Introduction
Column
Get to know the data
The data contains 2 spreadsheets. First spreadsheet contains information about the borrowers’ information such as account nummber, first name, last name, zip code, total mortgage balances, total home equity balances, number of trades past due and number of home equity trades. Second spreadsheet contains information about the loans such as account number, FICO score original and current, origination date, maturity date, commitments, outstandings, day past due and APR. Certain information have been excluded because they have too many missing data, or it had zero values such as annual income, collateral value and payment amount.
Account_Number FICO_Score_Original FICO_Score_Current Commitments
1 5000 677 0 0
2 5000 677 0 0
3 5000 677 0 0
4 5000 677 0 0
5 5000 677 0 0
6 5000 677 0 0
Outstandings LTV_Original LTV_Current Day_Past_Due Origination_DTI APR
1 0 0.816 0.137 0 0.327 0.0171
2 0 0.816 0.137 0 0.327 0.0176
3 0 0.816 0.136 0 0.327 0.0181
4 0 0.816 0.141 0 0.327 0.0178
5 0 0.816 0.140 0 0.327 0.0167
6 0 0.816 0.140 0 0.327 0.0176 Total_Mortgage_Balances Total_HE_Balances
1 0 0
2 0 133829
3 0 81498
4 18691 23091
5 0 13107
6 121705 2093Column
Motivation
As a lender, Peace Bank offers clients access to money in the form of loans or lines of credit with the understanding that some customers may fail to repay it. In order to maintain a profitable business, this expected loss must be factored into the credit decision-making process by pricing the risk into the interest rate charged to each client.
Delinquency rate is a pivotal credit risk metric used to assess the quality and health of Peace Bank’s credit portfolios. As an example, a customer who has paid all prior months’ loan bills on time is considered current on their obligations. However, once that customer misses a payment for the first time they become delinquent. As the portion of the portfolio considered delinquent grows, investors and managers quickly take notice.
In this project, we are tasked with predicting which customers from this month’s pool of “current” customers will become delinquent next month. Remove Column {.tabset data-width=400} this line and the corresponding tags in your rmarkdown file if you don’t want to have several tages here!
Data Cleaning
Column
Cleaning the dataset
Before we can start exploring the data, we first need to clean up the dataset, because there is a lot of empty columns that we need to remove and also some missing values.
Annual income, Collateral value, and Payment amount have zeroes values, so we didn’t take them into consideration.
Column
Credit Score Charting
First thing first, because it’s such a huge dataset, we want to clean the data so that we can better predict customers’ delinquincy.
Step 1: Using experian credit score chart, let’s have 1 for credit score higher than 800, 2 for credit score between 799 and 740, 3 for credit score between 739 and 670, 4 for credit score between 669 and 580, and 5 for credit score below 579.
Merging the two dataset
Step 2: Merge both loans and borrowers so that we only have one data set that we call df_all. The merge has been done by account number because that’s what they all have in common.
Removing accounts that have already been paid off
Step 3: Let’s use a for loop to remove the loans that have already been paid off. We don’t need to use logistic regression in those cases.
Indexing the accounts
Step 4: Most customers have 6 account numbers except for a few, and for that reason, we want to use the first account number that we will call 1 to judge whether or not the customer will pay for the second account number which we will call 2. So in summary, that’s why we have indexes i+1 and i in order to check whether or not the customer will be delinquent.
Results
Column
Logistic regression
Let’s apply logistic regression.
In statistics, the logistic model (or logit model) is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick.
Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression is estimating the parameters of a logistic model (a form of binary regression).
Mathematically, a binary logistic model has a dependent variable with two possible values, such as pass/fail which is represented by an indicator variable, where the two values are labeled “0” and “1”.
Logistic regression is used in various fields, including machine learning, most medical fields, and social sciences. For example, the Trauma and Injury Severity Score (TRISS), which is widely used to predict mortality in injured patients. Many other medical scales used to assess severity of a patient have been developed using logistic regression. Logistic regression may be used to predict the risk of developing a given disease (e.g. diabetes; coronary heart disease), based on observed characteristics of the patient (age, sex, body mass index, results of various blood tests, etc. Another example might be to predict whether a Nepalese voter will vote Nepali Congress or Communist Party of Nepal or Any Other Party, based on age, income, sex, race, state of residence, votes in previous elections, etc. The technique can also be used in engineering, especially for predicting the probability of failure of a given process, system or product.
