or, Ty Goes Into Far Too Much Detail About Cleaning Data
- Introduction
- Data cleaning
- Building the neural networks
- Saving the final model
- Building the API
- Further reading
Introduction
LendingClub is the world's largest peer-to-peer lending platform. Until recently (through the end of 2018), LendingClub published a public dataset of all loans issued since the company's launch in 2007. I'm accessing the dataset via Kaggle.
import pandas as pd
loans_raw = pd.read_csv(
"../input/lending-club/accepted_2007_to_2018q4.csv/accepted_2007_to_2018Q4.csv",
low_memory=False,
)
loans_raw.shape
(2260701, 151)
With 2,260,701 loans to look at and 151 potential variables, my goal is to create a neural network model to predict the fraction of an expected loan return that a prospective borrower will pay back. (Spoiler alert: my model can pick grade A loans better than LendingClub).
Afterward, I'll create a public API to serve that model.
Also, as you may have guessed from the preceding code block, this post is adapted from a Jupyter Notebook. If you'd like to follow along in your own notebook, go ahead and fork mine on Kaggle or GitHub.
Data cleaning
I'll first look at the data dictionary (downloaded directly from LendingClub's website) to get an idea of how to create the desired output variable and which remaining features are available at the point of loan application (to avoid data leakage).
dictionary_df = pd.read_excel("https://resources.lendingclub.com/LCDataDictionary.xlsx")
# Drop blank rows, strip white space, convert to Python dictionary, fix one key name
dictionary_df.dropna(axis="index", inplace=True)
dictionary_df = dictionary_df.applymap(lambda x: x.strip())
dictionary_df.set_index("LoanStatNew", inplace=True)
dictionary = dictionary_df["Description"].to_dict()
dictionary["verification_status_joint"] = dictionary.pop("verified_status_joint")
# Print in order of dataset columns (which makes more sense than dictionary's order)
for col in loans_raw.columns:
print(f"•{col}: {dictionary[col]}")
•id: A unique LC assigned ID for the loan listing.
•member_id: A unique LC assigned Id for the borrower member.
•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.
•funded_amnt: The total amount committed to that loan at that point in time.
•funded_amnt_inv: The total amount committed by investors for that loan at that point in time.
•term: The number of payments on the loan. Values are in months and can be either 36 or 60.
•int_rate: Interest Rate on the loan
•installment: The monthly payment owed by the borrower if the loan originates.
•grade: LC assigned loan grade
•sub_grade: LC assigned loan subgrade
•emp_title: The job title supplied by the Borrower when applying for the loan.*
•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.
•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
•annual_inc: The self-reported annual income provided by the borrower during registration.
•verification_status: Indicates if income was verified by LC, not verified, or if the income source was verified
•issue_d: The month which the loan was funded
•loan_status: Current status of the loan
•pymnt_plan: Indicates if a payment plan has been put in place for the loan
•url: URL for the LC page with listing data.
•desc: Loan description provided by the borrower
•purpose: A category provided by the borrower for the loan request.
•title: The loan title provided by the borrower
•zip_code: The first 3 numbers of the zip code provided by the borrower in the loan application.
•addr_state: The state provided by the borrower in the loan application
•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.
•delinq_2yrs: The number of 30+ days past-due incidences of delinquency in the borrower's credit file for the past 2 years
•earliest_cr_line: The month the borrower's earliest reported credit line was opened
•fico_range_low: The lower boundary range the borrower’s FICO at loan origination belongs to.
•fico_range_high: The upper boundary range the borrower’s FICO at loan origination belongs to.
•inq_last_6mths: The number of inquiries in past 6 months (excluding auto and mortgage inquiries)
•mths_since_last_delinq: The number of months since the borrower's last delinquency.
•mths_since_last_record: The number of months since the last public record.
•open_acc: The number of open credit lines in the borrower's credit file.
•pub_rec: Number of derogatory public records
•revol_bal: Total credit revolving balance
•revol_util: Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.
•total_acc: The total number of credit lines currently in the borrower's credit file
•initial_list_status: The initial listing status of the loan. Possible values are – W, F
•out_prncp: Remaining outstanding principal for total amount funded
•out_prncp_inv: Remaining outstanding principal for portion of total amount funded by investors
•total_pymnt: Payments received to date for total amount funded
•total_pymnt_inv: Payments received to date for portion of total amount funded by investors
•total_rec_prncp: Principal received to date
•total_rec_int: Interest received to date
•total_rec_late_fee: Late fees received to date
•recoveries: post charge off gross recovery
•collection_recovery_fee: post charge off collection fee
•last_pymnt_d: Last month payment was received
•last_pymnt_amnt: Last total payment amount received
•next_pymnt_d: Next scheduled payment date
•last_credit_pull_d: The most recent month LC pulled credit for this loan
•last_fico_range_high: The upper boundary range the borrower’s last FICO pulled belongs to.
•last_fico_range_low: The lower boundary range the borrower’s last FICO pulled belongs to.
•collections_12_mths_ex_med: Number of collections in 12 months excluding medical collections
•mths_since_last_major_derog: Months since most recent 90-day or worse rating
•policy_code: publicly available policy_code=1
new products not publicly available policy_code=2
•application_type: Indicates whether the loan is an individual application or a joint application with two co-borrowers
•annual_inc_joint: The combined self-reported annual income provided by the co-borrowers during registration
•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
•verification_status_joint: Indicates if the co-borrowers' joint income was verified by LC, not verified, or if the income source was verified
•acc_now_delinq: The number of accounts on which the borrower is now delinquent.
•tot_coll_amt: Total collection amounts ever owed
•tot_cur_bal: Total current balance of all accounts
•open_acc_6m: Number of open trades in last 6 months
•open_act_il: Number of currently active installment trades
•open_il_12m: Number of installment accounts opened in past 12 months
•open_il_24m: Number of installment accounts opened in past 24 months
•mths_since_rcnt_il: Months since most recent installment accounts opened
•total_bal_il: Total current balance of all installment accounts
•il_util: Ratio of total current balance to high credit/credit limit on all install acct
•open_rv_12m: Number of revolving trades opened in past 12 months
•open_rv_24m: Number of revolving trades opened in past 24 months
•max_bal_bc: Maximum current balance owed on all revolving accounts
•all_util: Balance to credit limit on all trades
•total_rev_hi_lim: Total revolving high credit/credit limit
•inq_fi: Number of personal finance inquiries
•total_cu_tl: Number of finance trades
•inq_last_12m: Number of credit inquiries in past 12 months
•acc_open_past_24mths: Number of trades opened in past 24 months.
•avg_cur_bal: Average current balance of all accounts
•bc_open_to_buy: Total open to buy on revolving bankcards.
•bc_util: Ratio of total current balance to high credit/credit limit for all bankcard accounts.
•chargeoff_within_12_mths: Number of charge-offs within 12 months
•delinq_amnt: The past-due amount owed for the accounts on which the borrower is now delinquent.
•mo_sin_old_il_acct: Months since oldest bank installment account opened
•mo_sin_old_rev_tl_op: Months since oldest revolving account opened
•mo_sin_rcnt_rev_tl_op: Months since most recent revolving account opened
•mo_sin_rcnt_tl: Months since most recent account opened
•mort_acc: Number of mortgage accounts.
•mths_since_recent_bc: Months since most recent bankcard account opened.
•mths_since_recent_bc_dlq: Months since most recent bankcard delinquency
•mths_since_recent_inq: Months since most recent inquiry.
•mths_since_recent_revol_delinq: Months since most recent revolving delinquency.
•num_accts_ever_120_pd: Number of accounts ever 120 or more days past due
•num_actv_bc_tl: Number of currently active bankcard accounts
•num_actv_rev_tl: Number of currently active revolving trades
•num_bc_sats: Number of satisfactory bankcard accounts
•num_bc_tl: Number of bankcard accounts
•num_il_tl: Number of installment accounts
•num_op_rev_tl: Number of open revolving accounts
•num_rev_accts: Number of revolving accounts
•num_rev_tl_bal_gt_0: Number of revolving trades with balance >0
•num_sats: Number of satisfactory accounts
•num_tl_120dpd_2m: Number of accounts currently 120 days past due (updated in past 2 months)
•num_tl_30dpd: Number of accounts currently 30 days past due (updated in past 2 months)
•num_tl_90g_dpd_24m: Number of accounts 90 or more days past due in last 24 months
•num_tl_op_past_12m: Number of accounts opened in past 12 months
•pct_tl_nvr_dlq: Percent of trades never delinquent
•percent_bc_gt_75: Percentage of all bankcard accounts > 75% of limit.
•pub_rec_bankruptcies: Number of public record bankruptcies
•tax_liens: Number of tax liens
•tot_hi_cred_lim: Total high credit/credit limit
•total_bal_ex_mort: Total credit balance excluding mortgage
•total_bc_limit: Total bankcard high credit/credit limit
•total_il_high_credit_limit: Total installment high credit/credit limit
•revol_bal_joint: Sum of revolving credit balance of the co-borrowers, net of duplicate balances
•sec_app_fico_range_low: FICO range (high) for the secondary applicant
•sec_app_fico_range_high: FICO range (low) for the secondary applicant
•sec_app_earliest_cr_line: Earliest credit line at time of application for the secondary applicant
•sec_app_inq_last_6mths: Credit inquiries in the last 6 months at time of application for the secondary applicant
•sec_app_mort_acc: Number of mortgage accounts at time of application for the secondary applicant
•sec_app_open_acc: Number of open trades at time of application for the secondary applicant
•sec_app_revol_util: Ratio of total current balance to high credit/credit limit for all revolving accounts
•sec_app_open_act_il: Number of currently active installment trades at time of application for the secondary applicant
•sec_app_num_rev_accts: Number of revolving accounts at time of application for the secondary applicant
•sec_app_chargeoff_within_12_mths: Number of charge-offs within last 12 months at time of application for the secondary applicant
•sec_app_collections_12_mths_ex_med: Number of collections within last 12 months excluding medical collections at time of application for the secondary applicant
•sec_app_mths_since_last_major_derog: Months since most recent 90-day or worse rating at time of application for the secondary applicant
•hardship_flag: Flags whether or not the borrower is on a hardship plan
•hardship_type: Describes the hardship plan offering
•hardship_reason: Describes the reason the hardship plan was offered
•hardship_status: Describes if the hardship plan is active, pending, canceled, completed, or broken
•deferral_term: Amount of months that the borrower is expected to pay less than the contractual monthly payment amount due to a hardship plan
•hardship_amount: The interest payment that the borrower has committed to make each month while they are on a hardship plan
•hardship_start_date: The start date of the hardship plan period
•hardship_end_date: The end date of the hardship plan period
•payment_plan_start_date: The day the first hardship plan payment is due. For example, if a borrower has a hardship plan period of 3 months, the start date is the start of the three-month period in which the borrower is allowed to make interest-only payments.
•hardship_length: The number of months the borrower will make smaller payments than normally obligated due to a hardship plan
•hardship_dpd: Account days past due as of the hardship plan start date
•hardship_loan_status: Loan Status as of the hardship plan start date
•orig_projected_additional_accrued_interest: The original projected additional interest amount that will accrue for the given hardship payment plan as of the Hardship Start Date. This field will be null if the borrower has broken their hardship payment plan.
•hardship_payoff_balance_amount: The payoff balance amount as of the hardship plan start date
•hardship_last_payment_amount: The last payment amount as of the hardship plan start date
•disbursement_method: The method by which the borrower receives their loan. Possible values are: CASH, DIRECT_PAY
•debt_settlement_flag: Flags whether or not the borrower, who has charged-off, is working with a debt-settlement company.
