Prediction of Google Stock price using LSTM Model of Keras
jupyter: colab: kernelspec: display_name: Python 3 name: python3 language_info: name: python nbformat: 4 nbformat_minor: 0
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM,Dropout,Densedataset_train = pd.read_csv('Google_Stock_Price_Train.csv')
training_set = dataset_train.iloc[:, 1:2].values
sc = MinMaxScaler(feature_range = (0, 1))
training_set_scaled = sc.fit_transform(training_set)
X_train = []
y_train = []
for i in range(60, 1258):
X_train.append(training_set_scaled[i-60:i, 0])
y_train.append(training_set_scaled[i, 0])X_train, y_train = np.array(X_train), np.array(y_train)
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))regressor = Sequential()
regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units = 50, return_sequences = True))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units = 50, return_sequences = True))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units = 50))
regressor.add(Dropout(0.2))
regressor.add(Dense(units = 1))
regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')
print(regressor.summary())Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm (LSTM) (None, 60, 50) 10400
dropout (Dropout) (None, 60, 50) 0
lstm_1 (LSTM) (None, 60, 50) 20200
dropout_1 (Dropout) (None, 60, 50) 0
lstm_2 (LSTM) (None, 60, 50) 20200
dropout_2 (Dropout) (None, 60, 50) 0
lstm_3 (LSTM) (None, 50) 20200
dropout_3 (Dropout) (None, 50) 0
dense (Dense) (None, 1) 51
=================================================================
Total params: 71,051
Trainable params: 71,051
Non-trainable params: 0
_________________________________________________________________
None
regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)Epoch 1/100
38/38 [==============================] - 14s 128ms/step - loss: 0.0422
Epoch 2/100
38/38 [==============================] - 6s 149ms/step - loss: 0.0067
Epoch 3/100
38/38 [==============================] - 5s 137ms/step - loss: 0.0053
Epoch 4/100
38/38 [==============================] - 5s 127ms/step - loss: 0.0054
Epoch 5/100
38/38 [==============================] - 6s 160ms/step - loss: 0.0056
Epoch 6/100
38/38 [==============================] - 5s 127ms/step - loss: 0.0052
Epoch 7/100
38/38 [==============================] - 6s 162ms/step - loss: 0.0049
Epoch 8/100
38/38 [==============================] - 5s 129ms/step - loss: 0.0044
Epoch 9/100
38/38 [==============================] - 5s 127ms/step - loss: 0.0041
Epoch 10/100
38/38 [==============================] - 6s 158ms/step - loss: 0.0046
Epoch 11/100
38/38 [==============================] - 5s 125ms/step - loss: 0.0043
Epoch 12/100
38/38 [==============================] - 6s 161ms/step - loss: 0.0042
Epoch 13/100
38/38 [==============================] - 5s 129ms/step - loss: 0.0037
Epoch 14/100
38/38 [==============================] - 5s 145ms/step - loss: 0.0040
Epoch 15/100
38/38 [==============================] - 6s 148ms/step - loss: 0.0038
Epoch 16/100
38/38 [==============================] - 5s 131ms/step - loss: 0.0035
Epoch 17/100
38/38 [==============================] - 6s 164ms/step - loss: 0.0037
Epoch 18/100
38/38 [==============================] - 5s 131ms/step - loss: 0.0042
Epoch 19/100
38/38 [==============================] - 6s 163ms/step - loss: 0.0032
Epoch 20/100
38/38 [==============================] - 5s 127ms/step - loss: 0.0037
Epoch 21/100
38/38 [==============================] - 5s 127ms/step - loss: 0.0034
Epoch 22/100
38/38 [==============================] - 6s 158ms/step - loss: 0.0036
Epoch 23/100
38/38 [==============================] - 5s 126ms/step - loss: 0.0031
Epoch 24/100
