Titanic Survival Prediction, Movie Rating Prediction With Python, Iris Flower Classification, Sales Prediction Using Python, Credit Card Fraud Detection

Use of different classification models to detect credit card frauds

Credit Card Fraud Detection using ML

contains project related to python

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.

Machine Learning for Credit Card Fraud Detection

This repository demonstrates how to build a robust fraud detection system that combines supervised learning techniques with anomaly detection models. It provides end-to-end implementation, from data preprocessing and model training to deploying a real-time fraud detection API using FastAPI.

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.

This repository contains all my Machine Learning projects.

This project aims at creating a classifier. It detects whether or not the card transaction is valid. Diverse machine learning algorithms are applied in this project to distinguish between a non-fraudulent and fraudulent transactions.

Credit-Card-Fraud-Detection-System - 6th Semester College Project

This repository focusses on credit card data analysis

This repository presents a credit card fraud detection system utilizing a Logistic Regression model trained on a dataset of 284,807 transactions with significant class imbalance. After employing under-sampling for balance, the model achieves a test accuracy of around 93.40%, showcasing the effectiveness of ML in identifying fraudulent transactions.

CreditCardFraudDetection

Classifying fraudulent transactions using K-Means SMOTE and ANN

Data Science Internship at CodSoft

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.

The aim of this project is to use the logistic regression mode as a binary classifier to analyse credit card risk. The recommended model helps to predict the high-risk cases. The accuracy, precision, and recall metrics are used to evaluate this model performance.

Implementation of an intelligence system to detect the fraud cases on the basis of classification.

The objective of this project is to develop and utilize autoencoders for detecting anomalies in credit card transactions.