<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine Learning on Zitao Liao</title><link>http://lzteddy.com/tags/machine-learning/</link><description>Recent content in Machine Learning on Zitao Liao</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Wed, 22 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="http://lzteddy.com/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Loan Default Prediction with Machine Learning</title><link>http://lzteddy.com/p/loan-default-prediction-ml/</link><pubDate>Sun, 01 Jun 2025 00:00:00 +0000</pubDate><guid>http://lzteddy.com/p/loan-default-prediction-ml/</guid><description>&lt;h2 id="background"&gt;Background
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&lt;li&gt;Time: Jun 2025&lt;/li&gt;
&lt;li&gt;Context: Machine learning project for loan risk modeling.&lt;/li&gt;
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&lt;h2 id="highlights"&gt;Highlights
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&lt;li&gt;Built an end-to-end binary classification pipeline for loan default prediction, including preprocessing, feature selection, training, and evaluation.&lt;/li&gt;
&lt;li&gt;Benchmarked Random Forest, Bagging ensemble, and XGBoost under a unified experiment setting.&lt;/li&gt;
&lt;li&gt;Tuned XGBoost hyperparameters and selected it as the final model based on accuracy and training efficiency.&lt;/li&gt;
&lt;li&gt;Generated final predictions for the test set and documented model limitations with practical improvement directions.&lt;/li&gt;
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&lt;h2 id="tech-stack"&gt;Tech Stack
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&lt;li&gt;Python&lt;/li&gt;
&lt;li&gt;Scikit-learn&lt;/li&gt;
&lt;li&gt;XGBoost&lt;/li&gt;
&lt;li&gt;Feature Engineering&lt;/li&gt;
&lt;li&gt;Binary Classification&lt;/li&gt;
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