Column
Data splitting
0 1
13932 8215 (Intercept) FICO_Score_Original2 FICO_Score_Original3
0.4029991 0.9927601 0.9865327
FICO_Score_Original4 FICO_Score_Original5 FICO_Score_Current2
0.9941188 1.1165182 1.0786934
FICO_Score_Current3 FICO_Score_Current4 FICO_Score_Current5
1.1484300 1.1147445 1.1771947
Commitments Outstandings LTV_Original
1.0000375 0.9999669 0.8723688
LTV_Current Day_Past_Due Origination_DTI
1.4936870 0.9997573 0.9953517
APR Total_Mortgage_Balances Total_HE_Balances
1.5568832 1.0000000 1.0000000 Results
0=good and 1=bad
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32 0.6290698 0.37093023
33 0.6335878 0.36641216
34 0.6240909 0.37590911
35 0.6314754 0.36852462
49 0.6569147 0.34308529
50 0.6542591 0.34574086
51 0.6601474 0.33985260
52 0.6638820 0.33611795
53 0.6655992 0.33440078
73 0.6373028 0.36269720
74 0.6248104 0.37518958
75 0.6353479 0.36465212
76 0.6339096 0.36609037
77 0.6461087 0.35389129
79 0.6379592 0.36204082
80 0.6548755 0.34512449
81 0.6555567 0.34444327
82 0.6543723 0.34562765
83 0.6398448 0.36015517
115 0.6643897 0.33561028
116 0.6440308 0.35596922
117 0.6535788 0.34642115
118 0.6581402 0.34185982
119 0.6698586 0.33014142
121 0.6124900 0.38751000
122 0.5950634 0.40493660
123 0.5962104 0.40378964
124 0.6015447 0.39845527
125 0.6453840 0.35461602
151 0.6787367 0.32126327
152 0.6763608 0.32363922
153 0.6768699 0.32313007
154 0.6823676 0.31763236
155 0.6787267 0.32127329
163 0.7585310 0.24146898
164 0.7476708 0.25232916
165 0.8092623 0.19073767
166 0.8368142 0.16318578
167 0.8392716 0.16072842
169 0.6360357 0.36396430
170 0.6182465 0.38175351
171 0.6531414 0.34685858
172 0.6628543 0.33714569
173 0.6777319 0.32226806
187 0.6580863 0.34191366
188 0.6577512 0.34224878
189 0.6521595 0.34784046
190 0.6523249 0.34767514
191 0.6496439 0.35035612
211 0.6355591 0.36444092
212 0.6381084 0.36189161
213 0.6318516 0.36814837
214 0.6261711 0.37382885
215 0.6504024 0.34959762
223 0.6302595 0.36974046
224 0.6335965 0.36640351
225 0.6441624 0.35583764
226 0.6468038 0.35319618
227 0.6639940 0.33600602
229 0.6663974 0.33360260
230 0.6692431 0.33075692
231 0.6553489 0.34465111
232 0.6533632 0.34663683
233 0.6866343 0.31336566
247 0.6680767 0.33192328
248 0.6836111 0.31638894
249 0.6865258 0.31347423
250 0.6843667 0.31563330
251 0.6864039 0.31359609
283 0.6659914 0.33400863
284 0.6650325 0.33496748
285 0.6305540 0.36944603
286 0.6271291 0.37287094
287 0.6278508 0.37214924
289 0.6509921 0.34900788
290 0.6456135 0.35438648
291 0.6478187 0.35218133
292 0.6482644 0.35173561
293 0.6608952 0.33910476
301 0.2002844 0.79971559
302 0.2095977 0.79040228
303 0.1874526 0.81254736
304 0.1908597 0.80914028
305 0.1662043 0.83379571
313 0.6239244 0.37607559
314 0.6427036 0.35729638
315 0.6319745 0.36802551
316 0.6041459 0.39585411
317 0.6326417 0.36735828
331 0.6254828 0.37451724
332 0.6400964 0.35990356
333 0.6240427 0.37595732
334 0.6323288 0.36767117
335 0.6084090 0.39159099
337 0.6250323 0.37496771
338 0.6249193 0.37508070
339 0.6492605 0.35073946
340 0.6614252 0.33857477
341 0.6352653 0.36473466
361 0.6378106 0.36218938
362 0.6427482 0.35725182
363 0.6407116 0.35928844
364 0.6436246 0.35637542
365 0.6408659 0.35913408
397 0.6560826 0.34391745
398 0.6477592 0.35224085
399 0.6458117 0.35418835
400 0.6476963 0.35230368
401 0.6494370 0.35056302
457 0.5302734 0.46972661
458 0.5588924 0.44110756
459 0.5903605 0.40963950
460 0.5705078 0.42949225
461 0.6141140 0.38588601
463 0.6203155 0.37968454
464 0.6428381 0.35716188
465 0.6175946 0.38240541
466 0.6388249 0.36117507
467 0.6385520 0.36144797
529 0.6422691 0.35773089
530 0.6402454 0.35975456
531 0.6380340 0.36196595
532 0.6504834 0.34951661
533 0.6487676 0.35123245
553 0.6863775 0.31362248
554 0.6864369 0.31356308
555 0.6884245 0.31157555
556 0.6885602 0.31143983
557 0.6887020 0.31129795
589 0.6337047 0.36629526
590 0.6004382 0.39956177
591 0.6413774 0.35862259
592 0.6644956 0.33550442
593 0.6170793 0.38292068
643 0.6460526 0.35394742
644 0.6454947 0.35450526
645 0.5492577 0.45074232
646 0.5891240 0.41087598
647 0.5818415 0.41815851
673 0.6239061 0.37609391
674 0.6240760 0.37592401
675 0.5529690 0.44703104
676 0.5541804 0.44581958
677 0.5680406 0.43195945
721 0.6341012 0.36589884
722 0.6326312 0.36736884
723 0.6309272 0.36907276
724 0.6438834 0.35611661
725 0.6386125 0.36138754
733 0.6454909 0.35450905
734 0.6411634 0.35883656
735 0.6470606 0.35293944
736 0.6421351 0.35786488
737 0.6496959 0.35030412
