Learn how to build a model that is able to detect fraudulent credit card transactions with high accuracy, recall and F1 score using Scikit-learn in Python.

Oct 28, 2024 · Solved End-to-End Credit Card Fraud Detection Data Science Project with Source Code in Python

🧠 Python, Scikit-learn, Pandas, NumPy 📊 Data Visualization (Matplotlib, Seaborn) ... FRAUD-DETECTION-ML-PROJECT/ ├── assets/ │ └── graphs/ │ ├── confusion_matrix.png │ ├── ROC_curve.png │ └── ui_screenshot.png │ ├── Code/ │ ├── Credit Card Fraud Detection - Decision Tree.ipynb ...

Apr 9, 2025 · In this article we will explore how to build a machine learning model to detect fraudulent credit card transactions using Python. We begin by importing the necessary Python libraries: numpy, pandas, matplotlib and seaborn for data handling, visualization and model building. Load the dataset into a pandas DataFrame and examine its structure.

This project implements a machine learning model to detect fraudulent credit card transactions using logistic regression. The code leverages Python's pandas for data manipulation, numpy for numerical operations, and scikit-learn for building and evaluating the machine learning model.

A machine learning project for detecting credit card fraud using Random Forest and Isolation Forest. It identifies fraudulent transactions with high accuracy, leveraging SMOTE for imbalanced data and feature scaling. Built with Python, it ensures scalable, automated fraud detection in real-time scenarios. - MayankSingh-MCA/CREDIT-CARD-FRAUD ...

We will be build a credit card fraud detection model. The goals of this notebook are the following: Table of Contents. I. Understanding Our Data. II. Preprocessing. III. Random...

Feb 1, 2023 · The objective of Credit Card Fraud Detection is to accurately identify fraudulent transactions from a large pool of credit card transactions by building a predictive model based on past transaction data.

Jan 4, 2020 · We will use various predictive models to see how accurate they are in detecting whether a transaction is a normal payment or a fraud. Classification techniques are the promising solutions to detect the fraud and non-fraud transactions.

The code develops a credit card fraud classifier using logistic regression and evaluates its performance using the confusion matrix, classification report, and ROC curve. The code also addresses the class imbalance issue in the dataset by oversampling the minority class using RandomOverSampler.