Скачать или смотреть Heart Stroke Prediction using Machine Learning: A Hybrid Feature Selection Approach

Discover how to build a powerful heart stroke prediction model that achieves up to 97.52% accuracy using a combination of low variance filtering and Principal Component Analysis (PCA). This step-by-step tutorial walks you through the entire machine learning pipeline - from data preprocessing to model evaluation.

What You'll Learn:
How to handle class imbalance in medical datasets using SMOTE
Implementing a hybrid feature selection approach with low variance filtering and PCA
Training and comparing 5 powerful classifiers (KNN, Decision Tree, Logistic Regression, Random Forest, XGBoost)
Techniques for evaluating model performance with reduced feature sets
Practical tips for handling missing values and outliers in healthcare data

This project demonstrates how machine learning can contribute to early stroke detection and potentially save lives through timely interventions.

#Dataset & Resources:
1. Kaggle Dataset: https://www.kaggle.com/datasets/sweth...
2. GitHub Repository: https://github.com/Harsha-chikkala/He...
3. Medium Article:   / predicting-heart-stroke-using-machine-lear...  

#MachineLearning #DataScience #HeartStrokePrediction #FeatureSelection #HealthcareAI #PCA #RandomForest #Python