Crime-Rate-Prediction-Using-K-means-Clustering

📌 Project Description

This project is a web-based crime analysis and visualization system built using React and K-Means clustering. It analyzes historical crime data and groups regions into clusters representing Low, Medium, and High crime risk zones. The system is designed to assist law enforcement agencies and researchers in understanding crime patterns through interactive dashboards and visual analytics.

The application simulates crime data, applies the K-Means algorithm on crime rate and frequency, and visualizes the results using charts and cluster plots.

🧠 Key Features

Interactive K-Means clustering

Dynamic selection of number of clusters (K)

Crime risk classification (Low / Medium / High)

Elbow Method for optimal K selection

Silhouette Score for cluster quality

Crime type and regional analysis

Real-time data regeneration

Clean, modern UI with multiple analysis tabs

🛠️ Tech Stack

Frontend: React.js

Visualization: Recharts

Icons: Lucide-react

Styling: Tailwind CSS

Algorithm: K-Means (implemented in JavaScript)

📂 Project Structure src/ ├── CrimePredictionSystem.jsx ├── index.js ├── App.js └── styles/

⚙️ How It Works

Crime data is generated with attributes like region, crime rate, frequency, and type

Data is preprocessed and normalized

K-Means clustering groups similar crime records

Clusters are analyzed to determine risk levels

Results are visualized using charts and scatter plots

📊 Modules

Dashboard: Overall crime statistics

Clustering: Interactive K-Means visualization

Analysis: Elbow & silhouette methods

Preprocessing: Data cleaning steps

Comparison: Algorithm comparison & limitations

🚀 How to Run npm install npm start

📈 Future Enhancements

Real crime dataset integration

Backend with Python/Flask

GIS-based crime heatmaps

Predictive crime forecasting models.

Built With

• ai
• claude
• dsa
• github
• ml
• react