Predicting Network Intrusions: Identifying Normal and Neptune Attack Activities Using Machine Learning

This project involves using a machine learning model to classify network activities as either normal or indicative of a Neptune attack, a type of denial-of-service (DoS) attack. The training dataset contains 86,845 records labeled as either normal or Neptune attack. The goal is to train a model using this dataset and then predict the status of 21,712 entries in the test set. The target variable is binary, where "normal" indicates no attack (attack = 0) and "neptune" indicates an attack (attack = 1).