This project focuses on solving the critical problem of detecting fraudulent online transactions using machine learning techniques. By leveraging libraries such as NumPy, scikit-learn, and other Python libraries, we develop robust models to identify fraudulent activities in real-time.
With the increasing prevalence of online transactions, the risk of fraudulent activities has also surged. Detecting these fraudulent transactions promptly is essential to prevent financial losses and maintain the integrity of online platforms. This project aims to build effective machine learning models to automatically identify fraudulent transactions based on various features associated with the transactions.
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Data Collection: We gather a dataset containing information about online transactions, including features such as transaction amount, location, time, and more.
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Data Preprocessing: Before training the models, we preprocess the data by handling missing values, scaling numerical features, and encoding categorical variables.
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Model Building: We employ two popular machine learning algorithms, logistic regression and random forest, to build our fraud detection models. These models are trained on labeled data to learn patterns indicative of fraudulent behavior.
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Model Evaluation: We evaluate the performance of our models using appropriate metrics such as accuracy, precision, recall, and ROC curve. This allows us to assess how well the models generalize to unseen data and their ability to correctly identify fraudulent transactions while minimizing false positives.