Fire and Gun detection using yolov3 in videos as well as images. Training code, dataset and trained weight file available.

classify crime into different categories using PySpark

Developing a system that could classify crime descriptions into different categories which would help the authorities to assign officers to crimes based on the report.

It is a machine learning project where we need to predict crimes happening in sanfrancisco city.Zip includes project report along with various tested models.

Machine Learning pipeline for large-scale crime classification using Scikit-learn, TensorFlow, PCA, and Optuna.

The Crime Classification Project implements machine learning models to classify crimes into six categories: Murder, Rape, Assault, Body Found, Kidnap, and Robbery. It features an interactive Streamlit dashboard for data visualization and a Flask web application for real-time predictions. Built with XGBoost and AdaBoost classifiers.