During recent times, plentiful advanced, self-reporting, and probabilistic data techniques have been implemented for analyzing financial literacy amongst the adult population. But, it will be quite tougher to discover student's perceptions based only on financial literacy without depending upon any data source. Hence, this work has been proposed for analyzing and assessing three financial factors such as financial attitude, financial knowledge, and financial behavior to decide upon which is the most vital financial factor amongst the currently pursuing and alumni students. From the countless techniques available, the Collaborative Filtering (CF) technique has been implemented in this prosed work as it is being highly recommended by most of the research institutes and enterprises. In general, the Collaborative Filtering (CF) technique is comprised of Memory-based CF and Model-based CF, but here in this study Memory-based CF recommendation algorithm is utilized combined with similarity measure S4 called MBCFWS4.The collaborative filtering technique utilized in this proposed work can just identify the relationship alone and hence, for the identification of the student's perception, the primary datasets, and secondary datasets are used depending upon the financial literacy. Using the precision, F-measure, and recall terms, the proposed work is checked for efficiency comparison. With the resulting comparison, it was proved that MBCFWS4 is found to have better performance than the other techniques with accurate prediction feature depending upon the financial literacy by calculating the similarity between the factors by S4 using the perceptions. A recently conducted financial literacy proportion analysis says that when compared with males, females were found to have more financial knowledge and financial literacy whereas males were found to have a higher financial behavior stance than females. According to recent research, it was found that 11.2% population of males and females were having the best financial literacy score on average.

Adult’s Financial Literacy,Collaborative Filtering, Financial factors, Memory-based CF,Model-based CF