•debt_settlement_flag_date: The most recent date that the Debt_Settlement_Flag has been set
•settlement_status: The status of the borrower’s settlement plan. Possible values are: COMPLETE, ACTIVE, BROKEN, CANCELLED, DENIED, DRAFT
•settlement_date: The date that the borrower agrees to the settlement plan
•settlement_amount: The loan amount that the borrower has agreed to settle for
•settlement_percentage: The settlement amount as a percentage of the payoff balance amount on the loan
•settlement_term: The number of months that the borrower will be on the settlement plan
For the output variable (the fraction of expected return that was recovered), I'll calculated the expected return by multiplying the monthly payment amount (installment) by the number of payments on the loan (term), and I'll calculate the amount actually received by summing the total principle, interest, late fees, and post-chargeoff gross recovery received (total_rec_prncp, total_rec_int, total_rec_late_fee, recoveries) and subtracting any collection fee (collection_recovery_fee).
cols_for_output = ["term", "installment", "total_rec_prncp", "total_rec_int", "total_rec_late_fee", "recoveries", "collection_recovery_fee"]
Several other columns contain either irrelevant demographic data or data not created until after a loan is accepted, so those will need to be removed. I'll hold onto issue_d (the month and year the loan was funded) for now, though, in case I want to compare variables to the date of the loan.
emp_title (the applicant's job title) does seem relevant in the context of a loan, but it may have too many unique values to be useful.
loans_raw["emp_title"].nunique()
512694
Too many unique values indeed. In a future version of this model I could perhaps try to generate a feature from this column by aggregating job titles into categories, but that effort may have a low return on investment, since there are already columns for annual income and length of employment.
Two other interesting columns that I'll also remove are title and desc ("description"), which are both freeform text entries written by the borrower. These could be fascinating subjects for natural language processing, but that's outside the scope of the current project. Perhaps in the future I could generate additional features from these fields using measures like syntactic complexity, word count, or keyword inclusion. (I ended up exploring this line of thinking in a later post using document vectors.)
cols_to_drop = ["id", "member_id", "funded_amnt", "funded_amnt_inv", "int_rate", "grade", "sub_grade", "emp_title", "pymnt_plan", "url", "desc", "title", "zip_code", "addr_state", "initial_list_status", "out_prncp", "out_prncp_inv", "total_pymnt", "total_pymnt_inv", "last_pymnt_d", "last_pymnt_amnt", "next_pymnt_d", "last_credit_pull_d", "last_fico_range_high", "last_fico_range_low", "policy_code", "hardship_flag", "hardship_type", "hardship_reason", "hardship_status", "deferral_term", "hardship_amount", "hardship_start_date", "hardship_end_date", "payment_plan_start_date", "hardship_length", "hardship_dpd", "hardship_loan_status", "orig_projected_additional_accrued_interest", "hardship_payoff_balance_amount", "hardship_last_payment_amount", "disbursement_method", "debt_settlement_flag", "debt_settlement_flag_date", "settlement_status", "settlement_date", "settlement_amount", "settlement_percentage", "settlement_term"]
loans = loans_raw.drop(columns=cols_to_drop)
Before creating the output variable, however, I must take a closer look at loan_status, to see if any loans in the dataset are still open.
loans.groupby("loan_status")["loan_status"].count()
loan_status
Charged Off 268559
Current 878317
Default 40
Does not meet the credit policy. Status:Charged Off 761
Does not meet the credit policy. Status:Fully Paid 1988
Fully Paid 1076751
In Grace Period 8436
Late (16-30 days) 4349
Late (31-120 days) 21467
Name: loan_status, dtype: int64
For practical purposes, I'll consider loans with statuses that don't contain "Fully Paid" or "Charged Off" to still be open, so I'll remove those from the dataset. I'll also merge the "credit policy" columns with their matching status.
credit_policy = "Does not meet the credit policy. Status:"
len_credit_policy = len(credit_policy)
remove_credit_policy = (
lambda status: status[len_credit_policy:]
if credit_policy in str(status)
else status
)
loans["loan_status"] = loans["loan_status"].map(remove_credit_policy)
rows_to_drop = loans[
(loans["loan_status"] != "Charged Off") & (loans["loan_status"] != "Fully Paid")
].index
loans.drop(index=rows_to_drop, inplace=True)
loans.groupby("loan_status")["loan_status"].count()
loan_status
Charged Off 269320
Fully Paid 1078739
Name: loan_status, dtype: int64
Now to create the output variable. I'll start by checking the null counts of the variables involved.
loans[cols_for_output].info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1348059 entries, 0 to 2260697
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 term 1348059 non-null object
1 installment 1348059 non-null float64
2 total_rec_prncp 1348059 non-null float64
3 total_rec_int 1348059 non-null float64
4 total_rec_late_fee 1348059 non-null float64
5 recoveries 1348059 non-null float64
6 collection_recovery_fee 1348059 non-null float64
dtypes: float64(6), object(1)
memory usage: 82.3+ MB
Every remaining row has each of these seven variables, but term's data type is object, so that needs to be fixed first.
loans.groupby("term")["term"].count()
term
36 months 1023181
60 months 324878
Name: term, dtype: int64
Ah, so term is a categorical feature with two options. I'll treat it as such when I use it as an input to the model, but to calculate the output variable I'll create a numerical column from it.
Also, I need to trim the whitespace from the beginning of those values—that's no good.
onehot_cols = ["term"]
loans["term"] = loans["term"].map(lambda term_str: term_str.strip())
extract_num = lambda term_str: float(term_str[:2])
loans["term_num"] = loans["term"].map(extract_num)
cols_for_output.remove("term")
cols_for_output.append("term_num")
Now I can create the output variable.
received = (
loans["total_rec_prncp"]
+ loans["total_rec_int"]
+ loans["total_rec_late_fee"]
+ loans["recoveries"]
- loans["collection_recovery_fee"]
)
expected = loans["installment"] * loans["term_num"]
loans["fraction_recovered"] = received / expected
loans.groupby("loan_status")["fraction_recovered"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| loan_status | ||||||||
| Charged Off | 269320.0 | 0.400162 | 0.219020 | 0.000000 | 0.224463 | 0.367554 | 0.550924 | 2.410680 |
| Fully Paid | 1078739.0 | 0.932705 | 0.100455 | 0.506053 | 0.897912 | 0.960100 | 0.997612 | 60.932353 |
There is at least one odd outlier on the right in both categories. But also, many of the "fully paid" loans do not quite reach 1. One potential explanation is that when the last payment comes in, the system just flips loan_status to "Fully Paid" without adding the payment amount to the system itself, or perhaps simply multiplying installation by the term number leaves off a few cents in the actual total. If I were performing this analysis for Lending Club themselves, I'd ask them, but this is just a personal project. I'll consider every loan marked "Fully Paid" to have fully recovered the expected return.
For that matter, I'll cap my fraction_recovered values for charged off loans at 1.0 as well, since at least one value is above that for some reason.
import numpy as np
loans["fraction_recovered"] = np.where(
(loans["loan_status"] == "Fully Paid") | (loans["fraction_recovered"] > 1.0),
1.0,
loans["fraction_recovered"],
)
loans.groupby("loan_status")["fraction_recovered"].describe()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| loan_status | ||||||||
| Charged Off | 269320.0 | 0.400152 | 0.218971 | 0.0 | 0.224463 | 0.367554 | 0.550924 | 1.0 |
| Fully Paid | 1078739.0 | 1.000000 | 0.000000 | 1.0 | 1.000000 | 1.000000 | 1.000000 | 1.0 |
For the sake of curiosity, I'll plot the distribution of fraction recovered for charged-off loans.
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.kdeplot(
data=loans["fraction_recovered"][loans["loan_status"] == "Charged Off"],
label="Charged Off",
shade=True,
)
plt.axis(xmin=0, xmax=1)
plt.title('Distribution of "fraction recovered"')
plt.show()
Now that the output is formatted, it's time to clean up the inputs. I'll check the null counts of each variable.
loans.drop(columns=cols_for_output, inplace=True)
loans.info(verbose=True, null_counts=True)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1348059 entries, 0 to 2260697
Data columns (total 97 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 loan_amnt 1348059 non-null float64
1 term 1348059 non-null object
2 emp_length 1269514 non-null object
3 home_ownership 1348059 non-null object
4 annual_inc 1348055 non-null float64
5 verification_status 1348059 non-null object
6 issue_d 1348059 non-null object
7 loan_status 1348059 non-null object
8 purpose 1348059 non-null object
9 dti 1347685 non-null float64
10 delinq_2yrs 1348030 non-null float64
11 earliest_cr_line 1348030 non-null object
12 fico_range_low 1348059 non-null float64
13 fico_range_high 1348059 non-null float64
14 inq_last_6mths 1348029 non-null float64
15 mths_since_last_delinq 668117 non-null float64
16 mths_since_last_record 229415 non-null float64
17 open_acc 1348030 non-null float64
18 pub_rec 1348030 non-null float64
19 revol_bal 1348059 non-null float64
20 revol_util 1347162 non-null float64
21 total_acc 1348030 non-null float64
22 collections_12_mths_ex_med 1347914 non-null float64
23 mths_since_last_major_derog 353750 non-null float64
24 application_type 1348059 non-null object
25 annual_inc_joint 25800 non-null float64
26 dti_joint 25797 non-null float64
27 verification_status_joint 25595 non-null object
28 acc_now_delinq 1348030 non-null float64
29 tot_coll_amt 1277783 non-null float64
30 tot_cur_bal 1277783 non-null float64
31 open_acc_6m 537597 non-null float64
32 open_act_il 537598 non-null float64
33 open_il_12m 537598 non-null float64
34 open_il_24m 537598 non-null float64
35 mths_since_rcnt_il 523382 non-null float64
36 total_bal_il 537598 non-null float64
37 il_util 465016 non-null float64
38 open_rv_12m 537598 non-null float64
39 open_rv_24m 537598 non-null float64
40 max_bal_bc 537598 non-null float64
41 all_util 537545 non-null float64
42 total_rev_hi_lim 1277783 non-null float64
43 inq_fi 537598 non-null float64
44 total_cu_tl 537597 non-null float64
45 inq_last_12m 537597 non-null float64
46 acc_open_past_24mths 1298029 non-null float64
47 avg_cur_bal 1277761 non-null float64
48 bc_open_to_buy 1284167 non-null float64
49 bc_util 1283398 non-null float64
50 chargeoff_within_12_mths 1347914 non-null float64
51 delinq_amnt 1348030 non-null float64
52 mo_sin_old_il_acct 1239735 non-null float64
53 mo_sin_old_rev_tl_op 1277782 non-null float64
54 mo_sin_rcnt_rev_tl_op 1277782 non-null float64
55 mo_sin_rcnt_tl 1277783 non-null float64
56 mort_acc 1298029 non-null float64
57 mths_since_recent_bc 1285089 non-null float64
58 mths_since_recent_bc_dlq 319020 non-null float64
59 mths_since_recent_inq 1171239 non-null float64
60 mths_since_recent_revol_delinq 449962 non-null float64
61 num_accts_ever_120_pd 1277783 non-null float64
62 num_actv_bc_tl 1277783 non-null float64
63 num_actv_rev_tl 1277783 non-null float64
64 num_bc_sats 1289469 non-null float64
65 num_bc_tl 1277783 non-null float64
66 num_il_tl 1277783 non-null float64
67 num_op_rev_tl 1277783 non-null float64
68 num_rev_accts 1277782 non-null float64
69 num_rev_tl_bal_gt_0 1277783 non-null float64
70 num_sats 1289469 non-null float64
71 num_tl_120dpd_2m 1227909 non-null float64
72 num_tl_30dpd 1277783 non-null float64
73 num_tl_90g_dpd_24m 1277783 non-null float64
74 num_tl_op_past_12m 1277783 non-null float64
75 pct_tl_nvr_dlq 1277629 non-null float64
76 percent_bc_gt_75 1283755 non-null float64
77 pub_rec_bankruptcies 1346694 non-null float64
78 tax_liens 1347954 non-null float64
79 tot_hi_cred_lim 1277783 non-null float64
80 total_bal_ex_mort 1298029 non-null float64
81 total_bc_limit 1298029 non-null float64
82 total_il_high_credit_limit 1277783 non-null float64
83 revol_bal_joint 18629 non-null float64
84 sec_app_fico_range_low 18630 non-null float64
85 sec_app_fico_range_high 18630 non-null float64
86 sec_app_earliest_cr_line 18630 non-null object
87 sec_app_inq_last_6mths 18630 non-null float64
88 sec_app_mort_acc 18630 non-null float64
89 sec_app_open_acc 18630 non-null float64
90 sec_app_revol_util 18302 non-null float64
91 sec_app_open_act_il 18630 non-null float64
92 sec_app_num_rev_accts 18630 non-null float64
93 sec_app_chargeoff_within_12_mths 18630 non-null float64
94 sec_app_collections_12_mths_ex_med 18630 non-null float64
95 sec_app_mths_since_last_major_derog 6645 non-null float64
96 fraction_recovered 1348059 non-null float64
dtypes: float64(86), object(11)
memory usage: 1007.9+ MB
Remaining columns with lots of null values seem to fall into three categories:
- Derogatory/delinquency metrics (where null means the borrower doesn't have any such marks)
- I'll also add
mths_since_recent_inqto this list, since its non-null count is below what seems to be the threshold for complete data, which is around 1,277,783. I'll assume a null value here means no recent inquiries.
