The main goal of this study is to build a prediction model based on stacked LSTM deep learning to forecast Netflix stock values on day-closing. The "Stock ticker" characteristic is used as an input in the prediction model, which forecasts stock market closing price as a chart using a web application written in Python. Date, Open, Close, High, Low, Volume, and Adj Close are the attributes that are included in the model. Data was gathered between the years 2002 and 2022, and I separated it into two parts: a training set and a testing set. Only the testing portion is to be used for the final forecast. The closing time is then displayed against time on a graph. The results suggest that NETFLIX functions effectively. Forecasting the index may be done using machine learning techniques.

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Keywords: Stock Prediction, NETFLIX, Machine Learning, LSTM Model.