This design aims to predict the stock price of Netflix using machine knowledge ways. Specifically, we will use intermittent neural networks (RNNs), a type of artificial neural network able of processing sequences of data, to dissect literal data on Netflix's stock prices and other fiscal variables similar as earnings, profit, and request trends. By training our model on this data, we will essay to identify patterns and trends that can be used to make prognostications about unborn stock prices. Other ways similar as decision trees, support vector machines (SVMs), and arbitrary timbers may also be explored. Still, it's important to keep in mind that prognosticating stock prices with machine literacy isn't an exact wisdom, and multiple sources of information and expert advice should be considered before making any investment opinions. Stock price prediction is a challenging task due to the complex and dynamic nature of the stock market. However, machine learning techniques have shown promising results in predicting stock prices. In this study, we explore the use of machine learning algorithms to predict the stock price of Netflix. We use a dataset containing historical stock prices of Netflix and other relevant variables, such as the company's financial metrics, news sentiment, and social media activity. We preprocess the data by cleaning, transforming, and feature engineering to extract useful information for prediction.

Keywords : Stock Prediction, NFLX, Machine Learning, Support Vector Machines (Svms).