Crime rate prediction - ML + Web

About This Project

A Crime Rate Prediction system that uses historical data and machine learning to forecast future crime rates based on factors like lighting, population, and education levels.

The Crime Rate Prediction project is a web-based Machine Learning application that analyzes historical crime data and predicts future crime rates for different areas. The system uses features such as population density, street lighting, education rates, unemployment, and other social indicators as input parameters.

The backend ML model is trained using supervised learning techniques (like Linear Regression, Random Forest, or Gradient Boosting) on cleaned and normalized historical crime datasets. The model predicts the likelihood of crimes occurring in a specific area, helping authorities or policymakers make data-driven decisions.

The application includes:

Data preprocessing: Handling missing values, normalization, and feature selection

Machine learning modeling: Training, testing, and evaluation

Web-based visualization: Interactive charts and maps to display predicted crime rates

Role-based access: Secure access for admins or analysts

This project demonstrates practical implementation of predictive analytics, supervised ML, data visualization, and web integration, making it a major project suitable for AI, ML, and Data Science students.