/Exploring-US-Bikeshare-data-in-3-major-cities-in-USA---python-script-web-app-deployment-using-flask

This repository contains a Python script for analyzing bikeshare data from various U.S. cities and a web application deployed using Flask. Users can filter data by city, month, and day to view statistics and insights into bike sharing trends. The interactive interface makes data exploration easy and engaging

Primary LanguagePythonMIT LicenseMIT

Exploring US Bikeshare data in 3 major cities in USA - python script, web app deployment using flask

bikeshare

Bikeshare Data Explorer

Bikeshare Data Explorer is a Python web application that allows users to explore and analyze bike sharing data from different cities. With this application, users can filter data by city, month, and day to view statistics and gain insights into bike sharing trends.

Table of Contents

Features

  • Load and analyze bike sharing data from Chicago, New York City, and Washington.
  • Filter data by city, month, and day to view specific insights.
  • Calculate statistics such as average trip duration, most popular start and end stations, total travel time, and more.
  • Interactive user interface for a seamless experience.

Getting Started

Prerequisites

Before you begin, ensure you have met the following requirements:

  • Python 3.x installed on your local machine.

  • Flask framework installed. You can install it using pip:

    pip install Flask

For quick analysis, you can just run the python script bikeshare.py

    python bikeshare.py

Installation

  1. Clone this repository:

    git clone https://github.com/yourusername/Bikeshare-Data-Explorer.git

  2. Navigate to the project directory:

    cd Bikeshare-Data-Explorer

  3. Run the Flask web application:

    python app.py

The application will start, and you will see output indicating the server is running. You can access the application in your web browser at http://localhost:5000.

Usage

  1. Launch the Bikeshare Data Explorer web application by following the installation instructions.

  2. Select your city (Chicago, New York City, or Washington) from the dropdown menu.

  3. Choose a specific month or select "All Months" to include all months in the analysis.

  4. Select a day of the week or choose "All Days" to include all days in the analysis.

  5. Click the "Explore Data" button to view statistics based on your selections.

  6. Explore the statistics, and you can also click the "Show Raw Data" button to view the raw data.

  7. Follow me on Twitter 🐦, connect with me on LinkedIn 🔗, and check out my GitHub 🐙 for more projects and updates!

Contributing

Contributions are welcome! If you'd like to contribute to this project, please follow these steps:

  1. Fork the project.
  2. Create your feature branch (git checkout -b feature/YourFeature).
  3. Commit your changes (git commit -m 'Add some feature').
  4. Push to the branch (git push origin feature/YourFeature).
  5. Open a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

If you have any questions or suggestions, feel free to contact me: