1. Introduction
2. Intrusion Detection System
3. Quick review about dataset
4. Load the Dataset
5.1 Feature Names List.html
5. Define features name
6. Dataframe info and describe
7.1 Set Class Value.html
7. Data preprocessing - set class values
8.1 Pie Plot Code Example.html
8. Pie Plot to know the data distribution
9.1 Data Scaling with RobustScaler Code Example.html
9. Data Preprocessing - Scaling the data with RobustScaler
10.1 Define features and target.html
10. Define x and y variable
11.1 PCA Code Example.html
11. Principal Component Analysis (PCA) - Feature Selection
12.1 Test Train Split Code Example.html
12. Splitting data for training and testing phase
13.1 Classifiers Executor Code Example.html
13. Create a method as classifier executor
14.1 The implementation of algorithms example.html
14. Classifiers Algorithms implementation
15.1 Feature Importances Code Example.html
15. Create a method to list down features importance
16.1 Plot Tree Code Example.html
16. Plot tree
17.1 Random Forest Code Example.html
17. Other Algorithms - Random Forest
18.1 Source Code Example of XGboost Regressor, Classifier, Mathplotlib Legend.html
18. XGBoost (Regressor, Classifier, Plot Legend Actual vs Predicted value)
19.1 Use Reduced Data.html
19. The use of reduced data
20.1 Metrics Performances value display in bar chart.html
20. All Algorithms score comparison in bar chart
21.1 Save, load data and model then make a new prediction.html
21. Load saved input and model then making new prediction
22.1 Source Code Example of Cross Validation (CV).html
22. Cross Validation
23.1 Grid Search CV Code Example.html
23. Gridsearch CV
