Stock Closing Price Forecasting using Machine Learning Models

A company's growth and development can be easily estimated by analyzing the stock market conditions of that company and this gives way for the development of the technology for stock prices forecasting to calculate what would be the repercussions of any event happening in the contemporary world. This paper will be used to describe the stock closing prices prediction of India's major automotive company Tata Motors. The stock price prediction of this company will be done through four supervised machine learning models -:multivariate linear regression, Facebook Prophet, ARIMA, LSTM. These models will predict the stock prices and RMSE was considered as a scale for determining the efficiency. The data Open, Close, High, and Low are taken into consideration to create variables to train the model with inputs. In this paper, the Prophet model using the logistic regression gave the least RMSE value, and hence it is more efficient than all the remaining algorithms.