#This program predicts stock prices with machine learning

#Install dependencies

import quandl
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
from sklearn.model_selection import train_test_split

#Get the data

df = quandl.get("WIKI/FB") #df = dataframe

#Take a look at the data

print(df.head())

#Get the adjusted close price

df = df[["Adj. Close"]]

#Take a look at the new data

print(df.head())

#Variable for the number of days predicting out in the future

forecast_out = 1

#Create another column (the target variable) shifted "n" units up

df['Prediction'] = df[['Adj. Close']].shift(-forecast_out)

print(df.tail())

#Create the independent data set (x)
#Convert the dataframe to numpy array

X = np.array(df.drop(["Prediction"]),1)

X = X [:-forecast_out]

print(x)