805 0.6266349 0.37336511
806 0.6330716 0.36692843
807 0.6528607 0.34713929
808 0.6553938 0.34460623
809 0.6538807 0.34611929
823 0.6684551 0.33154490
824 0.6684427 0.33155734
825 0.6697660 0.33023396
826 0.6875450 0.31245502
827 0.6869588 0.31304123
889 0.6448049 0.35519509
890 0.6465252 0.35347476
891 0.6424288 0.35757119
892 0.6777624 0.32223759
893 0.6382426 0.36175741
907 0.6375568 0.36244323
908 0.6382145 0.36178549
909 0.6451015 0.35489854
910 0.6702353 0.32976466
911 0.6596603 0.34033973
925 0.1233175 0.87668247
926 0.1598161 0.84018395
927 0.3876888 0.61231124
928 0.3404838 0.65951619
929 0.6993887 0.30061134
937 0.6045803 0.39541968
938 0.6080259 0.39197415
939 0.6026787 0.39732134
940 0.6028976 0.39710237
941 0.6058079 0.39419214
991 0.6449797 0.35502031
992 0.6312077 0.36879232
993 0.5501211 0.44987889
994 0.5419526 0.45804736
995 0.5611334 0.43886664
1015 0.6186310 0.38136898
1016 0.6126492 0.38735082
1017 0.5831024 0.41689763
1018 0.6188624 0.38113763
1019 0.5860416 0.41395835
1033 0.5924770 0.40752295
1034 0.5267817 0.47321826
1035 0.5707801 0.42921991
1036 0.5673055 0.43269450
1037 0.5898005 0.41019946
1051 0.6245985 0.37540145
1052 0.6091093 0.39089070
1053 0.6497423 0.35025769
1054 0.6519641 0.34803593
1055 0.6406343 0.35936571
1075 0.6670727 0.33292732
1076 0.6662662 0.33373375
1077 0.6646565 0.33534353
1078 0.6806807 0.31931933
1079 0.6835453 0.31645467
1099 0.6881887 0.31181128
1100 0.6716902 0.32830984
1101 0.6586664 0.34133357
1102 0.6587898 0.34121019
1103 0.6588859 0.34111408
1135 0.6381340 0.36186598
1136 0.6697040 0.33029603
1137 0.6675892 0.33241084
1138 0.6494171 0.35058286
1139 0.6504994 0.34950061
1141 0.6541256 0.34587440
1142 0.6496143 0.35038573
1143 0.6503977 0.34960231
1144 0.6718290 0.32817104
1145 0.5383194 0.46168056
1153 0.6378143 0.36218566
1154 0.6356833 0.36431669
1155 0.6416405 0.35835946
1156 0.6353617 0.36463831
1157 0.6384717 0.36152833
1183 0.7629654 0.23703459
1184 0.7567668 0.24323318
1185 0.7725447 0.22745530
1186 0.7679104 0.23208963
1187 0.7792268 0.22077318
1201 0.6544762 0.34552377
1202 0.6593122 0.34068781
1203 0.6446291 0.35537095
1204 0.6648447 0.33515532
1205 0.6389623 0.36103775
1213 0.6354461 0.36455394
1214 0.5970862 0.40291377
1215 0.6347139 0.36528606
1216 0.6456379 0.35436208
1217 0.6549037 0.34509634
1231 0.6465114 0.35348862
1232 0.6410140 0.35898598
1233 0.6339650 0.36603499
1234 0.6124825 0.38751746
1235 0.6293004 0.37069961
1261 0.6498028 0.35019716
1262 0.6463605 0.35363947
1263 0.6494068 0.35059316
1264 0.6497113 0.35028867
1265 0.6484648 0.35153521
1309 0.6657962 0.33420376
1310 0.6448975 0.35510252
1311 0.6083618 0.39163819
1312 0.6141710 0.38582902
1313 0.6437429 0.35625706
1357 0.6645680 0.33543198
1358 0.6389629 0.36103709
1359 0.6460590 0.35394098
1360 0.6763920 0.32360804
1361 0.6674843 0.33251575
1369 0.6266743 0.37332574
1370 0.6295778 0.37042225
1371 0.5989669 0.40103314
1372 0.5985256 0.40147438
1373 0.6043885 0.39561152
1399 0.6785331 0.32146689
1400 0.6785636 0.32143640
1401 0.6549063 0.34509369
1402 0.6700186 0.32998143
1403 0.6326767 0.36732327
1411 0.6681748 0.33182517
1412 0.6487443 0.35125572
1413 0.5806247 0.41937534
1414 0.5888756 0.41112444
1415 0.5946623 0.40533772
1429 0.6496955 0.35030448
1430 0.6458429 0.35415713
1431 0.6444690 0.35553101
1432 0.6619300 0.33807005
1433 0.6631654 0.33683465
1507 0.6493246 0.35067544
1508 0.6434325 0.35656752
1509 0.6201836 0.37981643
1510 0.6129027 0.38709735
1511 0.6133729 0.38662707
1523 0.6319180 0.36808200
1524 0.5841173 0.41588273
1525 0.6103733 0.38962665
1526 0.6028687 0.39713133
1527 0.6326610 0.36733899
1541 0.6530308 0.34696916
1542 0.6516234 0.34837659
1543 0.6458181 0.35418195
1544 0.6579762 0.34202382
1545 0.6499787 0.35002132
1553 0.5808392 0.41916082
1554 0.6250739 0.37492612
1555 0.6243773 0.37562271
1556 0.6038902 0.39610977
1557 0.6440589 0.35594113
1559 0.6466367 0.35336330
1560 0.6464856 0.35351440
1561 0.6502893 0.34971068
1562 0.6417463 0.35825369
1563 0.6396713 0.36032874
1565 0.6092255 0.39077455
1566 0.6365598 0.36344016
1567 0.6229044 0.37709561
1568 0.6136477 0.38635232
1569 0.6297913 0.37020875
1577 0.9067383 0.09326173
1578 0.9025703 0.09742965
1579 0.8857052 0.11429480
1580 0.8922763 0.10772372
1581 0.8911394 0.10886064
1595 0.6222503 0.37774970
1596 0.6495172 0.35048282
1597 0.5842268 0.41577318
1598 0.5771637 0.42283635
1599 0.5829022 0.41709779
1649 0.6248425 0.37515754
1650 0.6256900 0.37431001
