This project describes step-by-step procedure for building a machine learning (ML) model for stroke prediction and for analysing which features are most useful for the prediction.
The primary goal of this project is to predict whether a person could be at risk of having a stroke based on various features and medical data. Stroke prediction models like this can assist medical professionals in identifying high-risk individuals.
Dataset can be downloaded from kaggle(https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset?resource=download)
Linear Regreesion Model (71% accuracy) Decision Tree Model (90.6% accuracy) Support Vector Machine (95.5% accuracy) Random Forest Classifier (95.4% accuracy)