Crime Prediction Using Machine Learning

Crime Prediction Using Machine Learning – A Web Application for Predictive Policing

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Crime Prediction Using Machine Learning

Project Details

Project Name Crime Prediction Through Machine Learning
Language/s Used Python (Django Framework)
Python Version (Recommended) 3.8+
Database SQLite (db.sqlite3)
Type Web Application
   

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Available Features

  • Admin Login/Management
  • User Registration and Login
  • Crime Dataset Upload and Viewing
  • Machine Learning Model Training
  • Real-Time Crime Prediction Interface
  • Admin Activation/Deactivation of Users
  • Prediction Result Dashboard

 Introduction

Pattern Identification
Preventing crimes requires identifying patterns in historical crime data.

Techniques
Analytical techniques powered by Machine Learning can assist law enforcement by predicting where crimes are likely to happen. This project implements crime prediction using three algorithms:

  • K-Nearest Neighbor Classifier (KNN)
  • Multi-Layer Perceptron (MLP)
  • Random Forest Classifier (Best Performing)

Law Enforcement
Machine Learning supports law enforcement agencies in identifying high-risk locations and deploying resources effectively.

Predictive Policing
A proactive approach, empowering law enforcement to enhance public safety and minimize response time.

 Data Description

The dataset is structured with the following information:

  • Locations of crime occurrences
  • Types of crimes committed
  • Timestamps and incident-specific information

This project is intended for educational and learning purposes.

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