Keywords: Fraud detection,online fraud,machine learning,prediction
Online fraud detection is crucial for safeguarding financial systems and protecting users from fraudulent activities. The dataset contains transaction information such as step, type, amount, and account balances, along with indicators of fraud. Machine learning (ML) techniques offer the potential to analyze patterns and detect fraudulent transactions effectively.
Aim
The aim of this study is to develop a machine learning model for online fraud detection. The study aims to accurately identify and flag fraudulent transactions, preventing financial losses and enhancing security by analyzing the transaction data and patterns in data
Objective
1. Utilize ML algorithms like logistic regression, decision trees, and random forests for online fraud detection.
2. Train the ML model on a labeled dataset, distinguishing fraudulent and legitimate transactions based on provided features
The study involves preprocessing the data to handle missing values and encode categorical variables. ML algorithms like logistic regression, decision trees, random forests and other algorithms are implemented and trained on the labeled dataset. Feature engineering is performed to extract meaningful patterns from transaction data. The model's performance is evaluated using various metrics, and the best performing model is selected
Jupyter, Google Collab
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