This project is a machine learning-based crime prediction model that predicts various types of crimes and their likelihood of occurrence in different areas. The model is built using a database with more than 50,000 rows of data, and it utilizes a Random Forest Classifier for its predictions. The backend integration is handled using Flask, and the frontend is developed with React.
- Crime Prediction: Predicts the likelihood of various crimes such as sexual crimes, violent crimes, robbery/theft, fraud/scam, and less offensive crimes.
- Model Accuracy: Displays the accuracy of the prediction model.
- Safety Index: Provides a safety level for the selected area based on the predictions.
- User-Friendly Interface: Allows users to select their gender, age level, and area name to get predictions.
- Large Database: Utilizes a comprehensive database with over 50,000 rows of data to ensure accurate predictions.
- Backend Integration: Uses Flask for handling backend operations and integrating the machine learning model.
- Random Forest Classifier: Employs a Random Forest Classifier for making crime predictions.
- Frontend: React
- Backend: Flask
- Machine Learning: Random Forest Classifier
- Database: Extensive dataset with over 50,000 rows
- User Input: Users input their gender, age level, and area name.
- Prediction Request: The input data is sent to the backend where the machine learning model is hosted.
- ML Model: The Random Forest Classifier processes the input data and makes predictions.
- Result Display: The predictions are sent back to the frontend and displayed to the user.
Contributions are welcome! Please fork the repository and submit a pull request for any improvements or new features.
This project is licensed under the MIT License. See the LICENSE file for details.
For any inquiries or issues, please contact [[email protected]].