Naive Bayes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from IPython.display import display, clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30669</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Rural</td>\n",
       "      <td>95.12</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30468</td>\n",
       "      <td>Male</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>87.96</td>\n",
       "      <td>39.2</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16523</td>\n",
       "      <td>Female</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>110.89</td>\n",
       "      <td>17.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56543</td>\n",
       "      <td>Female</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>69.04</td>\n",
       "      <td>35.9</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46136</td>\n",
       "      <td>Male</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Never_worked</td>\n",
       "      <td>Rural</td>\n",
       "      <td>161.28</td>\n",
       "      <td>19.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "0  30669    Male   3.0             0              0           No   \n",
       "1  30468    Male  58.0             1              0          Yes   \n",
       "2  16523  Female   8.0             0              0           No   \n",
       "3  56543  Female  70.0             0              0          Yes   \n",
       "4  46136    Male  14.0             0              0           No   \n",
       "\n",
       "      work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "0      children          Rural              95.12  18.0              NaN   \n",
       "1       Private          Urban              87.96  39.2     never smoked   \n",
       "2       Private          Urban             110.89  17.6              NaN   \n",
       "3       Private          Rural              69.04  35.9  formerly smoked   \n",
       "4  Never_worked          Rural             161.28  19.1              NaN   \n",
       "\n",
       "   stroke  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset= pd.read_csv(\"stroke prediction.csv\")\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[\"gender\"]=dataset[\"gender\"].astype('category')\n",
    "dataset[\"ever_married\"]=dataset[\"ever_married\"].astype('category')\n",
    "dataset[\"Residence_type\"]=dataset[\"Residence_type\"].astype('category')\n",
    "dataset[\"work_type\"]=dataset[\"work_type\"].astype('category')\n",
    "dataset[\"smoking_status\"]=dataset.smoking_status.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_imputed=dataset.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=dataset_imputed.drop(columns=[\"id\",\"stroke\"],axis=1)\n",
    "y=dataset_imputed[\"stroke\"]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,y_train,y_test= train_test_split(X,y,test_size=0.2,random_state=88,stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5303: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "encoder=LabelEncoder()\n",
    "\n",
    "X_train.gender=encoder.fit_transform(X_train.gender)\n",
    "\n",
    "X_train.ever_married=encoder.fit_transform(X_train.ever_married)\n",
    "\n",
    "X_train.work_type=encoder.fit_transform(X_train.work_type)\n",
    "\n",
    "X_train.Residence_type=encoder.fit_transform(X_train.Residence_type)\n",
    "\n",
    "X_train.smoking_status=X_train.smoking_status.map({'never smoked':0,'formerly smoked':1,'smokes':2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "encoder=LabelEncoder()\n",
    "\n",
    "X_test.gender=encoder.fit_transform(X_test.gender)\n",
    "\n",
    "X_test.ever_married=encoder.fit_transform(X_test.ever_married)\n",
    "\n",
    "X_test.work_type=encoder.fit_transform(X_test.work_type)\n",
    "\n",
    "X_test.Residence_type=encoder.fit_transform(X_test.Residence_type)\n",
    "\n",
    "X_test.smoking_status=X_test.smoking_status.map({'never smoked':0,'formerly smoked':1,'smokes':2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import KNNImputer\n",
    "\n",
    "imputer=KNNImputer()\n",
    "\n",
    "X_trimputed=pd.DataFrame(imputer.fit_transform(X_train),columns=X_train.columns)\n",
    "\n",
    "X_teimputed=pd.DataFrame(imputer.transform(X_test),columns=X_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trimputed[\"smoking_status\"]=X_trimputed[\"smoking_status\"].apply(round)\n",
    "X_trimputed[\"smoking_status\"].value_counts()\n",
    "\n",
    "X_trimputed[\"smoking_status\"]=X_trimputed[\"smoking_status\"].astype('category')\n",
    "X_trimputed[\"work_type\"]=X_trimputed[\"work_type\"].apply(int)\n",
    "X_trimputed[\"work_type\"]=X_trimputed[\"work_type\"].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_teimputed[\"smoking_status\"]=X_teimputed[\"smoking_status\"].apply(round)\n",
    "X_teimputed[\"smoking_status\"].value_counts()\n",
    "\n",
    "X_teimputed[\"smoking_status\"]=X_teimputed[\"smoking_status\"].astype('category')\n",
    "X_teimputed[\"work_type\"]=X_teimputed[\"work_type\"].apply(int)\n",
    "X_teimputed[\"work_type\"]=X_teimputed[\"work_type\"].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trimputed=pd.concat([X_trimputed,pd.get_dummies(X_trimputed[[\"work_type\",\"smoking_status\"]]).drop(columns=[\"work_type_0\",\"smoking_status_0\"])],axis=1)\n",
    "X_teimputed=pd.concat([X_teimputed,pd.get_dummies(X_teimputed[[\"work_type\",\"smoking_status\"]]).drop(columns=[\"work_type_0\",\"smoking_status_0\"])],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trimputed.drop(columns=['work_type','smoking_status'],inplace=True)\n",
