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A Comparative Study on the Accuracy of Important Statistical Prediction Techniques for Marketing Data

마케팅 데이터를 대상으로 중요 통계 예측 기법의 정확성에 대한 비교 연구

  • Cho, Min-Ho (Dept. Computer System Engineering, JungWon University)
  • 조민호 (중원대학교 컴퓨터공학과)
  • Received : 2019.07.25
  • Accepted : 2019.08.15
  • Published : 2019.08.31

Abstract

Techniques for predicting the future can be categorized into statistics-based and deep-run-based techniques. Among them, statistic-based techniques are widely used because simple and highly accurate. However, working-level officials have difficulty using many analytical techniques correctly. In this study, we compared the accuracy of prediction by applying multinomial logistic regression, decision tree, random forest, support vector machine, and Bayesian inference to marketing related data. The same marketing data was used, and analysis was conducted by using R. The prediction results of various techniques reflecting the data characteristics of the marketing field will be a good reference for practitioners.

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그림 1. 설문조사 데이터 모습(일부) Fig. 1 Part of survey data

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그림 2. 다항회귀 분석 결과 Fig. 2 Result of multinomial regression analysis

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그림 3. CART 알고리즘 적용 결과 Fig. 3 Result of CART algorithm

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그림 4. 조건부추론나무 적용 결과 Fig. 4 Result of conditional inference tree

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그림 5. 랜덤포레스트 분석 결과 Fig. 5 Result of random forest analysis

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그림 6. 방사형 방법 분석 결과 Fig. 6 Result of radial method Analysis

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그림 7. 선형 방법 분석 결과 Fig. 7 Result of linear method Analysis

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그림 8. 다항 방법 분석 결과 Fig. 8 Result of Polynomial method Analysis

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그림 9. 베이지안방법론 분석 결과 Fig. 9 Result of Bayesian method Analysis

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