Note: Please fork the current Udacity repository so that you will have a remote repository in your Github account. Clone the remote repository to your local machine. Later, as a part of the project "Post your Work on Github", you will push your proposed changes to the remote repository in your Github account.

Bikeshare Data Analysis

Date Created

Created on: 2-2-2025

Project Title

Bikeshare Data Analysis

Description

This project provides an interactive command-line tool for exploring bikeshare data from three major U.S. cities: Chicago, New York City, and Washington, D.C. The program allows users to filter the data by city, month, and day of the week to analyze key statistics related to bikeshare usage.

Features

• Analyze trip data based on user-specified filters (city, month, day).
• Compute key statistics, including:
  • Most frequent travel times (month, day, and hour).
  • Most popular stations and trip combinations.
  • Total and average trip durations.
  • User statistics (user types, gender distribution, and birth year insights).
• Display raw data in chunks of 5 rows for deeper exploration.

Files Used

The following files are required for the project:

• chicago.csv – Bikeshare data for Chicago.
• new_york_city.csv – Bikeshare data for New York City.
• washington.csv – Bikeshare data for Washington, D.C.

These CSV files must be in the same directory as the Python script.

Requirements

To run this project, ensure you have the following dependencies installed:

• Python 3.x
• pandas library
• numpy library

You can install the required libraries using the following command:

pip install pandas numpy

Usage

1. Run the script using Python:

   python bikeshare.py

2. Follow the on-screen prompts to select a city, month, and day of the week.
3. View the computed statistics based on your selections.
4. Choose whether to display raw data for further analysis.

Credits

Proper credit is given to:

• Udacity for providing the project structure and dataset.