This notebook contains all the steps I took to clean my data and make it viable for all types of classificaton algo's.
At the end of this notebook, I load the dataframe into a PostgreSQL database. There isnt really a need for this, but it's a matter of demonstrating the skillset.
In [1]:
%matplotlib inline
import pickle
%run helper_functions.py
pd.options.display.max_columns = 1000
plt.rcParams["figure.figsize"] = (15,10)
from datetime import datetime
# from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
In [2]:
df = unpickle_object("non_current_df.pkl") #loans that are 'complete'
In [3]:
df.shape
Out[3]:
(538008, 110)
I will manipulate my dataset in order for it to be compatible with both GLM's and classification algorithms.
As such, I will create dummies and scale all of data. Scaling is incredibly important for KNN and will improve model performance for Logisitc Regression.
I am not particularly concerned with coeff interpretability as the purpose is the assign a class.
By manipulating my data in this way, I will be ready to be used by any ML model.
I will use 3 in particular:
Dummy Classifier. This will the global baseline I have to beat.
KNN (My most constrained model)
Logistic Regression
Random Forests
Note that Multi-collinearity does NOT matter for models like DT's and RF's - however, it will matter for Logistic regression. I will first throw all of my data at LGR, and remove variables (multi-collinear) accordingly (this creates a baselinne model for lgr).
My project will be concerned with classifying whether an individual will re-pay their loan on time. I will change the 'loan status' feature in this dataset into a binary form of "Fully Paid" or "Late"
In [4]:
df['loan_status'].unique()
Out[4]:
array(['Fully Paid', 'Charged Off', 'Late (31-120 days)',
'Late (16-30 days)', 'In Grace Period', 'Default',
'Does not meet the credit policy. Status:Fully Paid',
'Does not meet the credit policy. Status:Charged Off', 'Issued'], dtype=object)
In [5]:
mask = df['loan_status'] != "Fully Paid"
rows_to_change = df[mask]
rows_to_change.loc[:, 'loan_status'] = 'Late'
df.update(rows_to_change)
/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py:517: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
self.obj[item] = s
In [6]:
df['loan_status'].unique() #sweet!
Out[6]:
array(['Fully Paid', 'Late'], dtype=object)
In [7]:
df.shape # no dimensionality lost
Out[7]:
(538008, 110)
In [8]:
plot_corr_matrix(df)
Let's have a quick look at all of our columns, their descriptions and associated datatype.
Perhaps we can reduce the dimension of our dataset off the bat by dropping columns that are not pertinent
In [9]:
no_desc = []
for column in df.columns:
try:
print(column+":",lookup_description(column)," DataType:", df[column].dtype)
print()
except KeyError:
no_desc.append(column)
id: A unique LC assigned ID for the loan listing. DataType: float64
member_id: A unique LC assigned Id for the borrower member. DataType: float64
loan_amnt: The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value. DataType: float64
funded_amnt: The total amount committed to that loan at that point in time. DataType: float64
funded_amnt_inv: The total amount committed by investors for that loan at that point in time. DataType: float64
term: The number of payments on the loan. Values are in months and can be either 36 or 60. DataType: object
int_rate: Interest Rate on the loan DataType: float64
installment: The monthly payment owed by the borrower if the loan originates. DataType: float64
grade: LC assigned loan grade DataType: object
sub_grade: LC assigned loan subgrade DataType: object
emp_title: The job title supplied by the Borrower when applying for the loan.* DataType: object
emp_length: Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years. DataType: object
home_ownership: The home ownership status provided by the borrower during registration or obtained from the credit report. Our values are: RENT, OWN, MORTGAGE, OTHER DataType: object
annual_inc: The self-reported annual income provided by the borrower during registration. DataType: float64
verification_status: Indicates if income was verified by LC, not verified, or if the income source was verified DataType: object
issue_d: The month which the loan was funded DataType: object
loan_status: Current status of the loan DataType: object
pymnt_plan: Indicates if a payment plan has been put in place for the loan DataType: object
desc: Loan description provided by the borrower DataType: object
purpose: A category provided by the borrower for the loan request. DataType: object
title: The loan title provided by the borrower DataType: object
zip_code: The first 3 numbers of the zip code provided by the borrower in the loan application. DataType: object
addr_state: The state provided by the borrower in the loan application DataType: object
dti: A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income. DataType: float64
delinq_2yrs: The number of 30+ days past-due incidences of delinquency in the borrower's credit file for the past 2 years DataType: float64
earliest_cr_line: The month the borrower's earliest reported credit line was opened DataType: object
inq_last_6mths: The number of inquiries in past 6 months (excluding auto and mortgage inquiries) DataType: float64
mths_since_last_delinq: The number of months since the borrower's last delinquency. DataType: float64
mths_since_last_record: The number of months since the last public record. DataType: float64
open_acc: The number of open credit lines in the borrower's credit file. DataType: float64
pub_rec: Number of derogatory public records DataType: float64
revol_bal: Total credit revolving balance DataType: float64
revol_util: Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit. DataType: float64
total_acc: The total number of credit lines currently in the borrower's credit file DataType: float64
initial_list_status: The initial listing status of the loan. Possible values are – W, F DataType: object
out_prncp: Remaining outstanding principal for total amount funded DataType: float64
out_prncp_inv: Remaining outstanding principal for portion of total amount funded by investors DataType: float64
total_pymnt: Payments received to date for total amount funded DataType: float64
total_pymnt_inv: Payments received to date for portion of total amount funded by investors DataType: float64
total_rec_prncp: Principal received to date DataType: float64
total_rec_int: Interest received to date DataType: float64
total_rec_late_fee: Late fees received to date DataType: float64
recoveries: post charge off gross recovery DataType: float64
collection_recovery_fee: post charge off collection fee DataType: float64
last_pymnt_d: Last month payment was received DataType: object
last_pymnt_amnt: Last total payment amount received DataType: float64
next_pymnt_d: Next scheduled payment date DataType: object
last_credit_pull_d: The most recent month LC pulled credit for this loan DataType: object
collections_12_mths_ex_med: Number of collections in 12 months excluding medical collections DataType: float64
mths_since_last_major_derog: Months since most recent 90-day or worse rating DataType: float64
policy_code: publicly available policy_code=1
new products not publicly available policy_code=2 DataType: float64
application_type: Indicates whether the loan is an individual application or a joint application with two co-borrowers DataType: object
annual_inc_joint: The combined self-reported annual income provided by the co-borrowers during registration DataType: float64
dti_joint: A ratio calculated using the co-borrowers' total monthly payments on the total debt obligations, excluding mortgages and the requested LC loan, divided by the co-borrowers' combined self-reported monthly income DataType: float64
acc_now_delinq: The number of accounts on which the borrower is now delinquent. DataType: float64
tot_coll_amt: Total collection amounts ever owed DataType: float64
tot_cur_bal: Total current balance of all accounts DataType: float64
open_acc_6m: Number of open trades in last 6 months DataType: float64
open_il_6m: Number of currently active installment trades DataType: float64
open_il_12m: Number of installment accounts opened in past 12 months DataType: float64
open_il_24m: Number of installment accounts opened in past 24 months DataType: float64
mths_since_rcnt_il: Months since most recent installment accounts opened DataType: float64
total_bal_il: Total current balance of all installment accounts DataType: float64
il_util: Ratio of total current balance to high credit/credit limit on all install acct DataType: float64
open_rv_12m: Number of revolving trades opened in past 12 months DataType: float64
open_rv_24m: Number of revolving trades opened in past 24 months DataType: float64
max_bal_bc: Maximum current balance owed on all revolving accounts DataType: float64
all_util: Balance to credit limit on all trades DataType: float64
inq_fi: Number of personal finance inquiries DataType: float64
total_cu_tl: Number of finance trades DataType: float64
inq_last_12m: Number of credit inquiries in past 12 months DataType: float64
acc_open_past_24mths: Number of trades opened in past 24 months. DataType: float64
avg_cur_bal: Average current balance of all accounts DataType: float64
bc_open_to_buy: Total open to buy on revolving bankcards. DataType: float64
bc_util: Ratio of total current balance to high credit/credit limit for all bankcard accounts. DataType: float64
chargeoff_within_12_mths: Number of charge-offs within 12 months DataType: float64
delinq_amnt: The past-due amount owed for the accounts on which the borrower is now delinquent. DataType: float64
mo_sin_old_il_acct: Months since oldest bank installment account opened DataType: float64
mo_sin_old_rev_tl_op: Months since oldest revolving account opened DataType: float64
mo_sin_rcnt_rev_tl_op: Months since most recent revolving account opened DataType: float64
mo_sin_rcnt_tl: Months since most recent account opened DataType: float64
mort_acc: Number of mortgage accounts. DataType: float64
mths_since_recent_bc: Months since most recent bankcard account opened. DataType: float64
mths_since_recent_bc_dlq: Months since most recent bankcard delinquency DataType: float64
mths_since_recent_inq: Months since most recent inquiry. DataType: float64
mths_since_recent_revol_delinq: Months since most recent revolving delinquency. DataType: float64
num_accts_ever_120_pd: Number of accounts ever 120 or more days past due DataType: float64
num_actv_bc_tl: Number of currently active bankcard accounts DataType: float64
num_actv_rev_tl: Number of currently active revolving trades DataType: float64
num_bc_sats: Number of satisfactory bankcard accounts DataType: float64
num_bc_tl: Number of bankcard accounts DataType: float64
num_il_tl: Number of installment accounts DataType: float64
num_op_rev_tl: Number of open revolving accounts DataType: float64
num_rev_accts: Number of revolving accounts DataType: float64
num_rev_tl_bal_gt_0: Number of revolving trades with balance >0 DataType: float64
num_sats: Number of satisfactory accounts DataType: float64
num_tl_120dpd_2m: Number of accounts currently 120 days past due (updated in past 2 months) DataType: float64
num_tl_30dpd: Number of accounts currently 30 days past due (updated in past 2 months) DataType: float64
num_tl_90g_dpd_24m: Number of accounts 90 or more days past due in last 24 months DataType: float64
num_tl_op_past_12m: Number of accounts opened in past 12 months DataType: float64
pct_tl_nvr_dlq: Percent of trades never delinquent DataType: float64
percent_bc_gt_75: Percentage of all bankcard accounts > 75% of limit. DataType: float64
pub_rec_bankruptcies: Number of public record bankruptcies DataType: float64
tax_liens: Number of tax liens DataType: float64
tot_hi_cred_lim: Total high credit/credit limit DataType: float64
total_bal_ex_mort: Total credit balance excluding mortgage DataType: float64
total_bc_limit: Total bankcard high credit/credit limit DataType: float64
total_il_high_credit_limit: Total installment high credit/credit limit DataType: float64
In [10]:
columns_to_drop = ["id", "member_id", "emp_title","desc","title","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", "all_util", ]
In [11]:
# df.loc[:, ["loan_amnt","funded_amnt","out_prncp","out_prncp_inv","total_pymnt","total_pymnt_inv","total_rec_prncp","last_credit_pull_d"]]
In [12]:
no_desc
Out[12]:
['verification_status_joint', 'total_rev_hi_lim']
In [13]:
df['verification_status_joint'].unique()
Out[13]:
array([nan, 'Not Verified', 'Verified', 'Source Verified'], dtype=object)
In [14]:
df['total_rev_hi_lim'].unique()
Out[14]:
array([ nan, 29700., 23800., ..., 334560., 181280., 26940.])
In [15]:
df['verification_status_joint'].dtype
Out[15]:
dtype('O')
In [16]:
df['total_rev_hi_lim'].dtype
Out[16]:
dtype('float64')
After going through the list, I have decided to drop 5 columns!
These will not be relevant to the task at hand. Although, I could use some natural language processig via NLTK to parse job descriptions and loan descriptions. I will leave this for another day.
It is also important to note that I will be dropping variables that hint (i.e. information leakage) at what the final result will be.
In [17]:
df.drop(columns_to_drop, axis=1, inplace=True)
In [18]:
df.shape #just what we expected
Out[18]:
(538008, 89)
After reviewing the above, the following columns need to be changed to categorical datatypes from float64.
I will first make it an object datatype as later I will write a function that changed all object datatypes into categorical datatypes.
In [19]:
df["policy_code"] = df["policy_code"].astype('object')
I will have to transform the following columns as they are currently in percentages. I will take the natural log of these columns before proceeding:
pct_tl_nvr_dlq
percent_bc_gt_75
This will ensure better model performance for logistic regression as % may not follow a linear relationship.
In [20]:
df['pct_tl_nvr_dlq'] = df['pct_tl_nvr_dlq'].apply(lambda x: x/100)
df['percent_bc_gt_75'] = df['percent_bc_gt_75'].apply(lambda x: x/100)
My categorical features (those of type Object) have np.nan values, I will change these to something more meaningful like "Missing Data".
I will then create dummies for all of my categorical features. This will lead to an explosion in the number of columns - this will be more computationally expensive, however, this is NOT an explosion in the 'feature space' as our dataframe contains the same amount of information.
In [21]:
object_columns = df.select_dtypes(include=['object']).columns
for c in object_columns:
df.loc[df[df[c].isnull()].index, c] = "missing"
So, our dataset is comprised of features which are categorical and features that are numeric. We need to ensure that the object datatypes are converted to categorical datatypes.
Also, whether we use a GLM or classifier, we need to ensure that these datatypes stay consistent.
NOTE: changing columns to categorical datatypes will NOT change how a machine learning model interprets the data. i.e. The algorithm will still think that 5 > 4. As such, one hot encoding (i.e. making dummies) is the only way to ensure that a Machine Learning Model can detect the presence of a particular attribute.
I will be changing the object datatypes to categorical purely for data consistency within the dataframe.
In [22]:
obj_df = df.select_dtypes(include=['object'])
obj_df_cols = obj_df.columns
for col in obj_df_cols:
df[col] = df[col].astype("category")
df.dtypes.unique() #This is what we wanted!
