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🚖 Uber Ride Data Analysis (Google Colab)

📌 Project Overview

This project analyzes Uber ride data using Python in Google Colab to discover patterns in ride behavior, peak times, and trip purposes.

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🎯 Objectives

• Analyze trip duration
• Identify peak booking hours
• Understand trip purposes
• Find busiest days and months
• Analyze ride distribution
• Identify common pickup locations

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📂 Dataset

• File: Uber Rides.csv

• Features include:

  • Start Date & Time
  • End Date & Time
  • Category
  • Start & Stop Locations
  • Miles
  • Purpose

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🛠️ Tools & Technologies

• Python 🐍
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Google Colab

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📊 Analysis Performed

• Data Cleaning & Handling Missing Values
• Feature Engineering (Hour, Day, Month extraction)
• Time-based Analysis
• Purpose-wise Trip Analysis
• Location Analysis
• Visualization of trends

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📈 Key Insights

• Peak ride demand occurs during working hours
• Weekdays show higher business-related trips
• Certain locations are frequently used as pickup points
• Monthly trends reveal variations in ride demand

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▶️ Run the Project (Google Colab)

1. Open the notebook in Colab
2. Upload the dataset (Uber Rides.csv) when prompted
3. Run all cells step by step

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📁 Project Structure

├── Uber Ride Data Analysis Project.ipynb
├── Uber Rides.csv
└── README.md

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💡 Future Improvements

• Build a Power BI dashboard
• Add predictive analysis (ML models)
• Automate data pipeline

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🙌 Author

Bhuvaneswar G Aspiring Data Analyst & Web Developer

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