Mohammad Taghi Taghavifard - Department of Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran
Pegah Farazmand
Khatereh Farazmand - Department of Health, Faculty of Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Saeedreza Jadidi - Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, QC, H۳G ۱M۸, Canada

Advertising segmentation, also known as audience or market segmentation, constitutes a critical facet of marketing. It involves subdividing a large target population into more manageable groups or segments. By employing advertising grouping, businesses can tailor content and keywords to each group of advertisements, thereby enhancing the effectiveness of their marketing efforts. This project explores two machine learning models for advertising grouping, achieving an impressive efficiency rate of over ۹۰%. The study encompasses a dataset of ۲۰۰۰ online advertisements divided into five advertising groups, with ۱۶۰۰ data points used for training and ۴۰۰ for testing. The results indicate that the Support Vector Classifier (SVC) and Linear Regression (LR) models perform well in the categorization of advertisements.Advertising segmentation, also known as audience or market segmentation, constitutes a critical facet of marketing. It involves subdividing a large target population into more manageable groups or segments. By employing advertising grouping, businesses can tailor content and keywords to each group of advertisements, thereby enhancing the effectiveness of their marketing efforts. This project explores two machine learning models for advertising grouping, achieving an impressive efficiency rate of over ۹۰%. The study encompasses a dataset of ۲۰۰۰ online advertisements divided into five advertising groups, with ۱۶۰۰ data points used for training and ۴۰۰ for testing. The results indicate that the Support Vector Classifier (SVC) and Linear Regression (LR) models perform well in the categorization of advertisements.

Machine Learning Models, Advertising Segmentation, Target Audience, Online Ads