road-accident-analysis-python

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road-accident-analysis-python

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Road accident analysis project using Python for visualization, insights, and casualty prediction with machine learning.

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# Road Accident Analysis and Safety Trends Using Python 🚗📊

## Overview
This project analyzes road accident data to identify patterns, major risk factors, and trends that influence accident severity and casualties. Using Python and data science libraries, the dataset was cleaned, visualized, statistically tested, and used for predictive modeling.

## Dataset 📁
The dataset contains **3000 accident records** with features such as:

- State Name 🗺️  
- Number of Casualties 🚑  
- Number of Fatalities ⚠️  
- Speed Limit (km/h) 🛣️  
- Driver Age 👤  
- Accident Severity 🚨  
- Weather Conditions 🌦️  
- Road Type 🛤️  
- Road Condition 🧱  
- Lighting Conditions 💡  
- Alcohol Involvement 🍺  
- Traffic Control Presence 🚦  
- Accident Location Details 📍

## Tools & Libraries Used 🛠️
- Python 🐍  
- Pandas  
- NumPy  
- Matplotlib 📈  
- Seaborn  
- SciPy  
- Scikit-learn 🤖

## Project Features ✨
- Data Cleaning and Preprocessing  
- Exploratory Data Analysis (EDA) 🔍  
- Correlation Analysis  
- Outlier Detection  
- Statistical Testing 📚  
- Data Visualization 📊  
- Linear Regression Model  
- Casualty Prediction 🎯

## Visualizations Included 📷
- Correlation Heatmap 🔥  
- Alcohol Involvement Pie Chart 🥧  
- Top States by Accident Count  
- Driver Age Distribution  
- Casualties Boxplot  
- Weather vs Severity Analysis 🌧️  
- Actual vs Predicted Scatter Plot  
- Residual Analysis

## Statistical Methods Used 📐
- Shapiro-Wilk Test  
- Chi-Square Test  
- T-Test  
- IQR Outlier Detection

## Machine Learning 🤖
A **Linear Regression model** was developed to predict the number of casualties using selected features such as speed limit and accident location.

## Key Insights 💡
- Higher speed limits are associated with more casualties.  
- Alcohol significantly impacts accident severity.  
- Weather and road conditions affect accident outcomes.  
- Poor lighting conditions may increase casualty risk.

## Future Improvements 🚀
- Apply advanced models such as Random Forest or XGBoost  
- Build an interactive dashboard using Streamlit or Power BI 📊  
- Use real-time traffic accident datasets 🌐

## Author 👨‍💻
[Mitali]

## License 📜
This project is for educational and academic purposes.

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