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+# 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
+  }
+}