Estimation of success of entrepreneurship projects with data mining

Small and medium-sized enterprises (SMEs) have an important place in the economy due to the fact that 99.8% of businesses in Turkey are SMEs. It is important to survive for SMEs, especially newly founded enterprises. In order to help SMEs survive, KOSGEB provides the entrepreneurs with 3 year-support. However, the supported entrepreneurship projects still fail and cause to the waste of allocated resources for these projects. This study aimed to prevent waste of resource and to estimate the success and failure of proposed entrepreneurship projects with data mining algorithms. Thereby, the accuracy of the estimates increased and decisions about the projects were based on a scientific approach. As data of the study, the projects evaluated by KOSGEB Gaziantep Directorate between 2012-2014 were analyzed by taking some features such as age, gender, experience, education, partnership structure, market, location, sector, personnel, and capital into consideration. As a result of the analysis of the data, it has been examined whether entrepreneurial projects were successful or not. The data obtained from the entrepreneurship projects were pre-processed and adapted to WEKA 3.9.2 software. The dataset was classified using 10-fold cross-validation with C4.5, Naive Bayes, Logistic Regression, Random Forest and Support Vector algorithms. The results of the classification were compared and the C4.5 algorithm was found as the most successful algorithm with 70.75% prediction accuracy. In consequence of the C4.5 algorithm, the features affecting the tree were found as capital, partner, location, and age, respectively. The features that did not affect the tree were gender, education, market, sector, and personnel.