<reponame>Oreder/ML<filename>regression-intro-and-data.py
import pandas as pd

# Using machine learning with stock fundamentals for investing.
# In our case, we're able to at least start with simple stock price
# and volume information from Quandl.
import quandl

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. Low']) / df['Adj. Low'] * 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']]

print(df.head())
import pandas as pd

# Using machine learning with stock fundamentals for investing.
# In our case, we're able to at least start with simple stock price
# and volume information from Quandl.
import quandl

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. Low']) / df['Adj. Low'] * 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']]

print(df.head())
# Using machine learning with stock fundamentals for investing. # In our case, we're able to at least start with simple stock price # and volume information from Quandl.