Data mining analysis of economic indicators of countries

Data Mining is becoming a famous analysis day by day to reveal the hidden information within big data. In the study, we use data mining techniques on the economic indicators of the countries. The four data mining techniques are to be implemented on the dataset. Making homogenous groups of the countries whose economic characteristics are similar are obtained by the Clustering Algorithm. After the clustering algorithm is performed, we pass to Association Rule Data Mining to investigate the most exported products by Switzerland to the other countries. With the clustering and association rule mining, we complete the first stage of the data mining that is so-called as unsupervised learning. In the second stage, we build up both classification and regression models with panel data based on the new variables that are obtained by the Principal Component Analysis. The main aim of the second stage is to determine the most important economic predictor variables that have an effect on the grouping of the countries and have an effect on the main economic indicators such as Gross Domestic Product (GDP), Gross National Product (GNP), etc.