Predicting house prices in Ankara using machine learning

The focus of this thesis is to investigate whether machine learning predictions are accurate and viable enough to replace traditional real estate appraisal reports. To do this, we compare two datasets, one scraped from a real estate website and the other created from appraisal reports, and use various machine learning and neural network methods to find the best performing one and to determine the practicality of the study. Bagging and boosting ensemble methods are compared with the implementation of Extreme Gradient Boosting and Random Forest Models. Also, an Artificial Neural Network with five layers and Relu activation function is built as well as ensemble learning models. Hyperparameters of all models built throughout the study are chosen diligently for a comprehensive comparison. We evaluate the success of the models using root mean square error and accuracy score. Findings suggest that this approach has potential for improving the real estate valuation process, but further research is needed to determine its viability in the real world.