In [1]:

# Import necessary packages
import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator
from h2o.estimators.glm import H2OGeneralizedLinearEstimator

In [2]:

# Initialize instance of H2O
h2o.init()

Checking whether there is an H2O instance running at http://localhost:54321..... not found.
Attempting to start a local H2O server...
  Java Version: java version "1.7.0_80"; Java(TM) SE Runtime Environment (build 1.7.0_80-b15); Java HotSpot(TM) 64-Bit Server VM (build 24.80-b11, mixed mode)
  Starting server from /Users/megankurka/anaconda2/lib/python2.7/site-packages/h2o/backend/bin/h2o.jar
  Ice root: /var/folders/gv/w2f3zs_d33l3dt5j67k9nhhr0000gn/T/tmplmDKFI
  JVM stdout: /var/folders/gv/w2f3zs_d33l3dt5j67k9nhhr0000gn/T/tmplmDKFI/h2o_megankurka_started_from_python.out
  JVM stderr: /var/folders/gv/w2f3zs_d33l3dt5j67k9nhhr0000gn/T/tmplmDKFI/h2o_megankurka_started_from_python.err
  Server is running at http://127.0.0.1:54321
Connecting to H2O server at http://127.0.0.1:54321... successful.

H2O cluster uptime:	02 secs
H2O cluster version:	3.10.4.4
H2O cluster version age:	9 days
H2O cluster name:	H2O_from_python_megankurka_a8slf8
H2O cluster total nodes:	1
H2O cluster free memory:	3.556 Gb
H2O cluster total cores:	8
H2O cluster allowed cores:	8
H2O cluster status:	accepting new members, healthy
H2O connection url:	http://127.0.0.1:54321
H2O connection proxy:	None
H2O internal security:	False
Python version:	2.7.13 final

In [3]:

# If possible download from the s3 link and change the path to the dataset
path = "http://h2o-public-test-data.s3.amazonaws.com/bigdata/laptop/lending-club/LoanStats3a.csv"

In [4]:

# Specify some column types to "String" that we want to munge later
types = {"int_rate":"string", "revol_util":"string", "emp_length":"string", "verification_status":"string"}

In [5]:

# Task 1: Import the file and look at the frame
loan_stats = h2o.import_file(path=path, col_types= types)
loan_stats.describe()

Parse progress: |█████████████████████████████████████████████████████████| 100%
Rows:42538
Cols:52

