Generates decision trees based on training data

Decision Tree is a Supervised Machine Learning Algorithm, used to build classification and regression models in the form of a tree structure. Entropy is a measure of disorder or uncertainty and the goal of machine learning models and Data Scientists in general is to reduce uncertainty. Information gain is the reduction in entropy or surprise by transforming a dataset and is often used in training decision trees. Information gain is calculated by comparing the entropy of the dataset before and after a transformation and is the basic criterion to decide whether a feature should be used to split a node or not. I explained the concepts of Entropy and information gain, then I demonstrated how to use them to build a decision tree in Python. You are welcome to provide your comments and subscribe to my YouTube channel. https://www.youtube.com/watch?v=6i1lrF1T8VM

This project analyzes the entropy of decision trees to enhance decision-making processes. It includes Python code that calculates entropy based on input data, helping to identify optimal splits for classification tasks. The implementation leverages NumPy for efficient computation and provides insights into data-driven decision-making.