ANALISIS PREDIKSI KEPUTUSAN NASABAH DALAM BERLANGGANAN DEPOSITO BERJANGKA MENGGUNAKAN METODE MACHINE LEARNING PADA DATASET BANK MARKETING
Keywords:
Bank Marketing Dataset, Classification, Machine Learning, Random Forest, Synthetic Minority Oversampling Technique.Abstract
Advances in digital technology have increased the availability of data in the banking sector, which can be leveraged to support data-driven decision-making. This study aims to compare the performance of machine learning algorithms in predicting customers’ decisions to open time deposits using the Bank Marketing Dataset, which consists of 41,188 observations and 21 variables. The research stages included data cleaning, categorical data transformation, data standardization, splitting the data into training and testing sets, and addressing class imbalance using the Synthetic Minority Oversampling Technique. The algorithms used were Decision Tree, K-Nearest Neighbor, Logistic Regression, and Random Forest. The results show that Random Forest produced the best classification performance compared to the other models. Additionally, communication duration was the most influential factor affecting customers’ decisions to subscribe to time deposits. Thus, Random Forest can be used as an effective approach to support data-driven marketing strategies in the banking sector.
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