Integration Of CRISP-DM And Machine Learning In Residential Sales Decision Making In The Middle  And Upper Middle Class At Pt XYZ

Patrik Mario Agata; Hendra Achmadi

Authors

Patrik Mario Agata Faculty of Economy, Universitas Pelita Harapan, Jakarta
Hendra Achmadi Faculty of Economy, Universitas Pelita Harapan, Jakarta

Keywords:

Random Forest CRISP-DM Gender Age Group Martial Status Occupation Monthly Expenditure Monthly Income

Abstract

This study was conducted to predict the effect of monthly income on 6 other demographic variables. Research data was obtained through internal questionnaire data of PT XYZ involving 393 respondents of middle residential class and 47 respondents of upper middle residential class. Data collection done with a field questionnaire containing 6 demographic questions. The data was analyzed using the CRISP-DM integration method and the Random Forest analysis method. The results of this study state that middle-class residential targets with variables such as Monthly Expenditure and Age Group have a significant influence on Monthly Income and upper-middle-class residential targets with variables such as Age Group, Employment Status and Occupation have a significant influence on Monthly Income.

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