Analysis and classification using machine learning algorithms on the UCI Default of Credit Card Clients Dataset.
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Updated
Oct 27, 2023 - HTML
Analysis and classification using machine learning algorithms on the UCI Default of Credit Card Clients Dataset.
Official implementation for "Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images" https://arxiv.org/abs/2112.08810
Many algorithms for imbalanced data support binary and multiclass classification only. This approach is made for mulit-label classification (aka multi-target classification). 🌻
In this project, I explore different methods for detecting credit card fraud transactions; including using the Catboost algorithm with undersampling & oversampling methods, and using an almost new approach, by using deep learning and autoencoder.
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Predecir el abandono de futuros clientes
Detect potential frauds so that customers are not wrongly charged for items that they did not purchase.
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In this Classification Machine Learning Project, we will be analysing the dataset taken from www.kaggle.com related to details of credit card owners. This data consists of features like Gender, Income type, House type, marital status and many more. Our focus will be on analysing the data, getting the insights related to these features and there …
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