Credit Card Generator

Data Science Projects: Titanic Survival Prediction, Credit Card Fraud Detection, and Movie Recommendation System | CodSoft Internship

In this project, we implemented an ensemble learning approach using majority voting (hard voting) with five machine learning classifiers: DT, RF, XGBC, ANN, and KNN. The ensemble model achieved an impressive accuracy score of 99.95% and an F1 score of 85.51%.

Performing a comparative analysis of machine learning and deep learning models for Credit Card Fraud Detection in Python

AntiCCScam is a Python script that combats credit card fraud by sending fake data to scam websites, disrupting their malicious operations. This tool generates and automates the flooding of scam sites with random credit card information for educational and ethical purposes.

Explored various resampling techniques to learn from an imbalanced dataset for detecting Credit card frauds.

🤖 The Fraud Credit Card Detection project aims to identify fraudulent transactions in credit card usage through the application of various machine learning methods, thereby improving the security of credit card operations and safeguarding users' financial assets.

A few models were developed based on Decision trees and Logistic Regression to categorize fraudulent transactions

Detect Credit Card Fraud with Machine Learning in R

The Stratify Course Capstone Project focuses on developing and comparing different classification algorithms to determine the best performing model based on ROC-AUC scores.

Successful work completed as Intern at CodSoft in September 2023

This project analyzes credit card transactions to detect fraud using machine learning models like AdaBoost, CatBoost, XGBoost, and LightGBM. By comparing these models, the study identifies the most effective approach for accurate fraud detection, highlighting XGBoost for its superior performance.

Credit Card Default Prediction Model

The increase in credit card fraud brought on by weaknesses in the system. We employ machine learning algorithms such as Logistic Regression, Decision Trees and Support Vector Machine. The accuracy results in detecting fraudulent transactions appears promising.

My findings demonstrate that machine learning algorithms may effectively identify fraudulent transactions with a high degree of precision, giving financial institutions a useful tool to fight fraud.

This repository contains predictive ml model

An ensemble of machine learning models for detecting fraudulent credit card transactions, utilizing advanced techniques for feature selection, data imbalance handling, and hyperparameter tuning.

Python app for detecting credit card frauds using a graph database

Data Analytics and Predictive Models for the Default of Credit Card Clients dataset by UC Irvine