Kesciui

import pandas as pd
import quandl, math
import numpy as np
from sklearn import preprocessing, model_selection, svm
from sklearn.linear_model import LinearRegression


df = quandl.get('WIKI/GOOGL')

df = df [['Adj. Open','Adj. High','Adj. Low','Adj. Close','Adj. Volume']]
df['HL_PCT'] = (df['Adj. High']-df['Adj. Close']) / df['Adj. Close'] *100.0
df['PCT_change'] = (df['Adj. Close']-df['Adj. Open']) / df['Adj. Open'] *100.0

df=df[['Adj. Close','HL_PCT','PCT_change','Adj. Volume']]

forecast_col = 'Adj. Close'
df.fillna(-99999, inplace=True)
forecast_out = int(math.ceil(0.01*len(df)))
df['label'] = df[forecast_col].shift(-forecast_out)
df.dropna(inplace=True)

x = np.array(df.drop(['label'],1))
y = np.array(df['label'])
x = preprocessing.scale(x)
y = np.array(df['label'])


x_train, x_test, y_train, y_test = model_selection.train_test_split(x,y,test_size=0.2)
clf = LinearRegression()
clf.fit(x_train,y_train)
accuracy = clf.score((x_test,y_test,))

print(accuracy)
#gaunu error :   accuracy = clf.score((x_test,y_test,))
TypeError: score() missing 1 required positional argument: 'y'