Decision Tree

First we will look at what is Decision Tree? Decision Tree is a supervised machine learning and has a flowchart-like structure in which each internal node represents a test on a feature and each leaf node will represent a class label (the decision is taken after computing all the features). Moreover, the branches represent the conjunctions of features that lead to those class labels while the paths from the root to leaf represents classification rules. Decision Tree is built via an algorithmic approach that identifies ways to split the dataset based on certain conditions.

Now I will explain how each algorithm for predictive data mining is carried out. For predictive data mining, we used both Python and RapidMiner. However, the experiments and algorithms used for both Python and RapidMiner are the same. Thus, in this page 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 in which we used different datasets. The preprocessing my group did was explained on Preprocessing Page. To see in detail how each experiment was done, you can look at my group's Google Colab. Now let's begin!

Python

RapidMiner

No Hyperparameter Tuning

With Hyperparameter Tuning

All of the steps above for RapidMiner and Python are repeated for experiment 2 and 3.