Out[22]:
array([dtype('float64'), category], dtype=object)
In [23]:
df.shape
Out[23]:
(538008, 89)
In [24]:
df.head()
Out[24]:
loan_amnt
funded_amnt
funded_amnt_inv
term
int_rate
installment
grade
sub_grade
emp_length
home_ownership
annual_inc
verification_status
issue_d
loan_status
pymnt_plan
purpose
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
policy_code
application_type
annual_inc_joint
dti_joint
verification_status_joint
acc_now_delinq
tot_coll_amt
tot_cur_bal
open_acc_6m
open_il_6m
open_il_12m
open_il_24m
mths_since_rcnt_il
total_bal_il
il_util
open_rv_12m
open_rv_24m
max_bal_bc
total_rev_hi_lim
inq_fi
total_cu_tl
inq_last_12m
acc_open_past_24mths
avg_cur_bal
bc_open_to_buy
bc_util
chargeoff_within_12_mths
delinq_amnt
mo_sin_old_il_acct
mo_sin_old_rev_tl_op
mo_sin_rcnt_rev_tl_op
mo_sin_rcnt_tl
mort_acc
mths_since_recent_bc
mths_since_recent_bc_dlq
mths_since_recent_inq
mths_since_recent_revol_delinq
num_accts_ever_120_pd
num_actv_bc_tl
num_actv_rev_tl
num_bc_sats
num_bc_tl
num_il_tl
num_op_rev_tl
num_rev_accts
num_rev_tl_bal_gt_0
num_sats
num_tl_120dpd_2m
num_tl_30dpd
num_tl_90g_dpd_24m
num_tl_op_past_12m
pct_tl_nvr_dlq
percent_bc_gt_75
pub_rec_bankruptcies
tax_liens
tot_hi_cred_lim
total_bal_ex_mort
total_bc_limit
total_il_high_credit_limit
0
5000.0
5000.0
4975.0
36 months
10.65
162.87
B
B2
10+ years
RENT
24000.0
Verified
Dec-2011
Fully Paid
n
credit_card
860xx
AZ
27.65
0.0
Jan-1985
1.0
NaN
NaN
3.0
0.0
13648.0
83.7
9.0
f
1.0
INDIVIDUAL
NaN
NaN
missing
0.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
0.0
0.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
0.0
0.0
NaN
NaN
NaN
NaN
1
2500.0
2500.0
2500.0
60 months
15.27
59.83
C
C4
< 1 year
RENT
30000.0
Source Verified
Dec-2011
Late
n
car
309xx
GA
1.00
0.0
Apr-1999
5.0
NaN
NaN
3.0
0.0
1687.0
9.4
4.0
f
1.0
INDIVIDUAL
NaN
NaN
missing
0.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
0.0
0.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
0.0
0.0
NaN
NaN
NaN
NaN
2
2400.0
2400.0
2400.0
36 months
15.96
84.33
C
C5
10+ years
RENT
12252.0
Not Verified
Dec-2011
Fully Paid
n
small_business
606xx
IL
8.72
0.0
Nov-2001
2.0
NaN
NaN
2.0
0.0
2956.0
98.5
10.0
f
1.0
INDIVIDUAL
NaN
NaN
missing
0.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
0.0
0.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
0.0
0.0
NaN
NaN
NaN
NaN
3
10000.0
10000.0
10000.0
36 months
13.49
339.31
C
C1
10+ years
RENT
49200.0
Source Verified
Dec-2011
Fully Paid
n
other
917xx
CA
20.00
0.0
Feb-1996
1.0
35.0
NaN
10.0
0.0
5598.0
21.0
37.0
f
1.0
INDIVIDUAL
NaN
NaN
missing
0.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
0.0
0.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
0.0
0.0
NaN
NaN
NaN
NaN
4
3000.0
3000.0
3000.0
60 months
12.69
67.79
B
B5
1 year
RENT
80000.0
Source Verified
Dec-2011
Fully Paid
n
other
972xx
OR
17.94
0.0
Jan-1996
0.0
38.0
NaN
15.0
0.0
27783.0
53.9
38.0
f
1.0
INDIVIDUAL
NaN
NaN
missing
0.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
0.0
0.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
0.0
0.0
NaN
NaN
NaN
NaN
In [25]:
unique_val_dict = {}
for col in df.columns:
if col not in unique_val_dict:
unique_val_dict[col] = df[col].unique()
In [26]:
unique_val_dict #will use this later when making flask app.
Out[26]:
{'acc_now_delinq': array([ 0., 1., nan, 2., 3., 5., 4., 6.]),
'acc_open_past_24mths': array([ nan, 8., 4., 6., 3., 2., 0., 7., 9., 1., 5.,
10., 21., 11., 12., 13., 17., 14., 15., 16., 18., 22.,
19., 24., 20., 28., 23., 31., 25., 33., 40., 27., 29.,
26., 35., 34., 47., 39., 41., 32., 38., 30., 37., 42.,
53., 50., 56.]),
'addr_state': [AZ, GA, IL, CA, OR, ..., NE, ID, IN, ME, ND]
Length: 51
Categories (51, object): [AZ, GA, IL, CA, ..., ID, IN, ME, ND],
'annual_inc': array([ 24000., 30000., 12252., ..., 192057., 53535., 165840.]),
'annual_inc_joint': array([ nan, 77000. , 113314. , 140000. , 87000. ,
102000. , 108000. , 182000. , 122000. , 107000. ,
133000. , 82000. , 95000. , 70000. , 117000. ,
112194. , 74000. , 83200. , 115000. , 55000. ,
224000. , 106000. , 185000. , 52000. , 88500. ,
132600. , 76000. , 78200. , 105000. , 176000. ,
93000. , 40034. , 161000. , 69930. , 83456. ,
97000. , 109000. , 214000. , 136429. , 55200. ,
99000. , 189729. , 36000. , 51990. , 85000. ,
109440. , 96000. , 104800. , 116700. , 125000. ,
46000. , 98000. , 139000. , 63400. , 49926. ,
130000. , 73220. , 124000. , 75001. , 40988. ,
103500. , 270000. , 25142. , 151000. , 58750. ,
90000. , 69500. , 245000. , 152000. , 123400. ,
101771. , 77600. , 110000. , 61465. , 135000. ,
71136. , 142000. , 32760. , 149000. , 170000. ,
168472. , 202700. , 88400. , 81000. , 17950. ,
101318. , 213000. , 72653. , 64800. , 189490. ,
175000. , 104000. , 100000. , 126404. , 27600. ,
29448. , 175171. , 53000. , 111140. , 68000. ,
113000. , 87510. , 112862. , 141000. , 72500. ,
123000. , 50000. , 173000. , 126000. , 60000. ,
99566. , 76160. , 83000. , 67000. , 51192. ,
73000. , 112000. , 65000. , 103000. , 35132. ,
27000. , 128500. , 65136. , 88000. , 72000. ,
58000. , 160000. , 104954.4 , 80000. , 63985. ,
86000. , 30000. , 116000. , 153000. , 165000. ,
107640. , 66000. , 91700. , 180000. , 56000. ,
115049. , 50460. , 45500. , 200000. , 122500. ,
120000. , 95956. , 72227. , 76433.72, 134000. ,
155550. , 142160. , 58691.66, 101500. , 77668. ,
75000. , 63490.13, 79000. , 206000. , 106537.6 ,
118500. , 220000. , 131000. , 47000. , 31965.76,
78000. , 145040. , 170380. , 94000. , 68662. ,
177000. , 157000. , 61000. , 69508. , 51000. ,
108527.82, 150000. , 71000. , 101000. , 120200.7 ,
66800. , 77113. , 210000. , 199000. , 87900. ,
155000. , 86500. , 111000. , 114509. , 138000. ,
62925.24, 143423.91, 202555. , 62000. , 158000. ,
244260. , 180025. , 48760. , 307000. , 84000. ,
48000. , 118800. , 174000. , 86250. , 80600. ,
118000. , 124600. , 62500. , 149760. , 129000. ,
68200. , 121200. , 169000. , 196000. , 152600. ,
111849. , 91000. , 212000. , 59000. , 71770. ,
310000. , 158600. , 164000. , 48795.2 , 92000. ,
145000. , 162000. , 105500. , 44520. , 122964. ,
171000. , 40000. , 33672. , 32660. , 110574. ,
135940. , 125100. , 99800. , 181200. , 51672. ,
18072. , 121000. , 138921. , 225000. , 69000. ,
101666. , 195000. , 96350. , 26400. , 57000. ,
19200. , 33876. , 145400. , 87321. , 129500. ,
132000. , 133200. , 98760. , 136200. , 114607.6 ,
137000. , 119000. , 21276. , 53500. , 211800. ,
192000. , 53309.76, 45000. , 70320. , 115200. ,
72700. , 125400. , 148000. , 92160. , 56928. ,
143000. , 71200. , 147000. , 320000. , 95680. ,
159000. , 112320. , 272000. , 100606. , 150600. ,
117245. , 264000. , 215705. , 280000. , 128000. ,
154000. , 116100. , 37000. , 46488.8 , 323424. ,
108699. , 66692. , 114000. , 75700. , 56640. ,
42999. , 119084. , 77584. , 168000. , 228400. ,
94419. , 191126. , 77924. , 100446. , 40500. ,
40050. , 144000. , 186000. , 95682. , 127000. ,
140500. , 86800. , 91726. , 303000. , 82392. ,
66087. , 67200. , 99750. , 38500. , 208000. ,
126324. , 113500. , 201000. , 400000. , 126291. ,
84418. , 250000. , 66508.8 , 136000. , 163510. ,
78500. , 153600. , 92417. , 34000. , 231000. ,
190000. , 77454. , 83500. , 340000. , 63000. ,
44000. , 240000. , 98200. , 216000. , 163000. ,
83825. , 173600. , 87200. , 91305. , 86360. ,
79600. , 189000. , 166600. , 235570. , 41000. ,
114156. , 137372. , 73200. , 43000. , 56267.72,
178000. , 127500. , 260000. , 274000. , 140829.57,
79500. , 197000. , 78956.8 , 64000. , 52033. ,
83256. , 61372. , 248000. , 127600. , 83899.5 ,
156000. , 68800. , 90089. , 31000. , 39300. ,
118320. , 89000. , 85800. , 35500. , 290000. ,
175666. , 131150. , 149900. , 45990.82, 112400. ,
59500. , 91280. , 98156. , 104400. , 86001. ,
193000. , 84500. , 44160. , 116400. , 87501. ,
132280. , 234000. , 72238. , 69400. , 71947.2 ,
52500. , 79300. , 82800. , 139965. , 61500. ,
94300. , 76454. , 210178. , 79270. , 67080. ,
202000. , 255000. , 160632. ]),
'application_type': [INDIVIDUAL, JOINT, DIRECT_PAY]
Categories (3, object): [INDIVIDUAL, JOINT, DIRECT_PAY],
'avg_cur_bal': array([ nan, 476., 11783., ..., 56694., 69555., 65187.]),
'bc_open_to_buy': array([ nan, 15216., 2441., ..., 30707., 101758., 53336.]),
'bc_util': array([ nan, 15.9, 83.5, ..., 124.9, 122.6, 162. ]),
'chargeoff_within_12_mths': array([ 0., nan, 1., 2., 3., 4., 5., 7., 6.]),
'delinq_2yrs': array([ 0., 2., 3., 1., 4., 6., 5., 8., 7., 9., 11.,
nan, 13., 15., 10., 12., 17., 18., 29., 24., 14., 16.,
26., 27., 39., 20., 22., 19., 21.]),
'delinq_amnt': array([ 0.00000000e+00, nan, 2.70000000e+01,
6.05300000e+03, 3.21000000e+02, 4.56000000e+02,
1.03300000e+03, 3.53000000e+02, 2.24000000e+02,
2.60000000e+01, 6.57600000e+03, 1.53600000e+03,
4.50000000e+01, 3.23000000e+02, 1.56870000e+04,
2.35000000e+02, 8.60000000e+01, 5.40000000e+01,
3.44000000e+03, 8.31000000e+02, 5.20000000e+01,
1.33000000e+02, 2.50000000e+01, 3.67870000e+04,
1.12000000e+02, 5.70000000e+01, 2.12000000e+02,
1.42000000e+02, 4.82000000e+02, 5.30000000e+01,
1.70300000e+03, 1.10000000e+02, 4.67000000e+02,
4.88000000e+02, 5.50000000e+01, 4.13290000e+04,
3.94000000e+02, 5.68400000e+03, 8.11000000e+02,
5.12000000e+02, 1.61500000e+03, 2.51500000e+03,
5.60000000e+01, 1.34000000e+02, 5.04890000e+04,
1.85000000e+02, 3.42000000e+02, 6.37000000e+02,
3.00000000e+01, 3.01000000e+02, 2.04000000e+02,
1.04000000e+02, 7.60000000e+01, 9.03000000e+02,
5.00000000e+00, 2.20000000e+02, 3.74500000e+03,
6.30000000e+01, 3.50000000e+01, 3.09700000e+03,
