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from sklearn.preprocessing import MinMaxScaler

import numpy as np

import numpy as np # linear algebra

import pandas as pd

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset_train = pd.read_csv('../input/gooogle-stock-price/Google_Stock_Price_Train.csv')
training_set = dataset_train.iloc[:, 1:2].values
from sklearn.preprocessing import MinMaxScaler
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))

X_train.shape[1]