	id	member_id	loan_amnt	funded_amnt	funded_amnt_inv	term	int_rate	installment	grade	sub_grade	emp_title	emp_length	home_ownership	annual_inc	verification_status	issue_d	loan_status	pymnt_plan	url	desc	purpose	title	zip_code	addr_state	dti	delinq_2yrs	earliest_cr_line	inq_last_6mths	mths_since_last_delinq	mths_since_last_record	open_acc	pub_rec	revol_bal	revol_util	total_acc	initial_list_status	out_prncp	out_prncp_inv	total_pymnt	total_pymnt_inv	total_rec_prncp	total_rec_int	total_rec_late_fee	recoveries	collection_recovery_fee	last_pymnt_d	last_pymnt_amnt	next_pymnt_d	last_credit_pull_d	collections_12_mths_ex_med	mths_since_last_major_derog	policy_code
type	int	int	int	int	real	enum	string	real	enum	enum	enum	string	enum	real	string	time	enum	enum	string	enum	enum	enum	enum	enum	real	int	time	int	enum	enum	int	int	int	string	int	enum	real	real	real	real	real	real	real	real	real	time	real	time	time	int	enum	int
mins	54734.0	70473.0	500.0	500.0	0.0		NaN	15.67				NaN		1896.0	NaN	1.180656e+12			NaN						0.0	0.0	-7.573824e+11	0.0			1.0	0.0	0.0	NaN	1.0		0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.1964672e+12	0.0	1.1964672e+12	1.1779776e+12	0.0		1.0
mean	664579.85231	825702.55117	11089.7225814	10821.5857529	10139.8306012		NaN	322.625618667				NaN		69136.5564203	NaN	1.28564160993e+12			NaN						13.3730431409	0.152449066014	8.54295612817e+11	1.08142379899			9.34395144215	0.0581564955536	14297.8609145	NaN	22.1244059662		329.411123545	327.08996544	11651.753137	10948.5801657	9368.08058187	2209.04911203	1.45873268638	73.164719007	9.07681827201	1.36032037132e+12	2552.60725685	1.38132344523e+12	1.38597191711e+12	0.0		1.0
maxs	1077501.0	1314167.0	35000.0	35000.0	35000.0		NaN	1305.19				NaN		6000000.0	NaN	1.3226976e+12			NaN						29.99	13.0	1.2254976e+12	33.0			47.0	5.0	1207359.0	NaN	90.0		17749.51	17749.51	56809.0516288	56475.05	35000.03	21809.05	208.819529958	29623.35	7002.19	1.4331168e+12	36115.2	1.4383872e+12	1.4357088e+12	0.0		1.0
sigma	219302.219319	279540.905635	7410.93839055	7146.91467501	7131.68644868		NaN	208.928069				NaN		64096.3497189	NaN	31759283733.1			NaN						6.72631490173	0.512406485017	2.16068688522e+11	1.5274548353			4.49627387569	0.245713140651	22018.4410097	NaN	11.5928113373		1417.54568613	1409.61373349	8555.46989933	8486.98039403	6777.11981861	2502.52824092	7.57275617593	612.26018535	149.086823026	47801906399.7	4380.38476617	69074077358.6	49253773579.3	0.0		0.0
zeros	0	0	0	0	233		0	0				0		0	0	0			0						206	37771	4	19657			0	40130	1119	0	0		38972	38972	26	304	86	83	40214	38357	39033	0	95	0	0	42390		0
missing	3	3	3	3	3	3	3	3	3	3	3	3	3	7	3	3	3	3	3	3	3	3	3	3	3	32	32	32	3	3	32	32	3	3	32	3	3	3	3	3	3	3	3	3	3	86	3	36281	7	148	3	3
0	1077501.0	1296599.0	5000.0	5000.0	4975.0	36 months	10.65%	162.87	B	B2		10+ years	RENT	24000.0	VERIFIED - income	2011-12-01 00:00:00	Fully Paid	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1077501	Borrower added on 12/22/11 > I need to upgrade my business technologies.	credit_card	Computer	860xx	AZ	27.65	0.0	1985-01-01 00:00:00	1.0			3.0	0.0	13648.0	83.7%	9.0	f	0.0	0.0	5861.07141425	5831.78	5000.0	861.07	0.0	0.0	0.0	2015-01-01 00:00:00	171.62		2015-01-01 00:00:00	0.0		1.0
1	1077430.0	1314167.0	2500.0	2500.0	2500.0	60 months	15.27%	59.83	C	C4	Ryder	< 1 year	RENT	30000.0	VERIFIED - income source	2011-12-01 00:00:00	Charged Off	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1077430	Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike. I only need this money because the deal im looking at is to good to pass up. Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike.I only need this money because the deal im looking at is to good to pass up. I have finished college with an associates degree in business and its takingmeplaces	car	bike	309xx	GA	1.0	0.0	1999-04-01 00:00:00	5.0			3.0	0.0	1687.0	9.4%	4.0	f	0.0	0.0	1008.71	1008.71	456.46	435.17	0.0	117.08	1.11	2013-04-01 00:00:00	119.66		2013-09-01 00:00:00	0.0		1.0
2	1077175.0	1313524.0	2400.0	2400.0	2400.0	36 months	15.96%	84.33	C	C5		10+ years	RENT	12252.0	not verified	2011-12-01 00:00:00	Fully Paid	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1077175		small_business	real estate business	606xx	IL	8.72	0.0	2001-11-01 00:00:00	2.0			2.0	0.0	2956.0	98.5%	10.0	f	0.0	0.0	3003.65364445	3003.65	2400.0	603.65	0.0	0.0	0.0	2014-06-01 00:00:00	649.91		2015-06-01 00:00:00	0.0		1.0
