🌳 Decision Tree Explorer
Welcome to Decision Tree Explorer! 🚀 This repository houses a custom implementation of the decision tree algorithm without relying on external libraries. The decision tree is fueled by data fetched dynamically through an API, adding a dynamic and real-time dimension to your decision-making process.
📚 Overview
In this project, we leverage the power of decision trees – a fundamental algorithm in machine learning – to analyze and make predictions based on data retrieved through API calls. This README will guide you through the essentials of the project and provide a glimpse into the fascinating world of decision trees.
🚀 Key Features
Custom Decision Tree: No external libraries were harmed in the making of this decision tree. Dive into the code to explore the inner workings of our tailored implementation.
Dynamic Data Retrieval: The algorithm thrives on real-time data fetched through API calls, making it adaptable to evolving datasets.
Versatile Application: From classification to regression, witness the decision tree in action across various problem domains.
🌐 Data Fetching with API
The decision tree in this project is not static; it's dynamic! We fetch data seamlessly through API calls, ensuring that the model is always up-to-date and capable of handling real-world scenarios. The API integration adds an extra layer of flexibility to your decision-making endeavors.
🌲 Decision Tree Insights
Decision trees are versatile and widely used in machine learning. Here are some fascinating tidbits about decision trees:
Tree Structure: Decision trees mimic the human decision-making process, breaking down complex problems into a tree-like structure of decisions.
Information Gain: Nodes in the tree are split based on information gain, maximizing the knowledge gained at each step.
Interpretability: Decision trees offer transparency and interpretability, making them valuable for understanding model predictions.