Mar 3, 2024 · Using the training data (X_train and Y_train), we fit the Logistic Regression model to learn patterns in the data. This process entails adjusting model parameters to minimize the difference...

Explore and run machine learning code with Kaggle Notebooks | Using data from Credit Card Fraud Detection

Jan 28, 2025 · This paper presents a hybrid machine learning approach using logistic regression, decision trees, and random forests that could help enhance the accuracy and reliability of fraud detection systems.

Jun 25, 2023 · Logistic Regression is a statistical method that we use for binary classification problems, i.e., situations where the outcome can take one out of two possible states, ‘0’ or ‘1’. When it comes to fraud detection, the two states would typically be ‘fraud’ and ‘not fraud’.

In this work, fraud detection using artificial intelligence is proposed. The proposed system uses logistic regression to build the classifier to prevent frauds in credit card transactions. To handle dirty data and to ensure a high degree of detection accuracy, a pre-processing step is used.

Jan 20, 2025 · In this article, we’ll take you step-by-step through building a fraud detection model using logistic regression in R. By the end, you’ll not only understand how this technique works but...

This project aims to build a logistic regression model to predict fraudulent transactions, incorporating regularization techniques to improve accuracy and prevent overfitting.

Logistic Regression models can effectively discern patterns indicative of fraudulent activity. Feature selection and engineering is key process in machine learning credit card fraud detection project. Model evaluation techniques used in this project …

The proposed technique will use logistic regression, decision trees, random forest and other algorithms to classify transactions as fraudulent or valid using a credit card transaction data set.

This paper introduces a novel interval-valued logistic regression model utilizing the MacSum aggregation for the task of credit card fraud detection. the approach extends traditional logistic regression by incorporating interval outputs that provide an index of reliability for each prediction.