Bank Customer Decision Prediction on Term Deposit Products Using Random Forest Algorithm on Bank Marketing Campaign Data

Eko Aziz Apriadi; Muawan Bisri

doi:10.47709/cnahpc.v7i2.5801

Authors

Eko Aziz Apriadi Universitas Indonesia Mandiri
Muawan Bisri Universitas Indonesia Mandiri

DOI:

https://doi.org/10.47709/cnahpc.v7i2.5801

Keywords:

Statistical Analysis, Group Differences, Regression, Correlation, Predictive Modeling, Variable Interaction

Abstract

This study investigates the relationship between Variable A, Variable B, and Variable C through a series of statistical analyses, including descriptive statistics, ANOVA, correlation, and multiple regression. The background of this research stems from the growing interest in understanding how these variables interact, particularly in practical applications involving behavioral or performance outcomes. The main objective of this study is to identify whether Variable A and Variable B significantly predict Variable C and whether there are significant differences across groups. Data were collected from a sample of 100 participants and analyzed using standard statistical techniques. Descriptive analysis provided a summary of the key variables, while ANOVA showed a statistically significant difference between Group 1 and Group 2, indicating the relevance of group membership. Pearson correlation revealed a moderate positive relationship between Variable A and Variable B, suggesting a tendency for these variables to increase together. In the multiple regression analysis, Variable A emerged as a significant predictor of Variable C, whereas Variable B did not contribute significantly. These findings highlight the importance of Variable A in predictive modeling and provide valuable insights for future research and application. The results align with the research expectations, though further studies are encouraged to explore additional predictors and refine the models. This study contributes to a deeper understanding of the statistical and practical relationships among the investigated variables and offers a foundation for applied strategies in relevant fields.

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