Bikeshare Data Analysis – Udacity Nanodegree Project

This project is part of the Udacity Programming for Data Science with Python Nanodegree.
The goal is to explore US bikeshare data from three major cities — Chicago, New York City, and Washington — and analyze usage patterns based on user input.

Project Objective

The Python script (bikeshare.py) runs in an interactive terminal. It allows users to:

• Select a city to analyze (Chicago, New York City, or Washington)
• Optionally filter the data by month (January to June) or day of the week
• View various statistics about:
  • Most frequent travel times
  • Most popular stations
  • Trip durations
  • User demographics (e.g., gender, birth year)

Technologies Used

• Python 3
• Pandas for data manipulation
• NumPy for numerical operations
• Jupyter Notebook / Terminal for execution

Skills Demonstrated

• Data wrangling and cleaning
• Conditional logic and loops
• Handling user input and validation
• Basic statistical computations
• Reading CSV files and filtering DataFrames

Data Sources

The data used in this project includes CSV files provided by Udacity:

• chicago.csv
• new_york_city.csv
• washington.csv

🚀 How to Run

Clone the repository, then execute the Python script in your terminal:

python bikeshare.py