Uber Data Analysis with Python
📊 Uber Drive Data Analysis with Python This project performs exploratory data analysis (EDA) on Uber trip data using Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn. The dataset contains detailed trip information including start and end dates, trip categories, miles traveled, and trip purposes. The goal is to uncover patterns in user behavior and trip characteristics through data visualization and preprocessing.
🔍 Key Features Cleans and preprocesses Uber drive data
Converts timestamps and extracts datetime components (hour, day, month, weekday)
Analyzes:
Frequency of trip categories
Miles traveled histogram
Peak hours for Uber usage
Trip purposes distribution
Busiest days and months
Most popular starting locations
Generates multiple visualizations for insights
🛠️ Technologies Used Python
Pandas
NumPy
Matplotlib
Seaborn
📁 Dataset Uber Drives - .csv (included or expected in root directory)