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.

Ruby package for Prompt API's BIN Checker API

Technocolabs Machine Learning Developer Internship Project 2

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

Credit Card Fraud Detection using ML

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

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.

Data Science Internship at CodSoft

Use of different classification models to detect credit card frauds

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.

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

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.