LinearRegressionYerba

import pandas as pd
import quandl, math, datetime
import numpy as np
from sklearn import preprocessing, model_selection, svm
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from matplotlib import style

style.use('ggplot')

quandl.ApiConfig.api_key = 'Not gonna show the API key'

#Get data from quandl
df = quandl.get('WIKI/GOOGL')

#Deleting not important data
df = df[['Adj. Open', 'Adj. High', 'Adj. Low', 'Adj. Close', 'Adj. Volume',]]

#Calc HL_PCT
df['HL_PCT'] = (df['Adj. High'] - df['Adj. Low']) / df['Adj. Low'] * 100.0

#Calc PCT_change
df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open']) / df['Adj. Open'] * 100.0

#Getting rid of useless data
df = df[['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']]

forecast_col = 'Adj. Close'

#Fill empty data with -99999
df.fillna(-99999, inplace=True)

#Calculate the value we want to see in the future
forecast_out = math.ceil(0.01*len(df))

#Shifting the table up
df['label'] = df[forecast_col].shift(-forecast_out)

#Define X
X = np.array(df.drop(['label'], 1))
X = preprocessing.scale(X)
X = X[:-forecast_out]
X_lately = X[-forecast_out:]

df.dropna(inplace=True)

#Define y
y = np.array(df['label'])
y = np.array(df['label'])


X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2)

#Choosing algorythm
clf = svm.SVR()

#Fitting the data
clf.fit(X_train, y_train)

#Calculating accuracy
accuracy = clf.score(X_test, y_test)

print('Accuracy: ', (accuracy*100.00), '%')

#Predicting values
forecast_predict = clf.predict(X_lately)

#Printing predicted values
print(forecast_predict, forecast_out)

df['Forecast'] = np.nan


#Date calculation for visualisation
last_date = df.iloc[-1].name
last_unix = last_date.timestamp()
one_day = 86400
next_unix = last_unix + one_day

for i in forecast_predict:
    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]

#Displaying the graph
df['Adj. Close'].plot()
df['Forecast'].plot()
plt.legend(loc=4)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()