The aim of the project is to construct a model for predicting property prices in Israel.
This initiative advocates the application of machine learning to develop a predictive model capable of assessing real estate assets with enhanced accuracy compared to conventional methods, ultimately saving time and resources. The predictor takes specific asset descriptors as input, and outputs the current value of the asset. The project spans all phases of the machine learning process, commencing with extensive data mining and database creation, followed by information pre-processing, model training, evaluation, and refinement. The outcomes and insights derived from this project affirm the viability of employing machine learning algorithms for the general prediction of real estate prices, with a specific focus on Israel. The code segments crafted for the project establish a robust foundation for ongoing research, expansion, and the implementation and examination of future models.