Uber Data Analysis Using Python

📌 Project Overview

This project analyzes Uber ride data using Python, focusing on data preprocessing and visualization. By cleaning and transforming the dataset, we extract meaningful insights about ride patterns, peak hours, pricing trends, and geographic distribution of rides.

📂 Dataset

The dataset used in this project contains information such as:

• Pickup and drop-off locations
• Date and time of rides
• Trip distance
• Fare amount
• Passenger count

🛠 Technologies Used

• Python (for data analysis and visualization)
• Pandas (for data cleaning and preprocessing)
• NumPy (for numerical operations)
• Matplotlib & Seaborn (for data visualization)
• Jupyter Notebook (for interactive analysis)

🚀 Features

• Data preprocessing (handling missing values, filtering, and transforming data)
• Exploratory Data Analysis (EDA) with statistical insights
• Visualization of ride patterns using graphs and maps Identification of peak hours and busiest locations
• Analysis of fare trends and distance-based pricing

📊 Data Analysis Insights

• The busiest hours for Uber rides
• Areas with high Uber demand
• Relationship between fare and distance
• Seasonal trends in ride frequency

📜 How to Run the Project

# Clone the repository
git clone https://github.com/Rushi029/Uber-Data-Analysis-using-python.git

# Install required dependencies
pip install -r requirements.txt

# Open the Jupyter Notebook
jupyter notebook