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a30c2ac | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | # 🏋️♂️ 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]])
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