Road Accident Analysis – Data Analytics Project

Project Overview

This project analyzes road accident data using Power BI, SQL, Excel and Python . It highlights accident trends by state, time, and road type. Dashboards and visuals help identify high-risk areas and suggest data-driven safety improvements.

Tools & Technologies Used

Tool	Purpose
Excel	Cleaned raw data, removed errors, and organized information
SQL	Extracted useful data from large tables using queries
Power BI	Created interactive dashboards and visuals to understand trends
Python	Did deeper analysis using pandas and matplotlib (charts, data cleaning)
Tableau	Built simple visuals to compare data across states and years

What I Did in Each Tool

Excel

• Removed blank rows and errors
• Used formulas like IF, VLOOKUP, and Pivot Tables
• Converted date and time into readable formats

SQL

• Filtered accidents by state, time, and severity
• Grouped data using GROUP BY, WHERE, and ORDER BY

Python

• Cleaned data using Pandas
• Plotted charts using Matplotlib
• Handled missing values

Power BI

• Created dashboards to:
  • Show accident hotspots
  • Compare state-wise data
  • Visualize accident trends by year/month/hour

Tableau

• Compared different states and vehicle types
• Made user-friendly charts for reports

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Key Insights from the Analysis

1. Most accidents happen during evening hours (5 PM to 9 PM)
2. Urban areas reported more accidents than rural areas
3. Two-wheelers were involved in the majority of accidents
4. Certain states had significantly higher accident rates
5. Accidents increased on highways and during weekends
6. Poor lighting and road conditions also contributed to accidents