Stock Price Prediction using KNN and Linear Regression
Poornima S P1, Priyanka C N2, Reshma P3, Suraj Kr Jaiswal4, Surendra Babu K N5
1Mr. Surendra Babu K N, REVA University, Bangalore (Karnataka), India.
2Priyanka C N, Department of C&IT, REVA University, Bangalore (Karnataka), India.
3Suraj Kr Jaiswal, Department of C&IT, REVA University, Bangalore (Karnataka), India.
4Reshma P, Department of C&IT, REVA University, Bangalore (Karnataka), India.
5Poornima S P, Department of C&IT, REVA University, Bangalore (Karnataka), India.
Manuscript received on 05 June 2019 | Revised Manuscript received on 14 June 2019 | Manuscript Published on 29 June 2019 | PP: 142-145 | Volume-8 Issue-5S, May 2019 | Retrieval Number: E10300585S19/19©BEIESP
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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: Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. With the ever increasing amounts of data becoming available there is good reason to believe that smart data analysis will become even more pervasive as a necessary ingredient for technological progress. It is an important challenge for the people who invest their money to forecast the daily stock prices, which helps them to put money into stock market with credence by taking risks and also variations into considerations. In this paper, we are going to apply KNN method and linear regression for predicting the stocks. The performance of linear Regression model on the selected data set is better when compared to KNN algorithm technique. The stock holders can invest confidently based on the results obtained from the model.
Keywords: Prediction Regression Smart Data Analysis Method Performance.
Scope of the Article: Regression and Prediction