Regression_Training_and_Testing_p4.py

import pandas as pd
import quandl, math
import numpy as np
from sklearn import preprocessing, cross_validation, 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
df['PCT_change']=(df['Adj. Close']-df['Adj. Open'])/df['Adj. Open']*100
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)))
print(forecast_out)

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=cross_validation.train_test_split(X,y,test_size=0.2)


#clf=LinearRegression(n_jobs=-1)
#clf=LinearRegression(n_jobs=10)
#clf=svm.SVR(kernel='poly')
clf=LinearRegression()
clf.fit(X_train,y_train)
accuracy=clf.score(X_test,y_test)

print(accuracy)