STOCK MARKET PREDICTION USING MACHINE LEARNING
Author(s):
Shubhangi Jagdish Kadam, Shweta Narute, Shrikant Ramaiah Kamineni, Shresth Jain, PROF. MOHAN PAWAR
Keywords:
Stock market prediction, Linear Regression, Support Vector Machine, and Support Vector Regression are some of the terms used in this paper.
Abstract
The goal of Stock Market Prediction is to forecast the closing prices of the firm Bajaj-Finance on a given day using machine learning algorithms such as Linear Regression and Support Vector Machine. The company's historical and real-time data (close, high, low, volume, open, and Adj-Close) were used to make the prediction. The results of the investigation revealed that machine learning algorithms were capable of producing very accurate forecasting results. The use of machine learning to produce predictions that support the current stock closing price by training on historical values is a recent trend in available stock market prediction technologies. Machine Learning makes use of a variety of models to make accurate predictions. Stock trading is one of the most essential activities in the financial sector. The act of attempting to anticipate the long-term future value of a stock or other financial instrument traded on a financial exchange is known as the stock market prediction. The prediction of a stock using Machine Learning is demonstrated in this study. Most stockbrokers use technical and fundamental analysis, also known as static analysis when making stock predictions. Python is the recommended programming language for applying machine learning to anticipate the stock market. In this research, we present a Machine Learning approach that will be taught using publicly available stock data to build intelligence and then use that intelligence to make an accurate prediction. For shorter base period lengths and forecast horizons, all algorithms performed better.
Article Details
Unique Paper ID: 152191

Publication Volume & Issue: Volume 8, Issue 2

Page(s): 877 - 883
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