{ "cells": [ { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "data_base = pd.read_csv(\"csv/option4.csv\")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | id | \n", "gender | \n", "age | \n", "hypertension | \n", "heart_disease | \n", "ever_married | \n", "work_type | \n", "Residence_type | \n", "avg_glucose_level | \n", "bmi | \n", "smoking_status | \n", "stroke | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", "9046 | \n", "Male | \n", "67.0 | \n", "0 | \n", "1 | \n", "Yes | \n", "Private | \n", "Urban | \n", "228.69 | \n", "36.6 | \n", "formerly smoked | \n", "1 | \n", "
1 | \n", "51676 | \n", "Female | \n", "61.0 | \n", "0 | \n", "0 | \n", "Yes | \n", "Self-employed | \n", "Rural | \n", "202.21 | \n", "NaN | \n", "never smoked | \n", "1 | \n", "
2 | \n", "31112 | \n", "Male | \n", "80.0 | \n", "0 | \n", "1 | \n", "Yes | \n", "Private | \n", "Rural | \n", "105.92 | \n", "32.5 | \n", "never smoked | \n", "1 | \n", "
3 | \n", "60182 | \n", "Female | \n", "49.0 | \n", "0 | \n", "0 | \n", "Yes | \n", "Private | \n", "Urban | \n", "171.23 | \n", "34.4 | \n", "smokes | \n", "1 | \n", "
4 | \n", "1665 | \n", "Female | \n", "79.0 | \n", "1 | \n", "0 | \n", "Yes | \n", "Self-employed | \n", "Rural | \n", "174.12 | \n", "24.0 | \n", "never smoked | \n", "1 | \n", "