3	1076863.0	1277178.0	10000.0	10000.0	10000.0	36 months	13.49%	339.31	C	C1	AIR RESOURCES BOARD	10+ years	RENT	49200.0	VERIFIED - income source	2011-12-01 00:00:00	Fully Paid	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1076863	Borrower added on 12/21/11 > to pay for property tax (borrow from friend, need to pay back) & central A/C need to be replace. I'm very sorry to let my loan expired last time.	other	personel	917xx	CA	20.0	0.0	1996-02-01 00:00:00	1.0	35		10.0	0.0	5598.0	21%	37.0	f	0.0	0.0	12226.3022123	12226.3	10000.0	2209.33	16.97	0.0	0.0	2015-01-01 00:00:00	357.48		2015-01-01 00:00:00	0.0		1.0
4	1075358.0	1311748.0	3000.0	3000.0	3000.0	60 months	12.69%	67.79	B	B5	University Medical Group	1 year	RENT	80000.0	VERIFIED - income source	2011-12-01 00:00:00	Current	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1075358	Borrower added on 12/21/11 > I plan on combining three large interest bills together and freeing up some extra each month to pay toward other bills. I've always been a good payor but have found myself needing to make adjustments to my budget due to a medical scare. My job is very stable, I love it.	other	Personal	972xx	OR	17.94	0.0	1996-01-01 00:00:00	0.0	38		15.0	0.0	27783.0	53.9%	38.0	f	1168.04	1168.04	2767.64	2767.64	1831.96	935.68	0.0	0.0	0.0	2015-06-01 00:00:00	67.79	2015-07-01 00:00:00	2015-06-01 00:00:00	0.0		1.0
5	1075269.0	1311441.0	5000.0	5000.0	5000.0	36 months	7.90%	156.46	A	A4	Veolia Transportaton	3 years	RENT	36000.0	VERIFIED - income source	2011-12-01 00:00:00	Fully Paid	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1075269		wedding	My wedding loan I promise to pay back	852xx	AZ	11.2	0.0	2004-11-01 00:00:00	3.0			9.0	0.0	7963.0	28.3%	12.0	f	0.0	0.0	5631.37775318	5631.38	5000.0	631.38	0.0	0.0	0.0	2015-01-01 00:00:00	161.03		2015-06-01 00:00:00	0.0		1.0
6	1069639.0	1304742.0	7000.0	7000.0	7000.0	60 months	15.96%	170.08	C	C5	Southern Star Photography	8 years	RENT	47004.0	not verified	2011-12-01 00:00:00	Current	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1069639	Borrower added on 12/18/11 > I am planning on using the funds to pay off two retail credit cards with 24.99% interest rates, as well as a major bank credit card with a 18.99% rate. I pay all my bills on time, looking for a lower combined payment and lower monthly payment.	debt_consolidation	Loan	280xx	NC	23.51	0.0	2005-07-01 00:00:00	1.0			7.0	0.0	17726.0	85.6%	11.0	f	2853.21	2853.21	6946.28	6946.28	4146.8	2799.48	0.0	0.0	0.0	2015-06-01 00:00:00	170.08	2015-08-01 00:00:00	2015-06-01 00:00:00	0.0		1.0
7	1072053.0	1288686.0	3000.0	3000.0	3000.0	36 months	18.64%	109.43	E	E1	MKC Accounting	9 years	RENT	48000.0	VERIFIED - income source	2011-12-01 00:00:00	Fully Paid	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1072053	Borrower added on 12/16/11 > Downpayment for a car.	car	Car Downpayment	900xx	CA	5.35	0.0	2007-01-01 00:00:00	2.0			4.0	0.0	8221.0	87.5%	4.0	f	0.0	0.0	3938.14433376	3938.14	3000.0	938.14	0.0	0.0	0.0	2015-01-01 00:00:00	111.34		2014-12-01 00:00:00	0.0		1.0
8	1071795.0	1306957.0	5600.0	5600.0	5600.0	60 months	21.28%	152.39	F	F2		4 years	OWN	40000.0	VERIFIED - income source	2011-12-01 00:00:00	Charged Off	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1071795	Borrower added on 12/21/11 > I own a small home-based judgment collection business. I have 5 years experience collecting debts. I am now going from a home office to a small office. I also plan to buy a small debt portfolio (eg. $10K for $1M of debt) My score is not A+ because I own my home and have no mortgage.	small_business	Expand Business & Buy Debt Portfolio	958xx	CA	5.55	0.0	2004-04-01 00:00:00	2.0			11.0	0.0	5210.0	32.6%	13.0	f	0.0	0.0	646.02	646.02	162.02	294.94	0.0	189.06	2.09	2012-04-01 00:00:00	152.39		2012-08-01 00:00:00	0.0		1.0
9	1071570.0	1306721.0	5375.0	5375.0	5350.0	60 months	12.69%	121.45	B	B5	Starbucks	< 1 year	RENT	15000.0	VERIFIED - income	2011-12-01 00:00:00	Charged Off	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1071570	Borrower added on 12/16/11 > I'm trying to build up my credit history. I live with my brother and have no car payment or credit cards. I am in community college and work full time. Im going to use the money to make some repairs around the house and get some maintenance done on my car. Borrower added on 12/20/11 > $1000 down only $4375 to go. Thanks to everyone that invested so far, looking forward to surprising my brother with the fixes around the house.	other	Building my credit history.	774xx	TX	18.08	0.0	2004-09-01 00:00:00	0.0			2.0	0.0	9279.0	36.5%	3.0	f	0.0	0.0	1476.19	1469.34	673.48	533.42	0.0	269.29	2.52	2012-11-01 00:00:00	121.45		2013-03-01 00:00:00	0.0		1.0