4.65800000e+03, 2.91800000e+03, 7.90000000e+01,
4.31000000e+02, 5.00380000e+04, 1.44000000e+02,
6.90000000e+01, 1.17000000e+03, 4.87000000e+02,
3.37000000e+02, 6.46000000e+02, 2.71200000e+03,
1.15500000e+03, 8.59000000e+02, 6.56000000e+02,
6.07000000e+02, 3.10000000e+01, 9.50000000e+01,
1.49000000e+02, 6.10000000e+01, 2.64200000e+03,
8.50000000e+01, 2.94000000e+02, 5.00000000e+01,
2.40000000e+02, 2.66670000e+04, 3.51000000e+02,
2.78300000e+04, 1.19500000e+03, 3.62300000e+03,
1.80000000e+02, 2.49000000e+02, 5.26000000e+02,
1.64000000e+02, 2.42400000e+03, 5.37600000e+04,
5.63000000e+02, 8.37000000e+02, 7.50000000e+01,
4.00000000e+01, 1.04800000e+03, 3.09900000e+03,
3.00000000e+00, 8.40000000e+01, 7.20000000e+01,
3.25600000e+03, 3.03000000e+02, 4.44000000e+02,
6.13950000e+04, 2.32200000e+03, 2.45000000e+02,
6.05000000e+02, 1.68790000e+04, 1.70540000e+04,
1.87290000e+04, 5.97000000e+02, 5.10000000e+01,
8.00000000e+01, 1.24100000e+03, 3.05900000e+03,
4.39300000e+03, 7.78000000e+02, 2.00000000e+01,
4.20000000e+01, 8.95700000e+03, 4.30000000e+01,
7.00000000e+01, 5.19200000e+03, 2.61000000e+02,
2.12920000e+04, 7.67000000e+02, 1.00000000e+01,
3.73000000e+02, 1.43000000e+02, 3.61000000e+02,
2.20000000e+01, 1.65000000e+02, 1.97000000e+02,
4.40900000e+03, 9.52200000e+03, 3.56000000e+02,
1.75000000e+02, 4.01400000e+03, 6.55000000e+03,
1.21900000e+03, 9.04000000e+02, 6.50000000e+04,
2.49230000e+04, 3.80600000e+03, 2.11650000e+04,
1.59000000e+02, 3.32000000e+02, 9.80000000e+01,
1.74810000e+04, 1.52000000e+02, 1.10190000e+04,
3.80000000e+01, 9.50000000e+02, 7.24000000e+02,
2.42600000e+03, 3.11080000e+04, 6.45700000e+03,
4.22800000e+03, 1.57900000e+03, 5.98400000e+03,
2.81600000e+03, 1.63200000e+03, 2.40000000e+01,
2.35500000e+03, 1.16000000e+02, 1.04020000e+04,
7.75300000e+03, 1.07000000e+02, 3.33000000e+02,
3.82000000e+02, 6.06000000e+02, 6.68000000e+02,
4.73000000e+02, 6.20000000e+01, 7.27500000e+03,
7.80000000e+01, 7.02000000e+02, 5.64000000e+02,
1.90470000e+04, 2.54000000e+02, 4.17600000e+03,
7.43000000e+02, 4.23990000e+04, 1.89360000e+04,
1.94800000e+03, 5.80000000e+01, 1.92100000e+03,
3.50000000e+02, 2.92000000e+02, 1.45900000e+03,
7.96000000e+02, 7.83000000e+02, 1.17000000e+02,
5.30000000e+02, 7.39800000e+03, 2.47600000e+03,
1.55200000e+03, 1.69000000e+02, 2.06000000e+02,
8.76900000e+03, 2.16500000e+03, 7.26100000e+03,
9.20000000e+01, 7.65000000e+02, 4.37000000e+02,
2.30000000e+01, 1.50000000e+01, 4.63770000e+04,
1.04360000e+04, 4.39200000e+03, 2.17500000e+03,
1.78990000e+04, 7.38000000e+02, 3.20000000e+01,
4.00000000e+00, 3.40000000e+01, 1.26010000e+04,
5.82000000e+02, 2.10000000e+01, 1.01690000e+04,
3.90000000e+01, 1.78900000e+03, 1.96000000e+02,
1.94100000e+03, 6.60000000e+01, 6.70000000e+01,
1.43500000e+03, 3.85400000e+03, 2.09300000e+03,
6.63000000e+02, 4.20530000e+04, 6.29000000e+02,
6.34530000e+04, 1.90000000e+01, 5.36900000e+03,
3.12190000e+04, 1.06100000e+03, 1.70000000e+01,
1.30630000e+04, 1.26410000e+04, 4.80000000e+01,
2.37800000e+03, 1.08500000e+03, 1.90280000e+04,
4.04000000e+02, 4.40000000e+02, 6.85500000e+03,
2.45210000e+04, 4.24600000e+03, 2.10590000e+04,
3.54400000e+03, 1.38000000e+03, 1.16400000e+03,
1.18000000e+02, 1.39400000e+03, 7.84900000e+03,
4.59000000e+03, 1.84000000e+02, 1.80000000e+01,
1.26000000e+02, 9.17000000e+02, 2.53000000e+02,
8.82160000e+04, 1.77520000e+04, 6.67000000e+02,
6.81000000e+02, 1.54000000e+02, 8.20000000e+01,
2.46600000e+03, 1.19000000e+02, 1.32000000e+02,
2.60000000e+02, 8.56000000e+02, 1.71000000e+03,
6.65000000e+02, 6.00000000e+01, 1.27000000e+02,
5.50100000e+03, 2.93000000e+02, 6.36000000e+02,
1.00870000e+04, 1.04900000e+03, 2.95000000e+02,
2.32000000e+02, 1.39000000e+02, 4.89000000e+02,
1.77000000e+02, 1.08000000e+02, 1.31100000e+03,
1.30000000e+01, 1.57000000e+02, 8.34000000e+03,
3.60000000e+01, 3.59600000e+03, 1.01900000e+03,
1.45000000e+02, 1.28400000e+03, 1.06000000e+02,
2.75400000e+03, 2.56900000e+04, 2.11000000e+02,
3.96000000e+02, 6.58400000e+03, 5.28000000e+02,
4.63000000e+02, 4.25300000e+03, 4.18000000e+02,
2.48190000e+04, 1.33180000e+04, 2.62000000e+02,
8.00000000e+00, 6.40000000e+01, 1.73300000e+03,
4.61000000e+02, 5.25000000e+02, 2.08600000e+03,
3.02000000e+02, 7.41000000e+02, 1.79000000e+02,
1.13880000e+04, 2.24500000e+03, 1.05000000e+02,
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5.00000000e+02, 3.96600000e+03, 6.33000000e+02,
9.90000000e+01, 7.57000000e+02, 2.41100000e+03,
5.33000000e+02, 6.49000000e+02, 1.25200000e+03,
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1.81800000e+03, 9.00000000e+00, 4.40000000e+01,
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1.00100000e+03, 7.67350000e+04, 1.03000000e+02,
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1.10000000e+01, 1.35000000e+02, 7.30000000e+01,
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1.85800000e+03, 3.03500000e+03, 2.66900000e+03,
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3.48000000e+02, 1.87000000e+02, 2.47400000e+03,
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2.51000000e+02, 4.23000000e+02, 9.70000000e+02,
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1.82000000e+02, 4.10000000e+02, 1.36000000e+02,
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1.63800000e+03, 9.24000000e+02, 4.42000000e+02,
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1.02800000e+03, 1.20000000e+02, 1.02210000e+04,
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9.40000000e+01, 2.23000000e+02, 2.57000000e+02,
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1.73100000e+04, 5.18000000e+02, 6.40500000e+03,
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3.78000000e+02, 3.36000000e+02, 1.67000000e+02,
1.72000000e+02, 5.84500000e+03, 1.74000000e+02,
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1.38000000e+02, 6.29900000e+03, 2.80000000e+01,
2.43100000e+03, 3.12100000e+03, 6.00000000e+02,
1.20360000e+04, 4.45000000e+02, 2.68000000e+02,
4.38070000e+04, 3.63020000e+04, 3.85000000e+02,
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6.64400000e+03, 6.15000000e+02, 2.07000000e+02,
4.19000000e+02, 1.51000000e+02, 4.24500000e+03,
5.76000000e+02, 2.29300000e+03, 1.63400000e+03,
1.82300000e+03, 1.48550000e+04, 2.65600000e+03,
2.31000000e+02, 4.28180000e+04, 6.22400000e+03,
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1.51330000e+04, 3.43000000e+02, 1.71000000e+02,
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1.60000000e+01, 2.56000000e+02, 1.39600000e+03,
3.83000000e+02, 1.02000000e+03, 2.48710000e+04,
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1.30700000e+03, 5.26300000e+03, 4.02000000e+02,
8.18000000e+02, 3.35000000e+02, 3.16600000e+03,
4.81000000e+02, 1.04420000e+04, 1.89060000e+04,
1.42000000e+03, 2.50000000e+02, 2.60200000e+03,
8.21000000e+02, 4.59000000e+02, 2.78000000e+02,
1.30000000e+02, 1.18680000e+04, 4.66000000e+02,
1.79810000e+04, 7.93000000e+02, 1.53000000e+02,
1.75600000e+03, 2.79000000e+02, 1.46400000e+03,
9.25000000e+02, 3.90000000e+02, 4.50000000e+02,
5.88200000e+03, 9.39000000e+02, 3.28000000e+02,
5.69200000e+03, 4.29000000e+02, 5.34000000e+02,
5.22100000e+03, 2.13000000e+02, 2.63400000e+03,
1.53200000e+03, 5.53830000e+04, 1.25900000e+03,
3.47000000e+02, 1.03510000e+04, 3.92100000e+03,
1.91000000e+02, 4.61800000e+03, 3.80000000e+02,
2.72300000e+03, 1.24500000e+03, 8.09000000e+02,
8.89900000e+03, 7.50100000e+03, 4.58000000e+02,
5.02180000e+04, 4.95000000e+02, 1.68450000e+04,
3.72000000e+02, 1.01400000e+03, 1.13900000e+03,
1.72600000e+03, 1.32560000e+04, 6.01000000e+02,
2.35800000e+03, 5.96000000e+03, 1.37900000e+03,
5.72100000e+03, 1.50000000e+02, 6.42900000e+03,
3.31000000e+02, 1.90400000e+03, 2.02300000e+03,
2.00000000e+00, 4.21000000e+02, 2.03400000e+03,
1.36700000e+03, 1.47500000e+03, 4.09000000e+02,
2.30700000e+03, 1.63000000e+02, 6.94100000e+03,
4.01000000e+02, 1.32100000e+03, 1.33400000e+03,
4.63400000e+03, 4.55000000e+02, 1.23330000e+04,
5.81000000e+02, 1.07570000e+04, 1.50810000e+04,
5.47000000e+02, 3.24000000e+03, 6.97000000e+02,
3.64000000e+02, 2.33000000e+02, 5.89000000e+02,
7.31000000e+02, 9.84600000e+03, 1.97470000e+04,
1.47000000e+02, 2.63200000e+03, 1.00000000e+00,
2.21800000e+04, 8.58400000e+03, 1.11400000e+03,
2.26000000e+02, 1.35400000e+03, 5.07900000e+03,
3.43800000e+03, 4.70920000e+04, 8.03000000e+02,
1.40000000e+01, 3.19800000e+03, 5.34200000e+03,
3.32990000e+04, 8.67000000e+02, 8.36000000e+02,
8.80000000e+02, 2.22000000e+02, 1.99320000e+04,
5.68000000e+02, 2.98000000e+02, 9.43000000e+02,
3.29400000e+03, 7.04000000e+02, 4.64000000e+02,
6.08900000e+03, 1.55010000e+04, 4.06000000e+02,
1.00000000e+02, 3.00000000e+02, 1.84900000e+03,
4.55650000e+04, 6.61000000e+03, 7.12000000e+02,
1.75910000e+04, 1.89570000e+04, 6.21300000e+03,
2.06200000e+03, 2.72900000e+03, 3.69300000e+03,
1.60280000e+04, 4.86600000e+03, 4.27700000e+03,
3.58000000e+02, 3.15800000e+04, 7.44800000e+03,
4.12000000e+03, 1.33100000e+03, 1.33700000e+03,
4.68400000e+03, 1.59200000e+03, 1.62000000e+02,
9.05000000e+02, 1.21300000e+03, 5.60000000e+02,
1.31600000e+03, 1.07700000e+03, 2.41000000e+02,
5.52000000e+02, 1.46000000e+02, 1.88000000e+02,
1.92620000e+04, 1.06900000e+03, 2.25000000e+02,
9.08000000e+02, 3.24450000e+04, 2.43600000e+03,
1.25500000e+03, 4.12000000e+02, 3.65300000e+03,
9.79100000e+03, 3.36000000e+03, 7.66900000e+03,
6.07580000e+04, 5.93700000e+03, 1.51780000e+04,
6.96600000e+03, 7.53000000e+02, 1.03600000e+03,
