A Python project that leverages machine learning to predict potential terror activities based on historical data and relevant features. The goal is to assist analysts and authorities in identifying high-risk events or locations, supporting proactive security measures.
- Cleans and preprocesses terror-related datasets
- Extracts relevant features (location, date, method, etc.)
- Trains and evaluates machine learning models for prediction
- Provides risk scores or classifications for new/unseen data
- Visualizes trends and model results