In [6]:

# Task 2: Look at the levels int he response column, "loan_status"
# Hint: Use .table() function on the response column

loan_status	Count
Charged Off	5435
Current	3351
Default	7
Does not meet the credit policy. Status:Charged Off	761
Does not meet the credit policy. Status:Current	53
Does not meet the credit policy. Status:Fully Paid	1933
Does not meet the credit policy. Status:In Grace Period	2
Fully Paid	30843
In Grace Period	60
Late (16-30 days)	16

Out[6]:

In [7]:

# Task 3: Drop all loans that are still in progess and therefore cannot be deemed good/bad loans
# Hint: "Current", "In Grace Period", "Late (16-30 days)", "Late (31-120 days)" are ongoing loans

In [8]:

loan_stats.show()

id	member_id	loan_amnt	funded_amnt	funded_amnt_inv	term	int_rate	installment	grade	sub_grade	emp_title	emp_length	home_ownership	annual_inc	verification_status	issue_d	loan_status	pymnt_plan	url	desc	purpose	title	zip_code	addr_state	dti	earliest_cr_line	inq_last_6mths	mths_since_last_delinq	open_acc	revol_bal	revol_util	total_acc	initial_list_status	total_pymnt	total_pymnt_inv	total_rec_prncp	total_rec_int	total_rec_late_fee	recoveries	collection_recovery_fee	last_pymnt_d	last_pymnt_amnt	last_credit_pull_d	policy_code
1.0775e+06	1.2966e+06	5000	5000	4975	36 months	10.65%	162.87	B	B2		10+ years	RENT	24000	VERIFIED - income	2011-12-01 00:00:00	Fully Paid	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1077501	Borrower added on 12/22/11 > I need to upgrade my business technologies.	credit_card	Computer	860xx	AZ	27.65	1985-01-01 00:00:00	1		3	13648	83.7%	9	f	5861.07	5831.78	5000	861.07	0	0	0	2015-01-01 00:00:00	171.62	2015-01-01 00:00:00	1
1.07743e+06	1.31417e+06	2500	2500	2500	60 months	15.27%	59.83	C	C4	Ryder	< 1 year	RENT	30000	VERIFIED - income source	2011-12-01 00:00:00	Charged Off	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1077430	Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike. I only need this money because the deal im looking at is to good to pass up. Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike.I only need this money because the deal im looking at is to good to pass up. I have finished college with an associates degree in business and its takingmeplaces	car	bike	309xx	GA	1	1999-04-01 00:00:00	5		3	1687	9.4%	4	f	1008.71	1008.71	456.46	435.17	0	117.08	1.11	2013-04-01 00:00:00	119.66	2013-09-01 00:00:00	1
1.07718e+06	1.31352e+06	2400	2400	2400	36 months	15.96%	84.33	C	C5		10+ years	RENT	12252	not verified	2011-12-01 00:00:00	Fully Paid	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1077175		small_business	real estate business	606xx	IL	8.72	2001-11-01 00:00:00	2		2	2956	98.5%	10	f	3003.65	3003.65	2400	603.65	0	0	0	2014-06-01 00:00:00	649.91	2015-06-01 00:00:00	1
1.07686e+06	1.27718e+06	10000	10000	10000	36 months	13.49%	339.31	C	C1	AIR RESOURCES BOARD	10+ years	RENT	49200	VERIFIED - income source	2011-12-01 00:00:00	Fully Paid	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1076863	Borrower added on 12/21/11 > to pay for property tax (borrow from friend, need to pay back) & central A/C need to be replace. I'm very sorry to let my loan expired last time.	other	personel	917xx	CA	20	1996-02-01 00:00:00	1	35	10	5598	21%	37	f	12226.3	12226.3	10000	2209.33	16.97	0	0	2015-01-01 00:00:00	357.48	2015-01-01 00:00:00	1
