🏋️♂️ 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
import pickle
with open("winning_model.pkl", "rb") as f:
model = pickle.load(f)
prediction = model.predict([[weight, height, backsquat, snatch]])