1651 0.6438627 0.35613731
1652 0.6353035 0.36469655
1653 0.6383228 0.36167717
1655 0.6309329 0.36906711
1656 0.6324122 0.36758778
1657 0.6194062 0.38059384
1658 0.6367745 0.36322554
1659 0.6397319 0.36026806
1661 0.6570288 0.34297120
1662 0.6230275 0.37697250
1663 0.5335077 0.46649233
1664 0.5752307 0.42476931
1665 0.5744579 0.42554211
1667 0.6858115 0.31418852
1668 0.6667997 0.33320029
1669 0.6710825 0.32891751
1670 0.6879672 0.31203283
1671 0.6703385 0.32966151
1673 0.6312084 0.36879160
1674 0.6379389 0.36206108
1675 0.5656051 0.43439491
1676 0.5820787 0.41792134
1677 0.5876536 0.41234639
1709 0.6398277 0.36017231
1710 0.6382344 0.36176560
1711 0.6635606 0.33643936
1712 0.6592950 0.34070497
1713 0.6471488 0.35285124
1715 0.6301453 0.36985468
1716 0.6086564 0.39134363
1717 0.6155818 0.38441822
1718 0.5988081 0.40119186
1719 0.5943603 0.40563973
1739 0.6261495 0.37385048
1740 0.6118331 0.38816694
1741 0.6550251 0.34497489
1742 0.6519951 0.34800488
1743 0.6449881 0.35501195
1769 0.6295330 0.37046704
1770 0.6449362 0.35506383
1771 0.6605435 0.33945649
1772 0.6723338 0.32766619
1773 0.6607107 0.33928934
1775 0.6321511 0.36784893
1776 0.6338578 0.36614224
1777 0.6355876 0.36441237
1778 0.6441398 0.35586022
1779 0.6603218 0.33967824
1787 0.6336349 0.36636506
1788 0.6278765 0.37212346
1789 0.6486804 0.35131960
1790 0.6510184 0.34898156
1791 0.6610882 0.33891179
1817 0.6561407 0.34385926
1818 0.6562361 0.34376392
1819 0.6767099 0.32329009
1820 0.6656381 0.33436189
1821 0.6548863 0.34511375
1835 0.6621563 0.33784366
1836 0.6544049 0.34559508
1837 0.6841230 0.31587700
1838 0.6870362 0.31296379
1839 0.6667287 0.33327133
1877 0.5768578 0.42314218
1878 0.5936760 0.40632397
1879 0.5478308 0.45216919
1880 0.5526779 0.44732208
1881 0.5391451 0.46085488
1883 0.5985327 0.40146727
1884 0.6285266 0.37147344
1885 0.4788350 0.52116502
1886 0.5317109 0.46828909
1887 0.5215479 0.47845214
1901 0.5992678 0.40073222
1902 0.6251860 0.37481398
1903 0.6274752 0.37252476
1904 0.6143583 0.38564171
1905 0.6171040 0.38289599
1907 0.6440810 0.35591898
1908 0.6429743 0.35702567
1909 0.6411338 0.35886616
1910 0.6426967 0.35730330
1911 0.6550895 0.34491049
1913 0.6139748 0.38602518
1914 0.5894401 0.41055995
1915 0.5880542 0.41194584
1916 0.6181244 0.38187559
1917 0.6212038 0.37879620
1919 0.6461265 0.35387351
1920 0.6474915 0.35250855
1921 0.6442306 0.35576940
1922 0.6432658 0.35673418
1923 0.6531099 0.34689011
1943 0.6373383 0.36266167
1944 0.6495233 0.35047675
1945 0.6638948 0.33610518
1946 0.6366587 0.36334133
1947 0.6424706 0.35752940
1973 0.6338361 0.36616386
1974 0.6326481 0.36735194
1975 0.6391630 0.36083697
1976 0.6245228 0.37547723
1977 0.6348580 0.36514199
1979 0.6344989 0.36550108
1980 0.6605430 0.33945697
1981 0.6502397 0.34976025
1982 0.6434641 0.35653595
1983 0.6437346 0.35626545
1997 0.6035100 0.39648997
1998 0.5902299 0.40977005
1999 0.5985377 0.40146228
2000 0.6019353 0.39806466
2001 0.5696317 0.43036826
2009 0.6328095 0.36719049
2010 0.6300276 0.36997240
2011 0.5762495 0.42375051
2012 0.5783423 0.42165772
2013 0.6026973 0.39730272
2027 0.6078392 0.39216082
2028 0.6339534 0.36604658
2029 0.6103220 0.38967802
2030 0.6180885 0.38191155
2031 0.6360598 0.36394016
2039 0.6710788 0.32892118
2040 0.6761317 0.32386831
2041 0.6854916 0.31450842
2042 0.6851196 0.31488039
2043 0.6811140 0.31888601
2045 0.6535947 0.34640529
2046 0.6436667 0.35633334
2047 0.6666630 0.33333701
2048 0.6623964 0.33760358
2049 0.6575294 0.34247056
2051 0.6445892 0.35541084
2052 0.6442859 0.35571407
2053 0.6341060 0.36589395
2054 0.6464844 0.35351560
2055 0.6321682 0.36783178
2057 0.6355245 0.36447547
2058 0.6210722 0.37892776
2059 0.6459742 0.35402577
2060 0.6621644 0.33783556
2061 0.6423192 0.35768081
2075 0.6505271 0.34947289
2076 0.6312351 0.36876489
2077 0.6266826 0.37331741
2078 0.6379755 0.36202453
2079 0.6286673 0.37133270
2087 0.4095905 0.59040945
2088 0.3490248 0.65097523
2089 0.3730798 0.62692020
2090 0.3608742 0.63912577
2091 0.3102924 0.68970763
2093 0.6477175 0.35228248
2094 0.6486912 0.35130877
2095 0.6449260 0.35507399
2096 0.6525810 0.34741897
2097 0.6543717 0.34562829
2105 0.6181764 0.38182357
2106 0.6285524 0.37144763
2107 0.6401421 0.35985786
2108 0.6407577 0.35924233
2109 0.6545098 0.34549024
2147 0.6379242 0.36207580
2148 0.6226274 0.37737263
2149 0.6432056 0.35679444
2150 0.6461335 0.35386654
2151 0.6412083 0.35879170
2153 0.5387555 0.46124451
2154 0.5494756 0.45052438
2155 0.5426408 0.45735919
