Crime Prediction and Prevention

Overview

Crime Prediction and Prevention is a machine learning-based project designed to analyze past crime data and predict future crime trends. The system leverages data analytics, predictive modeling, and visualization techniques to enhance public safety by identifying high-risk areas and recommending preventive measures.

Features

• Predictive Analysis: Uses machine learning algorithms to predict potential crime occurrences based on historical data.
• Interactive Visualization: Displays crime-prone areas on a map for easy interpretation.
• Data-Driven Insights: Analyzes trends and patterns in crime data.
• User-Friendly Interface: Provides an intuitive front-end for inputting data and viewing results.

Technologies Used

• Frontend: HTML, CSS
• Backend: Flask
• Machine Learning: Pandas, Scikit-learn, Matplotlib
• Database: CSV/SQL-based storage for crime data

Installation and Setup

Prerequisites

Ensure you have the following installed:

• Python 3.x
• pip (Python package manager)

Steps to Run

1. Clone the repository:

   git clone https://github.com/Saurabhthakur023/Crime-prediction-and-prevention.git

2. Navigate to the project directory:

   cd Crime-prediction-and-prevention

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the Flask application:

   python app.py

5. Open your browser and go to http://127.0.0.1:5000/ to access the application.

Usage

• Enter crime-related details such as year, case type, and state.
• Click the Predict button to analyze and visualize crime trends.
• View crime hotspots on an interactive map.

Dataset

The project utilizes publicly available crime datasets, processed and formatted for training the prediction model.