Analysis of Credit Card Fraud Detection Performance Using Random Forest Classifier & Neural Networks Model
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This research discusses credit card fraud detection using machine learning algorithms, specifically Random Forest Classifier and Neural Networks. Research methods include the EDA (Exploratory Data Analysis) stage, data preprocessing, building Random Forest and Neural Networks models, as well as model evaluation. The data used comes from the Kaggle dataset that has been provided. Data analysis is carried out using Pandas to understand the structure and content of the data, while preprocessing involves checking for duplicates, displaying data information, and statistical descriptions. The research results show that the Random Forest model achieved an accuracy of 96% in detecting credit card fraud, while Neural Networks also provided good results. A comparison of the performance of the two algorithms shows that both are effective in detecting fraud. Suggestions for further development include comparing the performance of the model with other algorithms, exploring the factors that influence fraud detection, and developing a more complex and adaptive detection system. The positive implication of the results of this research is increased efficiency in credit card fraud detection, which can provide major benefits in protecting consumers and financial institutions from detrimental fraudulent activities. References used in the research are also included to support the validity and accuracy of the findings obtained
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