| # 🏋️♂️ 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]]) | |