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<!DOCTYPE HTML>
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<title>GiveMeSomeCredit</title>
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<h2 class="bld">GiveMeSomeCredit - Kaggle</h2>
<p>Improve on the state of the art in credit scoring by predicting the probability that
somebody will experience financial distress in the next two years.</p>
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<a href="#" class="image fit"><img src="images/givemesomecredit.webp" alt="GiveMeSomeCredit" /></a>
<div class="pmid">
<h3>What is GiveMeSomeCredit?</h3>
<p>GiveMeSomeCredit is a competition on Kaggle where participants aim to improve
the state of the art in credit scoring. The task involves predicting the probability
that an individual will experience financial distress in the next two years. In this
Kaggle competition, participants use their data science and machine learning skills
to develop models that can enhance the accuracy of credit scoring systems. By predicting
the likelihood of financial distress within a two-year timeframe, the goal is to contribute
advancements to the field, ultimately aiding in more informed decision-making processes
for lending institutions.</p>
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<li><a href="https://www.kaggle.com/competitions/GiveMeSomeCredit/overview" class="button">Learn More</a></li>
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<h3>What am I doing?</h3>
<p>I conducted data analysis to understand the data's structure. Following that, I processed
the data by removing outliers, eliminating duplicates, and verifying if any issues persisted
after processing. Subsequently, I performed modeling using RandomForest, XGBoost, and
LGBM. I consolidated all these results with an Ensemble Classifier, utilizing the soft
method to observe probability outcomes. Once I ensured everything was correct, I submitted
the results to Kaggle and obtained a Private score of 0.86634 and a public score of 0.86009.</p>
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<li><a href="https://www.kaggle.com/code/kizuke/givemesomecredit-esemble-voting" class="button">Learn More</a></li>
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