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Predicting Loan Defaults with QUEST: A Step-by-Step Guide

Welcome to our in-depth tutorial on using the QUEST decision tree algorithm in SPSS to predict loan defaults! In this video, we'll cover everything you need to know, from the advantages and disadvantages of the QUEST algorithm to a step-by-step demonstration of its implementation in SPSS. 📚 What You'll Learn: 1. Introduction to QUEST Algorithm: An overview of the QUEST (Quick, Unbiased, Efficient Statistical Tree) algorithm and its use cases. 2. Advantages of QUEST: Unbiased variable selection Efficient handling of large datasets Quick computational performance 3. Disadvantages of QUEST: Can be complex to interpret Limited support for categorical variables with many levels 4. Key Features of QUEST in SPSS: Automatic handling of missing values Integrated statistical tests for split selection Customizable tree growth options 5. Step-by-Step Demonstration: Data Import and Preparation: Learn how to load and preprocess your dataset in SPSS. Building the QUEST Model: Detailed instructions on setting up and running the QUEST algorithm in SPSS. Evaluating Model Performance: How to interpret the output and assess the accuracy of your model. Generate Rules: Practical application of the model to predict loan defaults. 💻 Tools Used: SPSS Statistics Software 📈 Why Use QUEST in SPSS? SPSS provides a user-friendly interface for implementing and visualizing decision trees. QUEST's statistical rigor ensures unbiased and reliable results. Ideal for financial analytics and risk assessment tasks. 📌 **Don't Forget to Like, Share, and Subscribe for More Data Science and Analytics Tutorials! If you have any questions or suggestions, please leave them in the comments below. Happy analyzing! 🎓 #DataScience #MachineLearning #QUESTAlgorithm #LoanPrediction #SPSS #DecisionTrees #FinancialAnalytics