Naive Bayes is one classification techniques based on Bayes' Theorem with an assumption of independence among predictors. In other terms, Naive Bayes classifier assumes that the presence of a particular feature in a class is not related to the presence of any other features. Naive Bayes is an easy model to build and very useful for large datasets.
I will only explain how we did the algorithm for only one experiment since the only difference between all these experiments are just the preprocessing. The preprocessing my group did was explained on Preprocessing Page. To see in detail how each experiment was done for Python, you can look at my group's Google Colab. Now let's see how to build the Naive Bayes model!
If you wish to see how the Decision Tree models were built, you may look on this page!
This video will explain the steps of the Decision Tree model with Python.
This video will explain the steps of the Decision Tree model with Python.
This video will explain the steps of the Decision Tree model with Python. This video will explain what operator was used for hyperparameter tuning and how we build this model.
All of the steps above for RapidMiner and Python are repeated for experiment 2 and 3.