    "X_teimputed.drop(columns=['work_type','smoking_status'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>work_type_1</th>\n",
       "      <th>work_type_2</th>\n",
       "      <th>work_type_3</th>\n",
       "      <th>work_type_4</th>\n",
       "      <th>smoking_status_1</th>\n",
       "      <th>smoking_status_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>136.51</td>\n",
       "      <td>17.1</td>\n",
       "      <td>0</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>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.55</td>\n",
       "      <td>27.3</td>\n",
       "      <td>0</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>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>108.69</td>\n",
       "      <td>29.6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>84.57</td>\n",
       "      <td>31.2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.43</td>\n",
       "      <td>26.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34715</th>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>58.90</td>\n",
       "      <td>29.1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34716</th>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.82</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34717</th>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>98.66</td>\n",
       "      <td>28.3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34718</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>150.00</td>\n",
       "      <td>17.4</td>\n",
       "      <td>0</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>34719</th>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>77.38</td>\n",
       "      <td>39.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34720 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gender   age  hypertension  heart_disease  ever_married  \\\n",
       "0         1.0   4.0           0.0            0.0           0.0   \n",
       "1         1.0   9.0           0.0            0.0           0.0   \n",
       "2         1.0  29.0           0.0            0.0           1.0   \n",
       "3         1.0  63.0           0.0            0.0           1.0   \n",
       "4         0.0  67.0           0.0            0.0           1.0   \n",
       "...       ...   ...           ...            ...           ...   \n",
       "34715     0.0  62.0           0.0            0.0           1.0   \n",
       "34716     0.0  43.0           0.0            0.0           0.0   \n",
       "34717     0.0  23.0           0.0            0.0           0.0   \n",
       "34718     1.0   9.0           0.0            0.0           0.0   \n",
       "34719     0.0  31.0           0.0            0.0           1.0   \n",
       "\n",
       "       Residence_type  avg_glucose_level   bmi  work_type_1  work_type_2  \\\n",
       "0                 1.0             136.51  17.1            0            0   \n",
       "1                 0.0              90.55  27.3            0            0   \n",
       "2                 0.0             108.69  29.6            0            1   \n",
       "3                 1.0              84.57  31.2            0            1   \n",
       "4                 0.0              70.43  26.3            0            0   \n",
       "...               ...                ...   ...          ...          ...   \n",
       "34715             1.0              58.90  29.1            0            1   \n",
       "34716             1.0              94.82  29.7            0            0   \n",
       "34717             1.0              98.66  28.3            0            1   \n",
       "34718             1.0             150.00  17.4            0            0   \n",
       "34719             1.0              77.38  39.6            0            0   \n",
       "\n",
       "       work_type_3  work_type_4  smoking_status_1  smoking_status_2  \n",
       "0                0            1                 0                 0  \n",
       "1                0            1                 0                 0  \n",
       "2                0            0                 0                 0  \n",
       "3                0            0                 1                 0  \n",
       "4                1            0                 0                 0  \n",
       "...            ...          ...               ...               ...  \n",
       "34715            0            0                 1                 0  \n",
       "34716            0            0                 0                 0  \n",
       "34717            0            0                 1                 0  \n",
       "34718            0            1                 0                 0  \n",
       "34719            1            0                 0                 0  \n",
       "\n",
       "[34720 rows x 14 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_trimputed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler=MinMaxScaler()\n",
    "\n",
    "scaler.fit(X_trimputed)\n",
    "\n",
    "X_trimputed=pd.DataFrame(scaler.transform(X_trimputed),columns=X_trimputed.columns)\n",
    "\n",
    "X_teimputed=pd.DataFrame(scaler.transform(X_teimputed),columns=X_teimputed.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import B"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}