Network Activity Anomaly Detection Chandani Gupta

The goal of this project is to detect Neptune attacks in network traffic. The given dataset contains detailed records of network activities. Each record is labelled as a normal or neptune attack. For preprocessing the data, I have used OneHotEncoder & StandardScaler. I used the train_test_spilt model for splitting data into training and validation sets, then I have used the Logistic Regression model to train the model using preprocessed data & evaluate the performance of the model on the validation set using f1_score metrics then preprocessed the test data features & used the trained model to predict Neptune attacks in test data then created submission CSV file with predicted attack labels for the test data.