Stock Price Prediction Using Python & Machine Learning (LSTM).
In this video you will learn how to create an artificial neural network called Long Short Term Memory to predict the future price of stock.

Disclaimer: The material in this video is purely educational and should not be taken as professional investment advice. Invest at your own discretion.

NOTE: Some errors in the video:
(1) In the video to calculate the RMSE I put the following statement:
rmse=np.sqrt(np.mean((predictions y_test)**2))

When in fact I meant to put:
rmse=np.sqrt(np.mean(((predictions y_test)**2)))

You can also use the following statements to calculate RMSE:
1. rmse =np.sqrt(np.mean(((predictions y_test)**2)))
2. rmse = np.sqrt(np.mean(np.power((np.array(y_test)np.array(predictions)),2)))
3. rmse = np.sqrt(((predictions y_test) ** 2).mean())

(2) The preprocessing of data using MinMaxScaler resulted in data leakage, leading to the creation of future bias. Despite this, the code can still be utilized to obtain a basic understanding of Neural Network implementation in Python for classification and prediction purposes.

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