| # 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 | |
|  | |
| Heavier weight groups generally show higher deadlift performance. | |
| ### Average Deadlift by Height | |
|  | |
| Taller athletes tend to lift more, with higher variability at the upper height ranges. | |
| ### Average Deadlift by Age | |
|  | |
| Performance peaks around ages 25–34 and gradually declines afterward. | |
| ### Body Ratio and Deadlift | |
|  | |
| Higher weight-to-height ratios are associated with stronger lifts. | |
| ### Strength Metric Correlations | |
|  | |
| 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 | |
|  | |
| 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) | |
|  | |
| 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: | |
|  | |
| Random Forest: | |
|  | |
| Gradient Boosting: | |
|  | |
| --- | |
| ## 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. | |
| ## Presentation Video | |
| [](https://youtu.be/R0YGueMVqko) |