Cross Validation Help

cross_validation.py

import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
import statsmodels.api as sm
import statsmodels.formula.api as smf
from sklearn.metrics import mean_squared_error

loansData = pd.read_csv('https://github.com/Thinkful-Ed/curric-data-001-data-sets/raw/master/loans/loansData.csv')

#Remove '%' from 'Interest.Rate' column and contert to number
loansData['Interest.Rate']=loansData['Interest.Rate'].map(lambda x: round(float(x.rstrip('%')) / 100, 4))
#Remove 'months' from the 'Loan.Length' column
loansData['Loan.Length']=loansData['Loan.Length'].map(lambda x: int(x.rstrip('months')))
#Split 'FICO.Range' column on the '-' to make each item a list
loansData['FICO.Range']=loansData['FICO.Range'].map(lambda x: x.split('-'))
#Convert each FICO score in the list to an int
loansData['FICO.Range']=loansData['FICO.Range'].map(lambda x: [int(n) for n in x])
#Populate the column with the first number in the FICO range
loansData['FICO.Score']=[val[0] for val in loansData['FICO.Range']]

#Split data into 10 sets
kf = KFold(n_splits=10)

for train, test in kf.split(loansData):
	intrate = loansData['Interest.Rate']
	loanamt = loansData['Amount.Requested']
	fico = loansData['FICO.Score']

	#Reshape series 
	y = np.matrix(intrate).transpose()
	x1 = np.matrix(fico).transpose()
	x2 = np.matrix(loanamt).transpose()

	#Combine columns to create input matrix
	x = np.column_stack([x1,x2])

	#Create linear model
	X = sm.add_constant(x)
	model = sm.OLS(y,X)
	f = model.fit()

	#Print R value
	print ('R-Squared: ', f.rsquared)