ml2_regression.py

import pandas as pd
import quandl as qd
import math
import 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')

df = qd.get('WIKI/GOOGL')  # google stock data

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)  # fill in missing data with an 'outlier'
forecast_out = math.ceil(0.005 * len(df))
print(forecast_out)
df['label'] = df[forecast_col].shift(-forecast_out)

x = np.array(df.drop(['label'], 1))  # remove the label from features
x = preprocessing.scale(x)  # scale all the features to save on processing time
x_lately = x[-forecast_out:]
x = x[:-forecast_out]

df.dropna(inplace=True)
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(n_jobs=-1)  # run as many processes as supported
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()