📊 Exploratory Data Analysis on Uber Dataset

In this project, I focuses on performing a detailed Exploratory Data Analysis (EDA) on an Uber ride dataset to uncover key insights, trends, and patterns that can inform better business and operational decisions.

Objective

The primary objective of the project is to:

• Understand the distribution and trends in Uber ride requests
• Analyze ride demand over time
• Identify patterns in pickup locations and times
• Suggest improvements for ride availability and service efficiency

Dataset

The dataset contains Uber ride details with the following columns:

Column	Description
START_DATE	Date and time the ride began
END_DATE	Date and time the ride ended
CATEGORY	Type of ride – Business or Personal
START	Starting city or location of the trip
STOP	Destination city or location
MILES	Distance traveled (in miles)
PURPOSE	Purpose of the trip (e.g., Meeting, Errand, Customer Visit)

Insights & Observations

• Peak demand occurs during evening hours (5–8 PM) on weekdays
• Higher ride volumes were seen on Fridays and Saturdays
• Clusters of high pickup activity in central NYC zones
• Weekends show increased activity during late hours

Recommendations

• Increase driver availability during peak hours and weekends
• Focus marketing strategies around high-demand zones
• Monitor weather and events for surge predictions