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oversampling-technique

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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.

  • Updated Dec 5, 2021
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Data from a website that provides job reviews. The website wants to analyze texts and the corresponding rating that is provided by the user about startups. Based on the texts, try to verify if it corresponds to the score provided by the reviewer. the task helps the website to rank user's reviews or ratings

  • Updated Feb 22, 2022
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Competition conducted by American Express on HackerEarth Platform to Predict Credit Card Defaulters by building Machine Learning Models for the given data.

  • Updated Jan 14, 2022
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Assess credit risk of applicants using supervised machine learning. Several different machine learning techniques such as SMOTE, SMOTEENN, RANDOM FOREST, EASY ENSEMBLE were applied, the models were assessed using accuracy score, precision and accuracy to choose the best technique that applies to this type of problem.

  • Updated Sep 22, 2021
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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 …

  • Updated Oct 10, 2021
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