Machine Learning Project for DA5030 Course
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Presenter: Chen Zeng
Meetup page: https://www.meetup.com/LearnDataScience/events/fbczqqyznbpc/
Slides: https://docs.google.com/presentation/d/189ge4CeULqPjInKwucMjst9u29AkgI9MwWKnEh1IkOs/edit?usp=sharing
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This project will help you predict the credit card frauds using four algorithms (Logistic Regression, Decision Tree, Random Forest and XGBoost. Based on the accuracy and AUC(Area Under Curve) value, the results are inferred. Watch full video to see which algorithm works best for predicting the frauds.
The github link for the project: https://github.com/Hima29nshi/Predicting_Credit_Card_Using_R.git
Dataset link: https://www.kaggle.com/mlg-ulb/creditcardfraud
The aim of this R project is to build a classifier that can detect credit card fraudulent transactions. We will use a variety of machine learning algorithms that will be able to discern fraudulent from non-fraudulent one. By the end of this machine learning project, you will learn how to implement machine learning algorithms to perform classification.
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In this credit card fraud detection, project in machine learning video you will learn what are the various frauds online, how to approach when you detect fraud using data science, what are the challenges you might face and a demo on creditcard fraud detection using machine learning in detail.
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