Credit Card Fraud Detection System

Self-Paced Projects

Fee: ₹499

The Credit Card Fraud Detection System is a machine learning project that aims to detect fraudulent transactions in real-time using historical transaction data. The project involves preprocessing the data, selecting relevant features, training and evaluating machine learning models, and deploying the best-performing model in a production environment.

Objectives

The project will use a dataset of credit card transactions with labeled instances of fraud and non-fraud transactions. The data will be preprocessed to remove outliers and missing values, and relevant features will be selected using techniques such as correlation analysis and principal component analysis. Various machine learning algorithms such as logistic regression, decision trees, and neural networks will be trained and evaluated to select the best-performing model. The final model will be deployed in a production environment for real-time fraud detection.

The project provides an opportunity to learn various machine learning techniques such as data pre-processing, feature selection, model training, and evaluation.

It also involves learning about different machine-learning algorithms and how to select the best-performing model.

Additionally, the project provides an understanding of real-time systems and the deployment of machine-learning models in production environments.

What Will You Learn

Data preprocessing techniques such as outlier detection and missing value imputation. Feature selection techniques such as correlation analysis and principal component analysis. Machine learning algorithms such as logistic regression, decision trees, and neural networks. Model evaluation metrics such as accuracy, precision, recall, and F1 score. Techniques for deploying machine learning models in a production environment.