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https://github.com/alessandroryo/bike-rental-data-analysis
A data analysis project focused on understanding and predicting bike rental patterns. This project utilizes data processing, visualization, and predictive modeling techniques to gain insights into bike rental usage, fulfilling the final submission requirement for Dicoding Indonesia's Data Analysis course.
https://github.com/alessandroryo/bike-rental-data-analysis
bike-rental data-analysis data-visualization jupyter-notebook machine-learning python streamlit
Last synced: about 1 month ago
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A data analysis project focused on understanding and predicting bike rental patterns. This project utilizes data processing, visualization, and predictive modeling techniques to gain insights into bike rental usage, fulfilling the final submission requirement for Dicoding Indonesia's Data Analysis course.
- Host: GitHub
- URL: https://github.com/alessandroryo/bike-rental-data-analysis
- Owner: alessandroryo
- License: mit
- Created: 2024-08-12T20:21:12.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-08-22T12:48:43.000Z (5 months ago)
- Last Synced: 2024-11-05T10:38:29.691Z (3 months ago)
- Topics: bike-rental, data-analysis, data-visualization, jupyter-notebook, machine-learning, python, streamlit
- Language: Jupyter Notebook
- Homepage: https://bike-sharing-data-analysis-ryo.streamlit.app/
- Size: 654 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Bike Rental Data Analysis Project - Dicoding
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.
## Table of Contents
- [Bike Rental Data Analysis Project - Dicoding](#bike-rental-data-analysis-project---dicoding)
- [Table of Contents](#table-of-contents)
- [Running the Streamlit Application](#running-the-streamlit-application)
- [Folder Structure](#folder-structure)
- [Project Objective](#project-objective)
- [Results](#results)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
## Running the Streamlit Application
To run the Streamlit application, use the following command:
```bash
streamlit run dashboard/dashboard.py
```
This command will launch the interactive dashboard, allowing you to explore the insights derived from the bike rental data.
## Folder Structure
Below is the folder structure for this project:
```text
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.
## Project Objective
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.
## Results
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.
## Contributing
Contributions are welcome! If you have suggestions for improvements or encounter any issues, please open an issue or submit a pull request.
## License
This project is licensed under the MIT License. See the [LICENSE](./LICENSE) file for more details.
## Contact
For any questions or inquiries, please contact:
- **Email**: