🤖💡 AI Algorithms Explained: Understanding Coefficients of Determination 📊

🔍 Understanding Coefficients of Determination | Bank Loan Prediction Project In this video, we delve into the world of Coefficients of Determination using a real-world example from a Bank Loan Prediction Project. This project utilizes machine learning models like Bagged Classifier, Random Tree Classifier, and Gradient Boosted Classifier to predict loan approval statuses. 🤔 What are Coefficients of Determination? We'll break down this important metric used to understand how well our models fit the data, crucial for making accurate predictions in various scenarios. 🚀 Key Points: - Overview of the Bank Loan Prediction Project - Explanation of Machine Learning Models Used - Coefficients of Determination in Action - Insights into Model Deployment via Flask REST API - Quick API Demo: Making Loan Status Predictions 💡 GitHub Repository: Find the complete code and details in the project's GitHub repository: [Link to GitHub](https://github.com/your/repository) ⚙️ Project Highlights: - Utilization of Bagged, Random Tree, and Gradient Boosted Classifiers - Models Created But Not Used: Logistical Regressor, AdaBoost Classifier - Deployment via Flask REST API with endpoints for health check and loan status prediction 📝 Notes: - This video aims to simplify Coefficients of Determination within a practical ML context. - The Flask API used in the project requires no authentication and lacks a front end, focusing solely on prediction. 🔗 Helpful Links: - GitHub Repository: [Link to GitHub](https://github.com/your/repository) - Flask API Deployment Instructions - Sample JSON Data for Loan Status Prediction Stay tuned as we explore Coefficients of Determination and their significance in model evaluation and prediction accuracy! Don't forget to like, share, and subscribe for more machine learning insights. #MachineLearning #DataScience #AI #CoefficientsofDetermination #BankLoanPrediction