42.104. Google Stock Price Prediction RNN

42.104.1. Load libraries

import numpy as np  # linear algebra
import pandas as pd  # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt

dataset_train = pd.read_csv("https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/RNN/trainset.csv")

dataset_train

trainset = dataset_train.iloc[:, 1:2].values

trainset

from sklearn.preprocessing import MinMaxScaler

sc = MinMaxScaler(feature_range=(0, 1))
training_scaled = sc.fit_transform(trainset)

training_scaled

x_train = []
y_train = []

for i in range(60, 1259):
    x_train.append(training_scaled[i - 60 : i, 0])
    y_train.append(training_scaled[i, 0])
x_train, y_train = np.array(x_train), np.array(y_train)

x_train.shape

x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout

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")

regressor.fit(x_train, y_train, epochs=50, batch_size=32)

# if you need a model trained, you can use this cell
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.utils import get_file

model_url = "https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/RNN/RNN_model.h5"
model_path = get_file("RNN_model.h5", model_url)
LSTM_model = load_model(model_path)

dataset_test = pd.read_csv("https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/RNN/testset.csv")

real_stock_price = dataset_test.iloc[:, 1:2].values

dataset_total = pd.concat((dataset_train["Open"], dataset_test["Open"]), axis=0)
dataset_total

inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60 :].values
inputs

inputs = inputs.reshape(-1, 1)

inputs

inputs = sc.transform(inputs)
inputs.shape

x_test = []
for i in range(60, 185):
    x_test.append(inputs[i - 60 : i, 0])

x_test = np.array(x_test)
x_test.shape

x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
x_test.shape

predicted_price = regressor.predict(x_test)

predicted_price = sc.inverse_transform(predicted_price)
predicted_price

plt.plot(real_stock_price, color="red", label="Real Price")
plt.plot(predicted_price, color="blue", label="Predicted Price")
plt.title("Google Stock Price Prediction")
plt.xlabel("Time")
plt.ylabel("Google Stock Price")
plt.legend()
plt.show()

42.105. Acknowledgements

Thanks to Priya for creating Google stock price prediction - RNN. It inspires the majority of the content in this chapter.