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PROJECT

# Implement the Decision Tree Classifier from Scratch

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

#### 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.

#### Skills

Machine Learning

Recursion

#### Prerequisites

Intermediate programming skills in Python

Basic knowledge of machine learning

#### Technologies

NumPy

Python

Pandas

#### Project Description

In this project, weâ€™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, weâ€™ll implement the

#### Project Tasks

1

Getting Started

Task 0: Get Started

Task 1: Prepare the Environment

2

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

3

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

4

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

Congratulations!