 27,
     "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>counts</th>\n",
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       "      <th>column</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Residence_type</th>\n",
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       "      <td>2514</td>\n",
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       "      <th>Urban</th>\n",
       "      <td>2596</td>\n",
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       "      <th rowspan=\"2\" valign=\"top\">ever_married</th>\n",
       "      <th>No</th>\n",
       "      <td>1757</td>\n",
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       "      <th>Yes</th>\n",
       "      <td>3353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">gender</th>\n",
       "      <th>Other</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>2115</td>\n",
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       "    <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": 28,
     "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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": 29,
     "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 30,
     "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <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",
       "      <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",
       "    </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": 31,
     "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": 32,
   "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": 32,
     "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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <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": 33,
     "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": 34,
   "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>work_type</th>\n",
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       "      <th>Residence_type_Urban</th>\n",
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       "      <th>0</th>\n",
       "      <td>67.0</td>\n",
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       "      <td>202.21</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",
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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",
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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": 34,
     "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",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <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>105.92</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</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",
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       "      <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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       "      <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": 35,
     "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": 36,
   "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",
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       "    </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",
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       "    <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": 36,
     "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": 37,
   "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>0</th>\n",
       "      <td>67.0</td>\n",
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       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <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",
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       "      <td>1</td>\n",
       "      <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>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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'bmi'}>]], dtype=object)"
      ]
     },
     "execution_count": 38,
     "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": 39,
   "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_stroke_df.sort_values(by=['bmi'], ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.349999999999994\n",
      "47.150000000000006\n",
      "117\n"
     ]
    }
   ],
   "source": [
    "#Determine 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",
    "#Display upper & lower bounds and number of outliers\n",
    "print(lower_bound)\n",
    "print(upper_bound)\n",
    "print(len(outliers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#quick look at the data\n",
    "encoded_stroke_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 42,
     "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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.421719128329308"
      ]
     },
     "execution_count": 43,
     "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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
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       "    </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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       "      <td>1</td>\n",
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       "      <th>5107</th>\n",
       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>82.99</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "    <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",
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       "      <td>1</td>\n",
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       "  </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": 44,
     "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": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = replace_stroke_df[\"stroke\"]\n",
    "target_names = [\"0\", \"1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>3</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>5109</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>4313 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  hypertension  heart_disease  work_type  avg_glucose_level  \\\n",
       "0     67.0             0              1          2             228.69   \n",
       "1     61.0             0              0          3             202.21   \n",
       "2     80.0             0              1          2             105.92   \n",
       "3     49.0             0              0          2             171.23   \n",
       "4     79.0             1              0          3             174.12   \n",
       "...    ...           ...            ...        ...                ...   \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  gender_Male  ever_married_Yes  \\\n",
       "0     36.600000               1            1                 1   \n",
       "1     30.421719               2            0                 1   \n",
       "2     32.500000               2            1                 1   \n",
       "3     34.400000               3            0                 1   \n",
       "4     24.000000               2            0                 1   \n",
       "...         ...             ...          ...               ...   \n",
       "5105  30.421719               2            0                 1   \n",
       "5106  40.000000               2            0                 1   \n",
       "5107  30.600000               2            0                 1   \n",
       "5108  25.600000               1            1                 1   \n",
       "5109  26.200000               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 10 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = replace_stroke_df.drop(\"stroke\", axis=1)\n",
    "feature_names = data.columns\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model work starts here\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, target, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC \n",
    "model = SVC(kernel='linear')\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the GridSearch estimator along with a parameter object containing the values to adjust\n",
    "#from sklearn.model_selection import GridSearchCV\n",
    "#param_grid = {'C': [1, 5, 10, 50],'gamma': [0.0001, 0.0005, 0.001, 0.005]}\n",
    "#grid = GridSearchCV(model, param_grid, verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#GridSearch Code\n",
    "#grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#GridSearch Code\n",
    "#print(grid.best_params_)\n",
    "# List the best score\n",
    "#print(grid.best_score_)\n",
    "# Make predictions with the hypertuned model\n",
    "#predictions = grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#GridSearch Code\n",
    "#print('Test Acc: %.3f' % grid.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "predictions = model.predict(X_test)\n",
    "print(classification_report(y_test, predictions,\n",
    "                            target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Acc: 0.934\n"
     ]
    }
   ],
   "source": [
    "# Model Accuracy for SVM\n",
    "print('Test Acc: %.3f' % model.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_matrix = confusion_matrix(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-56-f02d75517392>:14: 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, 'Support Vector Machine Model')"
      ]
     },
     "execution_count": 56,
     "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",
    "#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",
    "#Convert all of the labels to an array and reshape\n",
    "labels = np.asarray(labels).reshape(2,2)\n",
    "#Change the 0,1 to no stroke and stroke for labels\n",
    "tick_labels = ['No Stroke', 'Stroke']\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",
    "#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",
    "#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(\"Support Vector Machine Model\", fontsize = 14)"
   ]
  },
  {
   "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
}