Analysis of Various Regressions for Stock Data Prediction

Prediction of prices in the Stock Market is a complex task. It involves more contact between humans and computers. We will use more efficient algorithms to get the result more accurate. The proposed methodology here is Linear Regression, Ridge Regression, Lasso Regression and Polynomial Regression. This case will provide us the accurate results and this experiment results are effective and suitable for prediction. Firstly we will collect the data from the kaggle, then we will apply the proposed algorithms and the code is changed according to the results we get the accuracy we are getting. Finally this includes the workflow of the prediction of the share market. The results from the experiment can show that the methodology suggested is remarkably productive and also appropriate for predicting before a short period of time.