🚴‍♂️ Bike-Sharing-Demand-Forecasting-Using-Time-Series-Analysis 📈⏳

Bike-Sharing-Demand-Forecasting-Using-Time-Series-Analysis is a data science project that applies time-series forecasting techniques to predict the demand for bike-sharing services. By analyzing historical usage data, weather conditions, and seasonal patterns, this project demonstrates how predictive models can improve urban mobility planning, resource allocation, and operational efficiency.

✨ Key Features

📂 Data Preprocessing – Cleaning, handling missing values, feature engineering

🌦️ Feature Integration – Incorporating weather, holidays, and working days for better accuracy

📊 Exploratory Data Analysis (EDA) – Trends, seasonal decomposition, and visualization

🧮 Time-Series Models – ARIMA, SARIMA, Prophet, LSTM, GRU

📈 Forecasting – Hourly/daily bike rental demand predictions

🔍 Model Evaluation – RMSE, MAE, R², and residual analysis

📉 Visualization – Interactive plots of demand trends and forecasts

🚀 Deployment (Optional) – Streamlit app for real-time demand forecasting

🧰 Tech Stack

Programming: Python 🐍

Libraries: Pandas, NumPy, Matplotlib, Seaborn, Plotly

Time-Series & ML: Statsmodels, Scikit-learn, Facebook Prophet, TensorFlow / PyTorch

Deployment (Optional): Streamlit / Flask

📁 Project Structure 📁 data/ # Historical bike-sharing datasets 📁 notebooks/ # Jupyter notebooks for EDA and modeling 📁 src/ # Preprocessing, modeling, and evaluation scripts 📁 results/ # Forecast plots, metrics, and reports 📁 app/ # (Optional) Forecasting web app

🚀 Getting Started git clone https://github.com/yourusername/Bike-Sharing-Demand-Forecasting-Using-Time-Series-Analysis.git cd Bike-Sharing-Demand-Forecasting-Using-Time-Series-Analysis pip install -r requirements.txt jupyter notebook

📌 Use Cases

🚲 Bike-Sharing Companies – Optimize fleet distribution and availability

🌆 Smart Cities – Improve traffic and urban mobility planning

📊 Data Science Research – Apply and compare time-series forecasting methods

🎓 Education – Learn time-series modeling with a real-world dataset

🤝 Contributing

Contributions are welcome! Add new forecasting techniques, improve models, or enhance dashboards and submit a PR.

📜 License

MIT License – Free to use for research, education, and personal projects.