This repository contains a project that implements a Decision Tree Classifier from scratch. The classifier is built using ID3, C4.5, and Gini Index algorithms. The project also includes a tree visualizer to enhance the view of the decision tree. The dataset used in this project is a mobile dataset, and the goal is to predict the price range of a phone based on its properties.
The goal of this project is to predict the price range of mobile phones using a Decision Tree Classifier. The classifier is built from scratch using ID3, C4.5, and Gini Index algorithms. The dataset used for this project contains various properties of mobile phones.
The Decision Tree Classifier is a simple yet powerful classification algorithm. Decision Tree Classifier poses a series of carefully crafted questions about the attributes of the test record. Each time it receives an answer, a follow-up question is asked until a conclusion about the class label of the record is reached.
In this project, we have implemented the Decision Tree Classifier using ID3, C4.5, and Gini Index algorithms. These algorithms are used to decide the attribute that best divides the dataset into distinct classes based on entropy gain (for ID3), gain ratio (for C4.5), or gini index.
A tree visualizer is also implemented in this project to enhance the view of the decision tree. It provides a clear and intuitive graphical representation of the decision tree.
Clone this repository. Run the Python script on the mobile dataset.
Please note that this project is finished. Even though suggestions are welcome!