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

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

Credit Card Fraud Prediction in ASP.NET Core using ML.NET

NPM package for Prompt API's BIN Checker API

Golang wrapper for Prompt API's BIN Checker API

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.

the application of an intelligence system to classify and identify fraud situations.

Ruby package for Prompt API's BIN Checker API

Technocolabs Machine Learning Developer Internship Project 2

A machine learning project to detect fraudulent credit card transactions using real-world data.

contains project related to python

Use of different classification models to detect credit card frauds

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

Credit Card Fraud Detection using ML

Machine Learning for Credit Card Fraud Detection

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.

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.

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.

This repository focusses on credit card data analysis