- I'll also add
- Metrics that only apply to joint applications (where null means it was a single application)
- An inexplicable series of 14 credit history–related columns that only have around 537,000 entries. Are these newer metrics?
negative_mark_cols = ["mths_since_last_delinq", "mths_since_last_record", "mths_since_last_major_derog", "mths_since_recent_bc_dlq", "mths_since_recent_inq", "mths_since_recent_revol_delinq", "mths_since_recent_revol_delinq", "sec_app_mths_since_last_major_derog"]
joint_cols = ["annual_inc_joint", "dti_joint", "verification_status_joint", "revol_bal_joint", "sec_app_fico_range_low", "sec_app_fico_range_high", "sec_app_earliest_cr_line", "sec_app_inq_last_6mths", "sec_app_mort_acc", "sec_app_open_acc", "sec_app_revol_util", "sec_app_open_act_il", "sec_app_num_rev_accts", "sec_app_chargeoff_within_12_mths", "sec_app_collections_12_mths_ex_med", "sec_app_mths_since_last_major_derog"]
confusing_cols = ["open_acc_6m", "open_act_il", "open_il_12m", "open_il_24m", "mths_since_rcnt_il", "total_bal_il", "il_util", "open_rv_12m", "open_rv_24m", "max_bal_bc", "all_util", "inq_fi", "total_cu_tl", "inq_last_12m"]
I'll first look at those more confusing columns to find out whether or not they're a newer set of metrics. That'll require converting issue_d to date format first.
loans["issue_d"] = loans["issue_d"].astype("datetime64[ns]")
# Check date range of confusing columns
loans[confusing_cols + ["issue_d"]].dropna(axis="index")["issue_d"].agg(
["count", "min", "max"]
)
count 464325
min 2015-12-01 00:00:00
max 2018-12-01 00:00:00
Name: issue_d, dtype: object
# Compare to all entries from Dec 2015 onward
loans["issue_d"][loans["issue_d"] >= np.datetime64("2015-12-01")].agg(
["count", "min", "max"]
)
count 557708
min 2015-12-01 00:00:00
max 2018-12-01 00:00:00
Name: issue_d, dtype: object
It appears that these are indeed newer metrics, their use only beginning in December 2015, but even after that point usage is spotty. I'm curious to see if these additional metrics would make a model more accurate, though, so once I'm done cleaning the data I'll copy the rows with these new metrics into a new dataset and create another model using the new metrics.
new_metric_cols = confusing_cols
As for the derogatory/delinquency metrics, taking a cue from Michael Wurm, I'm going to take the inverse of all the "months since recent/last" fields, which will turn each into a proxy for the frequency of the event and also let me set all the null values (when an event has never happened) to 0. For the "months since oldest" fields, I'll just set the null values to 0 and leave the rest untouched.
mths_since_last_cols = [
col_name
for col_name in loans.columns
if "mths_since" in col_name or "mo_sin_rcnt" in col_name
]
mths_since_old_cols = [
col_name for col_name in loans.columns if "mo_sin_old" in col_name
]
for col_name in mths_since_last_cols:
loans[col_name] = [
0.0 if pd.isna(months) else 1 / 1 if months == 0 else 1 / months
for months in loans[col_name]
]
loans.loc[:, mths_since_old_cols].fillna(0, inplace=True)
# Rename inverse columns
rename_mapper = {}
for col_name in mths_since_last_cols:
rename_mapper[col_name] = col_name.replace("mths_since", "inv_mths_since").replace(
"mo_sin_rcnt", "inv_mo_sin_rcnt"
)
loans.rename(columns=rename_mapper, inplace=True)
def replace_list_value(l, old_value, new_value):
i = l.index(old_value)
l.pop(i)
l.insert(i, new_value)
replace_list_value(new_metric_cols, "mths_since_rcnt_il", "inv_mths_since_rcnt_il")
replace_list_value(
joint_cols,
"sec_app_mths_since_last_major_derog",
"sec_app_inv_mths_since_last_major_derog",
)
Now to look closer at joint loans.
loans.groupby("application_type")["application_type"].count()
application_type
Individual 1322259
Joint App 25800
Name: application_type, dtype: int64
joint_loans = loans[:][loans["application_type"] == "Joint App"]
joint_loans[joint_cols].info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 25800 entries, 2 to 2260663
Data columns (total 16 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 annual_inc_joint 25800 non-null float64
1 dti_joint 25797 non-null float64
2 verification_status_joint 25595 non-null object
3 revol_bal_joint 18629 non-null float64
4 sec_app_fico_range_low 18630 non-null float64
5 sec_app_fico_range_high 18630 non-null float64
6 sec_app_earliest_cr_line 18630 non-null object
7 sec_app_inq_last_6mths 18630 non-null float64
8 sec_app_mort_acc 18630 non-null float64
9 sec_app_open_acc 18630 non-null float64
10 sec_app_revol_util 18302 non-null float64
11 sec_app_open_act_il 18630 non-null float64
12 sec_app_num_rev_accts 18630 non-null float64
13 sec_app_chargeoff_within_12_mths 18630 non-null float64
14 sec_app_collections_12_mths_ex_med 18630 non-null float64
15 sec_app_inv_mths_since_last_major_derog 25800 non-null float64
dtypes: float64(14), object(2)
memory usage: 3.3+ MB
It seems there may be a case of newer metrics for joint applications as well. I'll investigate.
joint_new_metric_cols = ["revol_bal_joint", "sec_app_fico_range_low", "sec_app_fico_range_high", "sec_app_earliest_cr_line", "sec_app_inq_last_6mths", "sec_app_mort_acc", "sec_app_open_acc", "sec_app_revol_util", "sec_app_open_act_il", "sec_app_num_rev_accts", "sec_app_chargeoff_within_12_mths", "sec_app_collections_12_mths_ex_med", "sec_app_inv_mths_since_last_major_derog"]
joint_loans[joint_new_metric_cols + ["issue_d"]].dropna(axis="index")["issue_d"].agg(
["count", "min", "max"]
)
count 18301
min 2017-03-01 00:00:00
max 2018-12-01 00:00:00
Name: issue_d, dtype: object
# Check without `sec_app_revol_util` column
joint_new_metric_cols_2 = ["revol_bal_joint", "sec_app_fico_range_low", "sec_app_fico_range_high", "sec_app_earliest_cr_line", "sec_app_inq_last_6mths", "sec_app_mort_acc", "sec_app_open_acc", "sec_app_open_act_il", "sec_app_num_rev_accts", "sec_app_chargeoff_within_12_mths", "sec_app_collections_12_mths_ex_med", "sec_app_inv_mths_since_last_major_derog"]
joint_loans[joint_new_metric_cols_2 + ["issue_d"]].dropna(axis="index")["issue_d"].agg(
["count", "min", "max"]
)
count 18629
min 2017-03-01 00:00:00
max 2018-12-01 00:00:00
Name: issue_d, dtype: object
Newer than the previous set of new metrics, even—these didn't start getting used till March 2017. Now I wonder when joint loans were first introduced.
joint_loans["issue_d"].agg(["count", "min", "max"])
count 25800
min 2015-10-01 00:00:00
max 2018-12-01 00:00:00
Name: issue_d, dtype: object
2015. I think I'll save the newer joint metrics for perhaps a third model, but I believe I can include annual_inc_joint, dti_joint, and verification_status_joint in the main model—I'll just binary-encode application_type, and for individual applications I'll set annual_inc_joint, dti_joint, and verification_status_joint equal to their non-joint counterparts.
