Binary Classification for Predicting the Investment Trends of The Younger Generation Based on Machine Learning
DOI:
https://doi.org/10.30871/jaic.v9i6.11549Keywords:
Machine Learning, Feature Engineering, Binary Classification, Multi-class Classification, Hybrid Modeling, Data PreprocessingAbstract
This computational study examines investment behavior patterns among a specialized cohort of 115 final year and thesis writing university students, implementing sophisticated feature engineering to transform categorical survey responses into quantifiable financial metrics. The research methodology leverages this unique dataset where respondents' advanced academic standing provides particularly relevant insights into near-term investment decisions. Experimental outcomes reveal distinct algorithmic performance patterns: Random Forest achieved 69.6% accuracy in multi-class classification with weighted averages of 0.662 precision, 0.696 recall, and 0.678 F1-score, while Logistic Regression demonstrated superior binary classification capability with 82.6% accuracy, supported by 0.818 precision, 0.826 recall, and 0.814 F1-score (weighted averages). The hybrid architecture integrating machine learning with business rules achieved peak performance of 85.2% accuracy, successfully balancing predictive power with operational interpretability. These findings underscore how strategically engineered features combined with a carefully selected respondent pool can effectively decode complex financial behaviors, providing financial institutions with actionable frameworks for developing targeted investment solutions for the graduate student demographic while advancing methodological approaches for specialized survey data in fintech applications.
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