4.14400000e+03, 1.54800000e+03, 3.00000000e+03,
4.77000000e+02, 2.03000000e+03, 5.41650000e+04,
2.53900000e+03, 9.60000000e+02, 4.83000000e+02,
1.05400000e+03, 4.72000000e+02, 1.13610000e+04,
3.73740000e+04, 1.53500000e+03, 1.70000000e+02,
5.27500000e+03, 3.26470000e+04, 4.47000000e+02,
3.74300000e+03, 1.94000000e+04, 2.29800000e+03,
1.99100000e+03, 8.07000000e+02, 1.15900000e+03,
1.37700000e+03, 3.95000000e+02, 2.36800000e+03,
9.81000000e+02, 1.96200000e+03, 1.55800000e+03,
3.33570000e+04, 1.00000000e+03, 2.14100000e+03]),
'dti': array([ 27.65, 1. , 8.72, ..., 83.36, 266.77, 55.5 ]),
'dti_joint': array([ nan, 15.4 , 25.44, 21.69, 22.98, 17.51, 23.4 , 6.44,
32.21, 15.11, 22.48, 9.22, 23.99, 6.81, 12.99, 14.96,
13.88, 26.76, 19.26, 16.65, 12.92, 25.35, 31.92, 21.03,
20.2 , 9.29, 21.43, 12.18, 27.27, 17.19, 29.23, 17.89,
23.08, 10.15, 26.39, 22.33, 6.52, 7.87, 18.46, 23.26,
25.77, 31.23, 11.85, 22.18, 23.24, 10.55, 15.51, 12.19,
17.6 , 17.08, 26.07, 17.35, 20.65, 20.12, 10.92, 17.99,
20.16, 21.98, 11.08, 10.32, 14.59, 34.59, 18.72, 11.17,
30.13, 22.64, 10.02, 14.09, 8.06, 18.07, 25.05, 17.22,
28.72, 13.02, 17.14, 9.7 , 19.86, 3.09, 20.45, 30.04,
25.3 , 15.16, 13.9 , 12. , 9.26, 14.82, 19.65, 20.76,
16.61, 13.48, 19.64, 8.57, 20.6 , 29.29, 31.51, 17.52,
10.21, 25.2 , 10.48, 6.97, 4.55, 7.75, 25.68, 21.36,
25.54, 21.91, 6.66, 14.36, 20.39, 16.26, 15.07, 23.79,
27.85, 26.93, 14.92, 18.56, 9.01, 6.35, 13.67, 14.11,
23.68, 21.54, 28.46, 27.52, 16.69, 20.42, 24.56, 24.39,
13.16, 17.24, 18.68, 17.72, 8.34, 15.72, 26.59, 23.71,
25.89, 24.05, 16.76, 20.06, 15.7 , 7.78, 23.66, 20.14,
10.61, 11.99, 22.52, 10.78, 7.59, 20.26, 7.9 , 25.26,
31.84, 28.15, 15.95, 13.51, 10.63, 41.59, 6.05, 31.76,
21.65, 11.3 , 27.56, 4.5 , 22.84, 18.21, 26.35, 21.27,
34.28, 30.96, 21.44, 12.2 , 16.24, 14.1 , 10.82, 7.09,
10.05, 22.61, 8.04, 29.89, 14.07, 14.19, 24.66, 10.45,
25.84, 19.13, 21.16, 20.01, 14.44, 6.15, 6.45, 17.34,
18.57, 11.61, 16.7 , 14.68, 16.74, 18.95, 4.84, 26.23,
39.66, 28.93, 13.92, 22.88, 10.85, 20.81, 16.41, 19.08,
17.11, 12.56, 19.01, 10.75, 16.2 , 35.82, 22.22, 8.07,
29.4 , 26.87, 15.79, 15.75, 20.63, 7.97, 14.85, 18.93,
20.17, 11.66, 24.57, 12.84, 18.82, 20.57, 10.51, 19.04,
20.55, 21.02, 5.67, 22.65, 19.84, 26.74, 26.91, 16.73,
32.63, 13.12, 20.82, 21.76, 23.52, 15.26, 24.49, 19.68,
21.2 , 19.51, 18.32, 27.88, 15.33, 12.43, 15.32, 29.11,
3.48, 17.57, 22.29, 16.6 , 19.3 , 34.72, 12.89, 26.27,
22.05, 11.86, 16.38, 17.07, 15.38, 26.3 , 9.37, 26.96,
17.7 , 23.35, 16.47, 37.35, 14.14, 25.99, 15.92, 19.48,
22.54, 12.11, 19.74, 24.92, 6.48, 22.59, 38.07, 26.05,
14.15, 12.16, 20.97, 12.12, 18.06, 1.11, 27.2 , 10.09,
22.17, 6.11, 1.86, 20.86, 25.18, 27.39, 11.95, 6.17,
31.11, 23.64, 24.72, 24.68, 20.47, 12.23, 5.12, 27.35,
17.3 , 21.94, 33.54, 14.95, 14.17, 23.6 , 23.47, 26.29,
16.79, 27.31, 13.27, 25.5 , 9.71, 10.36, 20.1 , 25.64,
24.94, 9.67, 15.3 , 13.1 , 20.73, 26.53, 29.01, 20.48,
26.7 , 14.7 , 38.45, 14.78, 14.54, 31.59, 24.85, 18.02,
8.56, 13.72, 10.26, 23.22, 14.46, 17.73, 3.27, 7.15,
21.33, 22.46, 17.9 , 12.97, 22.72, 6.94, 23.59, 22.53,
19.76, 13.82, 23.73, 19.33, 18.81, 8.69, 63.66, 14.91,
33.4 , 18.61, 24.62, 12.28, 5.26, 13.37, 34.05, 33.45,
28.69, 8.23, 19.06, 30.66, 27.1 , 8.38, 17.97, 33.67,
30.77, 14.66, 21.38, 12.03, 8.8 , 6.76, 14.5 , 13.91,
28.7 , 10.03, 15.74, 20.19, 23.84, 16.5 , 23.21, 5.64,
14.94, 21.5 , 33.26, 3.56, 25.02, 13.54, 21.61, 16.11,
18.47, 13.11, 19.79, 11.18, 19.1 , 7.26, 10.14, 21.72,
37.95, 8.15, 24.21, 14.8 , 14.58, 21.18, 25.27, 23.88,
27.55, 20.05, 15.94, 9.47, 17.01, 12.54, 14.45, 16.17,
22.39, 25.76, 11.36, 20.09, 6.14, 22.62, 24.97, 14.4 ,
13.79, 15.99, 13.75, 19.93, 32.54, 8.73, 21.96, 9.92,
13.33, 14.16, 16.49, 16.51, 17.81, 26.44, 13.95, 14.63,
7.81, 14.97, 18.1 , 24.69, 21.32, 7.08, 12.07, 6.46,
6.72, 32.47, 10.91, 19.12, 16.53, 18.76, 23.67, 22.83,
9.06, 5.31, 14.57, 21.8 , 13.05, 31.35, 19.88, 22.31,
17.28, 18.65, 15.06, 16.23, 18.27, 9.34, 9.96, 45.39,
15.09, 26.9 , 14.73, 25.67, 28.27, 23.23, 39.91, 13.09,
36.02, 17.05, 24.7 , 13.57, 21.12, 15.83, 20.94, 27.91,
29.61, 8.95, 6.57, 10.98, 12.01, 15.9 , 18.63, 8.74,
5.39, 14.81, 14.64, 29.62, 11.62, 3.41, 17.36, 16. ,
22.4 , 18.26, 11.51, 13.61, 10.49, 23.36, 18.59, 12.8 ,
18.3 , 15.03, 23.57, 8.7 , 12.15, 25.87, 16.21, 2.95,
19.89, 13.32, 22.37, 25.83, 22.81, 19.83, 15.25, 18.9 ,
12.62, 33.76, 16.87, 27.13, 14.89, 28.41, 15.5 , 25.92,
11.79, 18.52, 10.39, 17.46, 3.12, 11.07, 27.89, 23.32,
6.26, 18.53, 19.03, 17.95, 33.58, 11.82, 11.14, 34.48,
31.01, 18.62, 19.57, 19.56, 23.12, 19.14, 28.35, 32.37,
15.76, 13.46, 17.2 , 21.42, 13.19, 48.58, 25.31, 16.62,
26.38, 24.35, 15.87, 18.14, 26.16, 7.05, 7.36, 15.49,
22.11, 19.15, 7.91, 24.65, 21.39, 25.01, 25.49, 25.36,
13.62, 23.13, 43.49, 11.32, 22.66, 4.24, 15.71, 12.86,
24.47, 21.35, 10.01, 20.51, 19.77, 25.09, 25.71, 11.1 ,
10.38, 18.12, 26.92, 35.05, 26.75, 17.1 , 10.64, 21.97,
13.74, 6.63, 12.59, 14.29, 10.43, 24.77, 18.74, 29.52,
29.75, 11.92, 19.63, 15.41, 28.26, 13.81, 14.86, 16.32,
25.93, 14.79, 8.54, 11.23, 21.67, 27.67, 27. , 28.57,
22.87, 29.09, 27.11, 6.88, 29.31, 23.49, 36.79, 25. ,
18.96, 18.87, 11.48, 17. , 2.15, 4.79, 6.33, 24.12,
42.39, 19.75, 20.25, 18.91, 3.03, 17.06, 32.46, 17.12,
6.59, 15.55, 21.47, 21.01, 12.74, 8.19, 33.46, 21.83,
21.95, 20.23, 20.08, 19.95, 20.36, 15.1 , 3.95, 18.08,
21.58, 18.6 , 17.92, 1.78, 11.2 , 19.94, 29.78, 13.49,
17.32, 19.24, 13.69, 20.3 , 31.8 , 25.29, 14.9 , 25.59,
17.8 , 19.22, 20.32, 12.94, 28.98, 22.78, 15.63, 28.76,
13.86, 22.01, 12.87, 17.33, 25.73, 17.65, 7.47, 9.73,
29.8 , 14.35, 35.87, 39.82, 13.7 , 22.08, 10.93, 24.76,
20.49, 19.69, 20.74, 22.27, 13.06, 24.9 , 27.79, 27.38,
28.32, 19.98, 23.03, 20.33, 12.88, 21.19, 10.42, 19.31,
18.19, 14.62, 8.2 , 20.98, 24.89, 5.09, 21.24, 16.03,
18.04, 21.41, 11.54, 22.93, 16.84, 13.66, 25.4 , 17.67,
15.42, 19.44, 14.75, 30.24, 8.94, 10.2 , 11.31, 22.85,
25.79, 29.3 , 14.12, 26.14, 16.93, 14.99, 20.7 , 22.95,
22.41, 27.42, 20.59, 14.27, 11.4 , 22.71, 34.6 , 15.62,
20.91, 22.15, 20.4 , 20.99, 24.46, 20.34, 14.33, 23.94,
17.94, 11.83, 27.32, 12.25, 15.6 , 31.74, 23.46, 9.62,
13.89, 6.36, 17.29, 28.84, 21.6 , 12.67]),
'earliest_cr_line': [Jan-1985, Apr-1999, Nov-2001, Feb-1996, Jan-1996, ..., Aug-2013, Jun-2013, Oct-2013, Sep-2013, Nov-2013]
Length: 697
Categories (697, object): [Jan-1985, Apr-1999, Nov-2001, Feb-1996, ..., Jun-2013, Oct-2013, Sep-2013, Nov-2013],
'emp_length': [10+ years, < 1 year, 1 year, 3 years, 8 years, ..., 5 years, 6 years, 2 years, 7 years, n/a]
Length: 12
Categories (12, object): [10+ years, < 1 year, 1 year, 3 years, ..., 6 years, 2 years, 7 years, n/a],
'funded_amnt': array([ 5000., 2500., 2400., ..., 36625., 38375., 39675.]),
'funded_amnt_inv': array([ 4975., 2500., 2400., ..., 36625., 38375., 39675.]),
'grade': [B, C, A, E, F, D, G]
Categories (7, object): [B, C, A, E, F, D, G],
'home_ownership': [RENT, OWN, MORTGAGE, OTHER, NONE, ANY]
Categories (6, object): [RENT, OWN, MORTGAGE, OTHER, NONE, ANY],
'il_util': array([ nan, 58., 51., 97., 84., 73., 63., 74., 94.,
75., 76., 30., 52., 81., 42., 91., 22., 43.,
64., 70., 86., 96., 106., 34., 87., 62., 90.,
55., 65., 85., 99., 92., 0., 38., 80., 82.,
23., 57., 83., 48., 61., 88., 93., 89., 60.,
27., 77., 98., 69., 68., 78., 35., 100., 109.,
39., 53., 71., 95., 59., 37., 33., 44., 26.,
67., 41., 66., 45., 122., 101., 46., 49., 16.,
150., 72., 6., 31., 50., 3., 19., 40., 79.,
111., 105., 151., 56., 121., 8., 54., 14., 112.,
120., 125., 118., 124., 47., 7., 18., 5., 104.,
108., 17., 21., 117., 28., 126., 29., 15., 13.,
107., 103., 9., 110., 149., 10., 24., 12., 115.,
147., 116., 32., 36., 114., 102., 119., 129., 25.,
2., 143., 20., 137., 1., 135., 130., 171., 127.,
113., 176., 139., 11., 128., 159., 173., 138., 133.,
4., 132., 136., 123., 131., 174., 160., 155., 140.,
152., 142., 234., 154., 134., 162., 153., 193., 156.,
141., 145., 146., 208., 175., 158., 161., 144., 184.,
148., 163., 165., 157., 166., 189., 186.]),
'initial_list_status': [f, w]
Categories (2, object): [f, w],
'inq_fi': array([ nan, 1., 2., 0., 4., 5., 3., 6., 16., 9., 7.,
10., 13., 11., 12., 8., 15., 20., 18., 14., 24., 19.,
23., 28., 17., 21., 22.]),
'inq_last_12m': array([ nan, 1., 5., 3., 6., 0., 2., 7., 9., 4., 10.,
14., 16., 8., 20., 11., 15., 13., 19., 17., 12., 18.,
30., 25., 21., 32., 27., 23., 26., 22., 24., 31., 29.,
28.]),
'inq_last_6mths': array([ 1., 5., 2., 0., 3., 4., 6., 7., 8., 9., 10.,
11., 12., 15., 14., 33., 17., 32., 24., 13., 18., 16.,
31., 28., 25., 27., 20., 19., nan]),
'installment': array([ 162.87, 59.83, 84.33, ..., 913.75, 502.75, 1272.39]),
'int_rate': array([ 10.65, 15.27, 15.96, 13.49, 12.69, 7.9 , 18.64, 21.28,
14.65, 9.91, 16.29, 6.03, 11.71, 12.42, 14.27, 16.77,
7.51, 8.9 , 18.25, 6.62, 19.91, 17.27, 17.58, 21.67,
19.42, 22.06, 20.89, 20.3 , 23.91, 19.03, 23.52, 23.13,
22.74, 22.35, 24.11, 6. , 22.11, 7.49, 11.99, 5.99,
10.99, 9.99, 18.79, 11.49, 8.49, 15.99, 16.49, 6.99,
12.99, 15.23, 14.79, 5.42, 10.59, 17.49, 15.62, 21.36,
19.29, 13.99, 18.39, 16.89, 17.99, 20.62, 20.99, 22.85,
19.69, 20.25, 23.22, 21.74, 22.48, 23.59, 12.62, 18.07,
11.63, 7.91, 7.42, 11.14, 20.2 , 12.12, 19.39, 16.11,
17.54, 22.64, 13.84, 16.59, 17.19, 12.87, 20.69, 9.67,
21.82, 19.79, 18.49, 22.94, 24.59, 24.4 , 21.48, 14.82,
14.17, 7.29, 17.88, 20.11, 16.02, 17.51, 13.43, 14.91,
13.06, 15.28, 15.65, 17.14, 11.11, 10.37, 16.4 , 7.66,
10. , 18.62, 10.74, 5.79, 6.92, 9.63, 14.54, 12.68,