38/38 [==============================] - 6s 160ms/step - loss: 0.0037
Epoch 25/100
38/38 [==============================] - 5s 126ms/step - loss: 0.0031
Epoch 26/100
38/38 [==============================] - 5s 137ms/step - loss: 0.0033
Epoch 27/100
38/38 [==============================] - 6s 147ms/step - loss: 0.0030
Epoch 28/100
38/38 [==============================] - 5s 127ms/step - loss: 0.0033
Epoch 29/100
38/38 [==============================] - 6s 161ms/step - loss: 0.0030
Epoch 30/100
38/38 [==============================] - 5s 128ms/step - loss: 0.0036
Epoch 31/100
38/38 [==============================] - 6s 151ms/step - loss: 0.0028
Epoch 32/100
38/38 [==============================] - 5s 134ms/step - loss: 0.0030
Epoch 33/100
38/38 [==============================] - 5s 126ms/step - loss: 0.0030
Epoch 34/100
38/38 [==============================] - 6s 160ms/step - loss: 0.0029
Epoch 35/100
38/38 [==============================] - 5s 127ms/step - loss: 0.0028
Epoch 36/100
38/38 [==============================] - 6s 160ms/step - loss: 0.0025
Epoch 37/100
38/38 [==============================] - 5s 127ms/step - loss: 0.0024
Epoch 38/100
38/38 [==============================] - 5s 127ms/step - loss: 0.0025
Epoch 39/100
38/38 [==============================] - 6s 160ms/step - loss: 0.0030
Epoch 40/100
38/38 [==============================] - 5s 127ms/step - loss: 0.0025
Epoch 41/100
38/38 [==============================] - 6s 161ms/step - loss: 0.0026
Epoch 42/100
38/38 [==============================] - 5s 126ms/step - loss: 0.0025
Epoch 43/100
38/38 [==============================] - 5s 135ms/step - loss: 0.0025
Epoch 44/100
38/38 [==============================] - 6s 150ms/step - loss: 0.0027
Epoch 45/100
38/38 [==============================] - 5s 128ms/step - loss: 0.0023
Epoch 46/100
38/38 [==============================] - 6s 162ms/step - loss: 0.0025
Epoch 47/100
38/38 [==============================] - 5s 126ms/step - loss: 0.0026
Epoch 48/100
38/38 [==============================] - 6s 152ms/step - loss: 0.0023
Epoch 49/100
38/38 [==============================] - 5s 135ms/step - loss: 0.0024
Epoch 50/100
38/38 [==============================] - 5s 127ms/step - loss: 0.0026
Epoch 51/100
38/38 [==============================] - 6s 159ms/step - loss: 0.0021
Epoch 52/100
38/38 [==============================] - 5s 128ms/step - loss: 0.0024
Epoch 53/100
38/38 [==============================] - 6s 161ms/step - loss: 0.0025
Epoch 54/100
38/38 [==============================] - 5s 128ms/step - loss: 0.0023
Epoch 55/100
38/38 [==============================] - 5s 128ms/step - loss: 0.0022
Epoch 56/100
38/38 [==============================] - 6s 158ms/step - loss: 0.0023
Epoch 57/100
38/38 [==============================] - 5s 125ms/step - loss: 0.0022
Epoch 58/100
38/38 [==============================] - 6s 162ms/step - loss: 0.0022
Epoch 59/100
38/38 [==============================] - 5s 127ms/step - loss: 0.0018
Epoch 60/100
38/38 [==============================] - 5s 138ms/step - loss: 0.0020
Epoch 61/100
38/38 [==============================] - 6s 146ms/step - loss: 0.0021
Epoch 62/100
38/38 [==============================] - 5s 127ms/step - loss: 0.0019
Epoch 63/100
38/38 [==============================] - 6s 160ms/step - loss: 0.0020
Epoch 64/100
38/38 [==============================] - 5s 125ms/step - loss: 0.0021
Epoch 65/100
38/38 [==============================] - 6s 147ms/step - loss: 0.0021
Epoch 66/100
38/38 [==============================] - 5s 135ms/step - loss: 0.0019
Epoch 67/100
38/38 [==============================] - 5s 126ms/step - loss: 0.0019
Epoch 68/100
38/38 [==============================] - 6s 159ms/step - loss: 0.0019