onehot_cols.append("application_type")
# Fill joint columns in individual applications
for joint_col, indiv_col in zip(
["annual_inc_joint", "dti_joint", "verification_status_joint"],
["annual_inc", "dti", "verification_status"],
):
loans[joint_col] = [
joint_val if app_type == "Joint App" else indiv_val
for app_type, joint_val, indiv_val in zip(
loans["application_type"], loans[joint_col], loans[indiv_col]
)
]
loans.info(verbose=True, null_counts=True)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1348059 entries, 0 to 2260697
Data columns (total 97 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 loan_amnt 1348059 non-null float64
1 term 1348059 non-null object
2 emp_length 1269514 non-null object
3 home_ownership 1348059 non-null object
4 annual_inc 1348055 non-null float64
5 verification_status 1348059 non-null object
6 issue_d 1348059 non-null datetime64[ns]
7 loan_status 1348059 non-null object
8 purpose 1348059 non-null object
9 dti 1347685 non-null float64
10 delinq_2yrs 1348030 non-null float64
11 earliest_cr_line 1348030 non-null object
12 fico_range_low 1348059 non-null float64
13 fico_range_high 1348059 non-null float64
14 inq_last_6mths 1348029 non-null float64
15 inv_mths_since_last_delinq 1348059 non-null float64
16 inv_mths_since_last_record 1348059 non-null float64
17 open_acc 1348030 non-null float64
18 pub_rec 1348030 non-null float64
19 revol_bal 1348059 non-null float64
20 revol_util 1347162 non-null float64
21 total_acc 1348030 non-null float64
22 collections_12_mths_ex_med 1347914 non-null float64
23 inv_mths_since_last_major_derog 1348059 non-null float64
24 application_type 1348059 non-null object
25 annual_inc_joint 1348055 non-null float64
26 dti_joint 1348056 non-null float64
27 verification_status_joint 1347854 non-null object
28 acc_now_delinq 1348030 non-null float64
29 tot_coll_amt 1277783 non-null float64
30 tot_cur_bal 1277783 non-null float64
31 open_acc_6m 537597 non-null float64
32 open_act_il 537598 non-null float64
33 open_il_12m 537598 non-null float64
34 open_il_24m 537598 non-null float64
35 inv_mths_since_rcnt_il 1348059 non-null float64
36 total_bal_il 537598 non-null float64
37 il_util 465016 non-null float64
38 open_rv_12m 537598 non-null float64
39 open_rv_24m 537598 non-null float64
40 max_bal_bc 537598 non-null float64
41 all_util 537545 non-null float64
42 total_rev_hi_lim 1277783 non-null float64
43 inq_fi 537598 non-null float64
44 total_cu_tl 537597 non-null float64
45 inq_last_12m 537597 non-null float64
46 acc_open_past_24mths 1298029 non-null float64
47 avg_cur_bal 1277761 non-null float64
48 bc_open_to_buy 1284167 non-null float64
49 bc_util 1283398 non-null float64
50 chargeoff_within_12_mths 1347914 non-null float64
51 delinq_amnt 1348030 non-null float64
52 mo_sin_old_il_acct 1239735 non-null float64
53 mo_sin_old_rev_tl_op 1277782 non-null float64
54 inv_mo_sin_rcnt_rev_tl_op 1348059 non-null float64
55 inv_mo_sin_rcnt_tl 1348059 non-null float64
56 mort_acc 1298029 non-null float64
57 inv_mths_since_recent_bc 1348059 non-null float64
58 inv_mths_since_recent_bc_dlq 1348059 non-null float64
59 inv_mths_since_recent_inq 1348059 non-null float64
60 inv_mths_since_recent_revol_delinq 1348059 non-null float64
61 num_accts_ever_120_pd 1277783 non-null float64
62 num_actv_bc_tl 1277783 non-null float64
63 num_actv_rev_tl 1277783 non-null float64
64 num_bc_sats 1289469 non-null float64
65 num_bc_tl 1277783 non-null float64
66 num_il_tl 1277783 non-null float64
67 num_op_rev_tl 1277783 non-null float64
68 num_rev_accts 1277782 non-null float64
69 num_rev_tl_bal_gt_0 1277783 non-null float64
70 num_sats 1289469 non-null float64
71 num_tl_120dpd_2m 1227909 non-null float64
72 num_tl_30dpd 1277783 non-null float64
73 num_tl_90g_dpd_24m 1277783 non-null float64
74 num_tl_op_past_12m 1277783 non-null float64
75 pct_tl_nvr_dlq 1277629 non-null float64
76 percent_bc_gt_75 1283755 non-null float64
77 pub_rec_bankruptcies 1346694 non-null float64
78 tax_liens 1347954 non-null float64
79 tot_hi_cred_lim 1277783 non-null float64
80 total_bal_ex_mort 1298029 non-null float64
81 total_bc_limit 1298029 non-null float64
82 total_il_high_credit_limit 1277783 non-null float64
83 revol_bal_joint 18629 non-null float64
84 sec_app_fico_range_low 18630 non-null float64
85 sec_app_fico_range_high 18630 non-null float64
86 sec_app_earliest_cr_line 18630 non-null object
87 sec_app_inq_last_6mths 18630 non-null float64
88 sec_app_mort_acc 18630 non-null float64
89 sec_app_open_acc 18630 non-null float64
90 sec_app_revol_util 18302 non-null float64
91 sec_app_open_act_il 18630 non-null float64
92 sec_app_num_rev_accts 18630 non-null float64
93 sec_app_chargeoff_within_12_mths 18630 non-null float64
94 sec_app_collections_12_mths_ex_med 18630 non-null float64
95 sec_app_inv_mths_since_last_major_derog 1348059 non-null float64
96 fraction_recovered 1348059 non-null float64
dtypes: datetime64[ns](1), float64(86), object(10)
memory usage: 1007.9+ MB
Now the only remaining steps should be removing rows with null values (in columns that aren't new metrics) and encoding categorical features.
I'm removing rows with null values in those columns because that should still leave the vast majority of rows intact, over 1 million, which is still plenty of data. But I guess I should make sure before I overwrite loans.
cols_to_search = [
col for col in loans.columns if col not in new_metric_cols + joint_new_metric_cols
]
loans.dropna(axis="index", subset=cols_to_search).shape
(1110171, 97)
Yes, still 1,110,171. That'll do.
loans.dropna(axis="index", subset=cols_to_search, inplace=True)
Then actually I'll tackle earliest_cr_line and its joint counterpart first before looking at the categorical features.
loans[["earliest_cr_line", "sec_app_earliest_cr_line"]]
| earliest_cr_line | sec_app_earliest_cr_line | |
|---|---|---|
| 0 | Aug-2003 | NaN |
| 1 | Dec-1999 | NaN |
| 2 | Aug-2000 | NaN |
| 4 | Jun-1998 | NaN |
| 5 | Oct-1987 | NaN |
| ... | ... | ... |
| 2260688 | Jul-2004 | NaN |
| 2260690 | Mar-2002 | NaN |
| 2260691 | Jun-2011 | NaN |
| 2260692 | Aug-1997 | NaN |
| 2260697 | Jul-1999 | NaN |
1110171 rows × 2 columns
I should convert that to the age of the credit line at the time of application (or the time of loan issuing, more precisely).
def get_credit_history_age(col_name):
earliest_cr_line_date = loans[col_name].astype("datetime64[ns]")
cr_hist_age_delta = loans["issue_d"] - earliest_cr_line_date
MINUTES_PER_MONTH = int(365.25 / 12 * 24 * 60)
cr_hist_age_months = cr_hist_age_delta / np.timedelta64(MINUTES_PER_MONTH, "m")
return cr_hist_age_months.map(
lambda value: np.nan if pd.isna(value) else round(value)
)
cr_hist_age_months = get_credit_history_age("earliest_cr_line")
cr_hist_age_months
0 148
1 192
2 184
4 210
5 338
...
2260688 147
2260690 175
2260691 64
2260692 230
2260697 207
Length: 1110171, dtype: int64
loans["earliest_cr_line"] = cr_hist_age_months
loans["sec_app_earliest_cr_line"] = get_credit_history_age(
"sec_app_earliest_cr_line"
).astype("Int64")
loans.rename(
columns={
"earliest_cr_line": "cr_hist_age_mths",
"sec_app_earliest_cr_line": "sec_app_cr_hist_age_mths",
},
inplace=True,
)
replace_list_value(
joint_new_metric_cols, "sec_app_earliest_cr_line", "sec_app_cr_hist_age_mths"
)
Now a look at those categorical features.
categorical_cols = ["term", "emp_length", "home_ownership", "verification_status", "purpose", "verification_status_joint"]
for i, col_name in enumerate(categorical_cols):
print(
loans.groupby(col_name)[col_name].count(),
"\n" if i < len(categorical_cols) - 1 else "",
)
term
36 months 831601
60 months 278570
Name: term, dtype: int64
emp_length
1 year 76868
10+ years 392883
2 years 106124
3 years 93784
4 years 69031
5 years 72421
6 years 54240
7 years 52229
8 years 53826
9 years 45210
< 1 year 93555
Name: emp_length, dtype: int64
home_ownership
ANY 250
MORTGAGE 559035
NONE 39
OTHER 40
OWN 114577
RENT 436230
Name: home_ownership, dtype: int64
verification_status
Not Verified 335350
Source Verified 463153
Verified 311668
Name: verification_status, dtype: int64
purpose
car 10754
credit_card 245942
debt_consolidation 653222
educational 1
home_improvement 71089
house 5720
major_purchase 22901
medical 12302
moving 7464
other 60986
renewable_energy 691
small_business 11137
vacation 7169
wedding 793
Name: purpose, dtype: int64
verification_status_joint
Not Verified 341073
Source Verified 461941
Verified 307157
Name: verification_status_joint, dtype: int64
First, in researching income verification, I learned that LendingClub only tries to verify income on a subset of loan applications based on the content of the application, so this feature is a source of target leakage. I'll remove the two offending columns (and a couple more I don't need anymore).
loans.drop(
columns=[
"verification_status",
"verification_status_joint",
"issue_d",
"loan_status",
],
inplace=True,
)
Once I create my pipeline, I'll binary encode term, one-hot encode home_ownership and purpose, and since emp_length is an ordinal variable, I'll convert it to the integers 0–10.
onehot_cols += ["home_ownership", "purpose"]
ordinal_cols = {
"emp_length": [
"< 1 year",
"1 year",
"2 years",
"3 years",
"4 years",
"5 years",
"6 years",
"7 years",
"8 years",
"9 years",
"10+ years",
]
}
That should cover all the cleaning necessary for the first model's data. I'll save the columns that'll be used in the first model to a new DataFrame, and while I'm at it, I'll start formatting the DataFrames for the two additional models adding the two sets of new metrics.
loans_1 = loans.drop(columns=new_metric_cols + joint_new_metric_cols)
loans_2 = loans.drop(columns=joint_new_metric_cols)
loans_2.info(verbose=True, null_counts=True)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1110171 entries, 0 to 2260697
Data columns (total 80 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 loan_amnt 1110171 non-null float64
1 term 1110171 non-null object
2 emp_length 1110171 non-null object
3 home_ownership 1110171 non-null object
4 annual_inc 1110171 non-null float64
5 purpose 1110171 non-null object
6 dti 1110171 non-null float64
7 delinq_2yrs 1110171 non-null float64
8 cr_hist_age_mths 1110171 non-null int64
9 fico_range_low 1110171 non-null float64
10 fico_range_high 1110171 non-null float64
11 inq_last_6mths 1110171 non-null float64
12 inv_mths_since_last_delinq 1110171 non-null float64
13 inv_mths_since_last_record 1110171 non-null float64
14 open_acc 1110171 non-null float64
15 pub_rec 1110171 non-null float64
16 revol_bal 1110171 non-null float64
17 revol_util 1110171 non-null float64
18 total_acc 1110171 non-null float64
19 collections_12_mths_ex_med 1110171 non-null float64
20 inv_mths_since_last_major_derog 1110171 non-null float64
21 application_type 1110171 non-null object
22 annual_inc_joint 1110171 non-null float64
23 dti_joint 1110171 non-null float64
24 acc_now_delinq 1110171 non-null float64
25 tot_coll_amt 1110171 non-null float64
26 tot_cur_bal 1110171 non-null float64
27 open_acc_6m 459541 non-null float64
28 open_act_il 459541 non-null float64
29 open_il_12m 459541 non-null float64
30 open_il_24m 459541 non-null float64
31 inv_mths_since_rcnt_il 1110171 non-null float64
32 total_bal_il 459541 non-null float64
33 il_util 408722 non-null float64
34 open_rv_12m 459541 non-null float64
35 open_rv_24m 459541 non-null float64
36 max_bal_bc 459541 non-null float64
37 all_util 459541 non-null float64
38 total_rev_hi_lim 1110171 non-null float64
39 inq_fi 459541 non-null float64
40 total_cu_tl 459541 non-null float64
41 inq_last_12m 459541 non-null float64
42 acc_open_past_24mths 1110171 non-null float64
43 avg_cur_bal 1110171 non-null float64
44 bc_open_to_buy 1110171 non-null float64
45 bc_util 1110171 non-null float64
46 chargeoff_within_12_mths 1110171 non-null float64
47 delinq_amnt 1110171 non-null float64
48 mo_sin_old_il_acct 1110171 non-null float64
49 mo_sin_old_rev_tl_op 1110171 non-null float64
50 inv_mo_sin_rcnt_rev_tl_op 1110171 non-null float64
51 inv_mo_sin_rcnt_tl 1110171 non-null float64
52 mort_acc 1110171 non-null float64
53 inv_mths_since_recent_bc 1110171 non-null float64
54 inv_mths_since_recent_bc_dlq 1110171 non-null float64
55 inv_mths_since_recent_inq 1110171 non-null float64
56 inv_mths_since_recent_revol_delinq 1110171 non-null float64
57 num_accts_ever_120_pd 1110171 non-null float64
58 num_actv_bc_tl 1110171 non-null float64
59 num_actv_rev_tl 1110171 non-null float64
60 num_bc_sats 1110171 non-null float64
61 num_bc_tl 1110171 non-null float64
62 num_il_tl 1110171 non-null float64
63 num_op_rev_tl 1110171 non-null float64
64 num_rev_accts 1110171 non-null float64
65 num_rev_tl_bal_gt_0 1110171 non-null float64
66 num_sats 1110171 non-null float64
67 num_tl_120dpd_2m 1110171 non-null float64
68 num_tl_30dpd 1110171 non-null float64
69 num_tl_90g_dpd_24m 1110171 non-null float64
70 num_tl_op_past_12m 1110171 non-null float64
71 pct_tl_nvr_dlq 1110171 non-null float64
72 percent_bc_gt_75 1110171 non-null float64
73 pub_rec_bankruptcies 1110171 non-null float64
74 tax_liens 1110171 non-null float64
75 tot_hi_cred_lim 1110171 non-null float64
76 total_bal_ex_mort 1110171 non-null float64
77 total_bc_limit 1110171 non-null float64
78 total_il_high_credit_limit 1110171 non-null float64
79 fraction_recovered 1110171 non-null float64
dtypes: float64(74), int64(1), object(5)
memory usage: 686.1+ MB
Before I drop a bunch of rows with nulls from loans_2, I'm concerned about il_util, as it's missing values in about 50,000 more rows than the rest of the new metric columns. Why would that be?