2156 0.5377058 0.46229421
2157 0.5474151 0.45258486
2177 0.6230565 0.37694352
2178 0.6144663 0.38553371
2179 0.5574150 0.44258500
2180 0.5478096 0.45219039
2181 0.5585239 0.44147611
2195 0.6705387 0.32946127
2196 0.6759627 0.32403728
2197 0.6625734 0.33742661
2198 0.6745448 0.32545525
2199 0.6771416 0.32285838
2243 0.6115960 0.38840395
2244 0.6206998 0.37930022
2245 0.6600916 0.33990838
2246 0.6353952 0.36460485
2247 0.6408209 0.35917905
2261 0.6399499 0.36005012
2262 0.6511004 0.34889961
2263 0.6522183 0.34778175
2264 0.6646415 0.33535852
2265 0.6498412 0.35015884
2309 0.6145007 0.38549930
2310 0.6131461 0.38685391
2311 0.5953751 0.40462486
2312 0.6080201 0.39197989
2313 0.5962863 0.40371367
2321 0.6297148 0.37028520
2322 0.6734306 0.32656945
2323 0.6876992 0.31230079
2324 0.6863618 0.31363822
2325 0.6656335 0.33436646
2387 0.6461329 0.35386710
2388 0.6427064 0.35729361
2389 0.6014006 0.39859935
2390 0.6007063 0.39929368
2391 0.6429319 0.35706809
2399 0.6320516 0.36794837
2400 0.6408831 0.35911693
2401 0.6273386 0.37266141
2402 0.6353051 0.36469493
2403 0.6228288 0.37717122
2411 0.6772640 0.32273596
2412 0.6791679 0.32083215
2413 0.6702673 0.32973268
2414 0.6709899 0.32901007
2415 0.6720278 0.32797222
2441 0.6456341 0.35436590
2442 0.6495492 0.35045076
2443 0.5864384 0.41356160
2444 0.5826242 0.41737584
2445 0.6087904 0.39120964
2489 0.6427071 0.35729285
2490 0.6264927 0.37350726
2491 0.5580334 0.44196664
2492 0.5597370 0.44026297
2493 0.6282844 0.37171562
2507 0.5881652 0.41183481
2508 0.5997321 0.40026795
2509 0.5898456 0.41015441
2510 0.6262975 0.37370247
2511 0.6238782 0.37612182
2513 0.6475634 0.35243655
2514 0.6512500 0.34874997
2515 0.6535282 0.34647178
2516 0.6455234 0.35447656
2517 0.6546481 0.34535190
2681 0.5778274 0.42217264
2682 0.6135499 0.38645014
2683 0.5605732 0.43942676
2684 0.5811303 0.41886974
2685 0.5750885 0.42491152
2687 0.6118030 0.38819702
2688 0.6291201 0.37087987
2689 0.6185692 0.38143076
2690 0.6303834 0.36961660
2691 0.6359999 0.36400013
2693 0.7437281 0.25627188
2694 0.7447804 0.25521960
2695 0.7370471 0.26295286
2696 0.7185739 0.28142611
2697 0.7338744 0.26612560
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19082 0.5851402 0.41485977
19083 0.5649299 0.43507011
19084 0.6338248 0.36617520
19085 0.5976660 0.40233404
19093 0.4952758 0.50472424
19094 0.4323453 0.56765475
19095 0.4554485 0.54455147
19096 0.5344115 0.46558845
19097 0.4268630 0.57313696
19111 0.7096859 0.29031409
19112 0.6921342 0.30786584
19113 0.6955241 0.30447587
19114 0.6944374 0.30556260
19115 0.6933436 0.30665642
19135 0.6200719 0.37992813
19136 0.6046673 0.39533266
19137 0.6064273 0.39357266
19138 0.5667554 0.43324461
19139 0.6215364 0.37846362
19153 0.5826877 0.41731234
19154 0.5523663 0.44763375
19155 0.5969803 0.40301972
19156 0.5847078 0.41529224
19157 0.6002140 0.39978599
19183 0.6400802 0.35991984
19184 0.6472393 0.35276073
19185 0.6444104 0.35558965
19186 0.6375986 0.36240142
19187 0.6496573 0.35034267
19207 0.6510262 0.34897378
19208 0.5854150 0.41458496
19209 0.5901139 0.40988613
19210 0.5974844 0.40251555
19211 0.5982928 0.40170716
19213 0.6197905 0.38020950
19214 0.6236814 0.37631858
19215 0.6607294 0.33927060
19216 0.6339545 0.36604550
19217 0.6288412 0.37115879
19249 0.6371289 0.36287112
19250 0.6455853 0.35441466
19251 0.6451772 0.35482283
19252 0.6414979 0.35850211
19253 0.6459371 0.35406291
19267 0.4861587 0.51384126
19268 0.4675368 0.53246315
19269 0.5200207 0.47997933
19270 0.4688203 0.53117968
19271 0.4611109 0.53888909
19315 0.5846519 0.41534808
19316 0.5876634 0.41233658
19317 0.5462137 0.45378625
19318 0.5590254 0.44097456
19319 0.5379318 0.46206817
19345 0.6738838 0.32611624
19346 0.6892170 0.31078299
19347 0.6751521 0.32484788
19348 0.6745803 0.32541974
19349 0.6763179 0.32368207
19369 0.6756733 0.32432669
19370 0.6752231 0.32477694
19371 0.6787378 0.32126218
19372 0.6771863 0.32281375
19373 0.6774954 0.32250460
19375 0.5139835 0.48601653
19376 0.5254101 0.47458990
19377 0.4945896 0.50541044
19378 0.5631979 0.43680209
19379 0.3051908 0.69480920
19387 0.6579552 0.34204477
19388 0.6735262 0.32647385
19389 0.6706737 0.32932630
19390 0.6683304 0.33166960
19391 0.6592212 0.34077884
19393 0.6703013 0.32969867
19394 0.6562638 0.34373623
19395 0.6565656 0.34343445
19396 0.6639820 0.33601797
19397 0.6565920 0.34340798
19435 0.6708428 0.32915715
19436 0.6752641 0.32473593
19437 0.6867623 0.31323767
19438 0.6750844 0.32491556
