Classification of Predicting Customer Ad Clicks Using Logistic Regression and k-Nearest Neighbors

Yasi Dani - Bina Nusantara University


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.7.1.1017

Abstract


In the past, marketing techniques were done conventionally (non-digital), moreover in recent years conventional marketing techniques are starting to turn into online (digital) marketing techniques where this marketing requires internet access. Since online marketing techniques have many advantages, especially in terms of cost efficiency and also sending information to the public more quickly and widely. Therefore, many companies are interested in online marketing and they advertise on social media platforms and websites. However, one of the challenges for companies or businesses in online marketing is to determine the right target or consumers who are not interested in buying their products, the advertising costs will be high. One use of online advertising is clicks on ads which is a marketing measurement of how many users click on the online ad. Thus, companies need a click prediction system so that they know the right target consumers.  Nowadays, different types of advertisers and search engines rely on modeling to predict ad clicks accurately. This paper constructs the advertisement customer ad clicks prediction model using the machine learning approach, since machine learning systems have become more sophisticated in their ability to effectively predict the probability of a click. We proposed two types of classification algorithms namely logistic regression (LR) that produce probabilistic outputs and k-nearest neighbors (k-NN) classifier that produce non-probabilistic outputs.  Furthermore, this study compares the two classification algorithms and determines the best algorithm based on their performance, we calculate confusion matrix and several metrics that are precision, recall, accuracy, F1-score, and AUC-ROC. The higher the metric values, the better the classification algorithm for predictive analysis of users clicking on ads where the data set comes from an advertising dataset from a marketing agency. The purpose of this research is to help companies or businesses use the right method for predictive analysis to reach the right target consumers

Keywords


machine learning algorithm; logistic regression; k-nearest neighbors; supervised classification; ad-click

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