According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths.This dataset is used to predict whether a patient is likely to get a stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relevant information about the patient.

This project describes step-by-step procedures for building a Machine Learning (ML) Model for Stroke Prediction and for analysing which features are most useful for the prediction.

=> About Dataset

The dataset contains 5110 real world observations and 10 different attributes:

• gender : "Male", "Female" or "Other"
• age : age of the patient
• hypertension:
  • 0: if the patient doesn't have hypertension
  • 1: if the patient has hypertension
• heart_disease:
  • 0: if the patient doesn't have any heart diseases
  • 1: if the patient has a heart disease
• ever_married : "No" or "Yes"
• Residence_type : "Rural" or "Urban"
• avg_glucose_level : average glucose level in blood
• bmi : body mass index
• smoking_status : "formerly smoked", "never smoked", "smokes" or "Unknown"
• stroke : 1 if the patient had a stroke or 0 if not

=> Working of System

• SVM: Support Vector Machine
• Input: Dataset can be consider as input
• Output: Classification into 0 (no stroke) or 1 (stroke)

=> Dataset

Refer dataset to download from here.