Epoch 69/100
38/38 [==============================] - 5s 126ms/step - loss: 0.0017
Epoch 70/100
38/38 [==============================] - 6s 159ms/step - loss: 0.0017
Epoch 71/100
38/38 [==============================] - 5s 125ms/step - loss: 0.0019
Epoch 72/100
38/38 [==============================] - 5s 126ms/step - loss: 0.0020
Epoch 73/100
38/38 [==============================] - 6s 160ms/step - loss: 0.0020
Epoch 74/100
38/38 [==============================] - 5s 127ms/step - loss: 0.0017
Epoch 75/100
38/38 [==============================] - 6s 160ms/step - loss: 0.0017
Epoch 76/100
38/38 [==============================] - 5s 126ms/step - loss: 0.0017
Epoch 77/100
38/38 [==============================] - 5s 128ms/step - loss: 0.0019
Epoch 78/100
38/38 [==============================] - 6s 154ms/step - loss: 0.0017
Epoch 79/100
38/38 [==============================] - 5s 126ms/step - loss: 0.0016
Epoch 80/100
38/38 [==============================] - 6s 160ms/step - loss: 0.0016
Epoch 81/100
38/38 [==============================] - 5s 126ms/step - loss: 0.0015
Epoch 82/100
38/38 [==============================] - 5s 138ms/step - loss: 0.0016
Epoch 83/100
38/38 [==============================] - 6s 144ms/step - loss: 0.0016
Epoch 84/100
38/38 [==============================] - 5s 127ms/step - loss: 0.0015
Epoch 85/100
38/38 [==============================] - 6s 162ms/step - loss: 0.0015
Epoch 86/100
38/38 [==============================] - 5s 127ms/step - loss: 0.0016
Epoch 87/100
38/38 [==============================] - 6s 154ms/step - loss: 0.0016
Epoch 88/100
38/38 [==============================] - 5s 132ms/step - loss: 0.0016
Epoch 89/100
38/38 [==============================] - 5s 127ms/step - loss: 0.0016
Epoch 90/100
38/38 [==============================] - 6s 160ms/step - loss: 0.0016
Epoch 91/100
38/38 [==============================] - 5s 127ms/step - loss: 0.0015
Epoch 92/100
38/38 [==============================] - 6s 161ms/step - loss: 0.0016
Epoch 93/100
38/38 [==============================] - 5s 129ms/step - loss: 0.0016
Epoch 94/100
38/38 [==============================] - 5s 135ms/step - loss: 0.0013
Epoch 95/100
38/38 [==============================] - 6s 154ms/step - loss: 0.0014
Epoch 96/100
38/38 [==============================] - 5s 130ms/step - loss: 0.0015
Epoch 97/100
38/38 [==============================] - 6s 163ms/step - loss: 0.0015
Epoch 98/100
38/38 [==============================] - 5s 128ms/step - loss: 0.0015
Epoch 99/100
38/38 [==============================] - 6s 150ms/step - loss: 0.0018
Epoch 100/100
38/38 [==============================] - 5s 133ms/step - loss: 0.0015
<keras.callbacks.History at 0x7ff988308e80>
dataset_test = pd.read_csv('Google_Stock_Price_Test.csv')
real_stock_price = dataset_test.iloc[:, 1:2].values# Getting the predicted stock price of 2017
dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis = 0)
inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values
inputs = inputs.reshape(-1,1)
inputs = sc.transform(inputs)
X_test = []
for i in range(60, 80):
X_test.append(inputs[i-60:i, 0])
X_test = np.array(X_test)
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
predicted_stock_price = regressor.predict(X_test)
predicted_stock_price = sc.inverse_transform(predicted_stock_price)1/1 [==============================] - 2s 2s/step
# Visualising the results
plt.plot(real_stock_price, color = 'red', label = 'Real Google Stock Price')
plt.plot(predicted_stock_price, color = 'green', label = 'Predicted Google Stock Price')
plt.title('Google Stock Price Prediction')
plt.xlabel('Time')
plt.ylabel('Google Stock Price')
plt.legend()
plt.show()