19439 0.6710621 0.32893794
19453 0.5645293 0.43547073
19454 0.5529915 0.44700849
19455 0.4426378 0.55736217
19456 0.5543735 0.44562649
19457 0.5387157 0.46128431
19495 0.6700859 0.32991407
19496 0.6678788 0.33212121
19497 0.6683577 0.33164228
19498 0.6694011 0.33059888
19499 0.6866188 0.31338121
19561 0.6948905 0.30510949
19562 0.6955576 0.30444243
19563 0.6955938 0.30440621
19564 0.6960871 0.30391294
19565 0.6969623 0.30303773
19609 0.6048320 0.39516798
19610 0.5708749 0.42912506
19611 0.6102162 0.38978379
19612 0.5902689 0.40973107
19613 0.5345661 0.46543389
19741 0.6760198 0.32398023
19742 0.6753163 0.32468370
19743 0.6744750 0.32552502
19744 0.6722932 0.32770684
19745 0.6777213 0.32227868
19771 0.6298308 0.37016915
19772 0.6099011 0.39009886
19773 0.6105514 0.38944856
19774 0.6153788 0.38462116
19775 0.6102002 0.38979978
19783 0.6955672 0.30443279
19784 0.7135145 0.28648553
19785 0.7080760 0.29192400
19786 0.7050245 0.29497548
19787 0.6914755 0.30852449
19807 0.4154428 0.58455724
19808 0.5591346 0.44086543
19809 0.5674040 0.43259599
19810 0.5271239 0.47287611
19811 0.5182223 0.48177767
19813 0.6766736 0.32332641
19814 0.6703327 0.32966729
19815 0.6764813 0.32351872
19816 0.6722238 0.32777619
19817 0.6779390 0.32206096
19867 0.6731258 0.32687417
19868 0.6732064 0.32679356
19869 0.6699654 0.33003459
19870 0.6726214 0.32737861
19871 0.6678016 0.33219840
19873 0.6173542 0.38264583
19874 0.6135854 0.38641456
19875 0.6153042 0.38469575
19876 0.6129329 0.38706711
19877 0.6423482 0.35765179
19879 0.6568122 0.34318778
19880 0.6501224 0.34987760
19881 0.6581226 0.34187737
19882 0.6347485 0.36525151
19883 0.6592140 0.34078598
19957 0.6245058 0.37549422
19958 0.6308858 0.36911424
19959 0.6340854 0.36591459
19960 0.6111065 0.38889354
19961 0.6411699 0.35883005
19969 0.6895954 0.31040459
19970 0.6723733 0.32762668
19971 0.6724419 0.32755811
19972 0.6793779 0.32062205
19973 0.6684156 0.33158444
19987 0.6555513 0.34444873
19988 0.6679014 0.33209856
19989 0.6523107 0.34768928
19990 0.6735851 0.32641495
19991 0.6706037 0.32939631
19999 0.6625737 0.33742631
20000 0.6675774 0.33242256
20001 0.6748446 0.32515540
20002 0.6766137 0.32338634
20003 0.6297262 0.37027378
20017 0.6783574 0.32164258
20018 0.6807504 0.31924955
20019 0.6814426 0.31855743
20020 0.6837745 0.31622549
20021 0.6822473 0.31775270
20059 0.5732882 0.42671183
20060 0.6030167 0.39698325
20061 0.5959982 0.40400179
20062 0.5339033 0.46609669
20063 0.5346341 0.46536592
20107 0.5607689 0.43923106
20108 0.5481846 0.45181536
20109 0.5478601 0.45213992
20110 0.5733715 0.42662845
20111 0.5182154 0.48178458
20149 0.6722216 0.32777837
20150 0.6519314 0.34806864
20151 0.6607685 0.33923155
20152 0.6333178 0.36668219
20153 0.6333662 0.36663376
20239 0.6541229 0.34587714
20240 0.6667316 0.33326840
20241 0.6670038 0.33299621
20242 0.6673195 0.33268048
20243 0.6679222 0.33207779
20263 0.6912965 0.30870351
20264 0.6916849 0.30831508
20265 0.6931008 0.30689918
20266 0.6891577 0.31084233
20267 0.6922660 0.30773404
20269 0.6054986 0.39450145
20270 0.6338395 0.36616049
20271 0.6161150 0.38388496
20272 0.6330098 0.36699023
20273 0.6144325 0.38556747
20281 0.6742970 0.32570304
20282 0.6496652 0.35033483
20283 0.6474554 0.35254460
20284 0.6829787 0.31702125
20285 0.6601291 0.33987089
20335 0.6468233 0.35317673
20336 0.6455160 0.35448401
20337 0.6469590 0.35304097
20338 0.6466669 0.35333314
20339 0.6477174 0.35228258
20353 0.6850538 0.31494624
20354 0.6864093 0.31359065
20355 0.6854445 0.31455554
20356 0.7073373 0.29266270
20357 0.6664844 0.33351559
20365 0.6481913 0.35180866
20366 0.6544020 0.34559803
20367 0.6612852 0.33871484
20368 0.6326208 0.36737918
20369 0.6437131 0.35628694
20407 0.6363848 0.36361525
20408 0.6684314 0.33156858
20409 0.6466886 0.35331139
20410 0.6431926 0.35680735
20411 0.6505320 0.34946801
20425 0.6405586 0.35944143
20426 0.6480525 0.35194748
20427 0.6138100 0.38619000
20428 0.6373564 0.36264356
20429 0.6236783 0.37632175
20443 0.6139210 0.38607901
20444 0.6210199 0.37898009
20445 0.6429706 0.35702938
20446 0.6062059 0.39379407
20447 0.6224869 0.37751309
20449 0.7039947 0.29600531
20450 0.6896178 0.31038218
20451 0.6764738 0.32352625
20452 0.7067559 0.29324409
20453 0.7199562 0.28004381
20473 0.6266068 0.37339321
20474 0.6255154 0.37448459
20475 0.6403451 0.35965486
20476 0.6169025 0.38309753
20477 0.6597698 0.34023023
20491 0.6889872 0.31101279
20492 0.6801246 0.31987541
20493 0.6863509 0.31364908
20494 0.6806929 0.31930707