loans_2["il_util"][loans_2["il_util"].notna()].describe()
count 408722.000000
mean 71.832894
std 22.311439
min 0.000000
25% 59.000000
50% 75.000000
75% 87.000000
max 464.000000
Name: il_util, dtype: float64
Peeking back up to the data dictionary, il_util is the "ratio of total current balance to high credit/credit limit on all install acct". The relevant balance (total_bal_il) and credit limit (total_il_high_credit_limit) metrics appear to already be in the data, so perhaps this utilization metric doesn't contain any new information. I'll compare il_util (where it's present) to the ratio of the other two variables.
query_df = loans[["il_util", "total_bal_il", "total_il_high_credit_limit"]].dropna(
axis="index", subset=["il_util"]
)
query_df["il_util_compute"] = (
query_df["total_bal_il"] / query_df["total_il_high_credit_limit"]
).map(lambda x: float(round(x * 100)))
query_df[["il_util", "il_util_compute"]]
| il_util | il_util_compute | |
|---|---|---|
| 0 | 36.0 | 36.0 |
| 1 | 73.0 | 73.0 |
| 2 | 73.0 | 73.0 |
| 4 | 84.0 | 84.0 |
| 5 | 99.0 | 99.0 |
| ... | ... | ... |
| 2260688 | 52.0 | 39.0 |
| 2260690 | 50.0 | 74.0 |
| 2260691 | 47.0 | 47.0 |
| 2260692 | 79.0 | 79.0 |
| 2260697 | 78.0 | 76.0 |
408722 rows × 2 columns
(query_df["il_util"] == query_df["il_util_compute"]).describe()
count 408722
unique 2
top True
freq 307589
dtype: object
query_df["compute_diff"] = abs(query_df["il_util"] - query_df["il_util_compute"])
query_df["compute_diff"][query_df["compute_diff"] != 0].describe()
count 101133.000000
mean 14.638684
std 16.409913
min 1.000000
25% 3.000000
50% 10.000000
75% 21.000000
max 1108.000000
Name: compute_diff, dtype: float64
That's weird. il_util is equal to the computed ratio three-quarters of the time, but when it's off, the median difference is 10 points off. Perhaps there's new information there sometimes after all. Maybe whatever credit bureau is reporting the utilization rate uses a different formula than just a simple ratio? Again, something I could ask if I were performing this analysis for a client, but that's not the case. I'll assume that this variable is still valuable, and where il_util is null I'll impute the value to make it equal to the ratio of total_bal_il to total_il_high_credit_limit (or 0 if the limit is 0). And I'll add one more boolean field to mark the imputed entries.
Also, that 1,108 is a doozy of an outlier, but I think I'll just leave it be, as it appears that outliers aren't too big a deal if the neural network architecture is sufficiently deep.
loans["il_util_imputed"] = [
True if pd.isna(util) & pd.notna(bal) & pd.notna(limit) else False
for util, bal, limit in zip(
loans["il_util"], loans["total_bal_il"], loans["total_il_high_credit_limit"]
)
]
new_metric_onehot_cols = ["il_util_imputed"]
loans["il_util"] = [
0.0
if pd.isna(util) & pd.notna(bal) & (limit == 0)
else float(round(bal / limit * 100))
if pd.isna(util) & pd.notna(bal) & pd.notna(limit)
else util
for util, bal, limit in zip(
loans["il_util"], loans["total_bal_il"], loans["total_il_high_credit_limit"]
)
]
loans_2 = loans.drop(columns=joint_new_metric_cols)
loans_2.info(verbose=True, null_counts=True)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1110171 entries, 0 to 2260697
Data columns (total 81 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 loan_amnt 1110171 non-null float64
1 term 1110171 non-null object
2 emp_length 1110171 non-null object
3 home_ownership 1110171 non-null object
4 annual_inc 1110171 non-null float64
5 purpose 1110171 non-null object
6 dti 1110171 non-null float64
7 delinq_2yrs 1110171 non-null float64
8 cr_hist_age_mths 1110171 non-null int64
9 fico_range_low 1110171 non-null float64
10 fico_range_high 1110171 non-null float64
11 inq_last_6mths 1110171 non-null float64
12 inv_mths_since_last_delinq 1110171 non-null float64
13 inv_mths_since_last_record 1110171 non-null float64
14 open_acc 1110171 non-null float64
15 pub_rec 1110171 non-null float64
16 revol_bal 1110171 non-null float64
17 revol_util 1110171 non-null float64
18 total_acc 1110171 non-null float64
19 collections_12_mths_ex_med 1110171 non-null float64
20 inv_mths_since_last_major_derog 1110171 non-null float64
21 application_type 1110171 non-null object
22 annual_inc_joint 1110171 non-null float64
23 dti_joint 1110171 non-null float64
24 acc_now_delinq 1110171 non-null float64
25 tot_coll_amt 1110171 non-null float64
26 tot_cur_bal 1110171 non-null float64
27 open_acc_6m 459541 non-null float64
28 open_act_il 459541 non-null float64
29 open_il_12m 459541 non-null float64
30 open_il_24m 459541 non-null float64
31 inv_mths_since_rcnt_il 1110171 non-null float64
32 total_bal_il 459541 non-null float64
33 il_util 459541 non-null float64
34 open_rv_12m 459541 non-null float64
35 open_rv_24m 459541 non-null float64
36 max_bal_bc 459541 non-null float64
37 all_util 459541 non-null float64
38 total_rev_hi_lim 1110171 non-null float64
39 inq_fi 459541 non-null float64
40 total_cu_tl 459541 non-null float64
41 inq_last_12m 459541 non-null float64
42 acc_open_past_24mths 1110171 non-null float64
43 avg_cur_bal 1110171 non-null float64
44 bc_open_to_buy 1110171 non-null float64
45 bc_util 1110171 non-null float64
46 chargeoff_within_12_mths 1110171 non-null float64
47 delinq_amnt 1110171 non-null float64
48 mo_sin_old_il_acct 1110171 non-null float64
49 mo_sin_old_rev_tl_op 1110171 non-null float64
50 inv_mo_sin_rcnt_rev_tl_op 1110171 non-null float64
51 inv_mo_sin_rcnt_tl 1110171 non-null float64
52 mort_acc 1110171 non-null float64
53 inv_mths_since_recent_bc 1110171 non-null float64
54 inv_mths_since_recent_bc_dlq 1110171 non-null float64
55 inv_mths_since_recent_inq 1110171 non-null float64
56 inv_mths_since_recent_revol_delinq 1110171 non-null float64
57 num_accts_ever_120_pd 1110171 non-null float64
58 num_actv_bc_tl 1110171 non-null float64
59 num_actv_rev_tl 1110171 non-null float64
60 num_bc_sats 1110171 non-null float64
61 num_bc_tl 1110171 non-null float64
62 num_il_tl 1110171 non-null float64
63 num_op_rev_tl 1110171 non-null float64
64 num_rev_accts 1110171 non-null float64
65 num_rev_tl_bal_gt_0 1110171 non-null float64
66 num_sats 1110171 non-null float64
67 num_tl_120dpd_2m 1110171 non-null float64
68 num_tl_30dpd 1110171 non-null float64
69 num_tl_90g_dpd_24m 1110171 non-null float64
70 num_tl_op_past_12m 1110171 non-null float64
71 pct_tl_nvr_dlq 1110171 non-null float64
72 percent_bc_gt_75 1110171 non-null float64
73 pub_rec_bankruptcies 1110171 non-null float64
74 tax_liens 1110171 non-null float64
75 tot_hi_cred_lim 1110171 non-null float64
76 total_bal_ex_mort 1110171 non-null float64
77 total_bc_limit 1110171 non-null float64
78 total_il_high_credit_limit 1110171 non-null float64
79 fraction_recovered 1110171 non-null float64
80 il_util_imputed 1110171 non-null bool
dtypes: bool(1), float64(74), int64(1), object(5)
memory usage: 687.1+ MB
Good. Ready to drop rows with nulls in loans_2 and move on to the DataFrame for the model that adds the new metrics for joint applications.
