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🚲 Bike Sharing Demand Prediction – Python Project

Forecast how many bikes will be rented using historical data and predictive analytics. Ideal for those working on urban mobility, time-series forecasting, or ML in smart cities.

Key Features
• Uses historical bike-sharing data with features like weather, hour, day, season, and holidays
• Applies regression models such as Linear Regression, Random Forest, Gradient Boosting, or XGBoost
• Visualizes actual vs. predicted demand trends
• Built with Python data science libraries like pandas, scikit-learn, and possibly Streamlit or Flask for interface

🔗 Explore the full project here:
Bike Sharing Demand Prediction – View Project

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