Data for Adaptive Neuro-Fuzzy Inference System for Customizing Investment Type based on the Potential Investors’ Demographics

The data is related to the paper by Asemi, A. & Ko, A. (2022), in the title: "Adaptive Neuro-Fuzzy Inference System for Customizing Investment Type based on the Potential Investors’ Demographics and feedback". The study aimed to present a novel customized investment recommender system based on the potential investors’ demographic data and their feedback using fuzzy neural inference solutions. In the proposed model, by combining the experts' knowledge and potential investors’ demographic data, we customized the investment type by the adaptive neuro-fuzzy inference recommender system. According to different functions, the design of the model is processed into three phases: data gathering, data analysis, and decision. The proposed algorithm was deployed in JMP and MATLAB. We designed a novel combined recommender system framework which recommends relevant and accurate recommendations to the investor for the most appropriate investment type or product.