Machine learning supported decision support system for small and medium-sized companies

Nowadays, with improvements occurring in information technology, management of rapidly growing data and incorporate into business processes it is of great importance for businesses. Therefore, processing the data and making it ready for analysis enables companies to use their resources effectively and to make better planning. At the same time, data supports companies in helping the decision-making process, understanding complex processes, identifying and solving problems. Within the scope of this study, a system has been designed to support the decision-making process of Small and Medium Sized companies, to enable them to use the data they have more effectively and to make predictions from the data. In the prepared study, the all process from organizing the raw data to its final estimation has been planned and the application has been designed properly. The application aims to help decision makers make instant and future predictions from the available data. In practice, a data set of 200 thousand data was used for testing. This data was first cleared on the data editing screen and made ready for use. Basically, reports can be obtained from these data under four main headings. These are descriptive reports where we can analyze instant data from the reports, forecasting reports based on machine learning methods, time series analysis that can analyze the new values that the data can receive over time, and finally cluster analysis reports where we can group the data according to their similarities. All these analyzes and estimates have been reported for decision makers in various graphs and tables.