#Uber Data Analysis Project (Exploratory Data Analysis using SQL, Excel & Python/pandas)

Project Description This project focuses on performing Exploratory Data Analysis (EDA) on a dataset of Uber ride requests to identify trends, patterns, and operational bottlenecks. The analysis was carried out using SQL for querying, Excel for dashboard visualizations, and Python with pandas for data manipulation and feature engineering. Tools & Technologies Used Python (pandas) – Data cleaning and feature extraction

SQL – Querying and aggregation tasks

Microsoft Excel – Dashboard visualizations

GitHub – Version control and sharing

🔍 Key Insights Identified peak demand hours and categorized them into time slots (Early Morning, Morning, Afternoon, Evening, Late Night).

Analyzed the number of ride requests based on pickup points.

Found that certain times of the day had a high number of ride cancellations or unavailability.

Performed feature engineering using pandas to create custom columns like Time_Slot and Status_Count.

📁 Project Structure

Uber-EDA-Project/ │ ├── uber_data8.csv # Cleaned dataset ├── eda_analysis.ipynb # Python notebook with pandas EDA ├── sql_queries.sql # SQL queries used for analysis ├── dashboard_screenshots/ # Excel dashboard images