Which algorithms are used in this traffic accident prediction project?

The system implements and compares two regularized logistic regression algorithms: Lasso (L1 regularization) and Ridge (L2 regularization). Ridge Regression achieved superior performance with 84.21% accuracy and 0.9213 ROC-AUC.

What is the accuracy of the traffic accident prediction model?

The Ridge Regression model achieves 84.21% accuracy with an F1-Score of 0.8542 and ROC-AUC of 0.9213, demonstrating strong predictive capability for accident risk assessment.

Who can benefit from this traffic accident prediction system?

Traffic safety authorities, transportation departments, insurance companies, driver education programs, and urban planners can use this system for risk assessment, resource allocation, and safety policy development.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, and imbalanced-learn for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

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Machine Learning

Traffic Accident Prediction Using Machine Learning

The Traffic Accident Prediction Using Machine Learning system is a comprehensive machine learning solution that predicts traffic accident occurrence using driver, vehicle, road, and environmental factors. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements Lasso and Ridge Regression with SMOTE, achieving 84.21% accuracy and 0.9213 ROC-AUC.

The system utilizes the traffic_accident_prediction1.csv dataset containing 10,000 records with 15 features including driver demographics, vehicle characteristics, road conditions, and environmental factors. It provides a user-friendly web interface for data upload, exploratory data analysis, model training, and real-time accident risk prediction, enabling traffic safety authorities to make data-driven decisions for road safety improvement.

Python 3.8+ Machine Learning Lasso Regression Ridge Regression Scikit-learn SMOTE Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Model Accident Prediction
Ridge Regression (84.21% Accuracy)
Lasso Regression (83.47% Accuracy)
Feature Engineering (risk scores, interaction features)
SMOTE for Class Imbalance Handling
Interactive Visualizations
Real-time Risk Predictions
Model Performance Comparison
Feature Importance Analysis
Flask Web Application

Algorithms Used

📊 Lasso Regression (L1)

L1-regularized logistic regression with feature selection, C=0.8, solver='liblinear'

🎯 Accuracy: 83.47%

📈 Ridge Regression (L2)

L2-regularized logistic regression with stable coefficients, C=0.8, solver='lbfgs'

🎯 Accuracy: 84.21%

🔧 Feature Engineering

vehicle_speed_ratio, age_experience_ratio, total_risk_score, density_speed_interaction

📊 Top Feature: Total Risk Score

🔄 SMOTE Oversampling

Synthetic minority oversampling for class imbalance (8% accident rate)

📊 CV AUC: 0.9284

Methodology & Workflow

1 Data Collection
10,000 records with 15 traffic features

2 Data Preprocessing
Cleaning, encoding, feature engineering, scaling

3 Exploratory Data Analysis
Statistical analysis and visualization of patterns

4 Model Training
Lasso and Ridge Regression with leak-free SMOTE

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC

6 Web Deployment
Flask web app with real-time risk prediction

Model Performance Comparison

Metric	Lasso Regression	Ridge Regression	Best
Test Accuracy	83.47%	84.21%	Ridge
Test Precision	85.63%	86.18%	Ridge
Test Recall	83.47%	84.21%	Ridge
Test F1-Score	84.52%	85.42%	Ridge
Test ROC-AUC	91.48%	92.13%	Ridge
CV Mean ROC-AUC	92.18%	92.84%	Ridge

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Traffic Accident Dataset Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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