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import quandl, datetime
import sklearn
import pandas as pd
import math as mt
import numpy as np
from sklearn import preprocessing, cross_validation, svm
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from matplotlib import style
style.use('ggplot')
df=quandl.get('WIKI/GOOGL')
#print (df.head())
df=df[['Adj. Open', 'Adj. High', 'Adj. Low', 'Adj. Close', 'Adj. Volume',]]
df['HL_PCT']=(df['Adj. Close']-df['Adj. Open'])/df['Adj. Close']*100.0
#print (df['HL_PCT']) #shows high/low percent
df['PCT_change']=(df['Adj. High']-df['Adj. Close'])/df['Adj. Open']*100.0 #shows % change
df=df[['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']]
#print (df.head())
forecast_col='Adj. Close'
df.fillna('-99999', inplace=True)
forecast_out=int(mt.ceil(0.01*len(df)))
df['label']=df[forecast_col].shift(-forecast_out)
#print (df.head())
#print (df.tail())
X=np.array(df.drop(['label'],1))
X=X[:-forecast_out]
X=preprocessing.scale(X)
X_lately=X[-forecast_out:]
df.dropna(inplace=True)
y=np.array(df['label'])
y=np.array(df['label'])
print (len(X),len(y))
X_train, X_test, y_train, y_test= cross_validation.train_test_split(X,y, test_size=0.2)
clf=LinearRegression(n_jobs=10)
clf.fit(X_train, y_train)
accuracy=clf.score(X_test, y_test)
forecast_set=clf.predict(X_lately)
print (forecast_set, accuracy, forecast_out)
df['forecast']=np.nan
last_date=df.iloc[-1].name
last_unix=last_date.timestamp()
one_day=86400
next_unix=last_unix + one_day
for i in forecast_set:
next_date=datetime.datetime.fromtimestamp(next_unix)
next_unix+=one_day
df.loc[next_date]=[np.nan for _ in range(len(df.columns)-1)]+[i]
df['Adj. Close'].plot()
df['forecast'].plot()
plt.legend(loc=4)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()
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