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Ad Click Prediction Using Machine Learning Algorithms
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Uncu, Nazlı Tuğçe; Hande Küçükaydın
Online advertising has a great potential to boost business’ revenue. One of the key metrics that defines the success of online ad campaigns is click through rate (CTR) which indicates the total number of clicks received in relation to the total impression. Therefore, the click prediction systems, which have the aim of increasing the click through rates of online advertising campaigns by predicting the clicks, have become essential for businesses. For this reason, predicting whether an advertisement will receive a click fromthe user or not attracts the attention of researchers from the both industry and academia. In this capstone project, the click prediction is studied by using Avazu’s click logs dataset. The effects of having high cardinality categorical features and imbalanced data are examined during data preprocessing phase and then relevant features are selected to be used in modeling. The methods that are used for this classification problem are decision trees, random forest, k-nearest neighbor, extreme gradient boosting, and logistic regression. According to the results of the study, extreme gradient boosting shows the best performance.
Price Prediction Using Machine Learning Techniques: an Application To Vacation Rental Properties
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Ay, Oğuz; Hande Küçükaydın
Pricing is a subjective process that highly depends on person. There is no general rule to price a house. That is why there is both overpriced and underpriced rental houses in rental listings in websites such as AirBnB. In order to reduce the effect of subjective pricing, a general machine learning model is built in this project to make more objective price predictions.In the literature, there are different machine learning models to make numeric predictions. Physical features of houses are used as an input to make inferences about the price of a house. These machine learning models can identify the relations between features and the price and make the predictions with respect to features of a new listing house that has not been priced before.In this project, six different machine learning models are developed. These are linear regression, ridge regression, support vector regressor, random forest regressor, light gradient boosting machine regressor and extreme gradient boosting regressor. The performances of all models are compared, and the best model is selected for hyper-parameter tuning to make more accurate predictions.

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