Yapay zeka kullanılarak kullanıcı tüketim verilerine dayalı tüketici profillerinin sınıflandırılması

Over-The-Top (OTT) platforms have grown dramatically in popularity in recent years, giving consumers access to a variety of multimedia material. Understanding consumer behavior has therefore become essential for platform providers and advertising. In order to categorize users based on their consumption rate which may be classed as low, medium, or high this research suggests the construction of a user consumption categorization system based on machine learning (ML) as Decision Tree (DT) , Random Forest ( RF) , Regression Algorithm , k-Nearest Neighbour (KNN), Bayesian Algorithm (BA), Gradient Boosting (GB) and XGBoost Classifier . In order to tailor the data and get it ready for clustering, the research will employ data preprocessing techniques. Next, it will evaluate several ML classifiers to see which one is the most accurate at predicting the user's consumption rate. Each algorithm's limits will be investigated, and the system will be combined with data analytics and mining software. By offering a useful application for categorizing user consumption habits, this research will advance the fields ML and may be useful to OTT platform providers, advertisers, and data analytics experts.