main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>2</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>3</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>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "0    Male  67.0             0              1          Yes        Private   \n",
       "1    Male  80.0             0              1          Yes        Private   \n",
       "2  Female  49.0             0              0          Yes        Private   \n",
       "3  Female  79.0             1              0          Yes  Self-employed   \n",
       "4    Male  81.0             0              0          Yes        Private   \n",
       "\n",
       "  Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "0          Urban             228.69  36.6  formerly smoked       1  \n",
       "1          Rural             105.92  32.5     never smoked       1  \n",
       "2          Urban             171.23  34.4           smokes       1  \n",
       "3          Rural             174.12  24.0     never smoked       1  \n",
       "4          Urban             186.21  29.0  formerly smoked       1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"stroke_dataset.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4981, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# esploracion de datos\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'age', 'hypertension', 'heart_disease', 'ever_married',\n",
       "       'work_type', 'Residence_type', 'avg_glucose_level', 'bmi',\n",
       "       'smoking_status', 'stroke'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4981 entries, 0 to 4980\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   gender             4981 non-null   object \n",
      " 1   age                4981 non-null   float64\n",
      " 2   hypertension       4981 non-null   int64  \n",
      " 3   heart_disease      4981 non-null   int64  \n",
      " 4   ever_married       4981 non-null   object \n",
      " 5   work_type          4981 non-null   object \n",
      " 6   Residence_type     4981 non-null   object \n",
      " 7   avg_glucose_level  4981 non-null   float64\n",
      " 8   bmi                4981 non-null   float64\n",
      " 9   smoking_status     4981 non-null   object \n",
      " 10  stroke             4981 non-null   int64  \n",
      "dtypes: float64(3), int64(3), object(5)\n",
      "memory usage: 428.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4981.000000</td>\n",
       "      <td>4981.000000</td>\n",
       "      <td>4981.000000</td>\n",
       "      <td>4981.000000</td>\n",
       "      <td>4981.000000</td>\n",
       "      <td>4981.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>43.419859</td>\n",
       "      <td>0.096165</td>\n",
       "      <td>0.055210</td>\n",
       "      <td>105.943562</td>\n",
       "      <td>28.498173</td>\n",
       "      <td>0.049789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22.662755</td>\n",
       "      <td>0.294848</td>\n",
       "      <td>0.228412</td>\n",
       "      <td>45.075373</td>\n",
       "      <td>6.790464</td>\n",
       "      <td>0.217531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.120000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>77.230000</td>\n",
       "      <td>23.700000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.850000</td>\n",
       "      <td>28.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>113.860000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>0.000000</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>271.740000</td>\n",
       "      <td>48.900000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age  hypertension  heart_disease  avg_glucose_level  \\\n",
       "count  4981.000000   4981.000000    4981.000000        4981.000000   \n",
       "mean     43.419859      0.096165       0.055210         105.943562   \n",
       "std      22.662755      0.294848       0.228412          45.075373   \n",
       "min       0.080000      0.000000       0.000000          55.120000   \n",
       "25%      25.000000      0.000000       0.000000          77.230000   \n",
       "50%      45.000000      0.000000       0.000000          91.850000   \n",
       "75%      61.000000      0.000000       0.000000         113.860000   \n",
       "max      82.000000      1.000000       1.000000         271.740000   \n",
       "\n",
       "               bmi       stroke  \n",
       "count  4981.000000  4981.000000  \n",
       "mean     28.498173     0.049789  \n",
       "std       6.790464     0.217531  \n",
       "min      14.000000     0.000000  \n",
       "25%      23.700000     0.000000  \n",
       "50%      28.100000     0.000000  \n",
       "75%      32.600000     0.000000  \n",
       "max      48.900000     1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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                  0\n",
       "smoking_status       0\n",
       "stroke               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# valores perdidos\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                  2\n",
       "age                   104\n",
       "hypertension            2\n",
       "heart_disease           2\n",
       "ever_married            2\n",
       "work_type               4\n",
       "Residence_type          2\n",
       "avg_glucose_level    3895\n",
       "bmi                   342\n",
       "smoking_status          4\n",
       "stroke                  2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# valores unicos\n",
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum().sum() /df.shape[0] *100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'heart_disease Distribution')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6)).patch.set_facecolor(\"white\")\n",
    "df['heart_disease'].value_counts().plot.pie(textprops={'fontsize':16},shadow=True,explode=[0.1,0.1],autopct=\"%1.1f%%\").set_title(\"heart_disease Distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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                  0\n",
       "smoking_status       0\n",
       "stroke               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ingenieria de caracteristicas\n",
    "\n",
    "# Datos faltantes\n",
    "# valores que faltan\n",
    "\n",
    "\n",
    "df.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>2</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>3</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>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "0    Male  67.0             0              1          Yes        Private   \n",
       "1    Male  80.0             0              1          Yes        Private   \n",
       "2  Female  49.0             0              0          Yes        Private   \n",
       "3  Female  79.0             1              0          Yes  Self-employed   \n",
       "4    Male  81.0             0              0          Yes        Private   \n",
       "\n",
       "  Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "0          Urban             228.69  36.6  formerly smoked       1  \n",
       "1          Rural             105.92  32.5     never smoked       1  \n",
       "2          Urban             171.23  34.4           smokes       1  \n",
       "3          Rural             174.12  24.0     never smoked       1  \n",
       "4          Urban             186.21  29.0  formerly smoked       1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df\n",
    "y = df['heart_disease']\n",
    "X.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: heart_disease, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4981 entries, 0 to 4980\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   gender             4981 non-null   object \n",
      " 1   age                4981 non-null   float64\n",
      " 2   hypertension       4981 non-null   int64  \n",
      " 3   heart_disease      4981 non-null   int64  \n",
      " 4   ever_married       4981 non-null   object \n",
      " 5   work_type          4981 non-null   object \n",
      " 6   Residence_type     4981 non-null   object \n",
      " 7   avg_glucose_level  4981 non-null   float64\n",
      " 8   bmi                4981 non-null   float64\n",
      " 9   smoking_status     4981 non-null   object \n",
      " 10  stroke             4981 non-null   int64  \n",
      "dtypes: float64(3), int64(3), object(5)\n",
      "memory usage: 428.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  }
 ],
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