🚲 US Bikeshare Data Exploration Project

Python + pandas + CLI App | Udacity Nanodegree Project

This project explores bikeshare data from three major US cities — Chicago, New York City, and Washington — through a command-line interface (CLI) application built in Python. Users can interactively filter data by city, month, and day to uncover commuting patterns, usage behavior, and demographic trends.

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🎯 Project Objective

To develop a Python-based CLI app that enables users to explore real-world bikeshare data, applying data wrangling and filtering techniques to extract insights on how and when people use the service.

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🧰 Tools & Skills

• 🐍 Language: Python 3
• 📦 Libraries: pandas, numpy, time
• 🧹 Techniques: Data wrangling, datetime filtering, CLI interaction
• 💻 Skills Applied: Exploratory data analysis (EDA), user input validation, programmatic reporting

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📂 Dataset

This project uses bikeshare usage data provided by Udacity for:

• Chicago: chicago.csv
• New York City: new_york_city.csv
• Washington: washington.csv

Each file contains details about individual trips made in the bikeshare system, including timestamps, trip duration, station locations, and user demographics.

⚠️ Note: The datasets are not included in this repository due to size limitations and licensing. You can obtain them from Udacity's course materials or request them from official sources.

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📁 Project Structure

bikeshare-analysis/
├── bikeshare.py              # Main Python script (CLI app)
└── README.md                 # Project overview and instructions

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🔍 Key Insights

• 🕔 Peak usage occurs around 5–6 PM, indicating strong commuter activity.
• 📅 Wednesdays show high trip volumes across all cities.
• 🚉 Major hubs like Columbus Circle and Union Station are frequently used.
• 👥 Younger riders dominate in cities with available birth year data.
• 🧭 While patterns vary slightly, weekday commuting is a strong common trend.

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💡 Recommendations

• 🚲 Peak Hour Optimization: Increase bike availability between 5–7 PM, the busiest usage window.
• 🛤️ Station Planning: Add stations near the most common start–end point pairs to improve access.
• 🧑‍🤝‍🧑 Demographic Targeting: Leverage weekday vs. weekend trends to cater to commuters vs. leisure users.

These insights can help bikeshare companies and urban planners make data-informed operational and strategic decisions.

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🚀 How to Use

1. Clone this repository or download the bikeshare.py file and datasets.
2. Run the script in your terminal:

python bikeshare.py

3. Follow the prompts to explore the data interactively.

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📬 Author: Hams Saeed Alhakim 📚 Udacity Data Analyst Nanodegree 🔗 GitHub: github.com/techwithhams 🗓 Date: 2024