19.36, 13.8 , 18.99, 21.59, 20.85, 21.22, 19.74, 20.48,
6.91, 12.23, 12.61, 10.36, 6.17, 6.54, 9.25, 16.69,
15.95, 8.88, 13.35, 9.62, 16.32, 12.98, 14.83, 13.72,
14.09, 14.46, 20.03, 17.8 , 15.2 , 15.57, 18.54, 19.66,
17.06, 18.17, 17.43, 20.4 , 20.77, 18.91, 21.14, 17.44,
13.23, 7.88, 11.12, 13.61, 10.38, 17.56, 17.93, 15.58,
13.98, 14.84, 15.21, 6.76, 6.39, 11.86, 7.14, 14.35,
16.82, 10.75, 14.72, 16.45, 18.67, 20.53, 19.41, 20.16,
21.27, 18.3 , 19.04, 20.9 , 21.64, 12.73, 10.25, 13.11,
10.62, 13.48, 14.59, 16.07, 15.7 , 9.88, 11.36, 15.33,
13.85, 14.96, 14.22, 7.74, 13.22, 13.57, 8.59, 17.04,
14.61, 8.94, 12.18, 11.83, 11.48, 16.35, 13.92, 15.31,
14.26, 19.13, 12.53, 16.7 , 16. , 17.39, 18.09, 7.4 ,
18.43, 17.74, 7.05, 20.52, 20.86, 19.47, 18.78, 21.21,
19.82, 20.17, 13.16, 8. , 13.47, 12.21, 16.63, 9.32,
12.84, 11.26, 15.68, 15.37, 10.95, 11.89, 14.11, 13.79,
7.68, 11.58, 7.37, 16.95, 15.05, 18.53, 14.74, 14.42,
18.21, 17.26, 18.84, 17.9 , 19.16, 13.67, 9.38, 12.72,
13.36, 11.46, 10.51, 9.07, 13.04, 11.78, 12.41, 10.83,
12.09, 17.46, 14.3 , 17.15, 15.25, 10.2 , 15.88, 14.93,
16.2 , 18.72, 14.62, 8.32, 14.12, 10.96, 10.33, 10.01,
12.86, 11.28, 11.59, 8.63, 12.54, 12.22, 11.91, 15.38,
16.96, 13.17, 9.7 , 16.33, 14.75, 15.07, 16.01, 10.71,
10.64, 9.76, 11.34, 10.39, 13.87, 11.03, 11.66, 13.24,
10.08, 9.45, 13.55, 12.29, 11.97, 12.92, 15.45, 14.5 ,
14.18, 15.13, 16.08, 15.76, 17.03, 17.34, 16.71, 9.83,
13.62, 10.46, 9.51, 9.2 , 13.3 , 10.78, 7.75, 8.38,
12.36, 12.67, 11.72, 13.93, 8.07, 7.43, 12.04, 14.25,
14.88, 11.41, 11.09, 10.14, 16.15, 15.83, 7.12, 18.36,
9.64, 9.96, 11.22, 9.01, 9.33, 11.54, 12.17, 12.8 ,
14.38, 13.75, 14.7 , 12.49, 14.07, 10.91, 13.12, 10.28,
8.7 , 14.67, 15.01, 17.78, 16.83, 17.59, 14.43, 16.65,
17.91, 17.28, 18.86, 18.61, 17.66, 18.29, 17.97, 18.04,
14.57, 17.72, 17.09, 15.51, 16.46, 17.41, 17.22, 16.91,
16.28, 17.86, 7.62, 14.98, 13.53, 12.85, 14.47, 16.24,
15.61, 19.97, 20.5 , 19.22, 22.9 , 23.7 , 17.57, 23.4 ,
16.99, 25.89, 22.4 , 25.8 , 24.99, 24.08, 25.99, 25.57,
26.06, 24.5 , 25.83, 17.76, 15.1 , 18.55, 21.7 , 17.1 ,
19.52, 19.2 , 23.1 , 21. , 6.97, 8.6 , 11.55, 13.68,
15.22, 9.71, 14.33, 22.7 , 22.2 , 16.78, 18.85, 13.05,
23.5 , 21.6 , 12.35, 24.89, 21.15, 20.8 , 20.31, 25.28,
18.75, 19.05, 15.8 , 10.16, 19.72, 23.76, 17.77, 23.28,
20.49, 21.98, 24.7 , 23.83, 23.63, 22.95, 22.47, 21.49,
24.83, 21.97, 15.81, 22.45, 22.78, 23.33, 19.99, 24.2 ,
24.76, 24.33, 23.26, 24.52, 14.28, 19.89, 9.17, 5.32,
24.24, 14.85, 15.77, 6.49, 9.8 , 12.88, 19.48, 27.49,
13.44, 28.49, 25.09, 28.99, 16.55, 26.99, 14.48, 15.41,
13.18, 12.59, 6.24, 21.99, 18.2 , 27.88, 7.89, 6.89,
23.99, 7.26, 22.99, 27.31, 26.77, 12.05, 8.18, 11.53,
13.33, 19.19, 25.78, 5.93, 6.68, 8.19, 19.24, 8.67,
11.44, 9.49, 13.66, 12.39, 15.59, 14.31, 14.99, 10.49,
7.69, 14.49, 10.15, 8.39, 22.15, 11.67, 18.24, 23.43,
13.65, 21.18, 14.64, 14.16, 18.92, 7.39, 9.75, 11.47,
9.16, 19.53, 20.75, 28.34, 26.57, 28.14, 28.67, 25.88,
27.34, 27.99, 24.49, 26.49, 22.39, 12.79, 25.69, 25.29,
7.99, 7.59, 8.99, 28.88, 28.18, 29.96, 27.79, 29.67,
23.32, 25.11, 26.14, 25.44, 30.99, 25.65, 12.74, 8.24,
11.39, 28.69, 25.49, 7.24, 26.24, 29.49, 29.99, 24.74,
30.89, 30.79, 30.49, 30.84, 30.74, 30.94]),
'issue_d': [Dec-2011, Nov-2011, Oct-2011, Sep-2011, Aug-2011, ..., Aug-2016, Jul-2016, Dec-2016, Nov-2016, Oct-2016]
Length: 115
Categories (115, object): [Dec-2011, Nov-2011, Oct-2011, Sep-2011, ..., Jul-2016, Dec-2016, Nov-2016, Oct-2016],
'loan_amnt': array([ 5000., 2500., 2400., ..., 36625., 38375., 39675.]),
'loan_status': [Fully Paid, Late]
Categories (2, object): [Fully Paid, Late],
'max_bal_bc': array([ nan, 8937., 653., ..., 37423., 15313., 22031.]),
'mo_sin_old_il_acct': array([ nan, 123., 125., 117., 173., 67., 124., 104., 129.,
179., 146., 111., 2., 147., 243., 115., 164., 160.,
98., 188., 162., 159., 235., 138., 38., 133., 137.,
101., 143., 199., 16., 153., 113., 165., 141., 154.,
291., 106., 116., 142., 217., 161., 131., 157., 118.,
134., 152., 20., 206., 163., 52., 29., 122., 114.,
93., 103., 172., 151., 150., 168., 192., 110., 28.,
213., 140., 85., 44., 258., 191., 158., 148., 187.,
218., 126., 40., 112., 19., 43., 167., 119., 128.,
145., 109., 149., 64., 78., 42., 227., 90., 127.,
170., 88., 166., 87., 100., 91., 74., 136., 259.,
107., 18., 135., 49., 92., 197., 75., 99., 55.,
108., 12., 120., 210., 121., 300., 132., 183., 51.,
205., 184., 30., 77., 62., 105., 97., 171., 96.,
261., 195., 130., 139., 364., 39., 155., 234., 10.,
193., 363., 196., 70., 32., 208., 31., 86., 59.,
304., 7., 89., 190., 41., 228., 66., 54., 219.,
303., 82., 60., 265., 23., 65., 45., 50., 207.,
280., 73., 26., 232., 156., 4., 180., 79., 185.,
169., 203., 175., 57., 34., 252., 72., 33., 216.,
241., 46., 209., 17., 58., 81., 225., 176., 230.,
292., 84., 63., 21., 69., 61., 37., 94., 144.,
178., 224., 214., 9., 53., 11., 95., 102., 80.,
221., 244., 71., 267., 25., 204., 222., 48., 76.,
174., 220., 211., 14., 268., 327., 68., 1., 264.,
340., 215., 181., 248., 83., 231., 24., 8., 15.,
263., 283., 35., 251., 279., 47., 255., 177., 6.,
27., 56., 182., 36., 22., 269., 198., 226., 278.,
239., 5., 201., 286., 194., 257., 242., 298., 249.,
200., 266., 13., 238., 297., 254., 296., 253., 0.,
186., 202., 290., 312., 302., 245., 348., 350., 212.,
189., 237., 262., 236., 315., 256., 295., 275., 250.,
386., 272., 314., 317., 233., 229., 3., 359., 322.,
271., 288., 223., 246., 385., 289., 240., 277., 294.,
357., 260., 299., 325., 324., 339., 323., 316., 281.,
399., 338., 274., 301., 273., 360., 456., 333., 393.,
282., 346., 285., 284., 362., 247., 270., 432., 293.,
371., 329., 287., 310., 405., 276., 334., 409., 313.,
372., 353., 369., 328., 349., 442., 308., 305., 383.,
397., 387., 395., 307., 326., 443., 306., 382., 337.,
347., 408., 446., 354., 394., 365., 321., 318., 343.,
351., 319., 438., 311., 361., 344., 341., 415., 309.,
384., 336., 420., 396., 373., 374., 491., 335., 342.,
401., 380., 355., 377., 407., 477., 376., 368., 331.,
412., 366., 320., 352., 410., 379., 411., 330., 413.,
406., 367., 332., 476., 392., 434., 404., 370., 375.,
345., 378., 400., 356., 421., 649., 429., 388., 651.,
455., 381., 391., 433., 427., 519., 724., 441., 358.,
416., 463., 640., 452., 474., 436., 390., 720., 422.,
424., 509., 469., 402., 483., 448., 447., 398., 478.,
471., 425., 507., 470., 423., 545., 490., 417., 428.,
414., 482., 418., 439., 465., 426., 403., 437., 389.,
472., 506., 459., 451., 494.]),
'mo_sin_old_rev_tl_op': array([ nan, 48., 118., 229., 326., 193., 150., 83., 182.,
220., 269., 299., 237., 103., 257., 189., 290., 186.,
38., 271., 139., 202., 115., 240., 188., 238., 144.,
92., 358., 86., 421., 336., 180., 117., 232., 243.,
285., 204., 381., 107., 275., 40., 99., 168., 123.,
213., 88., 177., 169., 80., 294., 68., 293., 136.,
76., 100., 106., 194., 149., 141., 178., 224., 210.,
246., 111., 37., 114., 233., 145., 276., 108., 69.,
158., 121., 147., 179., 154., 260., 361., 97., 214.,
42., 265., 134., 148., 396., 163., 77., 207., 98.,
223., 167., 206., 84., 119., 160., 349., 174., 474.,
53., 268., 313., 64., 112., 261., 142., 230., 416.,
79., 156., 284., 133., 231., 181., 264., 184., 146.,
228., 89., 247., 197., 221., 138., 47., 323., 62.,
185., 93., 41., 126., 350., 94., 60., 314., 87.,
209., 58., 353., 135., 153., 241., 166., 82., 190.,
491., 208., 192., 128., 51., 110., 458., 325., 211.,
102., 31., 244., 130., 104., 373., 253., 422., 162.,
248., 453., 116., 388., 267., 236., 129., 191., 124.,
109., 286., 157., 360., 296., 312., 418., 101., 120.,
200., 201., 85., 131., 438., 333., 295., 137., 105.,
140., 234., 283., 298., 96., 63., 225., 152., 32.,
303., 409., 252., 203., 56., 159., 33., 227., 389.,
212., 125., 226., 161., 254., 170., 324., 59., 173.,
363., 70., 274., 195., 90., 176., 71., 398., 183.,
61., 67., 297., 390., 196., 431., 292., 132., 311.,
205., 143., 74., 266., 218., 219., 113., 171., 216.,
320., 155., 309., 280., 436., 165., 440., 255., 222.,
73., 122., 371., 250., 291., 43., 235., 164., 199.,
273., 327., 18., 368., 175., 50., 39., 317., 330.,
245., 444., 95., 352., 332., 424., 172., 316., 239.,
307., 151., 385., 242., 354., 49., 81., 304., 380.,
65., 281., 342., 52., 288., 337., 556., 27., 432.,
321., 278., 54., 537., 127., 15., 198., 277., 425.,
259., 415., 557., 272., 334., 329., 91., 78., 376.,
287., 339., 66., 366., 29., 302., 318., 328., 251.,
23., 503., 439., 44., 215., 531., 359., 441., 57.,
217., 305., 343., 270., 28., 355., 394., 498., 75.,
256., 335., 46., 187., 72., 331., 387., 395., 357.,
20., 407., 346., 393., 356., 465., 548., 419., 310.,
263., 282., 369., 365., 34., 35., 279., 21., 414.,
538., 476., 36., 445., 308., 351., 364., 249., 45.,
319., 262., 449., 455., 300., 443., 427., 446., 404.,
370., 301., 525., 13., 289., 16., 460., 372., 55.,
30., 490., 386., 406., 518., 362., 384., 374., 258.,
340., 494., 442., 379., 430., 524., 378., 435., 408.,
345., 338., 452., 533., 322., 397., 480., 635., 479.,
19., 391., 341., 528., 315., 26., 483., 433., 22.,
535., 614., 594., 344., 462., 402., 411., 466., 413.,
25., 620., 420., 383., 471., 459., 482., 464., 423.,
484., 523., 470., 429., 448., 501., 558., 567., 544.,
400., 348., 549., 475., 14., 447., 403., 487., 367.,
451., 477., 546., 347., 450., 401., 502., 578., 461.,
426., 492., 485., 521., 410., 547., 569., 511., 382.,
306., 412., 512., 24., 585., 499., 467., 399., 513.,
508., 437., 478., 500., 463., 392., 684., 488., 434.,
