Özet:

The Organization for Economic Co-operation and Development (OECD) is an international organization that works to create better policies for better lives. For this aim, OECD collects data on countries in many indicators. In order to make more accurate analyses, these data must be complete. But there are deficiencies in the information collected from different national and international sources. These deficiencies are especially problematic for researchers who want to work using statistical analysis and machine learning methods. For such analysis, data sets must first be cleared of missing data. In general, incomplete data has a negative effect on statistical analysis. There are traditional and modern methods to solve this problem. The data can be missing completely at random (MCAR), missing at random (MAR), and not missing at random (MNAR). For this reason, each data must be handled separately. In the data set titled industrial production in the Main Economic Indicators database, there are 4046 values, 113 missing data and 3933 complete data belonging to 34 countries. In order to divide the data set into different groups, the study used a machine learning algorithm called K-Nearest Neighbor(kNN). Because the kNN algorithm is simple to use, it is widely used. The nearest neighborhood value of the algorithm used in the study was determined as k=15. There was an 86.8% success rate in estimating the missing data.

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