ML project

This project focuses on building a machine learning model for network intrusion detection, classifying network traffic data as either "attack" or "normal." The process involves loading and exploring datasets, preprocessing numerical and categorical features, and creating a pipeline for consistent data transformation. A RandomForestClassifier is used for the classification task. The model is trained, validated, and evaluated using accuracy and classification reports. Finally, the model is retrained on the full dataset to make predictions on test data, and a submission file with the predictions is prepared. This project demonstrates the end-to-end workflow of a machine learning solution for network security.