Decision Tree Implementation:

We aim to implement a decision tree classifier without using high-level libraries. For this purpose, we use a dataset whose details and raw data files are available in the DT-DATA.ZIP file. The objective of this exercise is to implement the decision tree algorithm from scratch, using the Gini index and Information Gain as criteria for splitting the tree. We will also examine the impact of limiting the tree depth on the accuracy of the algorithm on a test dataset. Finally, in the last cell, you should provide a comparison between the normal scenario and the scenario where the tree depth is limited."