Performance Comparison Supervised Machine Learning Models to Predict Customer Transaction Through Social Media Ads

Thohari, Afandi Nur Aziz and Ramadhani, Rima Dias (2022) Performance Comparison Supervised Machine Learning Models to Predict Customer Transaction Through Social Media Ads. Journal of computer Networks, Architecture and High Performance Computing.

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Abstract

The application of machine learning has been used in various sectors, one of which is digital marketing. This research compares the performance of six machine learning algorithms to predict customer transaction decisions. The six algorithms used for comparison are Perceptron, Linear Regression, K-Nearest neighbors, Naive Bayes, Decision Tree, and Random Forest. The dataset is obtained from Facebook ads transaction data in 2020. The goal is to get a model that has the best performance so that can be deployed to the web. The method that is used to compare the results is a confusion matrix and also uses visualization of the model to get the prediction error that occured. Based on the test results, the random forest algorithm has the highest accuracy, recall, and f1-score values, with scores of 96.35%, 95.45%, ad 93.32%. The logistic regression algorithm generated the highest precision value, which was 94.44%. Based on the data cisualization presented by the random forest algorithm has the best performance because it has the highest value in the three confusion matrix measurements and the smallest data prediction error. The model of the random forest algorithm is deployed to the web platform and can be accessed at the link iklan-sosmed.herokuapp.com.

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