1.07527e+06	1.31144e+06	5000	5000	5000	36 months	7.90%	156.46	A	A4	Veolia Transportaton	3 years	RENT	36000	VERIFIED - income source	2011-12-01 00:00:00	Fully Paid	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1075269		wedding	My wedding loan I promise to pay back	852xx	AZ	11.2	2004-11-01 00:00:00	3		9	7963	28.3%	12	f	5631.38	5631.38	5000	631.38	0	0	0	2015-01-01 00:00:00	161.03	2015-06-01 00:00:00	1
1.07205e+06	1.28869e+06	3000	3000	3000	36 months	18.64%	109.43	E	E1	MKC Accounting	9 years	RENT	48000	VERIFIED - income source	2011-12-01 00:00:00	Fully Paid	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1072053	Borrower added on 12/16/11 > Downpayment for a car.	car	Car Downpayment	900xx	CA	5.35	2007-01-01 00:00:00	2		4	8221	87.5%	4	f	3938.14	3938.14	3000	938.14	0	0	0	2015-01-01 00:00:00	111.34	2014-12-01 00:00:00	1
1.0718e+06	1.30696e+06	5600	5600	5600	60 months	21.28%	152.39	F	F2		4 years	OWN	40000	VERIFIED - income source	2011-12-01 00:00:00	Charged Off	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1071795	Borrower added on 12/21/11 > I own a small home-based judgment collection business. I have 5 years experience collecting debts. I am now going from a home office to a small office. I also plan to buy a small debt portfolio (eg. $10K for $1M of debt) My score is not A+ because I own my home and have no mortgage.	small_business	Expand Business & Buy Debt Portfolio	958xx	CA	5.55	2004-04-01 00:00:00	2		11	5210	32.6%	13	f	646.02	646.02	162.02	294.94	0	189.06	2.09	2012-04-01 00:00:00	152.39	2012-08-01 00:00:00	1
1.07157e+06	1.30672e+06	5375	5375	5350	60 months	12.69%	121.45	B	B5	Starbucks	< 1 year	RENT	15000	VERIFIED - income	2011-12-01 00:00:00	Charged Off	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1071570	Borrower added on 12/16/11 > I'm trying to build up my credit history. I live with my brother and have no car payment or credit cards. I am in community college and work full time. Im going to use the money to make some repairs around the house and get some maintenance done on my car. Borrower added on 12/20/11 > $1000 down only $4375 to go. Thanks to everyone that invested so far, looking forward to surprising my brother with the fixes around the house.	other	Building my credit history.	774xx	TX	18.08	2004-09-01 00:00:00	0		2	9279	36.5%	3	f	1476.19	1469.34	673.48	533.42	0	269.29	2.52	2012-11-01 00:00:00	121.45	2013-03-01 00:00:00	1
1.07008e+06	1.3052e+06	6500	6500	6500	60 months	14.65%	153.45	C	C3	Southwest Rural metro	5 years	OWN	72000	not verified	2011-12-01 00:00:00	Fully Paid	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1070078	Borrower added on 12/15/11 > I had recived a loan from Citi Financial about a year ago, I was paying 29.99 intrest, so the refinance is to cut that rate since cleaning up my credit I have been paying everything on time as shown on my credit report	debt_consolidation	High intrest Consolidation	853xx	AZ	16.12	1998-01-01 00:00:00	2		14	4032	20.6%	23	f	7677.52	7677.52	6500	1177.52	0	0	0	2013-06-01 00:00:00	1655.54	2013-07-01 00:00:00	1
1.06991e+06	1.30501e+06	12000	12000	12000	36 months	12.69%	402.54	B	B5	UCLA	10+ years	OWN	75000	VERIFIED - income source	2011-12-01 00:00:00	Fully Paid	n	https://www.lendingclub.com/browse/loanDetail.action?loan_id=1069908		debt_consolidation	Consolidation	913xx	CA	10.78	1989-10-01 00:00:00	0		12	23336	67.1%	34	f	13943.1	13943.1	12000	1943.08	0	0	0	2013-09-01 00:00:00	6315.3	2013-08-01 00:00:00	1

