This project analyzes bike ride trends in London, focusing on patterns of usage over time and across different conditions. Using data from the London bike sharing system, we apply statistical methods and visualization techniques to uncover insights into ride frequencies, effects of weather conditions on bike usage, and more.

• London Bike Rides - Moving Average and Heatmap.twbx: A Tableau workbook presenting visual analysis.
• london_merged.csv: The main dataset, including timestamps, weather conditions, and the number of bike shares.
• london_bikes_final.xlsx: An Excel workbook used for preliminary data analysis and cleaning.
• london_bikes.ipynb: A Jupyter notebook detailing the data analysis process, including data cleaning, exploration, and visualization.
• london-bike-sharing-dataset.zip: The raw dataset archive.

The data encompasses bike sharing information in London, including ride durations, weather conditions, and holiday information, aiming to identify patterns and correlations.

1. Data Exploration: Start with the Jupyter notebook (london_bikes.ipynb) for an overview of the data cleaning and exploration process.
2. Data Analysis: Delve into the Excel workbook (london_bikes_final.xlsx) for detailed analysis steps and initial findings.
3. Visualization and Insights: Use Tableau Desktop to open the TWBX file and explore interactive visualizations.
4. Further Analysis: For custom analysis, use the CSV file with Python, R, or any data analysis tool.

• Ensure you have Python, Jupyter, Tableau Desktop, and Excel installed.
• Clone this repository to your local machine.
• Navigate through each component as outlined in the "How to Use" section.

Contributions are welcome! Whether it's suggesting new analyses, improving the visualizations, or enhancing the dataset, feel free to fork the repository and submit your pull requests.