493., 504., 570., 481., 599., 375., 377., 473., 559.,
428., 454., 489., 495., 562., 611., 517., 563., 472.,
405., 505., 456., 469., 17., 554., 468., 601., 534.,
540., 457., 555., 519., 622., 536., 496., 486., 509.,
497., 623., 529., 565., 572., 515., 417., 506., 580.,
552., 527., 582., 589., 588., 520., 510., 539., 526.,
551., 507., 564., 550., 571., 597., 645., 561., 522.,
643., 560., 514., 573., 609., 543., 681., 579., 586.,
568., 545., 541., 532., 596., 728., 656., 612., 566.,
553., 583., 610., 587., 626., 542., 602., 615., 576.,
613., 642., 592., 654., 590., 581., 593., 7., 530.,
644., 649., 617., 708., 574., 605., 705., 603., 616.,
630., 12., 516., 730., 640., 760., 577., 627., 639.,
607., 575., 5., 698., 632., 682., 637., 638., 744.,
600., 659., 658., 595., 633., 631., 584., 655., 6.,
636., 686., 647., 674., 608., 726., 619., 598., 624.,
591., 10., 8., 657., 606., 666., 695., 769., 673.,
691., 618., 665., 9., 714., 625., 11., 604., 653.,
629., 672., 711., 663., 650., 775., 669., 641., 670.,
757., 4., 732., 768., 621., 634., 661., 717., 700.,
724., 842., 761., 793., 668., 734., 731., 758., 662.,
648., 687., 628.]),
'mo_sin_rcnt_rev_tl_op': array([ nan, 1., 10., 5., 16., 4., 11., 12., 25.,
14., 18., 20., 24., 7., 23., 0., 17., 8.,
29., 2., 6., 13., 9., 142., 3., 30., 36.,
69., 39., 50., 42., 37., 27., 22., 15., 21.,
65., 19., 31., 55., 63., 67., 59., 26., 32.,
106., 33., 44., 46., 87., 53., 96., 77., 35.,
56., 57., 45., 41., 49., 28., 60., 72., 58.,
66., 40., 79., 43., 47., 117., 73., 54., 75.,
38., 70., 113., 61., 52., 74., 91., 51., 82.,
81., 62., 126., 68., 76., 84., 34., 108., 102.,
221., 89., 64., 48., 71., 88., 111., 134., 99.,
86., 93., 151., 137., 97., 80., 78., 83., 128.,
103., 90., 228., 109., 124., 127., 101., 95., 114.,
118., 85., 119., 146., 104., 120., 100., 145., 110.,
94., 211., 122., 92., 144., 149., 112., 107., 135.,
153., 210., 98., 181., 143., 136., 115., 139., 132.,
156., 150., 116., 125., 157., 105., 193., 141., 148.,
159., 192., 188., 131., 130., 160., 121., 129., 123.,
158., 133., 155., 152., 165., 163., 167., 182., 138.,
168., 197., 178., 164., 174., 166., 171., 180., 315.,
254., 154., 290., 147., 186., 195., 196., 201., 173.,
140., 175., 241., 161., 184., 183., 304., 176., 177.,
224., 236., 187., 179., 170., 223., 162., 202., 194.,
205., 297., 372., 302., 217., 239., 190., 172., 218.,
189., 208., 253., 258., 169., 219., 200., 267., 199., 225.]),
'mo_sin_rcnt_tl': array([ nan, 1., 9., 2., 6., 4., 11., 12., 25.,
14., 7., 3., 13., 8., 0., 17., 27., 5.,
24., 16., 20., 69., 30., 21., 10., 15., 29.,
19., 55., 50., 33., 23., 22., 26., 18., 31.,
49., 32., 35., 36., 40., 63., 46., 57., 47.,
65., 39., 44., 28., 41., 53., 75., 73., 43.,
37., 48., 76., 84., 59., 38., 102., 68., 45.,
34., 42., 51., 78., 54., 89., 77., 66., 52.,
79., 70., 87., 80., 67., 64., 62., 61., 60.,
113., 82., 56., 119., 58., 88., 90., 81., 71.,
211., 74., 72., 101., 94., 83., 121., 86., 95.,
97., 85., 99., 98., 93., 106., 117., 91., 111.,
124., 116., 100., 127., 96., 114., 107., 118., 105.,
148., 109., 143., 92., 104., 122., 174., 137., 108.,
166., 192., 133., 197., 120., 110., 130., 154., 135.,
131., 140., 147., 115., 176., 151., 139., 136., 162.,
103., 144., 145., 194., 132., 125., 112.]),
'mort_acc': array([ nan, 0., 1., 5., 4., 3., 6., 10., 7., 2., 11.,
8., 9., 14., 15., 12., 13., 19., 24., 17., 18., 16.,
31., 27., 20., 30., 25., 23., 22., 29., 21., 32., 34.,
28., 26., 37.]),
'mths_since_last_delinq': array([ nan, 35., 38., 61., 8., 20., 18., 68., 45.,
48., 41., 40., 74., 25., 53., 39., 10., 26.,
56., 77., 28., 52., 24., 16., 60., 54., 23.,
9., 11., 13., 65., 19., 80., 22., 59., 79.,
44., 64., 57., 14., 63., 49., 15., 73., 70.,
29., 51., 5., 75., 55., 2., 30., 47., 69.,
4., 43., 33., 21., 27., 46., 81., 78., 82.,
31., 76., 62., 72., 42., 50., 3., 12., 67.,
36., 34., 58., 17., 71., 66., 32., 6., 37.,
7., 1., 83., 86., 115., 96., 103., 120., 106.,
89., 107., 85., 97., 95., 0., 110., 84., 135.,
88., 87., 122., 91., 146., 134., 114., 99., 93.,
127., 101., 94., 102., 129., 113., 139., 131., 143.,
109., 119., 149., 118., 130., 90., 141., 116., 148.,
100., 152., 98., 92., 108., 105., 112., 125., 176.,
137., 121., 133., 104., 140., 151., 159., 117., 132., 111.]),
'mths_since_last_record': array([ nan, 113., 105., 97., 33., 93., 52., 85., 90.,
91., 114., 92., 117., 87., 45., 83., 118., 38.,
101., 100., 112., 110., 88., 79., 77., 107., 102.,
98., 95., 103., 96., 116., 111., 89., 108., 29.,
106., 115., 53., 86., 57., 63., 94., 109., 99.,
104., 76., 61., 28., 23., 75., 47., 82., 21.,
62., 44., 80., 67., 119., 42., 34., 66., 58.,
22., 56., 72., 64., 50., 69., 49., 74., 35.,
12., 26., 78., 54., 37., 73., 11., 31., 59.,
32., 81., 68., 55., 39., 51., 70., 30., 41.,
71., 40., 43., 27., 65., 46., 19., 17., 25.,
13., 48., 36., 7., 60., 14., 6., 18., 0.,
20., 120., 129., 5., 24., 15., 84., 10., 16.,
8., 9., 3., 121., 4., 1., 2.]),
'mths_since_rcnt_il': array([ nan, 13., 19., 3., 14., 18., 9., 21., 47.,
91., 6., 24., 17., 16., 7., 5., 25., 12.,
50., 8., 1., 32., 141., 2., 29., 28., 15.,
27., 31., 26., 20., 118., 10., 4., 30., 36.,
22., 55., 129., 23., 100., 43., 230., 114., 90.,
61., 44., 11., 131., 52., 45., 33., 62., 35.,
95., 81., 103., 117., 60., 37., 38., 39., 113.,
86., 82., 51., 68., 42., 88., 83., 67., 101.,
158., 40., 89., 97., 98., 64., 69., 147., 149.,
34., 73., 0., 99., 77., 115., 92., 138., 46.,
123., 122., 139., 72., 56., 94., 63., 41., 108.,
107., 54., 127., 48., 70., 80., 165., 112., 71.,
57., 137., 134., 128., 174., 105., 84., 124., 96.,
78., 87., 110., 49., 132., 125., 74., 121., 153.,
144., 59., 66., 104., 152., 79., 65., 202., 244.,
76., 148., 85., 58., 93., 75., 201., 161., 219.,
211., 140., 126., 159., 116., 133., 130., 53., 136.,
325., 142., 170., 102., 120., 160., 204., 162., 157.,
146., 106., 212., 119., 150., 135., 169., 181., 109.,
151., 111., 255., 168., 178., 245., 145., 231., 154.,
242., 179., 246., 156., 163., 172., 243., 192., 171.,
200., 209., 166., 164., 183., 167., 173., 143., 260.,
186., 221., 184., 189., 187., 206., 155., 176., 263.,
177., 257., 309., 227., 193., 196., 191., 248., 213.,
288., 268., 182., 366., 279., 188., 217.]),
'mths_since_recent_bc': array([ nan, 1., 10., 5., 16., 85., 11., 12., 25.,
18., 20., 38., 7., 24., 0., 17., 14., 8.,
15., 29., 6., 31., 27., 26., 19., 9., 13.,
142., 136., 30., 33., 3., 65., 36., 102., 39.,
22., 50., 2., 45., 77., 90., 23., 97., 21.,
37., 72., 70., 82., 4., 78., 62., 84., 74.,
44., 35., 129., 76., 47., 55., 109., 63., 87.,
83., 89., 88., 28., 59., 69., 32., 106., 40.,
66., 54., 93., 60., 68., 108., 86., 49., 46.,
53., 41., 71., 114., 91., 139., 80., 79., 43.,
95., 56., 58., 57., 100., 73., 64., 51., 52.,
42., 112., 67., 137., 134., 117., 75., 61., 115.,
113., 118., 48., 140., 216., 116., 81., 107., 34.,
98., 104., 99., 151., 132., 156., 194., 103., 254.,
96., 143., 101., 105., 92., 135., 146., 221., 174.,
138., 220., 124., 204., 119., 111., 94., 191., 163.,
158., 121., 110., 122., 126., 170., 152., 125., 130.,
128., 295., 206., 224., 183., 154., 133., 329., 363.,
228., 145., 213., 291., 123., 169., 157., 197., 153.,
190., 205., 167., 120., 188., 144., 211., 207., 255.,
181., 210., 296., 141., 164., 243., 175., 320., 171.,
226., 235., 185., 283., 315., 150., 201., 149., 449.,
459., 348., 196., 223., 312., 147., 162., 155., 160.,
270., 127., 161., 276., 131., 267., 189., 192., 176.,
227., 292., 166., 231., 177., 208., 168., 288., 238.,
502., 159., 179., 290., 148., 209., 274., 193., 252.,
182., 245., 275., 327., 240., 172., 303., 187., 202.,
242., 232., 225., 237., 398., 285., 300., 261., 199.,
173., 198., 219., 222., 178., 180., 200., 165., 250.,
263., 284., 306., 239., 417., 230., 554., 383., 420.,
339., 215., 229., 277., 317., 195., 251., 186., 318.,
214., 234., 217., 354., 246., 351., 273., 343., 361.,
493., 313., 256., 218., 328., 266., 272., 279., 247.,
321., 278., 289., 316., 310., 307., 342., 311., 248.,
305., 233., 353., 358., 319., 326., 184., 360., 203.,
236., 302., 527., 262., 212., 241., 297., 293., 416.,
538., 268., 338., 309., 521., 287., 259., 299., 298.,
346., 412., 244., 356., 257., 375., 260., 362., 359.,
413., 308., 387., 282., 294., 350., 324., 451., 281.,
258., 331., 467., 462., 380., 333., 466., 381., 347.,
337., 280., 473., 249., 435., 301., 341., 404., 322.,
325., 334., 344., 422., 323., 345., 304., 395., 330.,
286., 335., 415., 407., 271., 265., 447., 611., 372.,
408., 390., 376., 436., 264., 336., 382., 533., 384.,
385., 386., 427., 365., 441., 371., 388., 364., 564.,
373., 355., 314., 370., 253., 340., 439., 393., 269.,
357., 546., 504.]),
'mths_since_recent_bc_dlq': array([ nan, 35., 53., 16., 75., 11., 70., 69., 48.,
45., 8., 14., 59., 10., 50., 25., 71., 76.,
20., 34., 7., 28., 41., 38., 62., 56., 74.,
60., 47., 18., 55., 49., 77., 66., 58., 15.,
12., 61., 22., 21., 37., 33., 31., 81., 57.,
6., 36., 5., 42., 17., 29., 23., 64., 9.,
13., 44., 46., 68., 4., 3., 26., 80., 73.,
24., 19., 72., 65., 43., 1., 79., 27., 52.,
78., 30., 40., 39., 63., 32., 67., 54., 84.,
51., 0., 82., 2., 106., 92., 90., 88., 95.,
86., 83., 102., 94., 87., 85., 89., 122., 93.,
109., 91., 99., 96., 145., 107., 127., 124., 143.,
149., 100., 116., 152., 97., 103., 98., 105., 101.,
114., 112., 141., 110., 111., 108., 128., 113., 176.,
121., 131., 115., 104., 135., 140., 134., 133., 151.,
159., 130., 139., 120.]),
'mths_since_recent_inq': array([ nan, 3., 10., 8., 4., 11., 20., 17., 6., 7., 2.,
