In this project, we’ll implement the decision tree classifier from scratch in Python. The decision tree classifier is a machine learning model that creates an N-ary tree where each node (or decision stump) represents a feature in the training data. Once the tree is constructed, it can be traversed by providing the classes for each feature in a row of the test dataset.

Moreover, we’ll implement the ID3Iterative Dichotomiser 3 variant of the decision tree classifier, train it, and then use it to perform classification over the test set. Finally, we’ll use the scikit-learn package to generate evaluation metrics and the seaborn package to visualize the results.