This repository contains a data analysis project focused on understanding and predicting bike rental usage patterns. The project is part of the final submission for Dicoding Indonesia's Data Analysis curriculum. The analysis leverages data processing and visualization techniques to derive insights and build predictive models.
To run the Streamlit application, use the following command:
streamlit run dashboard/dashboard.pyThis command will launch the interactive dashboard, allowing you to explore the insights derived from the bike rental data.
Below is the folder structure for this project:
submission/
├── dashboard/
│ ├── dashboard.ipynb
│ ├── dashboard.py
├── data/
│ ├── day.csv
│ ├── hour.csv
├── notebook.ipynb
├── README.md
├── requirements.txt
└── url.txt
This structure organizes the project files, including Jupyter notebooks, data files, and the Streamlit dashboard.
The primary goal of this project is to fulfill the final submission requirement for the Data Analysis course offered by Dicoding Indonesia. It aims to analyze bike rental data to uncover patterns and predict future usage, providing valuable insights for stakeholders.
The analysis reveals key trends in bike rental patterns, such as peak rental times and the influence of weather conditions. The predictive models built as part of this project demonstrate the potential to forecast rental demand, contributing to more effective resource management.
Contributions are welcome! If you have suggestions for improvements or encounter any issues, please open an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for more details.
For any questions or inquiries, please contact:
- Email: [email protected]