# Strength Performance Analysis and Modeling

## Overview

This project explores a dataset of athlete strength metrics to understand patterns in deadlift performance and to build models that can predict and classify athletes based on strength.

The workflow includes:

- Exploratory Data Analysis (EDA)
- Feature engineering
- Regression models
- Classification models
- Clustering
- Model selection and export

The final objective was to classify athletes into performance categories and evaluate which model performs best.

---

## Dataset

The dataset contains:

- Body weight
- Height
- Age
- Strength metrics: deadlift, back squat, snatch

After cleaning:

- Duplicate rows were removed
- Placeholder values were replaced
- Unrealistic values were filtered
- Missing key fields were dropped

---

## Exploratory Data Analysis (EDA)

### Average Deadlift by Body Weight
![img11](img11.png)

Heavier weight groups generally show higher deadlift performance.

### Average Deadlift by Height
![img12](img12.png)

Taller athletes tend to lift more, with higher variability at the upper height ranges.

### Average Deadlift by Age
![img13](img13.png)

Performance peaks around ages 25–34 and gradually declines afterward.

### Body Ratio and Deadlift
![img14](img14.png)

Higher weight-to-height ratios are associated with stronger lifts.

### Strength Metric Correlations
![img15](img15.png)

Deadlift and back squat show a strong positive correlation, while snatch is only weakly related.

---

## Regression Modeling

A baseline linear regression model was trained to predict deadlift performance.

### Actual vs Predicted Deadlift
![img16](img16.png)

The model follows the general trend but shows noise due to differences between athletes.

---

## Clustering

K-Means clustering was used to group athletes based on strength metrics.

### Cluster Visualization (PCA)
![img17](img17.png)

Three performance clusters were identified, separating athletes by overall strength level.

---

## Classification Modeling

Athletes were grouped into three balanced performance classes:

- Low
- Medium
- High

Models trained:

- Logistic Regression
- Random Forest
- Gradient Boosting

### Confusion Matrices

Logistic Regression:
![img18](img18.png)

Random Forest:
![img19](img19.png)

Gradient Boosting:
![img20](img20.png)

---

## Model Evaluation

All models performed well across accuracy, precision, recall, and F1-score.

Random Forest stood out because it:

- Made fewer major misclassifications
- Separated high and low performers better
- Achieved the highest F1-score

---

## Final Model

The final selected model:

**Random Forest Classifier**

It was trained on the full dataset and exported as:

`best_classifier.pkl`

---

## How to Load the Model

```python
import pickle

with open("best_classifier.pkl", "rb") as f:
    model = pickle.load(f)

prediction = model.predict(X_sample)
```

---

## Conclusion

This project provided several key insights:

- Weight, height, and body ratio strongly influence deadlift performance
- Performance peaks in the late 20s and declines afterward
- Deadlift and back squat are closely related
- Classification models performed very well due to clear class separation
- Random Forest proved to be the most reliable model

The work demonstrates a full machine learning workflow, including:

- Data exploration
- Feature engineering
- Model training
- Evaluation
- Model selection
- Export

The final Random Forest model delivers strong performance and can be used to classify athletes into strength categories based on their physical and strength metrics.


[![Project Presentation](https://img.youtube.com/vi/R0YGueMVqko/0.jpg)](https://youtu.be/R0YGueMVqko)