12., 0., 9., 5., 14., 1., 13., 16., 21., 19., 18.,
22., 15., 23., 24., 25.]),
'mths_since_recent_revol_delinq': array([ nan, 35., 53., 43., 16., 75., 11., 70., 69.,
48., 45., 8., 14., 59., 10., 50., 17., 15.,
36., 25., 71., 37., 76., 20., 34., 5., 7.,
28., 41., 38., 62., 29., 54., 74., 60., 47.,
18., 55., 49., 77., 66., 12., 61., 4., 56.,
23., 39., 33., 32., 13., 3., 31., 9., 57.,
6., 80., 44., 42., 22., 30., 46., 68., 26.,
81., 64., 73., 24., 19., 72., 1., 79., 52.,
2., 67., 27., 40., 58., 78., 21., 51., 63.,
84., 65., 0., 82., 106., 135., 115., 88., 93.,
95., 86., 83., 102., 87., 85., 122., 94., 109.,
91., 146., 134., 89., 114., 99., 96., 90., 107.,
165., 127., 119., 101., 124., 129., 113., 120., 131.,
143., 103., 149., 118., 130., 141., 116., 100., 152.,
92., 97., 98., 105., 112., 110., 125., 108., 111.,
176., 121., 137., 133., 104., 140., 151., 159., 117., 132.]),
'num_accts_ever_120_pd': array([ nan, 0., 1., 6., 3., 7., 2., 5., 9., 10., 4.,
8., 20., 12., 17., 11., 13., 15., 14., 21., 19., 29.,
24., 26., 16., 18., 25., 22., 23., 39., 30.]),
'num_actv_bc_tl': array([ nan, 4., 2., 3., 6., 8., 1., 5., 10., 11., 7.,
15., 0., 9., 13., 17., 12., 20., 14., 16., 19., 21.,
26., 18., 23., 22., 30., 24., 25.]),
'num_actv_rev_tl': array([ nan, 7., 5., 4., 8., 2., 24., 3., 6., 12., 11.,
13., 18., 10., 9., 15., 19., 21., 1., 14., 16., 17.,
20., 0., 26., 22., 29., 23., 27., 28., 25., 37., 31.,
34., 38., 32., 35., 30., 44., 33., 36., 40., 39.]),
'num_bc_sats': array([ nan, 8., 4., 6., 3., 5., 13., 11., 2., 1., 16.,
7., 12., 18., 9., 0., 10., 15., 17., 14., 19., 21.,
20., 24., 22., 28., 27., 35., 25., 32., 29., 23., 26.,
33., 30., 31., 36., 37., 57., 63., 39., 44., 34., 40.]),
'num_bc_tl': array([ nan, 10., 8., 4., 11., 14., 7., 5., 9., 13., 6.,
17., 2., 12., 19., 25., 16., 18., 3., 20., 15., 23.,
0., 26., 24., 21., 1., 22., 29., 28., 31., 34., 30.,
38., 27., 36., 33., 39., 32., 65., 35., 37., 40., 46.,
42., 52., 44., 43., 41., 45., 54., 47., 57., 48., 50.,
70., 51., 58., 61., 53., 68., 59.]),
'num_il_tl': array([ nan, 0., 15., 11., 8., 1., 2., 17., 9.,
3., 4., 6., 12., 16., 5., 13., 14., 30.,
22., 7., 33., 10., 20., 27., 18., 31., 39.,
24., 19., 28., 36., 21., 35., 54., 29., 25.,
23., 40., 26., 43., 32., 37., 34., 42., 41.,
44., 48., 38., 46., 47., 50., 45., 52., 56.,
57., 49., 53., 58., 61., 51., 66., 55., 59.,
86., 82., 104., 64., 69., 68., 65., 60., 100.,
101., 67., 73., 117., 89., 63., 72., 83., 118.,
71., 62., 79., 93., 77., 78., 85., 70., 75.,
91., 74., 87., 88., 107., 97., 81., 121., 76.,
96., 84., 128.]),
'num_op_rev_tl': array([ nan, 15., 8., 9., 10., 5., 6., 3., 4., 29., 7.,
11., 16., 13., 12., 20., 19., 26., 18., 14., 17., 23.,
2., 22., 28., 1., 24., 21., 25., 0., 27., 31., 39.,
32., 37., 45., 30., 34., 38., 43., 33., 58., 36., 42.,
35., 47., 48., 46., 41., 44., 40., 49., 83., 62., 50.,
52., 56., 51., 73.]),
'num_rev_accts': array([ nan, 18., 14., 15., 8., 11., 20., 24., 7.,
19., 9., 10., 17., 13., 40., 5., 22., 30.,
23., 25., 6., 29., 27., 28., 12., 26., 46.,
16., 34., 32., 4., 31., 38., 50., 21., 35.,
51., 3., 2., 37., 43., 36., 44., 33., 48.,
39., 45., 42., 41., 47., 49., 94., 75., 52.,
53., 54., 55., 56., 58., 57., 62., 59., 91.,
60., 61., 1., 0., 80., 63., 77., 67., 64.,
69., 68., 72., 73., 74., 66., 65., 92., 90.,
102., 71., 87., 70., 96., 105., 81., 78., 89.,
86., 82., 103., 76.]),
'num_rev_tl_bal_gt_0': array([ nan, 7., 5., 4., 8., 2., 24., 3., 6., 12., 11.,
13., 18., 10., 9., 15., 19., 21., 1., 14., 16., 17.,
20., 0., 26., 22., 23., 29., 27., 28., 25., 37., 31.,
34., 30., 38., 42., 33., 39., 36., 32.]),
'num_sats': array([ nan, 15., 17., 14., 9., 12., 7., 3., 6., 16., 8.,
29., 4., 5., 13., 10., 11., 20., 21., 27., 19., 30.,
18., 25., 22., 24., 40., 2., 26., 23., 1., 28., 31.,
37., 32., 33., 42., 41., 0., 34., 39., 36., 35., 45.,
38., 49., 53., 51., 43., 62., 44., 48., 50., 52., 46.,
55., 58., 57., 47., 56., 90., 75., 84., 54., 76., 59.,
77.]),
'num_tl_120dpd_2m': array([ nan, 0., 2., 1., 6.]),
'num_tl_30dpd': array([ nan, 0., 1., 2., 3., 4.]),
'num_tl_90g_dpd_24m': array([ nan, 0., 1., 2., 7., 5., 4., 3., 6., 8., 9.,
10., 11., 17., 15., 13., 18., 12., 20., 24., 14., 16.,
26., 39., 22., 19.]),
'num_tl_op_past_12m': array([ nan, 4., 3., 5., 1., 2., 0., 6., 9., 7., 8.,
15., 10., 12., 11., 13., 14., 17., 16., 19., 24., 25.,
18., 20., 23., 22., 21., 26., 30., 28.]),
'open_acc': array([ 3., 2., 10., 15., 9., 7., 4., 11., 14., 12., 20.,
8., 6., 17., 5., 13., 16., 30., 21., 18., 19., 27.,
23., 34., 25., 22., 24., 26., 32., 28., 29., 33., 31.,
39., 35., 36., 38., 44., 41., 42., 1., 46., 37., 47.,
nan, 40., 45., 49., 53., 51., 43., 0., 62., 48., 50.,
52., 55., 58., 57., 56., 90., 76., 84., 54., 59., 77.]),
'open_acc_6m': array([ nan, 0., 6., 3., 1., 2., 5., 4., 9., 8., 7.,
14., 12., 10., 11., 16., 15., 17.]),
'open_il_12m': array([ nan, 0., 2., 1., 4., 3., 5., 9., 6., 7., 8.,
10., 12., 11., 13., 20., 25.]),
'open_il_24m': array([ nan, 1., 2., 3., 4., 0., 6., 5., 7., 10., 9.,
19., 11., 8., 12., 14., 13., 15., 18., 16., 26., 17.,
28., 51., 20.]),
'open_il_6m': array([ nan, 3., 1., 2., 10., 0., 4., 14., 12., 9., 7.,
6., 5., 11., 19., 17., 13., 8., 15., 18., 21., 20.,
31., 22., 16., 33., 23., 25., 24., 28., 26., 27., 47.,
30., 29., 34., 38., 35., 32., 45.]),
'open_rv_12m': array([ nan, 0., 12., 2., 4., 1., 6., 3., 5., 11., 7.,
8., 9., 15., 14., 10., 13., 17., 18., 28., 16., 20.]),
'open_rv_24m': array([ nan, 1., 16., 3., 7., 0., 10., 9., 4., 5., 2.,
6., 11., 8., 24., 13., 17., 12., 15., 20., 21., 14.,
19., 22., 18., 25., 39., 23., 35., 27., 29., 26., 30.,
28.]),
'pct_tl_nvr_dlq': array([ nan, 1. , 0.75 , 0.812, 0.955, 0.786, 0.893, 0.773,
0.902, 0.913, 0.824, 0.771, 0.96 , 0.95 , 0.863, 0.682,
0.941, 0.969, 0.8 , 0.87 , 0.774, 0.964, 0.966, 0.932,
0.977, 0.921, 0.947, 0.981, 0.939, 0.952, 0.975, 0.974,
0.714, 0.872, 0.971, 0.933, 0.894, 0.958, 0.882, 0.917,
0.97 , 0.818, 0.885, 0.9 , 0.853, 0.953, 0.833, 0.912,
0.889, 0.65 , 0.929, 0.711, 0.852, 0.842, 0.88 , 0.923,
0.962, 0.846, 0.591, 0.897, 0.909, 0.957, 0.857, 0.931,
0.937, 0.871, 0.667, 0.862, 0.895, 0.956, 0.978, 0.905,
0.838, 0.968, 0.875, 0.892, 0.864, 0.92 , 0.762, 0.976,
0.967, 0.759, 0.949, 0.722, 0.915, 0.789, 0.81 , 0.742,
0.94 , 0.926, 0.84 , 0.86 , 0.946, 0.903, 0.381, 0.78 ,
0.919, 0.944, 0.973, 0.529, 0.93 , 0.943, 0.696, 0.739,
0.963, 0.815, 0.982, 0.881, 0.906, 0.972, 0.85 , 0.98 ,
0.979, 0.792, 0.7 , 0.873, 0.687, 0.706, 0.925, 0.82 ,
0.778, 0.767, 0.867, 0.735, 0.935, 0.911, 0.741, 0.76 ,
0.927, 0.71 , 0.538, 0.625, 0.727, 0.848, 0.826, 0.828,
0.733, 0.692, 0.643, 0.821, 0.865, 0.737, 0.56 , 0.72 ,
0.769, 0.806, 0.879, 0.841, 0.886, 0.984, 0.765, 0.684,
0.656, 0.861, 0.79 , 0.914, 0.609, 0.844, 0.951, 0.918,
0.674, 0.795, 0.808, 0.83 , 0.655, 0.816, 0.66 , 0.829,
0.783, 0.63 , 0.91 , 0.679, 0.67 , 0.983, 0.73 , 0.878,
0.62 , 0.724, 0.647, 0.562, 0.839, 0.68 , 0.89 , 0.904,
0.74 , 0.565, 0.6 , 0.333, 0.704, 0.621, 0.743, 0.793,
0.708, 0.794, 0.654, 0.884, 0.556, 0.822, 0.781, 0.615,
0.357, 0.854, 0.869, 0.907, 0.64 , 0.575, 0.922, 0.681,
0.652, 0.611, 0.633, 0.758, 0.57 , 0.607, 0.619, 0.689,
0.429, 0.725, 0.959, 0.961, 0.936, 0.868, 0.526, 0.533,
0.583, 0.676, 0.412, 0.891, 0.571, 0.775, 0.965, 0.636,
0.809, 0.825, 0.368, 0.928, 0.632, 0.567, 0.896, 0.757,
0.942, 0.69 , 0.804, 0.784, 0.934, 0.887, 0.694, 0.353,
0.5 , 0.745, 0.756, 0.594, 0.805, 0.657, 0.738, 0.545,
0.618, 0.791, 0.45 , 0.898, 0.55 , 0.593, 0.788, 0.613,
0.814, 0.731, 0.849, 0.703, 0.524, 0.41 , 0.673, 0.348,
0.719, 0.559, 0.787, 0.945, 0.658, 0.588, 0.4 , 0.686,
0.811, 0.763, 0.458, 0.837, 0.697, 0.481, 0.548, 0.754,
0.579, 0.744, 0.467, 0.948, 0.677, 0.51 , 0.622, 0.519,
0.568, 0.586, 0.851, 0.645, 0.736, 0.457, 0.44 , 0.985,
0.462, 0.796, 0.877, 0.531, 0.61 , 0.77 , 0.58 , 0.675,
0.54 , 0.46 , 0.53 , 0.18 , 0.17 , 0.33 , 0.59 , 0.48 ,
0.42 , 0.52 , 0.36 , 0.47 , 0.43 , 0.34 , 0.99 , 0.3 ,
0.25 , 0.38 , 0.27 , 0.35 , 0.39 , 0.31 , 0.37 , 0.28 ,
0.15 , 0.49 , 0.16 , 0.712, 0.378, 0.843, 0.831, 0.648,
0.552, 0.732, 0.987, 0.577, 0.536, 0.606, 0.986, 0.455,
0.542, 0.883, 0.836, 0.578, 0.768, 0.717, 0.698, 0.761,
0.576, 0.764, 0.541, 0.522, 0.605, 0.702, 0.845, 0.471,
0.421, 0.257, 0.776, 0.543, 0.649, 0.707, 0.312, 0.308,
0.635, 0.487, 0.646, 0.989, 0.389, 0.558, 0.581, 0.267,
0.721, 0.476, 0.441, 0.614, 0.827, 0.259, 0.803, 0.659,
0.908, 0.167, 0.437, 0.866, 0.433, 0.807, 0.938, 0.639,
0.444, 0.718, 0.629, 0.474, 0.282, 0.847, 0.924, 0.407,
0.797, 0.988, 0.705, 0.453, 0.683, 0.782, 0.424, 0.641,
0.316, 0.512, 0.587, 0.723, 0.726, 0.364, 0.755, 0.661,
0.779, 0.468, 0.954, 0.323, 0.206, 0.772, 0.292, 0.855,
0.514, 0.785, 0.766, 0.566, 0.604, 0.638, 0.393, 0.464,
0.286, 0.276, 0.874, 0.189, 0.242, 0.899, 0.375, 0.564,
0.595, 0.653, 0.394, 0.475, 0.685, 0.187, 0.617, 0.478,
0.644, 0.355, 0.32 , 0.125, 0.391, 0.695, 0.729, 0.916,
0.486, 0.817, 0.516, 0.263, 0.521, 0.553, 0.634, 0.387,
0.273, 0.823, 0.385, 0.304, 0.628, 0.484, 0.483, 0.859,
0.417, 0.448, 0.423, 0.992, 0.459, 0.662, 0.515, 0.409,
0.29 , 0.813, 0.901, 0.517, 0.19 , 0.469, 0.214, 0.537,
0.627, 0.746, 0.561, 0.343, 0.535, 0.418, 0.642, 0.819,
0.993, 0.278, 0.435, 0.888, 0.182, 0.709, 0.452, 0.592,
0.154, 0.688, 0.563, 0.438, 0.351]),
'percent_bc_gt_75': array([ nan, 0. , 1. , 0.167 , 0.25 , 0.01 , 0.5 ,
0.667 , 0.333 , 0.75 , 0.077 , 0.4 , 0.2 , 0.8 ,
0.375 , 0.727 , 0.571 , 0.818 , 0.6 , 0.143 , 0.545 ,
