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+# 🏋️‍♂️ Gradient Boosting Deadlift Predictor
+
+This repository contains the winning model from Assignment #2: Classification, Regression, Clustering & Evaluation.
+
+## 📌 Model Purpose
+The model predicts an athlete's **deadlift performance (lbs)** based on physical and strength-related features.
+
+## 🧠 Algorithm
+✅ Gradient Boosting Regressor  
+Selected as the final model after comparing:
+
+- Linear Regression
+- Random Forest
+- Gradient Boosting
+
+## 🏆 Performance (Test Set)
+
+- R²: 0.85
+- MAE: ~28.6 lbs
+- RMSE: ~37.2 lbs
+
+Gradient Boosting achieved the **highest accuracy and lowest error**, so it was chosen as the final model.
+
+## 📁 Files
+- `winning_model.pkl` – serialized model ready for loading and inference
+
+## 🔧 Usage
+```python
+import pickle
+
+with open("winning_model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+prediction = model.predict([[weight, height, backsquat, snatch]])