🏋️‍♂️ Active Session Predictor (Machine Learning Project)

A complete end-to-end Machine Learning project that predicts whether a gym session is Active or Not Active based on user fitness metrics. This project demonstrates core ML concepts including model building, evaluation, visualization, scaling, comparison, and hyperparameter tuning.

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🚀 Project Overview

This project uses real-world inspired fitness data to classify whether a workout session is active (high calorie burn) or not. It is built using multiple ML techniques and provides insights through evaluation metrics and visualizations.

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🎯 Objective

To predict whether a workout session is Active based on:

• Age
• Weight
• Height
• Session Duration
• Average BPM (Heart Rate)

👉 A session is considered Active if:

Calories_Burned > 400

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🧠 Machine Learning Concepts Used

• Logistic Regression
• Model Evaluation Metrics
• Confusion Matrix & ROC Curve
• Learning Curves (Overfitting Analysis)
• Feature Scaling (StandardScaler, MinMaxScaler)
• Model Comparison (KNN, Decision Tree, Random Forest)
• Cross Validation
• Hyperparameter Tuning (GridSearchCV)

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📂 Project Structure

Fitness_Model/
│
├── fitness.py                # Basic Logistic Regression model
├── evaluation.py             # Evaluation metrics + Confusion Matrix + ROC Curve
├── overfitting.py            # Learning Curve (Overfitting/Underfitting analysis)
├── scaling.py                # Feature Scaling comparison
├── multiple_models.py        # Model comparison (Multiple algorithms)
├── tuning.py                 # Hyperparameter tuning (GridSearchCV)
│
├── gym_members_exercise_tracking_synthetic_data.csv   # Dataset
│
├── output_curves/            # Saved plots
│   ├── confusion_matrix.png
│   ├── roc_curve.png
│   └── learning_curve.png
│
├── README.md
├── requirements.txt
└── .gitignore

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⚙️ Installation & Setup

1. Clone the repository:

git clone https://github.com/aeindri-tech/Fitness_Model.git

2. Navigate to the project folder:

cd Fitness_Model

3. Install dependencies:

pip install -r requirements.txt

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▶️ How to Run

Run any task file:

python fitness.py

You will be prompted to enter:

• Age
• Weight
• Height
• Session Duration
• Average BPM

👉 The model will predict:

Active Session / Not an Active Session

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📊 Model Outputs

📌 Confusion Matrix

📌 ROC Curve

📌 Learning Curve

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📈 Model Performance

• Achieved high accuracy (~98%)

• Evaluated using:

  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • ROC-AUC

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🔍 Key Learnings

• Importance of data preprocessing
• Effect of feature scaling on model performance
• Comparing multiple models improves decision making
• Cross-validation ensures model reliability
• Hyperparameter tuning boosts performance

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💡 Future Improvements

• Deploy as a web app (Streamlit / Flask)
• Add more features (Sleep, Diet, Steps, etc.)
• Use advanced models (XGBoost, Neural Networks)
• Improve dataset quality

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📜 License

This project is licensed under the MIT License.

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🙌 Author

Aeindri BTech CSE (AI) Student

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⭐ Show Your Support

If you liked this project:

• ⭐ Star the repository
• 🍴 Fork it
• 📢 Share it

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🚀 Final Note

This project showcases a complete beginner-to-intermediate level Machine Learning workflow and is a strong step towards building real-world ML systems.