🚗 Uber Ride Data Analysis

Exploratory data analysis on Uber ride patterns, demand forecasting, and pricing trends using Python.

📌 Overview

This project analyzes a real-world Uber rides dataset to uncover insights about:

• Peak demand times and days
• Most frequent pickup/dropoff locations
• Trip duration and distance distributions
• Ride purpose breakdown (Business vs Personal)

🗂️ Repository Structure

Uber_ride_data_analysis/
├── data_analysis.ipynb     # Interactive notebook with full EDA
├── data_analysis.py        # Python script version
├── UberDataset.csv         # Dataset (Uber rides log)
├── requirements.txt        # Python dependencies
└── README.md               # This file

📊 Key Insights

• Peak hours: 7–9 AM and 5–7 PM on weekdays
• Top purpose: Business travel (~85% of rides)
• Longest routes: Airport pickups average 2× longer than city rides
• Seasonal trends: Higher demand in Q4 (Oct–Dec)

⚙️ Setup

pip install -r requirements.txt
jupyter notebook data_analysis.ipynb

👩‍💻 Author

Devyani Deore — github.com/DevyaniD19

📄 License

MIT License — see LICENSE for details.

🔍 Analysis Sections

Section	Description
Data Loading	Read CSV, parse dates, inspect shape
Data Cleaning	Handle nulls, parse categorical fields
Demand Analysis	Hourly, daily, and monthly ride counts
Trip Distance	Distribution and outlier detection
Purpose Analysis	Business vs. Personal ride breakdown
Location Heatmap	Top start/stop categories

🔮 Future Work

• Interactive map visualization of pickup/dropoff hotspots
• Price surge prediction using weather and event data
• Time-series demand forecasting with ARIMA or Prophet
• Comparison with Lyft dataset for competitive analysis

💡 Sample Visualizations

The notebook generates the following charts:

• Hourly demand heatmap (hour × day of week)
• Trip duration histogram with KDE
• Ride purpose pie chart
• Monthly trend line chart