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This repo contains 4 different projects. Built various machine learning models for Kaggle competitions. Also carried out Exploratory Data Analysis, Data Cleaning, Data Visualization, Data Munging, Feature Selection etc
This is a classification problem to detect or classify the fraud with label 0 or 1. Class with label 1 means fraud is detected otherwise 0. The biggest challenge is to handle the imbalanced data set.
Credit card fraud is a significant global issue, posing challenges for financial institutions due to the low incidence of fraud amid a high volume of legitimate transactions.
It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.
This project uses logistic regression models to analyze credit risk. The recommended model, trained with resampled data, shows higher precision and recall scores for predicting high-risk loans. This model helps mitigate credit risk for lending companies.
In this Upgrad/IIIT-B Capstone project, we navigated the complex landscape of credit card fraud, employing advanced machine learning techniques to bolster banks against financial losses. With a focus on precision, we predicted fraudulent credit card transactions by analyzing customer-level data from Worldline and the Machine Learning Group.