Paper Title
Stock Market Prediction using Machine Learning Algorithm

Abstract
The stock market is a very volatile market so predicting the exactness in it is very difficult. Research endeavors in improving the exactness of determining models are expanding since the most recent decade. Therefore we are using different machine learning algorithms to see the accuracy of the models. The purpose of this project is to explore the field of machine learning models and in the future take it to next level so that we can train our data accurately. . In this work, LSTM, Naive Forecast, RNN, and dense forecasting models have been utilized for predicting the stock price. The financial data Open, High, Low, and Close prices of stock Spy Etf is taken in time-series format. The models are evaluated using mean absolute error(MAE), the lower the value of MAE higher is the accuracy of the model. Keywords - LSTM, CNN, STOCK MARKET PREDICTION, MAE.