In [9]:

# Task 4: Bin the response variable to good/bad oans only, use your best judgement for what qualifies as a good/bad loan
# Create a new column called "bad_loan" which should be a binary variable
# Hint: You can turn the bad_loan columm into a factor using .asfactor()

In [10]:

# Task 5: String munging to clearn string columns before converting to numeric
# Hint: Column that need munging include "int_rate," "revol_util," "emp_length"

#### Example for int_rate using gsub, trim, asnumeric ####
loan_stats["int_rate"] = loan_stats["int_rate"].gsub(pattern = "%", replacement = "") # strip %
loan_stats["int_rate"] = loan_stats["int_rate"].trim() # trim ws
loan_stats["int_rate"] = loan_stats["int_rate"].asnumeric() #change to a numeric 
loan_stats["int_rate"].show()

int_rate
10.65
15.27
15.96
13.49
7.9
18.64
21.28
12.69
14.65
12.69

In [11]:

# Now try for revol_util yourself

revol_util
83.7
9.4
98.5
21
28.3
87.5
32.6
36.5
20.6
67.1

In [12]:

# Now we're going to clean up emp_length
# Use gsub to remove " year" and " years" also translate n/a to "" 
loan_stats["emp_length"] = loan_stats["emp_length"].gsub(pattern = "([ ]*+[a-zA-Z].*)|(n/a)", 
                                              replacement = "") 

# Use trim to remove any trailing spaces 
loan_stats["emp_length"] = loan_stats["emp_length"].trim()

In [13]:

# Use sub to convert < 1 to 0 years and do the same for 10 + to 10
# Hint: Be mindful of spaces between characters

emp_length
10
0
10
10
3
9
4
0
5
10

In [14]:

# Task 6: Create new column called credit_length
# Hint: Do this by subtracting the earliest_cr year from the issue_d year

credit_length
26
12
10
15
7
4
7
7
13
22

In [15]:

# Task 7: Use the sub function to create two levels from the verification_status column. Ie "verified" and "not verified"

In [16]:

# Task 8: Do a test-train split (80-20)

In [17]:

# Task 9: Define your response and predictor variables 
y="bad_loan"
x=[]

In [18]:

# Task 10: Train GBM Model
# Set parameters for GBM model 

In [19]:

# Build your model

gbm Model Build progress: |███████████████████████████████████████████████| 100%

In [20]:

