Track: Machine Learning

Abstract

Due to the pandemic in this time economic scenario increased, credit cards or debit cards use has become extremely commonplace due to online payments. Credit cards enable individuals to make large payments without having to carry huge amounts of money. The number of users is increasing, so credit card fraud also increased as well. The credentials on a credit card can be obtained fraudulently and used to defraud. So we're going to use Machine Learning Algorithms to collect data and overcome this issue .This project compares supervised algorithms like Logistic regression, Support vector machine, KNN, Decision Tree, Xgboost, etc., and finds the best model through hyper parameter tuning, Grid search and applies resampling techniques. Due to the pandemic in this time economic scenario increased, credit cards or debit cards use has become extremely commonplace due to online payments. Credit cards enable individuals to make large payments without having to carry huge amounts of money. The number of users is increasing, so credit card fraud also increased as well. The credentials on a credit card can be obtained fraudulently and used to defraud. So we're going to use Machine Learning Algorithms to collect data and overcome this issue .This project compares supervised algorithms like Logistic regression, Support vector machine, KNN, Decision Tree, Xgboost, etc., and finds the best model through hyper parameter tuning, Grid search and applies resampling techniques.