Financial Inclusion: An Application of Machine Learning in Collaborative Filtering Recommender Systems
Girija Sankar Das1, Bhagirathi Nayak2
1Suja P Mathews*, Department of IT, Jain University, Bangalore, India.
2Dr. Raju R Gondkar, Department of PG Studies, CMR University, Bangalore, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 4243-4247 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9361038620/2020©BEIESP | DOI: 10.35940/ijrte.F9361.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the current scenario in finance, data play a major role for predicting stock market as well as verious financial instruments. For the estimation of financial data, the various algorithms and models have been used. The use of the advising method has been used in this paper. The advising programs are one of the main methodologies used in the present market scenario with machine learning technologies. This paper focuses on the impact of financial inclusion in Odisha using a machine learning approach such as the classification of k-Nearest Neighbors (k-NN). For financial inclusion systems, machine learning has become a commonly used method. The result takes into the ATMs, Banks and BCs ranking in different districts of Odisha. We used the k-Nearest Neighbor’s machine learning methodology classification algorithm to characterize the recommendation system based on users of the mentioned populations. Using our approach we equate conventional collective filtering. Our results show that the linear algorithm is more reliable than the current algorithm and is more efficient and stable than current methods.
Keywords: Recommender System, Collaborative filtering, k-Nearest Neighbors.
Scope of the Article: Machine Learning.