Automated approach from feature engineering to modeling on the Kaggle Home Credit Default Risk competition dataset
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Updated
Mar 3, 2021 - Jupyter Notebook
Automated approach from feature engineering to modeling on the Kaggle Home Credit Default Risk competition dataset
Classification and Oversampling Algorithms Comparison, using Deep Feature Synthesis and Feature Selection with RFE
Portfolio of data science projects completed by me for academic, self learning, and hobby purposes.
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The project provides a complete end-to-end workflow for building a binary classifier in Python to recognize the risk of housing loan default. It includes methods like automated feature engineering for connecting relational databases, comparison of different classifiers on imbalanced data, and hyperparameter tuning using Bayesian optimization.
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