Improving the Accuracy of the Logistic Regression Algorithm Model using SelectKBest in Customer Prediction Based on Purchasing Behavior Patterns
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Abstract
The development of increasingly sophisticated science and technology allows anyone to easily create and run a business. This provides convenience to consumers with a variety of shopping options that are more numerous in this era but also poses challenges in increasingly fierce business competition. Therefore, companies need to develop effective marketing strategies to achieve profitability and sustainable growth. The right marketing strategy should be aimed at meeting customer needs. Several studies have been conducted to classify customers based on their purchasing patterns, but have not applied a fixed combination of features so the accuracy obtained is still not optimal. The purpose of this research is to improve the accuracy of the logistic regression model in predicting customers based on their purchasing behavior patterns with SelectKBest. The proposed new algorithm model is Logistic Regression using feature selection in the form of chi-square scores to improve the combination of the use of features to better fit the characteristics of the predicted model. The first research process is pre-processing, namely performing feature selection with chi-square scores and normalizing data with a standard scaler. The second process is split data, dividing the data into training and testing data. The third process is modeling. Modeling is done with 7 algorithms, namely KNN, Gradient Boosting, Logistic Regression, Decision Tree, Naïve Bayes, SVM, and Random Forest to compare performance. And the fourth is model evaluation. The model is tested using datasets from the UCI Machine Learning repository platform. The evaluation results show that the Logistic Regression algorithm can produce the greatest accuracy of 93.18% with precision, recall, and f1-score of 95% each. This research shows that optimizing the Logistic Regression model with SelectKBest can improve the accuracy of predicting customers based on their purchasing patterns.
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