Random Forest + Cross Validation

This code preprocesses network activity data by encoding categorical features and scaling numerical features, then trains a Random Forest classifier to predict 'neptune' attacks. Cross-validation scores are calculated to assess model performance. The trained model is used to make predictions on test data, which are saved to a CSV file. Learning Points: Data Preprocessing: Label encoding categorical variables and scaling numerical features are crucial steps for preparing data for machine learning models. Model Training and Evaluation: Random Forest classifier is trained and evaluated using cross-validation and classification metrics. Prediction and Output: The model's predictions on test data are saved for further analysis or submission. Reproducibility: Ensuring consistent preprocessing and model training steps helps in achieving reproducible results.