Stock-Closing-Price-Prediction

Prediction of stock prices is a demanding task since the financial sector is highly volatile.For our project the data set utilized for analysis was selected from Yahoo Finance.It has over 97000 rows of the required Stock price and other pertinent information.The data set consisted of 4 attributes namely ”Open”,”High”,”Low”,”Volume”.Data prepossessing,analysis was done with the help of multiple python libraries.The test set was limited to 20 % of the total dataset. This project focuses on regression based model architecture. The Regression-based Model is used to predict continuous values from a set of autonomous values. Regression makes predictions by using a given linear function to predict continuous values of the attribute ”Close”.The stock prices vary from a minimum of 2.13 dollar of a stock to 272800 dollar.