This repository contains a simple tutorial for beginners on implementing a decision tree classifier to predict heart disease using a well-known dataset. You can directly access the colab file directly fom the DT.ipynb file
The project covers the following key aspects:
- Data Loading and Preprocessing: How to load the heart disease dataset and preprocess the data for modeling.
- Decision Tree Classifier: Step-by-step guide to building and training a decision tree classifier.
- Model Evaluation: Techniques for evaluating the model's performance using accuracy, confusion matrix, and other relevant metrics.
- Visualization: Methods to visualize the decision tree for better understanding and interpretation.
The tutorial is designed to be beginner-friendly, with clear explanations and commented code to help you understand the concepts and processes involved in decision tree classification.
Link to the google Colab notebook:
https://colab.research.google.com/drive/1YJVHOPFNixihNNhBSLicPQmeNF6CXlSb?usp=sharing
The dataset:
Here you can find, slightly tweaked version of the publicly available heart disease dataset.
You can access the original dataset from here,
https://ieee-dataport.org/open-access/heart-disease-dataset-comprehensive