rakibnsajib/Credit-Card-Fraud-Detection-on-Imbalanced-Data-Using-Machine-Learning
A Jupyter notebook that applies machine learning techniques to detect credit card fraud on imbalanced data. It covers data preprocessing, EDA, handling class imbalance, training classifiers (Logistic Regression, Decision Tree, RandomForest), and saving the trained models.
This helps financial analysts and fraud prevention specialists identify fraudulent credit card transactions. You provide raw credit card transaction data, and it outputs trained machine learning models that can predict which new transactions are likely fraudulent. This is ideal for anyone tasked with minimizing financial losses due to fraud.
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Use this if you need to build or evaluate a system for detecting credit card fraud, especially when fraudulent cases are rare in your data.
Not ideal if your primary goal is to understand the root causes of fraud or to prevent fraud proactively through policy changes rather than detection.
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Last pushed
Sep 13, 2024
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