Best Crime Rate Prediction Using Machine Learning

Crime Rate Prediction

Introduction

Crime Rate Prediction is a specialized web application designed to forecast crime rates across 19 major Indian metropolitan cities, helping authorities and policymakers plan more effectively for urban safety. As cities expand and populations grow, predicting and addressing crime trends becomes essential for efficient resource allocation and preventive measures.Built using Random Forest Regression, a powerful ensemble machine learning model, the system delivers highly accurate forecasts backed by real historical data. With an impressive 93.20% prediction accuracy, it offers dependable insights that can assist law enforcement, government agencies, and researchers in developing proactive safety strategies.

Overview

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About the Application

The Crime Rate Predictor is designed to serve as a decision-support tool for understanding crime patterns and anticipating future crime trends. It uses comprehensive datasets compiled from the National Crime Records Bureau (NCRB), spanning the years 2014 to 2021.

The predictions cover 10 major crime categories:

1. Murder
2. Kidnapping
3. Crime Against Women
4. Crime Against Children
5. Crime Committed by Juveniles
6. Crime Against Senior Citizens
7. Crime Against Scheduled Castes (SC)
8. Crime Against Scheduled Tribes (ST)
9. Economic Offences
10. Cybercrimes

By offering category-specific and city-specific forecasts, the application empowers agencies to focus their preventive measures on the areas and crime types that are most likely to rise.

Technology Behind the Prediction

The predictive model uses Random Forest Regression from the Scikit-learn library. This approach builds multiple decision trees during training and averages their predictions to generate final outputs. This method reduces the risk of overfitting and ensures high precision in predictions.

Technologies and tools integrated in the project include:

• Python – Backend logic, model training, and prediction processing.
• HTML – Structuring the web pages for user interaction.
• CSS – Styling the interface for a clean and professional look.
• JavaScript – Enhancing interactivity and handling dynamic content.
• Excel Dataset – Primary data storage for NCRB statistics.

Data Source

The dataset is manually prepared using NCRB’s official publications, ensuring credibility and accuracy. It stores city-wise crime statistics from 2014 to 2021 in Excel files (crp.xlsx and new_dataset.xlsx). These datasets serve as input for the machine learning model and are easy to update for future projections.

Available Features

From the project’s implementation, the core features include:

• Prediction for 10 Crime Categories – Offers forecasts for specific types of crimes that significantly impact public safety.
• 19 Metropolitan Cities Coverage – City-wise predictions for targeted safety planning.
• Historical Data Integration – Uses authentic NCRB data to strengthen prediction reliability.
• User-Friendly Web Interface – Allows selection of city, crime type, and year for instant predictions.
• High Prediction Accuracy – Achieves 93.20% accuracy with Random Forest Regression.

How It Works

1. Select City – Choose from the list of 19 metropolitan cities.
2. Select Crime Type – Pick one of the 10 major crime categories.
3. Enter Year – Input the future year for which prediction is needed.
4. Generate Prediction – View projected crime rates instantly based on historical patterns.

Benefits for Stakeholders

Law Enforcement Agencies

• Identify and monitor high-risk areas.
• Deploy personnel and resources strategically.
• Enhance preventive policing strategies.

Policymakers

• Integrate crime forecasts into long-term safety plans.
• Allocate budgets effectively for law enforcement infrastructure.

Researchers and Analysts

• Access reliable forecasts for academic and social studies.
• Study the impact of social and economic policies on crime trends.

Technical Details

Running the Project

1. Open VS Code – Launch Visual Studio Code on your system.
2. Open Project Folder – Use File > Open Folder and select the extracted project directory.
3. Install Dependencies – Open the terminal in VS Code and run: pip install -r requirements.txt
4. Start the Application – Run the following command in the terminal: python app.py
5. Access in Browser – Once running, open the given local server link (e.g., http://127.0.0.1:5000/) to use the application.

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