0.1 , 0.529 , 0.0033, 0.778 , 0.222 , 0.889 , 0.364 ,
0.833 , 0.429 , 0.444 , 0.857 , 0.615 , 0.286 , 0.625 ,
0.769 , 0.0067, 0.0075, 0.008 , 0.125 , 0.714 , 0.091 ,
0.111 , 0.556 , 0.083 , 0.875 , 0.182 , 0.005 , 0.0025,
0.006 , 0.267 , 0.273 , 0.154 , 0.231 , 0.3 , 0.002 ,
0.917 , 0.9 , 0.417 , 0.7 , 0.105 , 0.0038, 0.909 ,
0.19 , 0.636 , 0.923 , 0.0063, 0.0043, 0.071 , 0.455 ,
0.0029, 0.905 , 0.214 , 0.176 , 0.538 , 0.133 , 0.308 ,
0.583 , 0.462 , 0.067 , 0.004 , 0.846 , 0.059 , 0.385 ,
0.867 , 0.118 , 0.357 , 0.533 , 0.211 , 0.235 , 0.692 ,
0.263 , 0.045 , 0.588 , 0.062 , 0.684 , 0.762 , 0.929 ,
0.056 , 0.733 , 0.312 , 0.643 , 0.095 , 0.05 , 0.158 ,
0.187 , 0.437 , 0.042 , 0.467 , 0.053 , 0.294 , 0.031 ,
0.786 , 0.238 , 0.412 , 0.316 , 0.933 , 0.474 , 0.882 ,
0.706 , 0.937 , 0.526 , 0.687 , 0.278 , 0.812 , 0.941 ,
0.087 , 0.389 , 0.562 , 0.765 , 0.15 , 0.35 , 0.737 ,
0.944 , 0.647 , 0.281 , 0.842 , 0.368 , 0.227 , 0.043 ,
0.824 , 0.063 , 0.353 , 0.048 , 0.313 , 0.45 , 0.85 ,
0.188 , 0.722 , 0.471 , 0.034 , 0.381 , 0.174 , 0.04 ,
0.13 , 0.522 , 0.192 , 0.421 , 0.55 , 0.619 , 0.611 ,
0.037 , 0.138 , 0.074 , 0.95 , 0.036 , 0.84 , 0.524 ,
0.261 , 0.579 , 0.033 , 0.28 , 0.103 , 0.148 , 0.065 ,
0.947 , 0.0014, 0.136 , 0.0017, 0.0057, 0.0071, 0.955 ,
0.185 , 0.026 , 0.789 , 0.038 , 0.632 , 0.03 , 0.688 ,
0.438 ]),
'policy_code': [1.0]
Categories (1, float64): [1.0],
'pub_rec': array([ 0., 1., 2., 3., 4., 5., nan, 6., 9., 8., 7.,
49., 11., 10., 54., 15., 13., 12., 20., 86., 19., 40.,
17., 24., 14.]),
'pub_rec_bankruptcies': array([ 0., 1., 2., nan, 4., 3., 8., 5., 6., 7.]),
'purpose': [credit_card, car, small_business, other, wedding, ..., moving, vacation, house, renewable_energy, educational]
Length: 14
Categories (14, object): [credit_card, car, small_business, other, ..., vacation, house, renewable_energy, educational],
'pymnt_plan': [n]
Categories (1, object): [n],
'revol_bal': array([ 13648., 1687., 2956., ..., 120611., 178379., 60328.]),
'revol_util': array([ 83.7, 9.4, 98.5, ..., 105.6, 111.6, 162. ]),
'sub_grade': [B2, C4, C5, C1, B5, ..., G3, G2, G1, F5, G5]
Length: 35
Categories (35, object): [B2, C4, C5, C1, ..., G2, G1, F5, G5],
'tax_liens': array([ 0., nan, 1., 3., 2., 4., 8., 7., 5., 6., 48.,
11., 10., 53., 13., 12., 9., 85., 18., 15., 39., 16.,
23.]),
'term': [36 months, 60 months]
Categories (2, object): [36 months, 60 months],
'tot_coll_amt': array([ nan, 0., 15386., ..., 21101., 5677., 6165.]),
'tot_cur_bal': array([ nan, 7137., 200314., ..., 242670., 201817., 236497.]),
'tot_hi_cred_lim': array([ nan, 29700., 233004., ..., 267838., 270826., 557391.]),
'total_acc': array([ 9., 4., 10., 37., 38., 12., 11., 13., 3.,
23., 34., 29., 28., 42., 14., 22., 21., 17.,
7., 31., 44., 26., 16., 6., 18., 27., 24.,
25., 40., 35., 8., 20., 15., 19., 36., 51.,
32., 30., 33., 46., 5., 61., 56., 50., 41.,
39., 79., 62., 43., 47., 53., 45., 60., 55.,
52., 58., 54., 57., 49., 63., 48., 59., 77.,
87., 75., 72., 64., 67., 78., 76., 74., 66.,
81., 90., 80., 71., 69., 73., 70., 68., 65.,
2., 1., nan, 105., 83., 84., 98., 88., 82.,
91., 99., 94., 102., 112., 95., 135., 85., 92.,
86., 110., 106., 89., 96., 104., 151., 97., 113.,
169., 93., 129., 103., 111., 101., 124., 100., 114.,
116., 107., 150., 119., 117., 118., 176., 115., 108.,
133., 144.]),
'total_bal_ex_mort': array([ nan, 7137., 46738., ..., 154662., 201817., 138125.]),
'total_bal_il': array([ nan, 47194., 9358., ..., 20294., 45430., 93273.]),
'total_bc_limit': array([ nan, 18100., 14800., ..., 7790., 161780., 45602.]),
'total_cu_tl': array([ nan, 0., 1., 6., 3., 2., 7., 4., 8., 12., 5.,
11., 14., 16., 9., 15., 10., 18., 22., 13., 17., 21.,
20., 24., 19., 25., 43., 23., 31., 34., 38., 27., 35.,
33., 26., 28., 30., 37., 32., 36., 44., 41.]),
'total_il_high_credit_limit': array([ nan, 0., 53404., ..., 148273., 129934., 124725.]),
'total_rev_hi_lim': array([ nan, 29700., 23800., ..., 334560., 181280., 26940.]),
'verification_status': [Verified, Source Verified, Not Verified]
Categories (3, object): [Verified, Source Verified, Not Verified],
'verification_status_joint': [missing, Not Verified, Verified, Source Verified]
Categories (4, object): [missing, Not Verified, Verified, Source Verified],
'zip_code': [860xx, 309xx, 606xx, 917xx, 972xx, ..., 909xx, 516xx, 511xx, 901xx, 095xx]
Length: 916
Categories (916, object): [860xx, 309xx, 606xx, 917xx, ..., 516xx, 511xx, 901xx, 095xx]}
In [27]:
category_columns = df.select_dtypes(include=['category']).columns
df = pd.get_dummies(df, columns=category_columns, drop_first=True)
In [28]:
df.shape
Out[28]:
(538008, 1926)
Let's ensure that all of our missing values in float columns be nan values via the numpy library. I am doing this because Numpy is a highly optimized library.
In [29]:
float_columns = df.select_dtypes(include=['float64']).columns
for c in float_columns:
df.loc[df[df[c].isnull()].index, c] = np.nan
In [30]:
pickle_object(unique_val_dict, "unique_values_for_columns")
In [31]:
pickle_object(df, "dummied_dataset")
In [8]:
df = unpickle_object("dummied_dataset.pkl")
In [3]:
df.head()
Out[3]:
loan_amnt
funded_amnt
funded_amnt_inv
int_rate
installment
annual_inc
dti
delinq_2yrs
inq_last_6mths
mths_since_last_delinq
mths_since_last_record
open_acc
pub_rec
revol_bal
revol_util
total_acc
annual_inc_joint
dti_joint
acc_now_delinq
tot_coll_amt
tot_cur_bal
open_acc_6m
open_il_6m
open_il_12m
open_il_24m
mths_since_rcnt_il
total_bal_il
il_util
open_rv_12m
open_rv_24m
max_bal_bc
total_rev_hi_lim
inq_fi
total_cu_tl
inq_last_12m
acc_open_past_24mths
avg_cur_bal
bc_open_to_buy
bc_util
chargeoff_within_12_mths
delinq_amnt
mo_sin_old_il_acct
mo_sin_old_rev_tl_op
mo_sin_rcnt_rev_tl_op
mo_sin_rcnt_tl
mort_acc
mths_since_recent_bc
mths_since_recent_bc_dlq
mths_since_recent_inq
mths_since_recent_revol_delinq
num_accts_ever_120_pd
num_actv_bc_tl
num_actv_rev_tl
num_bc_sats
num_bc_tl
num_il_tl
num_op_rev_tl
num_rev_accts
num_rev_tl_bal_gt_0
num_sats
num_tl_120dpd_2m
num_tl_30dpd
num_tl_90g_dpd_24m
num_tl_op_past_12m
pct_tl_nvr_dlq
percent_bc_gt_75
pub_rec_bankruptcies
tax_liens
tot_hi_cred_lim
total_bal_ex_mort
total_bc_limit
total_il_high_credit_limit
term_ 60 months
grade_B
grade_C
grade_D
grade_E
grade_F
grade_G
sub_grade_A2
sub_grade_A3
sub_grade_A4
sub_grade_A5
sub_grade_B1
sub_grade_B2
sub_grade_B3
sub_grade_B4
sub_grade_B5
sub_grade_C1
sub_grade_C2
sub_grade_C3
sub_grade_C4
sub_grade_C5
sub_grade_D1
sub_grade_D2
sub_grade_D3
sub_grade_D4
sub_grade_D5
sub_grade_E1
sub_grade_E2
sub_grade_E3
sub_grade_E4
sub_grade_E5
sub_grade_F1
sub_grade_F2
sub_grade_F3
sub_grade_F4
sub_grade_F5
sub_grade_G1
sub_grade_G2
sub_grade_G3
sub_grade_G4
sub_grade_G5
emp_length_10+ years
emp_length_2 years
emp_length_3 years
emp_length_4 years
emp_length_5 years
emp_length_6 years
emp_length_7 years
emp_length_8 years
emp_length_9 years
emp_length_< 1 year
emp_length_n/a
home_ownership_MORTGAGE
home_ownership_NONE
home_ownership_OTHER
home_ownership_OWN
home_ownership_RENT
verification_status_Source Verified
verification_status_Verified
issue_d_Apr-2009
issue_d_Apr-2010
issue_d_Apr-2011
issue_d_Apr-2012
issue_d_Apr-2013
issue_d_Apr-2014
issue_d_Apr-2015
issue_d_Apr-2016
issue_d_Aug-2007
issue_d_Aug-2008
issue_d_Aug-2009
issue_d_Aug-2010
issue_d_Aug-2011
issue_d_Aug-2012
issue_d_Aug-2013
issue_d_Aug-2014
issue_d_Aug-2015
issue_d_Aug-2016
issue_d_Dec-2007
issue_d_Dec-2008
issue_d_Dec-2009
issue_d_Dec-2010
issue_d_Dec-2011
issue_d_Dec-2012
issue_d_Dec-2013
issue_d_Dec-2014
issue_d_Dec-2015
issue_d_Dec-2016
issue_d_Feb-2008
issue_d_Feb-2009
issue_d_Feb-2010
issue_d_Feb-2011
issue_d_Feb-2012
issue_d_Feb-2013
issue_d_Feb-2014
issue_d_Feb-2015
issue_d_Feb-2016
issue_d_Jan-2008
issue_d_Jan-2009
issue_d_Jan-2010
issue_d_Jan-2011
issue_d_Jan-2012
issue_d_Jan-2013
issue_d_Jan-2014
issue_d_Jan-2015
issue_d_Jan-2016
issue_d_Jul-2007
issue_d_Jul-2008
issue_d_Jul-2009
issue_d_Jul-2010
issue_d_Jul-2011
issue_d_Jul-2012
issue_d_Jul-2013
issue_d_Jul-2014
issue_d_Jul-2015
issue_d_Jul-2016
issue_d_Jun-2007
issue_d_Jun-2008
issue_d_Jun-2009
issue_d_Jun-2010
issue_d_Jun-2011
issue_d_Jun-2012
issue_d_Jun-2013
issue_d_Jun-2014
issue_d_Jun-2015
issue_d_Jun-2016
issue_d_Mar-2008
issue_d_Mar-2009
issue_d_Mar-2010
issue_d_Mar-2011
issue_d_Mar-2012
issue_d_Mar-2013
issue_d_Mar-2014
issue_d_Mar-2015
issue_d_Mar-2016
issue_d_May-2008
issue_d_May-2009
issue_d_May-2010
issue_d_May-2011
issue_d_May-2012
issue_d_May-2013
issue_d_May-2014
issue_d_May-2015
issue_d_May-2016
issue_d_Nov-2007
issue_d_Nov-2008
issue_d_Nov-2009
issue_d_Nov-2010
issue_d_Nov-2011
issue_d_Nov-2012
issue_d_Nov-2013
issue_d_Nov-2014
issue_d_Nov-2015
issue_d_Nov-2016
issue_d_Oct-2007
issue_d_Oct-2008
issue_d_Oct-2009
issue_d_Oct-2010
issue_d_Oct-2011
issue_d_Oct-2012
issue_d_Oct-2013
issue_d_Oct-2014
issue_d_Oct-2015
issue_d_Oct-2016
issue_d_Sep-2007
issue_d_Sep-2008
issue_d_Sep-2009
issue_d_Sep-2010
issue_d_Sep-2011
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application_type_INDIVIDUAL
application_type_JOINT
verification_status_joint_Source Verified
verification_status_joint_Verified
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0
5000.0
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4975.0
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27.65
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5 rows × 1926 columns
In [ ]:
Content source: igabr/Metis_Projects_Chicago_2017
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