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import numpy
import matplotlib.pyplot as plt
import pandas
import pandas_datareader as web
import datetime
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM, Dropout

# Load Data: 
company = 'FB'
start = datetime.datetime(2012, 1, 1)
end = datetime.datetime(2020, 1, 1)

data = web.DataReader(company, 'yahoo', start, end)

# Prepare Data:
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1, 1))

prediction_days = 60

x_train = []
y_train = []

for x in range(prediction_days, len(scaled_data)):
    x_train.append(scaled_data[x - prediction_days:x, 0])
    y_train.append(scaled_data[x, 0])

x_train, y_train = numpy.array(x_train), numpy.array(y_train)
x_train = numpy.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))

# Build Model:
model = Sequential()

model.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1], 1)))
model.add(Dropout(0.2))
model.add(LSTM(units=50, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=50))
model.add(Dropout(0.2))
model.add(Dense(units=1))

model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(x_train, y_train, epochs=25, batch_size=32)

'''# Test Model:'''
test_start = datetime.datetime(2020, 1, 1)
test_end = datetime.datetime.now()

test_data = web.DataReader(company, 'yahoo', test_start, test_end)
actual_prices = test_data['Close'].values

total_dataset = pandas.concat((data['Close'], test_data['Close']), axis=0)

model_inputs = total_dataset[len(total_dataset) - len(test_data) - prediction_days:].values
model_inputs = model_inputs.reshape(-1, 1)
model_inputs = scaler.transform(model_inputs)

x_test = []

for x in range(prediction_days, len(model_inputs)):
    x_test.append(model_inputs[x - prediction_days:x, 0])
    
x_test = numpy.array(x_test)
x_test = numpy.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))

predicted_prices = model.predict(x_test)
predicted_prices = scaler.inverse_transform(predicted_prices)

# Plot Data:
plt.plot(actual_prices, color='black', label=f'Actual {company} Price')
plt.plot(predicted_prices, color='green', label=f'Predicted {company} Price')
plt.title(f'{company} Share Price')
plt.xlabel('Time')
plt.ylabel(f'{company} Share Price')
plt.legend()
plt.show()

# Predict Next Day:
real_data = [model_inputs[len(model_inputs) + 1 - prediction_days:len(model_inputs + 1), 0]]
real_data = numpy.array(real_data)
real_data = numpy.reshape(real_data, (real_data.shape[0], real_data.shape[1], 1))

prediction = model.predict(real_data)
prediction = scaler.inverse_transform(prediction)

print(f'Prediction: {prediction}')
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