The credit card fraud detection model employs a Random Forest Classifier, a robust ensemble learning technique. It analyzes various transaction features to accurately identify fraudulent activities, leveraging the collective decision-making of multiple decision trees to enhance detection accuracy and resilience against data imbalances.

Credit card transactions fraud detection using classic algorithms

Fraud Detection model based on anonymized credit card transactions based on Isolation Forest Algorithm and Local Outlier Factor

This project aims to detect credit card fraud using Anamoly detection techniques such as Isolation Forest and Local Outlier Factor algorithms.

🛡️ Welcome to our Credit Card Fraud Detection project! 💳 Harnessing the formidable prowess machine learning, we're steadfast in our mission to fortify your financial stronghold against deceitful adversaries. Join our crusade for financial resilience,Ensuring every transaction is securely monitored! 🔐💯

Used Gaussian Naive Bayes Classifier and XG Boost Classifier for Credit Card Fault Detection

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.

A repository containing all my tasks/projects during my internship at CognoRise Infotech

This repository consists of assignments and Models trained on various ML, NLP, & Deep Learning Algorithms.

Utilizando algoritmos de classificação para criar um modelo preditivo que seja capaz de detectar fraudes de cartão de crédito.

Identify fraudulent credit card transactions.

The project focused on building a joint computational toolbox for credit card risk analysis. It consists of removing irrelevant attributes from dataset to get meaningful model while building decision trees from the possible subset or combination of attributes and further applying these pools of trees as an initial seed to Multi - Objective Evolu…

Final Project for CS-577 Principles and Techniques of Data Science at San Diego State University

Credit Card Fraud Detection using Machine Learning Algorithms

Credit Card Fraud Detection using KNN and K-means

Unsupervised machine learning model to predict fraudulent credit card transactions on a highly imbalanced dataset.

Includes Jupyter notebooks on some of my ML projects utilizing Sklearn, tensorflow, keras.These notebooks includes problems such as credit card fraud detection, digit recognization with clustering method, Stock market data analysis with KMeans clustering.Also, includes work on Natural Language Processing, Computer vision problems.

Credit Card Fraud Detection model is developed to classify online transaction as either fradulent or genuine.

Credit Card Fraud detection with neural networks(anomaly detection) and machine learning techniques (random forest classifier)