# Task 11: Plot the scoring history to make sure you're not overfitting
# Hint: Use plot function on the model object

In [21]:

# Task 12: Plot the ROC curve for the binomial models and get auc using h2o.auc
# Hint: Use h2o.performance and plot to grab the modelmetrics and then plotting the modelmetrics

Training AUC = 0.833931838694
Validation AUC = 0.793780377998

In [22]:

# Task 13: Check the variable importance and generate confusion matrix for max F1 threshold
# Hint: Use h2o.varimp for non-GLM model and use h2o.confusionMatrix

               variable  relative_importance  scaled_importance  percentage
0        inq_last_6mths          8205.389648           1.000000    0.484600
1            addr_state          1844.628906           0.224807    0.108941
2                  term          1479.474487           0.180305    0.087376
3         credit_length          1132.209595           0.137984    0.066867
4            revol_util          1127.222534           0.137376    0.066572
5             revol_bal           896.188965           0.109220    0.052928
6               purpose           609.911682           0.074331    0.036021
7            annual_inc           556.461426           0.067817    0.032864
8             loan_amnt           197.390945           0.024056    0.011658
9                   dti           164.333130           0.020027    0.009705
10          delinq_2yrs           154.244186           0.018798    0.009109
11             open_acc           129.676529           0.015804    0.007659
12            total_acc           126.753441           0.015448    0.007486
13              pub_rec           119.910492           0.014614    0.007082
14  verification_status            68.559044           0.008355    0.004049
15           emp_length            64.321648           0.007839    0.003799
16       home_ownership            55.632797           0.006780    0.003286
Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.224251521214:

	0	1	Error	Rate
0	5027.0	1061.0	0.1743	(1061.0/6088.0)
1	640.0	977.0	0.3958	(640.0/1617.0)
Total	5667.0	2038.0	0.2208	(1701.0/7705.0)

In [23]:

# Task 14: Score the entire data set using the model
# Hint: Use h2o.predict.

gbm prediction progress: |████████████████████████████████████████████████| 100%

In [24]:

# Extra: Calculate the money gain/loss if model is implemented
# Calculate the total amount of money earned or lost per loan
loan_stats["earned"] = loan_stats["total_pymnt"] - loan_stats["loan_amnt"]

In [25]:

# Calculate how much money will be lost to false negative, vs how much will be saved due to true positives
loan_stats["pred"] = pred["predict"]

grouped = loan_stats.group_by(["bad_loan", "pred"])
net = grouped.sum(col = "earned", na = "ignore").get_frame()

n1 = net[(net["bad_loan"] == "0") & (net["pred"] == "0")]["sum_earned"].round(digits = 0).max()
n2 = net[(net["bad_loan"] == "0") & (net["pred"] == "1")]["sum_earned"].round(digits = 0).max()
n3 = net[(net["bad_loan"] == "1") & (net["pred"] == "1")]["sum_earned"].round(digits = 0).max()
n4 = net[(net["bad_loan"] == "1") & (net["pred"] == "0")]["sum_earned"].round(digits = 0).max()

# Calculate the amount earned
print "Total amount of profit still earned using the model : %s" % '${:0,.0f}'.format(n1)
print "Total amount of profit forfeitted using the model : %s" % '${:0,.0f}'.format(n2)
print "Total amount of loss that could have been prevented : %s" % '${:0,.0f}'.format(n3)
print "Total amount of loss that still would've accrued : %s" % '${:0,.0f}'.format(n4)

# Calculate Net
print "Total profit by implementing model : $ %s" %'${:0,.0f}'.format((n1 - n2 + (-1*n3) - (-1*n4)))

Total amount of profit still earned using the model : $37,436,802
Total amount of profit forfeitted using the model : $12,838,849
Total amount of loss that could have been prevented : $-19,328,621
Total amount of loss that still would've accrued : $-12,083,433
Total profit by implementing model : $ $31,843,141

In [26]:

# Shutdown h2o instance

H2O session _sid_90d0 closed.

In [ ]: