This repository contains the tasks for data science internship at codsoft

🛡️ 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! 🔐💯

This Repository contains the 3 projects that I completed during my Machine Learning Internship with CODSOFT.

Credit Card Fraud Detection

CODSOFT Machine Learning Internship Tasks

WooCommerce plugin for using the BulletProof Checkout API

This code snippet performs fraud detection using machine learning models such as RandomForestClassifier and DecisionTreeClassifier.

Credit card fraud detection application using Machine Learning and Streamlit

A curated list of data mining papers about fraud 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.

Credit card fraud detection using machine learning. Web application using Streamlit framework

The challenge is to recognize fraudulent credit card transactions so that the customers of credit card companies are not charged for items that they did not purchase.

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

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 fraud is a significant problem, with billions of dollars lost each year. Machine learning can be used to detect credit card fraud by identifying patterns that are indicative of fraudulent transactions. Credit card fraud refers to the physical loss of a credit card or the loss of sensitive credit card information.

The Credit Card Fraud Detection project uses statistical techniques and machine learning for identifying fraudulent transactions. It includes data preprocessing, outlier detection using Boxplots and Z-scores, and a decision tree model. Evaluation goes beyond accuracy, considering precision, recall, F1-score, and ROC AUC.

Credit card transactions fraud detection using classic algorithms

Machine Learning based Credit Card Fraud Detection

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