loans_2.dropna(axis="index", inplace=True)
loans_3 = loans.dropna(axis="index")
loans_3.info(verbose=True, null_counts=True)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 14453 entries, 421222 to 2157147
Data columns (total 94 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 loan_amnt 14453 non-null float64
1 term 14453 non-null object
2 emp_length 14453 non-null object
3 home_ownership 14453 non-null object
4 annual_inc 14453 non-null float64
5 purpose 14453 non-null object
6 dti 14453 non-null float64
7 delinq_2yrs 14453 non-null float64
8 cr_hist_age_mths 14453 non-null int64
9 fico_range_low 14453 non-null float64
10 fico_range_high 14453 non-null float64
11 inq_last_6mths 14453 non-null float64
12 inv_mths_since_last_delinq 14453 non-null float64
13 inv_mths_since_last_record 14453 non-null float64
14 open_acc 14453 non-null float64
15 pub_rec 14453 non-null float64
16 revol_bal 14453 non-null float64
17 revol_util 14453 non-null float64
18 total_acc 14453 non-null float64
19 collections_12_mths_ex_med 14453 non-null float64
20 inv_mths_since_last_major_derog 14453 non-null float64
21 application_type 14453 non-null object
22 annual_inc_joint 14453 non-null float64
23 dti_joint 14453 non-null float64
24 acc_now_delinq 14453 non-null float64
25 tot_coll_amt 14453 non-null float64
26 tot_cur_bal 14453 non-null float64
27 open_acc_6m 14453 non-null float64
28 open_act_il 14453 non-null float64
29 open_il_12m 14453 non-null float64
30 open_il_24m 14453 non-null float64
31 inv_mths_since_rcnt_il 14453 non-null float64
32 total_bal_il 14453 non-null float64
33 il_util 14453 non-null float64
34 open_rv_12m 14453 non-null float64
35 open_rv_24m 14453 non-null float64
36 max_bal_bc 14453 non-null float64
37 all_util 14453 non-null float64
38 total_rev_hi_lim 14453 non-null float64
39 inq_fi 14453 non-null float64
40 total_cu_tl 14453 non-null float64
41 inq_last_12m 14453 non-null float64
42 acc_open_past_24mths 14453 non-null float64
43 avg_cur_bal 14453 non-null float64
44 bc_open_to_buy 14453 non-null float64
45 bc_util 14453 non-null float64
46 chargeoff_within_12_mths 14453 non-null float64
47 delinq_amnt 14453 non-null float64
48 mo_sin_old_il_acct 14453 non-null float64
49 mo_sin_old_rev_tl_op 14453 non-null float64
50 inv_mo_sin_rcnt_rev_tl_op 14453 non-null float64
51 inv_mo_sin_rcnt_tl 14453 non-null float64
52 mort_acc 14453 non-null float64
53 inv_mths_since_recent_bc 14453 non-null float64
54 inv_mths_since_recent_bc_dlq 14453 non-null float64
55 inv_mths_since_recent_inq 14453 non-null float64
56 inv_mths_since_recent_revol_delinq 14453 non-null float64
57 num_accts_ever_120_pd 14453 non-null float64
58 num_actv_bc_tl 14453 non-null float64
59 num_actv_rev_tl 14453 non-null float64
60 num_bc_sats 14453 non-null float64
61 num_bc_tl 14453 non-null float64
62 num_il_tl 14453 non-null float64
63 num_op_rev_tl 14453 non-null float64
64 num_rev_accts 14453 non-null float64
65 num_rev_tl_bal_gt_0 14453 non-null float64
66 num_sats 14453 non-null float64
67 num_tl_120dpd_2m 14453 non-null float64
68 num_tl_30dpd 14453 non-null float64
69 num_tl_90g_dpd_24m 14453 non-null float64
70 num_tl_op_past_12m 14453 non-null float64
71 pct_tl_nvr_dlq 14453 non-null float64
72 percent_bc_gt_75 14453 non-null float64
73 pub_rec_bankruptcies 14453 non-null float64
74 tax_liens 14453 non-null float64
75 tot_hi_cred_lim 14453 non-null float64
76 total_bal_ex_mort 14453 non-null float64
77 total_bc_limit 14453 non-null float64
78 total_il_high_credit_limit 14453 non-null float64
79 revol_bal_joint 14453 non-null float64
80 sec_app_fico_range_low 14453 non-null float64
81 sec_app_fico_range_high 14453 non-null float64
82 sec_app_cr_hist_age_mths 14453 non-null Int64
83 sec_app_inq_last_6mths 14453 non-null float64
84 sec_app_mort_acc 14453 non-null float64
85 sec_app_open_acc 14453 non-null float64
86 sec_app_revol_util 14453 non-null float64
87 sec_app_open_act_il 14453 non-null float64
88 sec_app_num_rev_accts 14453 non-null float64
89 sec_app_chargeoff_within_12_mths 14453 non-null float64
90 sec_app_collections_12_mths_ex_med 14453 non-null float64
91 sec_app_inv_mths_since_last_major_derog 14453 non-null float64
92 fraction_recovered 14453 non-null float64
93 il_util_imputed 14453 non-null bool
dtypes: Int64(1), bool(1), float64(86), int64(1), object(5)
memory usage: 10.4+ MB
Phew, the data's all clean now! Time for the fun part.
Building the neural networks
After a good deal of trial and error, I found that a network architecture with three hidden layers, each followed by a dropout layer of rate 0.3, was as good as I could find. I used ReLU activation in those hidden layers, and adam optimization and a loss metric of mean squared error in the model as a whole. I also settled on a mean squared logarithmic error loss function, since it performed better than mean absolute error, mean squared error, and mean absolute percentage error.
The dataset being so large, I had great results increasing the batch size for the first couple models.
from sklearn.model_selection import train_test_split
from sklearn_pandas import DataFrameMapper
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, StandardScaler
from tensorflow.keras import Sequential, Input
from tensorflow.keras.layers import Dense, Dropout
def run_pipeline(
data, onehot_cols, ordinal_cols, batch_size, validate=True,
):
X = data.drop(columns=["fraction_recovered"])
y = data["fraction_recovered"]
X_train, X_valid, y_train, y_valid = (
train_test_split(X, y, test_size=0.2, random_state=0)
if validate
else (X, None, y, None)
)
transformer = DataFrameMapper(
[
(onehot_cols, OneHotEncoder(drop="if_binary")),
(
list(ordinal_cols.keys()),
OrdinalEncoder(categories=list(ordinal_cols.values())),
),
],
default=StandardScaler(),
)
X_train = transformer.fit_transform(X_train)
X_valid = transformer.transform(X_valid) if validate else None
input_nodes = X_train.shape[1]
output_nodes = 1
model = Sequential()
model.add(Input((input_nodes,)))
model.add(Dense(64, activation="relu"))
model.add(Dropout(0.3, seed=0))
model.add(Dense(32, activation="relu"))
model.add(Dropout(0.3, seed=1))
model.add(Dense(16, activation="relu"))
model.add(Dropout(0.3, seed=2))
model.add(Dense(output_nodes))
model.compile(optimizer="adam", loss="mean_squared_logarithmic_error")
history = model.fit(
X_train,
y_train,
batch_size=batch_size,
epochs=100,
validation_data=(X_valid, y_valid) if validate else None,
verbose=1,
)
return history.history, model, transformer
print("Model 1:")
history_1, _, _ = run_pipeline(loans_1, onehot_cols, ordinal_cols, batch_size=128,)
print("\nModel 2:")
history_2, _, _ = run_pipeline(
loans_2, onehot_cols + new_metric_onehot_cols, ordinal_cols, batch_size=64,
)
print("\nModel 3:")
history_3, _, _ = run_pipeline(
loans_3, onehot_cols + new_metric_onehot_cols, ordinal_cols, batch_size=32,
)
Model 1:
Epoch 1/100
6939/6939 - 15s - loss: 0.0267 - val_loss: 0.0233
Epoch 2/100
6939/6939 - 15s - loss: 0.0235 - val_loss: 0.0232
Epoch 3/100
6939/6939 - 15s - loss: 0.0234 - val_loss: 0.0233
Epoch 4/100
6939/6939 - 15s - loss: 0.0233 - val_loss: 0.0234
Epoch 5/100
6939/6939 - 15s - loss: 0.0233 - val_loss: 0.0233
Epoch 6/100
6939/6939 - 15s - loss: 0.0233 - val_loss: 0.0233
Epoch 7/100
6939/6939 - 15s - loss: 0.0232 - val_loss: 0.0232
Epoch 8/100
6939/6939 - 15s - loss: 0.0232 - val_loss: 0.0230
Epoch 9/100
6939/6939 - 15s - loss: 0.0232 - val_loss: 0.0230
Epoch 10/100
6939/6939 - 15s - loss: 0.0232 - val_loss: 0.0231
Epoch 11/100
6939/6939 - 16s - loss: 0.0232 - val_loss: 0.0232
Epoch 12/100
6939/6939 - 15s - loss: 0.0232 - val_loss: 0.0232
Epoch 13/100
6939/6939 - 15s - loss: 0.0232 - val_loss: 0.0232
Epoch 14/100
6939/6939 - 16s - loss: 0.0232 - val_loss: 0.0230
Epoch 15/100
6939/6939 - 18s - loss: 0.0232 - val_loss: 0.0231
Epoch 16/100
6939/6939 - 15s - loss: 0.0232 - val_loss: 0.0232
Epoch 17/100
6939/6939 - 15s - loss: 0.0232 - val_loss: 0.0231
Epoch 18/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 19/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 20/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0230
Epoch 21/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0232
Epoch 22/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0233
Epoch 23/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0230
Epoch 24/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0233
Epoch 25/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 26/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0233
Epoch 27/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 28/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0232
Epoch 29/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0230
Epoch 30/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0232
Epoch 31/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0232
Epoch 32/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 33/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0230
Epoch 34/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0233
Epoch 35/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0230
Epoch 36/100
6939/6939 - 18s - loss: 0.0231 - val_loss: 0.0231
Epoch 37/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0232
Epoch 38/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0234
Epoch 39/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0232
Epoch 40/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 41/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0232
Epoch 42/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0230
Epoch 43/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0229
Epoch 44/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0230
Epoch 45/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0231
Epoch 46/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0232
Epoch 47/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0231
Epoch 48/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0232
Epoch 49/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0232
Epoch 50/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0233
Epoch 51/100
6939/6939 - 17s - loss: 0.0231 - val_loss: 0.0230
Epoch 52/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 53/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0232
Epoch 54/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0232
Epoch 55/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0230
Epoch 56/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 57/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0230
Epoch 58/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 59/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 60/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 61/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0232
Epoch 62/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 63/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 64/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0230
Epoch 65/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 66/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0230
Epoch 67/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0232
Epoch 68/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0232
Epoch 69/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0233
Epoch 70/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0232
Epoch 71/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0231
Epoch 72/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0234
Epoch 73/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 74/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 75/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 76/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 77/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0230
Epoch 78/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0232
Epoch 79/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0232
