# 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

  }

}