With the development of technology and the increase of the data sources, the size and variety of data collected from these sources has increased considerably. Thus, individuals and institutions have become able to store more data. However, it has become an important need to make meaning from this large and valuable data and transform into information and has become more complex. Business intelligence applications ensure that different types of data collected from different data sources are clustered and separated in a certain order and it provides the creation of reports by establishing a semantic relationship between these stored data. The aim of this study is identifying the business intelligence tools preferred by companies in Turkey. It is also aimed to give ideas to institutions and individual users so that they can choose the right business intelligence tool. Within the scope of the study, first of all, the general definition of business intelligence and the business intelligence applications preferred by the companies in Turkey in recent years are mentioned. Afterwards, the information obtained from the scanned scientific studies are analyzed and the findings are presented and then Afterwards, these tools were compared with the tables and it was aimed to give an idea to individuals and institutions. Scientific studies are very important in terms of revealing the current status of these business intelligence tools and seeing what kind of studies they can be used in the future.

Shelter is one of the most basic human needs. Besides housing needs, the housing market is also very important for investment. It is also a market where many people, such as engineers, architects, real estate agents make economic gain. When a house is bought for living in it, it is not desired to be changed for many years, and when it is bought for investment, it is a tool that requires good income. Therefore, the best decision should be made when buying a house, and it should be scrutinized. Correct estimation of house prices is very important for both buyers to make the right decision and for sellers to sell without a loss. There are many parameters for estimating house prices. In addition to variables such as the number of floors, location, and several bathrooms used in previous studies, economic factors (such as the price of bread, foreign currency price, new car price) and the housing loan interest rate of the banks were taken as inputs in this study. Sakarya province, where all parameters can be tested to make a more accurate determination, was chosen as the research area. A comparison of polynomial regression, random forest, and deep learning methods was made and it was concluded that the most accurate method was deep learning. At the same time, it was determined which parameters are more effective in house price estimation.