Epoch 80/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0232
Epoch 81/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0230
Epoch 82/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 83/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0233
Epoch 84/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 85/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0232
Epoch 86/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0230
Epoch 87/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0233
Epoch 88/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 89/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 90/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0231
Epoch 91/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0232
Epoch 92/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0232
Epoch 93/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0230
Epoch 94/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0230
Epoch 95/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0232
Epoch 96/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0231
Epoch 97/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0231
Epoch 98/100
6939/6939 - 20s - loss: 0.0231 - val_loss: 0.0232
Epoch 99/100
6939/6939 - 16s - loss: 0.0231 - val_loss: 0.0231
Epoch 100/100
6939/6939 - 15s - loss: 0.0231 - val_loss: 0.0232
Model 2:
Epoch 1/100
5745/5745 - 11s - loss: 0.0361 - val_loss: 0.0298
Epoch 2/100
5745/5745 - 11s - loss: 0.0302 - val_loss: 0.0295
Epoch 3/100
5745/5745 - 11s - loss: 0.0299 - val_loss: 0.0295
Epoch 4/100
5745/5745 - 11s - loss: 0.0298 - val_loss: 0.0296
Epoch 5/100
5745/5745 - 11s - loss: 0.0298 - val_loss: 0.0293
Epoch 6/100
5745/5745 - 11s - loss: 0.0297 - val_loss: 0.0292
Epoch 7/100
5745/5745 - 11s - loss: 0.0297 - val_loss: 0.0293
Epoch 8/100
5745/5745 - 11s - loss: 0.0296 - val_loss: 0.0292
Epoch 9/100
5745/5745 - 11s - loss: 0.0296 - val_loss: 0.0293
Epoch 10/100
5745/5745 - 11s - loss: 0.0296 - val_loss: 0.0292
Epoch 11/100
5745/5745 - 12s - loss: 0.0296 - val_loss: 0.0292
Epoch 12/100
5745/5745 - 11s - loss: 0.0296 - val_loss: 0.0292
Epoch 13/100
5745/5745 - 11s - loss: 0.0295 - val_loss: 0.0292
Epoch 14/100
5745/5745 - 11s - loss: 0.0295 - val_loss: 0.0292
Epoch 15/100
5745/5745 - 13s - loss: 0.0295 - val_loss: 0.0293
Epoch 16/100
5745/5745 - 12s - loss: 0.0295 - val_loss: 0.0291
Epoch 17/100
5745/5745 - 12s - loss: 0.0295 - val_loss: 0.0291
Epoch 18/100
5745/5745 - 11s - loss: 0.0295 - val_loss: 0.0290
Epoch 19/100
5745/5745 - 11s - loss: 0.0295 - val_loss: 0.0293
Epoch 20/100
5745/5745 - 11s - loss: 0.0295 - val_loss: 0.0292
Epoch 21/100
5745/5745 - 11s - loss: 0.0294 - val_loss: 0.0291
Epoch 22/100
5745/5745 - 12s - loss: 0.0294 - val_loss: 0.0291
Epoch 23/100
5745/5745 - 11s - loss: 0.0294 - val_loss: 0.0293
Epoch 24/100
5745/5745 - 13s - loss: 0.0294 - val_loss: 0.0293
Epoch 25/100
5745/5745 - 14s - loss: 0.0294 - val_loss: 0.0291
Epoch 26/100
5745/5745 - 11s - loss: 0.0294 - val_loss: 0.0292
Epoch 27/100
5745/5745 - 11s - loss: 0.0294 - val_loss: 0.0291
Epoch 28/100
5745/5745 - 11s - loss: 0.0294 - val_loss: 0.0292
Epoch 29/100
5745/5745 - 11s - loss: 0.0294 - val_loss: 0.0292
Epoch 30/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0291
Epoch 31/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0291
Epoch 32/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0292
Epoch 33/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0291
Epoch 34/100
5745/5745 - 11s - loss: 0.0294 - val_loss: 0.0291
Epoch 35/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0291
Epoch 36/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0291
Epoch 37/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0292
Epoch 38/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0291
Epoch 39/100
5745/5745 - 12s - loss: 0.0293 - val_loss: 0.0291
Epoch 40/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0291
Epoch 41/100
5745/5745 - 12s - loss: 0.0294 - val_loss: 0.0292
Epoch 42/100
5745/5745 - 13s - loss: 0.0293 - val_loss: 0.0293
Epoch 43/100
5745/5745 - 13s - loss: 0.0293 - val_loss: 0.0291
Epoch 44/100
5745/5745 - 12s - loss: 0.0293 - val_loss: 0.0291
Epoch 45/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0292
Epoch 46/100
5745/5745 - 12s - loss: 0.0293 - val_loss: 0.0290
Epoch 47/100
5745/5745 - 12s - loss: 0.0293 - val_loss: 0.0291
Epoch 48/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0292
Epoch 49/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0291
Epoch 50/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0294
Epoch 51/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0292
Epoch 52/100
5745/5745 - 14s - loss: 0.0293 - val_loss: 0.0291
Epoch 53/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0290
Epoch 54/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0291
Epoch 55/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0291
Epoch 56/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0293
Epoch 57/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0291
Epoch 58/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0293
Epoch 59/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0292
Epoch 60/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0291
Epoch 61/100
5745/5745 - 11s - loss: 0.0293 - val_loss: 0.0290
Epoch 62/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0292
Epoch 63/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0291
Epoch 64/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0291
Epoch 65/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0294
Epoch 66/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0292
Epoch 67/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0292
Epoch 68/100
5745/5745 - 12s - loss: 0.0292 - val_loss: 0.0292
Epoch 69/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0293
Epoch 70/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0292
Epoch 71/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0291
Epoch 72/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0291
Epoch 73/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0291
Epoch 74/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0291
Epoch 75/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0293
Epoch 76/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0294
Epoch 77/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0291
Epoch 78/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0292
Epoch 79/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0292
Epoch 80/100
5745/5745 - 13s - loss: 0.0292 - val_loss: 0.0295
Epoch 81/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0290
Epoch 82/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0291
Epoch 83/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0291
Epoch 84/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0293
Epoch 85/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0292
Epoch 86/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0292
Epoch 87/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0293
Epoch 88/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0292
Epoch 89/100
5745/5745 - 12s - loss: 0.0292 - val_loss: 0.0291
Epoch 90/100
5745/5745 - 12s - loss: 0.0292 - val_loss: 0.0293
Epoch 91/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0292
Epoch 92/100
5745/5745 - 11s - loss: 0.0292 - val_loss: 0.0291
Epoch 93/100
5745/5745 - 11s - loss: 0.0291 - val_loss: 0.0291
Epoch 94/100
5745/5745 - 12s - loss: 0.0292 - val_loss: 0.0291
Epoch 95/100
5745/5745 - 12s - loss: 0.0292 - val_loss: 0.0293
Epoch 96/100
5745/5745 - 12s - loss: 0.0292 - val_loss: 0.0292
Epoch 97/100
5745/5745 - 13s - loss: 0.0291 - val_loss: 0.0295
Epoch 98/100
5745/5745 - 11s - loss: 0.0291 - val_loss: 0.0291
Epoch 99/100
5745/5745 - 12s - loss: 0.0291 - val_loss: 0.0292
Epoch 100/100
5745/5745 - 12s - loss: 0.0292 - val_loss: 0.0292
Model 3:
Epoch 1/100
362/362 - 1s - loss: 0.1141 - val_loss: 0.0628
Epoch 2/100
362/362 - 1s - loss: 0.0575 - val_loss: 0.0527
Epoch 3/100
362/362 - 1s - loss: 0.0506 - val_loss: 0.0502
Epoch 4/100
362/362 - 1s - loss: 0.0486 - val_loss: 0.0486
Epoch 5/100
362/362 - 1s - loss: 0.0468 - val_loss: 0.0477
Epoch 6/100
362/362 - 1s - loss: 0.0463 - val_loss: 0.0474
Epoch 7/100
362/362 - 1s - loss: 0.0455 - val_loss: 0.0471
Epoch 8/100
362/362 - 1s - loss: 0.0445 - val_loss: 0.0466
Epoch 9/100
362/362 - 1s - loss: 0.0439 - val_loss: 0.0465
Epoch 10/100
362/362 - 1s - loss: 0.0438 - val_loss: 0.0465
Epoch 11/100
362/362 - 1s - loss: 0.0433 - val_loss: 0.0463
Epoch 12/100
362/362 - 1s - loss: 0.0430 - val_loss: 0.0462
Epoch 13/100
362/362 - 1s - loss: 0.0428 - val_loss: 0.0463
Epoch 14/100
362/362 - 1s - loss: 0.0427 - val_loss: 0.0460
Epoch 15/100
362/362 - 1s - loss: 0.0425 - val_loss: 0.0462
Epoch 16/100
362/362 - 1s - loss: 0.0422 - val_loss: 0.0461
Epoch 17/100
362/362 - 1s - loss: 0.0416 - val_loss: 0.0459
Epoch 18/100
362/362 - 1s - loss: 0.0416 - val_loss: 0.0460
Epoch 19/100
362/362 - 1s - loss: 0.0412 - val_loss: 0.0460
Epoch 20/100
362/362 - 1s - loss: 0.0414 - val_loss: 0.0461
Epoch 21/100
362/362 - 1s - loss: 0.0410 - val_loss: 0.0459
Epoch 22/100
362/362 - 1s - loss: 0.0409 - val_loss: 0.0459
Epoch 23/100
362/362 - 1s - loss: 0.0401 - val_loss: 0.0456
Epoch 24/100
362/362 - 1s - loss: 0.0400 - val_loss: 0.0460
Epoch 25/100
362/362 - 1s - loss: 0.0399 - val_loss: 0.0462
Epoch 26/100
362/362 - 1s - loss: 0.0394 - val_loss: 0.0461
Epoch 27/100
362/362 - 1s - loss: 0.0396 - val_loss: 0.0466
Epoch 28/100
362/362 - 1s - loss: 0.0394 - val_loss: 0.0462
Epoch 29/100
362/362 - 1s - loss: 0.0388 - val_loss: 0.0468
Epoch 30/100
362/362 - 1s - loss: 0.0384 - val_loss: 0.0465
Epoch 31/100
362/362 - 1s - loss: 0.0386 - val_loss: 0.0462
Epoch 32/100
362/362 - 1s - loss: 0.0379 - val_loss: 0.0467
Epoch 33/100
362/362 - 1s - loss: 0.0375 - val_loss: 0.0469
Epoch 34/100
362/362 - 1s - loss: 0.0382 - val_loss: 0.0468
Epoch 35/100
362/362 - 1s - loss: 0.0375 - val_loss: 0.0475
Epoch 36/100
362/362 - 1s - loss: 0.0374 - val_loss: 0.0468
Epoch 37/100
362/362 - 1s - loss: 0.0371 - val_loss: 0.0471
Epoch 38/100
362/362 - 1s - loss: 0.0370 - val_loss: 0.0474
Epoch 39/100
362/362 - 1s - loss: 0.0363 - val_loss: 0.0472
Epoch 40/100
362/362 - 1s - loss: 0.0366 - val_loss: 0.0477
Epoch 41/100
362/362 - 1s - loss: 0.0359 - val_loss: 0.0488
Epoch 42/100
362/362 - 1s - loss: 0.0356 - val_loss: 0.0476
Epoch 43/100
362/362 - 1s - loss: 0.0361 - val_loss: 0.0474
Epoch 44/100
362/362 - 1s - loss: 0.0357 - val_loss: 0.0476
Epoch 45/100
362/362 - 1s - loss: 0.0360 - val_loss: 0.0480
Epoch 46/100
362/362 - 1s - loss: 0.0351 - val_loss: 0.0476
Epoch 47/100
362/362 - 1s - loss: 0.0354 - val_loss: 0.0475
Epoch 48/100
362/362 - 1s - loss: 0.0348 - val_loss: 0.0487
Epoch 49/100
362/362 - 1s - loss: 0.0348 - val_loss: 0.0475
Epoch 50/100
362/362 - 1s - loss: 0.0351 - val_loss: 0.0477
Epoch 51/100
362/362 - 1s - loss: 0.0344 - val_loss: 0.0481
Epoch 52/100
362/362 - 1s - loss: 0.0348 - val_loss: 0.0482
Epoch 53/100
362/362 - 1s - loss: 0.0346 - val_loss: 0.0480
Epoch 54/100
362/362 - 1s - loss: 0.0344 - val_loss: 0.0487
Epoch 55/100
362/362 - 1s - loss: 0.0331 - val_loss: 0.0490
Epoch 56/100
362/362 - 1s - loss: 0.0339 - val_loss: 0.0488
Epoch 57/100
362/362 - 1s - loss: 0.0334 - val_loss: 0.0486
Epoch 58/100
362/362 - 1s - loss: 0.0336 - val_loss: 0.0497
Epoch 59/100
362/362 - 1s - loss: 0.0335 - val_loss: 0.0488
Epoch 60/100
362/362 - 1s - loss: 0.0336 - val_loss: 0.0485
Epoch 61/100
362/362 - 1s - loss: 0.0336 - val_loss: 0.0490
Epoch 62/100
362/362 - 1s - loss: 0.0327 - val_loss: 0.0493
Epoch 63/100
362/362 - 1s - loss: 0.0327 - val_loss: 0.0488
Epoch 64/100
362/362 - 1s - loss: 0.0331 - val_loss: 0.0489
Epoch 65/100
362/362 - 1s - loss: 0.0327 - val_loss: 0.0486
Epoch 66/100
362/362 - 1s - loss: 0.0324 - val_loss: 0.0500
Epoch 67/100
362/362 - 1s - loss: 0.0326 - val_loss: 0.0493
Epoch 68/100