20495 0.6795588 0.32044124
20503 0.6328767 0.36712335
20504 0.5712773 0.42872274
20505 0.5853238 0.41467624
20506 0.5708664 0.42913357
20507 0.6096096 0.39039037
20533 0.6009738 0.39902624
20534 0.5933520 0.40664799
20535 0.5905455 0.40945451
20536 0.5989566 0.40104337
20537 0.5371535 0.46284651
20539 0.6578786 0.34212136
20540 0.6902148 0.30978524
20541 0.6677329 0.33226713
20542 0.6893502 0.31064977
20543 0.6716329 0.32836713
20575 0.6516196 0.34838044
20576 0.6577448 0.34225520
20577 0.6641092 0.33589076
20578 0.6536602 0.34633980
20579 0.6548222 0.34517782
20593 0.6788783 0.32112167
20594 0.6820385 0.31796148
20595 0.6709302 0.32906981
20596 0.6813159 0.31868413
20597 0.6756086 0.32439144
20707 0.4607371 0.53926291
20708 0.4612328 0.53876722
20709 0.4784525 0.52154751
20710 0.5086793 0.49132074
20711 0.4165760 0.58342404
20737 0.5134154 0.48658464
20738 0.5322767 0.46772331
20739 0.5308614 0.46913859
20740 0.4341700 0.56583003
20741 0.5709027 0.42909727
20767 0.6848922 0.31510780
20768 0.6653455 0.33465446
20769 0.6788174 0.32118259
20770 0.6752830 0.32471697
20771 0.6585541 0.34144589
20773 0.6527954 0.34720460
20774 0.6341986 0.36580138
20775 0.6521303 0.34786968
20776 0.6392516 0.36074838
20777 0.6481622 0.35183775
20779 0.6806507 0.31934928
20780 0.6782016 0.32179844
20781 0.6667237 0.33327628
20782 0.6784188 0.32158118
20783 0.6681593 0.33184069
20791 0.6421265 0.35787353
20792 0.6437922 0.35620779
20793 0.6231165 0.37688350
20794 0.6369857 0.36301431
20795 0.6316209 0.36837913
20803 0.5854798 0.41452018
20804 0.5315228 0.46847721
20805 0.5861172 0.41388275
20806 0.6174026 0.38259737
20807 0.5801464 0.41985360
20809 0.6913619 0.30863809
20810 0.7049166 0.29508338
20811 0.7045934 0.29540659
20812 0.7050249 0.29497508
20813 0.7021328 0.29786723
20815 0.6472967 0.35270329
20816 0.6596082 0.34039184
20817 0.6621653 0.33783474
20818 0.6679786 0.33202139
20819 0.6690789 0.33092108
20827 0.5842062 0.41579379
20828 0.5669029 0.43309707
20829 0.5684470 0.43155302
20830 0.5774859 0.42251406
20831 0.5661292 0.43387076
20875 0.6140339 0.38596608
20876 0.5828583 0.41714170
20877 0.5927005 0.40729946
20878 0.6021940 0.39780601
20879 0.6536036 0.34639644
20923 0.6410795 0.35892046
20924 0.6319695 0.36803050
20925 0.6366783 0.36332171
20926 0.6248184 0.37518158
20927 0.5477960 0.45220397
20959 0.6738270 0.32617301
20960 0.6741535 0.32584654
20961 0.6843372 0.31566278
20962 0.6775550 0.32244502
20963 0.6748788 0.32512118
20965 0.5849472 0.41505281
20966 0.5165693 0.48343069
20967 0.5394916 0.46050840
20968 0.5444285 0.45557148
20969 0.5596173 0.44038272
20983 0.6821827 0.31781730
20984 0.7025294 0.29747058
20985 0.7081418 0.29185824
20986 0.7014415 0.29855851
20987 0.6884097 0.31159035
20989 0.7069646 0.29303536
20990 0.6785662 0.32143380
20991 0.7192102 0.28078975
20992 0.7194388 0.28056118
20993 0.6894167 0.31058333
20995 0.6765248 0.32347515
20996 0.6748297 0.32517027
20997 0.6781373 0.32186271
20998 0.6754127 0.32458733
20999 0.5844664 0.41553356
21007 0.6820556 0.31794443
21008 0.7116009 0.28839913
21009 0.6998382 0.30016178
21010 0.6972715 0.30272852
21011 0.6997783 0.30022171
21121 0.6615623 0.33843773
21122 0.6409254 0.35907457
21123 0.6420920 0.35790801
21124 0.6182963 0.38170373
21125 0.6132963 0.38670373
21127 0.6439527 0.35604726
21128 0.6456136 0.35438641
21129 0.6500680 0.34993199
21130 0.6504626 0.34953745
21131 0.5399369 0.46006308
21145 0.6331875 0.36681255
21146 0.6353696 0.36463036
21147 0.6579380 0.34206199
21148 0.6446513 0.35534867
21149 0.6412018 0.35879821
21157 0.7154793 0.28452074
21158 0.7172669 0.28273310
21159 0.6984894 0.30151062
21160 0.7069055 0.29309450
21161 0.5959804 0.40401965
21163 0.7079226 0.29207738
21164 0.7078568 0.29214316
21165 0.7070094 0.29299059
21166 0.6898828 0.31011718
21167 0.6918223 0.30817770
21205 0.7049117 0.29508834
21206 0.7031731 0.29682689
21207 0.6895850 0.31041500
21208 0.6902038 0.30979617
21209 0.7058674 0.29413256
21277 0.6877608 0.31223920
21278 0.6953092 0.30469076
21279 0.6949994 0.30500055
21280 0.6801862 0.31981383
21281 0.6958857 0.30411426
21313 0.6135150 0.38648505
21314 0.7112504 0.28874961
21315 0.6394408 0.36055924
21316 0.6690113 0.33098868
21317 0.6263608 0.37363920
21319 0.6863547 0.31364534
21320 0.7084249 0.29157507
21321 0.6980657 0.30193434
21322 0.6861721 0.31382785
21323 0.6880658 0.31193417
21337 0.6078638 0.39213617
21338 0.5721480 0.42785198
21339 0.6042361 0.39576389
