구글 데이터 분석하기

import numpy as np

import pandas as pd

from sklearn.preprocessing import MinMaxScaler

from keras.models import Sequential

from keras.layers import Dense, LSTM

import matplotlib.pyplot as plt

 

# Load the Google stock data into a pandas dataframe

df = pd.read_csv('GOOGL.csv', date_parser=True)

 

# Normalize the data using MinMaxScaler

scaler = MinMaxScaler(feature_range=(0,1))

df = scaler.fit_transform(df[['Close']])

 

# Split the data into training and testing sets

train_data = df[0:int(df.shape[0]*0.8),:]

test_data = df[int(df.shape[0]*0.8):,:]

 

# Convert the data into a numpy array

x_train = []

y_train = []

for i in range(60, train_data.shape[0]):

    x_train.append(train_data[i-60:i,0])

    y_train.append(train_data[i,0])

x_train, y_train = np.array(x_train), np.array(y_train)

 

x_test = []

y_test = []

for i in range(60, test_data.shape[0]):

    x_test.append(test_data[i-60:i,0])

    y_test.append(test_data[i,0])

x_test, y_test = np.array(x_test), np.array(y_test)

 

# Reshape the data for use with the LSTM model

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

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

 

# Build the LSTM model

model = Sequential()

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

model.add(LSTM(units=50))

model.add(Dense(1))

 

# Compile the model

model.compile(loss='mean_squared_error', optimizer='adam')

 

# Train the model

history = model.fit(x_train, y_train, epochs=30, batch_size=16)

 

# Evaluate the model on the test data

model.evaluate(x_test, y_test)

 

# Make predictions using the trained model

predictions = model.predict(x_test)

 

# Plot the predictions against the actual stock prices

plt.plot(y_test, color='red', label='Actual Google Stock Price')

plt.plot(predictions, color='blue', label='Predicted Google Stock Price')

plt.title('Google Stock Price Prediction')

plt.xlabel('Time')

plt.ylabel('Stock Price')

plt.legend()

plt.show()