Exploratory Data Analysis (EDA) on Uber ride data to identify demand patterns, time-based trends, and ride distribution. The project includes data cleaning, feature analysis, and visualizations using Python to extract meaningful insights from the dataset.
Uber Rides Exploratory Data Analysis (EDA)
Project Overview
This project performs Exploratory Data Analysis (EDA) on Uber ride data to identify demand patterns, time-based trends, and ride distribution. The analysis includes data cleaning, feature exploration, and visualizations using Python.
Description
Exploratory Data Analysis (EDA) on Uber ride data to identify demand patterns, time-based trends, and ride distribution. The project includes data cleaning, feature analysis, and visualizations using Python to extract meaningful insights from the dataset.
Methodology
• Handled missing and inconsistent values
• Removed duplicates and standardized columns
• Performed univariate and time-based analysis
• Created visualizations to understand ride demand patterns
Results
• Peak ride demand observed during specific hours and days
• Clear temporal patterns in Uber ride frequency
• Data cleaning improved consistency and analytical accuracy
• Visual analysis revealed trends useful for planning and forecasting
Tools and Libraries
• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
Objective
To demonstrate practical skills in data cleaning, exploratory data analysis, and insight generation using real-world transportation data.
Author: Sampada Kharat