Asli Kaya, Ozer Ozdemir, Ferdi Karakutuk
With the exponential increase of data, which is called the mine of our age, from year to year, processing it and accessing information has gained more importance than ever before. The interest in data mining methods, which enable us to reach information from unprocessed data, is increasing in parallel with the increase in the amount of data. In this study, it is aimed to classify the 2019 Life Satisfaction Survey Hope Level variable data using data mining and machine learning algorithms and compare the algorithm results. As a result of the experimental studies, it has been seen that the k-NN algorithm makes more accurate and effective classification in Likert Scale data types.
Classification, Data mining, k-NN algorithm, Support vector machine
Cite this paper
Asli Kaya, Ozer Ozdemir, Ferdi Karakutuk. (2021) Investigation of Life Satisfaction Data by Data Mining and Machine Learning Techniques. International Journal of Cultural Heritage, 6, 63-68