Uber Rides Data Analysis using Python

Overview

This project performs exploratory data analysis (EDA) on Uber ride data to uncover usage patterns, seasonal trends, and behavioral insights. The notebook focuses on data cleaning, feature extraction, visualization, and interpretation of ride demand across time.

Objectives

• Understand ride frequency across months, days, and hours
• Identify seasonal and temporal demand patterns
• Clean and prepare raw ride data for analysis
• Generate visual insights to support business decisions

Tools & Libraries

• Python
• Pandas – data manipulation and cleaning
• NumPy – numerical operations
• Matplotlib & Seaborn – data visualization
• Jupyter Notebook – interactive analysis

Dataset Description

The dataset contains Uber ride records with date-time information and categorical attributes such as ride category and purpose.

Key columns include:

• START_DATE – ride start timestamp
• END_DATE – ride end timestamp
• CATEGORY – business or personal
• PURPOSE – reason for the trip
• Engineered features: month, day, hour

Project Structure

Uber-Rides-Data-Analysis/
├── Data_set
├── Uber_Rides_Data_Analysis_using_Python.ipynb
├── README.md
├── analysis.md
├── requirements.txt
└── data/

How to Run

1. Clone the repository
2. Install dependencies
3. Open the notebook in Jupyter
4. Run all cells sequentially

pip install pandas numpy matplotlib seaborn scikit-learn

Key Visualizations

• Monthly ride distribution
• Hourly demand patterns
• Category and purpose frequency plots
• Correlation heatmap (numeric features)

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