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credit-card credit-card-fraud creditcard-validator credit-card-payments credit-card-validation credit-card-fraud-detection creditcardfrauddetection cc-checker credit-card-checker creditcard-checker cc-checker-bot cc-checker-live cc-checker-api cc-checker-sk-key
This project is focused on building a Credit Card Fraud Detection System that utilizes machine learning techniques to detect fraudulent transactions. The project will incorporate MLOps practices to ensure the entire lifecycle of the machine learning model is automated and scalable.
Credit Card Generator. A program for generating credit cards such as Visa, MasterCard and others.
This project aims to detect credit card fraud using various machine learning techniques. It explores the Credit Card Fraud Detection dataset, handles imbalanced data, trains models, and evaluates their performance. The project also investigates the impact of outlier removal on model accuracy.
A web app featuring five classification projects: Spam Mail Prediction, Titanic Survival Prediction, Wine Quality Prediction, Loan Status Prediction, and Credit Card Fraud Detection, all built with Streamlit.
WooCommerce plugin for using the BulletProof Checkout API
Credit Card Fraud Detection Using SQL
Validates popular debit and credit cards numbers against regular expressions and Luhn algorithm. Also validates the CVC and the expiration date.
In this project, I developed a machine learning model to detect fraudulent credit card transactions. The goal was to create a system that accurately identifies fraud while minimizing false positives, which is crucial in real-world financial applications.
I am currently pursuing an internship where I am honing my skills in data science and machine learning. My passion lies in uncovering insights from data and building predictive models that can drive meaningful impact.
This project aims to analyze credit card customer data, clean the dataset using libraries such as Pandas and NumPy then create a machine learning model using sklearn
This project employs machine learning and SMOTE to accurately identify fraudulent credit card transactions and address data imbalance.
Machine Learning based Credit Card Fraud Detection
the application of an intelligence system to classify and identify fraud situations.
Credit card fraud detection, Breast cancer prediction, Wine quality prediction, Bank note authentication, prediction of attrition of employees, Stock prediction, etc
Machine Learning Internship
This is an Online Transaction Fraud Detection System (FDS) to detect payment frauds. Made using Django.
Detect fraudulent credit card transactions using machine learning models and compare their performance. This project explores various techniques, including SMOTE, to handle imbalanced data and enhance detection accuracy.
This Python project uses Isolation Forest for credit card fraud detection, analyzing transaction data and visualizing distributions. It employs PyCaret for model comparison, highlighting Random Forest. The project is concise and effective for fraud detection.
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