Heart Failure Prediction Project

Overview

Machine learning models to predict heart failure severity and mortality risk using clinical data.

Machine learning models to predict:
- Severity Score (Regression)
- Mortality Risk (Classification)

📌 Key Features

Component	Techniques Used
Data Analysis	EDA, Correlation Heatmaps, Feature Importance
Regression Models	Linear, Ridge, Lasso, Kernel (RBF/Poly)
Classification	Logistic Regression, SVM, Random Forest
Model Evaluation	MSE, R², Accuracy, Precision-Recall

🚀 Results Highlight

Best Performing Models:
```python
{
"Regression": {
"Best Model": "RBF Kernel Regression",
"MSE": 0.7888,
"R² Score": 0.7500
},
"Classification": {
"Best Model": "Random Forest",
"Accuracy": 83.33%,
"Recall": 63.16% # Critical for mortality prediction
}
}