21340 0.6186725 0.38132746
21341 0.5971509 0.40284905
21343 0.6810701 0.31892986
21344 0.6787601 0.32123989
21345 0.7029612 0.29703879
21346 0.6848435 0.31515646
21347 0.6790441 0.32095592
21349 0.6922613 0.30773873
21350 0.7118276 0.28817242
21351 0.6957543 0.30424571
21352 0.7111985 0.28880147
21353 0.7116625 0.28833754
21373 0.6975189 0.30248110
21374 0.7136386 0.28636137
21375 0.7133390 0.28666097
21376 0.7138489 0.28615107
21377 0.6978999 0.30210008
21391 0.6825130 0.31748699
21392 0.6828440 0.31715600
21393 0.6826632 0.31733681
21394 0.6847775 0.31522247
21395 0.6856313 0.31436866
21415 0.6878808 0.31211920
21416 0.6908276 0.30917244
21417 0.6903184 0.30968158
21418 0.6903970 0.30960301
21419 0.6918406 0.30815945
21433 0.6221788 0.37782122
21434 0.6319866 0.36801341
21435 0.5929903 0.40700973
21436 0.6292484 0.37075157
21437 0.6469435 0.35305649
21457 0.6901444 0.30985555
21458 0.6800434 0.31995661
21459 0.6807794 0.31922062
21460 0.6810933 0.31890669
21461 0.6913583 0.30864169
21463 0.6625696 0.33743041
21464 0.6553054 0.34469463
21465 0.6400569 0.35994314
21466 0.6492119 0.35078811
21467 0.6628990 0.33710095
21475 0.6398938 0.36010623
21476 0.6448116 0.35518838
21477 0.6189578 0.38104219
21478 0.6412758 0.35872423
21479 0.6477625 0.35223755
21493 0.6207387 0.37926131
21494 0.6064662 0.39353380
21495 0.6016747 0.39832531
21496 0.6136502 0.38634977
21497 0.5720060 0.42799402
21583 0.6861319 0.31386807
21584 0.6645268 0.33547323
21585 0.6557402 0.34425980
21586 0.6606714 0.33932865
21587 0.6754036 0.32459639
21595 0.6632033 0.33679667
21596 0.6733076 0.32669236
21597 0.6621641 0.33783591
21598 0.6949602 0.30503982
21599 0.6625815 0.33741854
21613 0.6925151 0.30748485
21614 0.6954927 0.30450732
21615 0.6911864 0.30881358
21616 0.6890078 0.31099225
21617 0.6940374 0.30596264
21631 0.6516932 0.34830685
21632 0.6286518 0.37134824
21633 0.5758641 0.42413592
21634 0.5991990 0.40080103
21635 0.6222777 0.37772232
21661 0.7089342 0.29106577
21662 0.7077153 0.29228469
21663 0.7104769 0.28952306
21664 0.7091105 0.29088946
21665 0.7101523 0.28984766
21667 0.5588818 0.44111820
21668 0.5878848 0.41211517
21669 0.6120528 0.38794724
21670 0.5972004 0.40279957
21671 0.5388825 0.46111749
21709 0.6771704 0.32282961
21710 0.6320120 0.36798797
21711 0.6010434 0.39895659
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21713 0.6870485 0.31295149
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21764 0.6887957 0.31120431
21765 0.6880069 0.31199314
21766 0.6853331 0.31466691
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21784 0.6642401 0.33575986
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21788 0.6313873 0.36861273
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21790 0.6399810 0.36001896
21791 0.6529567 0.34704335
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21800 0.6857951 0.31420492
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21935 0.6434088 0.35659120
21979 0.6088285 0.39117154
21980 0.6338251 0.36617493
21981 0.6433615 0.35663850
21982 0.6472580 0.35274196
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Levels: 0 1
pred 0 1
0 3352 1954
1 119 115[1] 0.6258123Confusion Matrix and Statistics
Reference
Prediction 0 1
0 3352 1954
1 119 115
Accuracy : 0.6258
95% CI : (0.6129, 0.6386)
No Information Rate : 0.6265
P-Value [Acc > NIR] : 0.5502
Kappa : 0.0259
Mcnemar's Test P-Value : <2e-16
Sensitivity : 0.96572
Specificity : 0.05558
Pos Pred Value : 0.63174
Neg Pred Value : 0.49145
Prevalence : 0.62653
Detection Rate : 0.60505
Detection Prevalence : 0.95776
Balanced Accuracy : 0.51065
'Positive' Class : 0
Data Exploration
Column
Diagnostic Regression
Column
We start by computing an example of logistic regression model for predicting the probability of customers will make a payment the next month based on regressors.
The smoothed scatter plots show that variables commitments, outstandings, and origination DTI are all quite linearly associated with the delinquincy outcome in logit scale.
The variables APR, Day past due, LTV current, LTV original, Total HE balance, and Total mortgage balance are not linear and might need some transformations. Those variables doesn’t really affect the model, as we have shown earlier with the boxplots.
Column
Building a logistic regression model
[1] "Good" "Good" "Good" "Good" "Good" "Good"Column {.tabset data-width=500}
Linearity assumption
Column {.tabset data-width=500}
Create the scatter plots
