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Implement the Decision Tree Classifier from Scratch

Learn to implement the decision tree classifier from scratch in Python.

Implement the Decision Tree Classifier from Scratch

You will learn to:

Load the dataset for predicting heart diseases.

Convert the continuous features to binary.

Implement the ID3 algorithm for creating decision trees.

Evaluate the classifier.


Machine Learning



Intermediate programming skills in Python

Basic knowledge of machine learning





Project Description

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.

Project Tasks


Getting Started

Task 0: Get Started

Task 1: Prepare the Environment


The Decision Tree ID 3 Algorithm

Task 2: Calculate the Entropy of a Feature

Task 3: Select the Best Feature

Task 4: Retrieve the Subtable

Task 5: Build the Decision Tree

Task 6: Generate Predictions


Preprocess the Data

Task 7: Compute the Information Gain of the Feature

Task 8: Convert the Continuous Features to Binary

Task 9: Preprocess the Data


Train, Predict, and Evaluate the Model

Task 10: Train the Model and Make Predictions

Task 11: Display the Confusion Matrix

Task 12: Compute the Evaluation Metrics