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#Show the valid and predicted prices
valid
#This compares between the "Close" and "Predictions"

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#Plot the data
train = data[:training_data_len]
valid = data[training_data_len:]
valid["Predictions"] = predictions
#Visualize the data
plt.figure(figsize=(16,8))
plt.title("Model")
plt.xlabel("Data", fontsize=18)
plt.ylabel("Close Price USD ($)", fontsize=18)
plt.plot(train["Close"])

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#Evaluate the model: Getting the root square error (RMSE)
rmse = np.sqrt( np.mean( predictions - y_test )**2 )
rmse

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#Get the model predicted price values
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions)

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#Convert the data to a numpy array
x_test = np.array(x_test)#Reshape the data
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1 ))

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#Create the testing data set
#Create a new array containing scaled values from index 1543 to 2003
test_data = scaled_data[training_data_len - 60: , :]
#Create the data sets x_test and y_test
x_test = []
y_test = dataset[training_data_len:, :]

for i in range(60, len(test_data)):
    x_test.append(test_data[i-60:i, 0])

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#Train the model
model.fit(x_train, y_train, batch_size=1, epochs=5)#Batch size the number of Batch per training, while epochs is the number of Iteration

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#Compile the model
model.compile(optimizer="adam", loss="mean_squared_error")

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#Build the LSTM model
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape= (x_train.shape[1], 1)))#50 means the no of input neurons
model.add(LSTM(50, return_sequences= False))
model.add(Dense(25))
model.add(Dense(1))# Final output  

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#Reshape the data to a 3 dimensional shape
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
x_train.shape
#Now you'll notice it a 3 dimensional shape