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! 🔐💯

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

Tasks and Projects completed during the Data Science internship at CODSOFT

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

Identify fraudulent credit card transactions.

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.

Fraud Detection model based on anonymized credit card transactions

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.

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

This repository contains three machine learning projects completed during the internship at Encryptix: Titanic Survival Prediction, Sales Prediction, and Credit Card Fraud Detection. Each project includes data preprocessing, exploratory data analysis, model building, and evaluation steps.

CODSOFT Machine Learning Internship Tasks

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