Traffic Accident Prediction

[alo7lika]

#69
The Traffic Accident Prediction project aims to develop a machine learning model that predicts the likelihood of traffic accidents based on historical data, such as accident records, weather conditions, traffic volume, and road characteristics. By analyzing these factors, the model can provide insights into high-risk areas and conditions, helping local governments and traffic authorities implement targeted safety measures and improve traffic management strategies.

Traffic accidents are a significant public safety concern, leading to injuries, fatalities, and economic losses. Traditional methods of analyzing traffic incidents often lack predictive capabilities, making it challenging for authorities to proactively address potential risks. This project seeks to solve this problem by providing a data-driven approach to identify and predict high-risk scenarios, allowing for better allocation of resources and implementation of preventive measures.

The proposed solution involves:

• Data Collection: Gathering historical accident data, weather conditions, traffic patterns, and road information from reliable sources.
• Data Preprocessing: Cleaning and preparing the data for analysis, handling missing values, and encoding categorical variables.
• Model Development: Using Scikit-learn to develop a predictive model (e.g., Random Forest, Logistic Regression) to estimate the likelihood of accidents based on the input features.
• Explainability: Integrating the ExplainableAI package to provide insights into the model's predictions, highlighting the key factors contributing to accident likelihood.
• Deployment: Creating a user-friendly interface for local governments to input current conditions and receive risk assessments.

[github-actions]

🎉 Thank you for your contribution! Your pull request has been submitted successfully. A maintainer will review it as soon as possible. We appreciate your support in making this project better

[alo7lika]

@ombhojane the task has been completed. Kindly review it. Thank you!

[alo7lika]

@ombhojane the task has been completed. Kindly review it. Thank you!

@ombhojane

[ombhojane]

Perfect!
Thanks for contributing

[ombhojane]

hey @alo7lika
It's awesome, you may enhance more it via performing with explainableai package.

[alo7lika]

hey @alo7lika It's awesome, you may enhance more it via performing with explainableai package.

The task wasn't performing with explainableai so in alternative I had used SHAP.But will try doing more projects using explainableai