Machine Learning Finance: Application of Machine Learning in Collaborative Filtering Recommendation System for Financial Recommendations
Bhagirathi Nayak1, Rajesh K. Ojha2, P. S. Subbarao3, VijayaBatth4
1Dr. Bhagirathi Nayak, FCMS, Sri Sri University, Cuttack, Odisha, India.
2Mr. Rajesh K. Ojha, FCMS, Sri Sri University, Cuttack, Odisha, India.
3Dr. P. S. Subbarao, FCMS, Sri Sri University, Cuttack, Odisha, India.
4Dr. VijayaBatth, Xavier University Bhubaneswar, Odisha, India.

Manuscript received on 07 April 2019 | Revised Manuscript received on 13 May 2019 | Manuscript published on 30 May 2019 | PP: 905-909 | Volume-8 Issue-1, May 2019 | Retrieval Number: E2069017519/19©BEIESP
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Abstract: Prediction of stock market data have got a significant role in present scenario of finance. The various algorithms and models are used for forecasting of financial data. In this article we used application of recommender system. The recommender systems are one of the significant methodologies in machine learning technologies, which is using in current business scenario. This paper focuses on developing a stock market data recommender system using machine learning technique like k-Nearest Neighbors (k-NN) classification. Machine learning has become a widely operational tool in financial recommendation systems. Here the data considers the daily wise equity trading of Nifty 50 from National Stock Exchange (NSE) of 50 companies in 10 different sectors around 5986 days’ transactions. We used k-Nearest Neighbors classification algorithm of deep learning technique to classify users based recommender system. We explore the traditional collaborative filtering with our methodology and also to compare with them. Our outcomes display the predictable algorithm is more precise than the existing algorithm, besides it is less time and robust than the existing methods.
Keywords: Recommender System, Collaborative filtering,
k-Nearest Neighbors, Classification, Equity Trading.

Scope of the Article: Machine Learning