Train a decision tree, that trains on the data given back from load_test_data().
# Returns data in np array sorted as:
in_sf,beds,bath,price,year_built,sqft,price_per_sqft,elevation = load_data()
Then, write a function that uses this decision tree. Try to get at least 70% on
grade(). Your function is given the following arguments as arrays, and is expected to
return a numpy array that labels if the entry at the ith index is in San-Francisco, using
1 or 0.
# Arguments
beds,bath,price,year_built,sqft,price_per_sqft,elevation
Both these functions are available in data.py (import data).