362/362 - 1s - loss: 0.0320 - val_loss: 0.0499
Epoch 69/100
362/362 - 1s - loss: 0.0323 - val_loss: 0.0497
Epoch 70/100
362/362 - 1s - loss: 0.0321 - val_loss: 0.0495
Epoch 71/100
362/362 - 1s - loss: 0.0323 - val_loss: 0.0492
Epoch 72/100
362/362 - 1s - loss: 0.0321 - val_loss: 0.0487
Epoch 73/100
362/362 - 1s - loss: 0.0324 - val_loss: 0.0491
Epoch 74/100
362/362 - 1s - loss: 0.0325 - val_loss: 0.0487
Epoch 75/100
362/362 - 1s - loss: 0.0315 - val_loss: 0.0496
Epoch 76/100
362/362 - 1s - loss: 0.0317 - val_loss: 0.0503
Epoch 77/100
362/362 - 1s - loss: 0.0318 - val_loss: 0.0486
Epoch 78/100
362/362 - 1s - loss: 0.0312 - val_loss: 0.0504
Epoch 79/100
362/362 - 1s - loss: 0.0312 - val_loss: 0.0493
Epoch 80/100
362/362 - 1s - loss: 0.0315 - val_loss: 0.0490
Epoch 81/100
362/362 - 1s - loss: 0.0307 - val_loss: 0.0502
Epoch 82/100
362/362 - 1s - loss: 0.0310 - val_loss: 0.0499
Epoch 83/100
362/362 - 1s - loss: 0.0312 - val_loss: 0.0499
Epoch 84/100
362/362 - 1s - loss: 0.0306 - val_loss: 0.0507
Epoch 85/100
362/362 - 1s - loss: 0.0311 - val_loss: 0.0506
Epoch 86/100
362/362 - 1s - loss: 0.0305 - val_loss: 0.0506
Epoch 87/100
362/362 - 1s - loss: 0.0309 - val_loss: 0.0503
Epoch 88/100
362/362 - 1s - loss: 0.0314 - val_loss: 0.0504
Epoch 89/100
362/362 - 1s - loss: 0.0308 - val_loss: 0.0508
Epoch 90/100
362/362 - 1s - loss: 0.0304 - val_loss: 0.0500
Epoch 91/100
362/362 - 1s - loss: 0.0306 - val_loss: 0.0497
Epoch 92/100
362/362 - 1s - loss: 0.0305 - val_loss: 0.0510
Epoch 93/100
362/362 - 1s - loss: 0.0308 - val_loss: 0.0502
Epoch 94/100
362/362 - 1s - loss: 0.0300 - val_loss: 0.0504
Epoch 95/100
362/362 - 1s - loss: 0.0310 - val_loss: 0.0509
Epoch 96/100
362/362 - 1s - loss: 0.0306 - val_loss: 0.0509
Epoch 97/100
362/362 - 1s - loss: 0.0298 - val_loss: 0.0509
Epoch 98/100
362/362 - 1s - loss: 0.0303 - val_loss: 0.0503
Epoch 99/100
362/362 - 1s - loss: 0.0296 - val_loss: 0.0509
Epoch 100/100
362/362 - 1s - loss: 0.0301 - val_loss: 0.0509
The first model performed best, settling around a mean squared logarithmic error of 0.0231 (though it seems even after setting random_state inside train_test_split and seed inside the dropout layers, there's still a bit of entropy left in the training of the model, so if you run this notebook yourself, the course of your training may look a little different). Apparently the additional records in the first dataset did more to aid in training than the additional metrics in the subsequent sets. And the dropout layers didn't stop the third model from overfitting anyway.
sns.lineplot(x=range(1, 101), y=history_1["loss"], label="loss")
sns.lineplot(x=range(1, 101), y=history_1["val_loss"], label="val_loss")
plt.xlabel("epoch")
plt.title("Model 1 loss metrics during training")
plt.show()
Saving the final model
First I need to create the final model, training model_1's architecture on the full dataset. Then I'll save the model to disk with its save function and save the data transformer using joblib so I can use it in the API.
import joblib
_, final_model, final_transformer = run_pipeline(
loans_1, onehot_cols, ordinal_cols, batch_size=128, validate=False
)
final_model.save("loan_risk_model")
joblib.dump(final_transformer, "data_transformer.joblib")
Epoch 1/100
8674/8674 - 17s - loss: 0.0268
Epoch 2/100
8674/8674 - 17s - loss: 0.0234
Epoch 3/100
8674/8674 - 17s - loss: 0.0233
Epoch 4/100
8674/8674 - 17s - loss: 0.0233
Epoch 5/100
8674/8674 - 17s - loss: 0.0232
Epoch 6/100
8674/8674 - 17s - loss: 0.0232
Epoch 7/100
8674/8674 - 17s - loss: 0.0232
Epoch 8/100
8674/8674 - 17s - loss: 0.0232
Epoch 9/100
8674/8674 - 17s - loss: 0.0232
Epoch 10/100
8674/8674 - 22s - loss: 0.0231
Epoch 11/100
8674/8674 - 17s - loss: 0.0232
Epoch 12/100
8674/8674 - 17s - loss: 0.0231
Epoch 13/100
8674/8674 - 17s - loss: 0.0231
Epoch 14/100
8674/8674 - 17s - loss: 0.0231
Epoch 15/100
8674/8674 - 17s - loss: 0.0231
Epoch 16/100
8674/8674 - 17s - loss: 0.0231
Epoch 17/100
8674/8674 - 17s - loss: 0.0232
Epoch 18/100
8674/8674 - 19s - loss: 0.0231
Epoch 19/100
8674/8674 - 18s - loss: 0.0231
Epoch 20/100
8674/8674 - 17s - loss: 0.0231
Epoch 21/100
8674/8674 - 16s - loss: 0.0231
Epoch 22/100
8674/8674 - 17s - loss: 0.0231
Epoch 23/100
8674/8674 - 17s - loss: 0.0231
Epoch 24/100
8674/8674 - 17s - loss: 0.0231
Epoch 25/100
8674/8674 - 16s - loss: 0.0231
Epoch 26/100
8674/8674 - 17s - loss: 0.0231
Epoch 27/100
8674/8674 - 18s - loss: 0.0231
Epoch 28/100
8674/8674 - 18s - loss: 0.0231
Epoch 29/100
8674/8674 - 18s - loss: 0.0231
Epoch 30/100
8674/8674 - 17s - loss: 0.0231
Epoch 31/100
8674/8674 - 17s - loss: 0.0231
Epoch 32/100
8674/8674 - 17s - loss: 0.0231
Epoch 33/100
8674/8674 - 17s - loss: 0.0231
Epoch 34/100
8674/8674 - 17s - loss: 0.0231
Epoch 35/100
8674/8674 - 17s - loss: 0.0231
Epoch 36/100
8674/8674 - 17s - loss: 0.0231
Epoch 37/100
8674/8674 - 19s - loss: 0.0231
Epoch 38/100
8674/8674 - 17s - loss: 0.0231
Epoch 39/100
8674/8674 - 17s - loss: 0.0231
Epoch 40/100
8674/8674 - 17s - loss: 0.0231
Epoch 41/100
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Epoch 42/100
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Epoch 43/100
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Epoch 44/100
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Epoch 45/100
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Epoch 46/100
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Epoch 47/100
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Epoch 48/100
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Epoch 49/100
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Epoch 50/100
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Epoch 51/100
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Epoch 52/100
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Epoch 53/100
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Epoch 54/100
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Epoch 55/100
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Epoch 56/100
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Epoch 57/100
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Epoch 58/100
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Epoch 59/100
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Epoch 60/100
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Epoch 61/100
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Epoch 62/100
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Epoch 63/100
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Epoch 64/100
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Epoch 65/100
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Epoch 66/100
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Epoch 67/100
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Epoch 68/100
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Epoch 69/100
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Epoch 70/100
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Epoch 71/100
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Epoch 72/100
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Epoch 73/100
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Epoch 74/100
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Epoch 75/100
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Epoch 76/100
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Epoch 77/100
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Epoch 78/100
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Epoch 79/100
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Epoch 80/100
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Epoch 81/100
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Epoch 82/100
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Epoch 83/100
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Epoch 84/100
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Epoch 85/100
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Epoch 86/100
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Epoch 87/100
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Epoch 88/100
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Epoch 89/100
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Epoch 90/100
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Epoch 91/100
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Epoch 92/100
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Epoch 93/100
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Epoch 94/100
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Epoch 95/100
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Epoch 96/100
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Epoch 97/100
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Epoch 98/100
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Epoch 99/100
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Epoch 100/100
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['data_transformer.joblib']
Building the API
I first tried building this API and its demonstrational front end on Glitch, which, officially, only supports Node.js back ends, but unofficially you can get a Python server running there (which I've done before using Flask). When I was almost finished, though, I tried importing TensorFlow to load my model, and it was then that I discovered that unlike Node.js dependencies, Python dependencies get installed to your project's disk space on Glitch, and not even their pro plan provides enough space to contain the entire TensorFlow library. Which totally makes sense—I certainly wasn't using the platform as intended.
Then I discovered PythonAnywhere! They have plenty of common Python libraries already installed out-of-the-box, including TensorFlow, so I got everything working perfectly there.
So head on over if you'd like to check it out; the front end includes a form where you can fill in all the parameters for the API request, and there are a couple of buttons that let you fill the form with typical examples from the dataset (since there are a lot of fields to fill in). Or you can send a GET request to https://tywmick.pythonanywhere.com/api/predict if you really want to include every parameter in your query string. In either case, you're also more than welcome to take a look at its source on GitHub.
One of the best/worst things about machine learning is that your models always have room for improvement. I mentioned a couple ideas along the way above for how I could improve the model in the future, but what's the first thing you would tweak? I'd love to hear in the discussion below.
# Exports for "Can I Grade Loans Better than LendingClub?"
expected.rename("expected_return", inplace=True)
loans_for_eval = loans_1.join([loans_raw[["issue_d", "grade", "sub_grade"]], expected])
joblib.dump(loans_for_eval, "loans_for_eval.joblib")
# Exports for "Improving Loan Risk Prediction With Natural Language Processing"
loans_for_nlp = loans_1.join(loans_raw[["issue_d", "title", "desc"]])
joblib.dump(loans_for_nlp, "loans_for_nlp.joblib")
['loans_for_nlp.joblib']
Top comments (2)
Will this neural network be available to ordinary users or only to professionals?
I'd be interested in using it to calculate by myself all the risks for the mortgage I want to take this year. But I don't know much about finances, and I don't like all these calculations, so I want the program to be easy to understand for non-professionals.
I was recommended to go to Mortgage Advisors to get advice and help with getting a mortgage loan. I contacted Mortgage Advisor Derby and was given mortgage counseling. I also received help collecting the paperwork for the mortgage and selecting a bank and insurance company.
Mick it looks very promising, we should think about implementing something similar for our brand: unitedfinances.com where we deliver many types of online loans: cash loans, personal loans and payday loans.