🏃‍♂️ Distance Walked Prediction (Health Data)

This project uses Machine Learning models to predict the daily distance walked (in kilometers) based on health and lifestyle features such as step count, sleep duration, physical activity, and more.

The dataset was sourced from Kaggle and manually edited to suit the prediction objective.

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🔍 Models Implemented

1. Linear Regression
2. Support Vector Regression (SVR)
3. Random Forest Regressor

The models are compared using:

• R² Score
• Mean Absolute Error (MAE)
• Root Mean Squared Error (RMSE)

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📊 Visuals Included

• Histogram: Distribution of Distance Walked (km)
• Correlation Matrix: Heatmap of numeric feature relationships
• Pairplot: Pairwise relationships between core health metrics
• Scatter Plots: Actual vs Predicted Distance Walked (for all 3 models)
• Bar Plot Comparison: Model performance comparison (R², MAE, RMSE)

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🛠 Tech Stack

• Python
• pandas, numpy
• scikit-learn
• matplotlib, seaborn
• Jupyter Notebook

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📂 File Structure

Distance_Walked/
├── Distance.ipynb                   🔹 Main notebook with ML Workflow
├── Distance.py                      🔹 Python code of same ML Workflow
├── Health_dataset.csv               🔹 Cleaned and customized dataset (from: kaggle)
├── LICENSE                          🔹 MIT License
└── README.md                        🔹 This file !!

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🔽 Model Performance Comparison

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🚀 Future Scope

This project may soon be integrated into a web app or website, where users can:

• Enter their health stats
• Get distance walked predictions
• Visualize trends over time

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👤 Author

Anuj Kulkarni - aka - steam-bell-92