Crime Rate Predictor using Machine Learning

Crime Rate Predictor using Machine Learning

Introduction

In today’s world, ensuring public safety is a top priority. The Crime Rate Predictor is an advanced application that leverages machine learning to predict crime rates across 19 Indian metropolitan cities. By analyzing historical crime data, this system enables law enforcement agencies to better understand crime patterns, allocate resources efficiently, and ultimately reduce crime rates.

With this predictive system, we aim to create safer communities by providing data-driven insights into crime trends, helping authorities make informed decisions and take preventive measures.

🛠️ About the Application

Crime rate prediction is a powerful tool that allows law enforcement agencies to:

✅ Identify crime trends and anticipate where crimes are likely to occur.
✅ Optimize resource allocation by focusing on high-risk areas.
✅ Develop proactive strategies to prevent crimes before they happen.
✅ Improve public safety by reducing crime rates through data-driven decision-making.

This project is based on data collected from the Indian National Crime Rate Bureau (NCRB), covering statistics from 2014 to 2021. The model predicts crime rates for 10 different crime categories, including:

• Murder
• Kidnapping
• Crime Against Women
• Crime Against Children
• Juvenile Crimes
• Crime Against Senior Citizens
• Crime Against Scheduled Castes (SC)
• Crime Against Scheduled Tribes (ST)
• Economic Offenses
• Cybercrimes

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🧠 How It Works – Machine Learning Model

The Crime Rate Predictor is built using Scikit-learn’s Random Forest Regression Model, which provides accurate predictions by analyzing past crime trends.

📌 Model Overview

✔ Algorithm Used: Random Forest Regression
✔ Inputs: Year, City Name, Crime Type
✔ Prediction Accuracy: 93.20% on the testing dataset
✔ Data Source: NCRB crime reports (2014-2021)

🔍 Why Random Forest Regression?

Random Forest Regression is an ensemble learning technique that creates multiple decision trees and combines their predictions to produce more accurate results. By averaging predictions from multiple decision trees, it reduces errors and improves accuracy compared to a single decision tree model.

🚀 Features

✔️ Crime Rate Prediction for 10 crime categories.
✔️ Prediction for 19 Indian metropolitan cities.
✔️ Historical crime data analysis (2014-2021).
✔️ High accuracy (93.20%) using Random Forest Regression.
✔️ User-friendly interface to select city, crime type, and year for predictions.
✔️ Data-driven insights to support law enforcement planning.

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📌 How to Use the Application

1️⃣ Launch the application by running app.py.
2️⃣ Select the city, crime type, and year for which you want to predict the crime rate.
3️⃣ Click on the “Predict” button to generate the crime rate prediction.
4️⃣ The predicted crime rate will be displayed on the screen.

🌍 Real-World Impact

The Crime Rate Predictor is designed to help:

✔ Police Departments – Identify crime-prone areas and deploy resources effectively.
✔ Government Authorities – Develop policies to reduce crime and improve public safety.
✔ Researchers & Analysts – Analyze crime trends and study societal impacts.
✔ Public Awareness – Help citizens understand crime risks in their cities.

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🔮 Future Enhancements

🚀 Integration with Live Crime Data for real-time predictions.
📊 Data Visualization using interactive heatmaps and trend analysis.
🔍 Crime Hotspot Detection using geospatial mapping.