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agrisampath-blog · 6 years

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UP SELLING OF PERSONAL LOANS TO LIABILITY CUSTOMERS-ASSIGNMENT 2

SAS CODE:

libname mydata '/home/agrisampath0/my_courses ';

proc import datafile= '/home/agrisampath0/Wesleyian University/Bank_Personal_Loan_Modelling.xlsx' out=mydata.loanmodelling dbms=xlsx replace;sheet='Data'; getnames=yes;run;

data new; set mydata.loanmodelling; label age='Age Distribution' Experience=" Experience distribution" Personal Loan= "PL Availment"; rename 'personal loan'n= Personal_Loan 'zip Code'n= Zip_Code 'Securities Account'n =Securities_Account 'CD Account'n = CD_Account; if age<60; if experience>1;

run; proc sort data=new; by id;run; proc freq data= new; tables age experience Personal_Loan; run;

RESULTS

The FREQ Procedure

Age Distribution Age Frequency Percent Cumulative Frequency Cumulative Percent 26 27 0.65 27 0.65 27 56 1.35 83 2.01 28 100 2.42 183 4.43 29 119 2.88 302 7.31 30 135 3.27 437 10.57 31 120 2.90 557 13.47 32 120 2.90 677 16.38 33 120 2.90 797 19.28 34 134 3.24 931 22.52 35 151 3.65 1082 26.17 36 107 2.59 1189 28.76 37 106 2.56 1295 31.33 38 115 2.78 1410 34.11 39 133 3.22 1543 37.32 40 125 3.02 1668 40.35 41 136 3.29 1804 43.64 42 126 3.05 1930 46.69 43 149 3.60 2079 50.29 44 121 2.93 2200 53.22 45 127 3.07 2327 56.29 46 127 3.07 2454 59.36 47 113 2.73 2567 62.09 48 118 2.85 2685 64.95 49 115 2.78 2800 67.73 50 138 3.34 2938 71.07 51 129 3.12 3067 74.19 52 145 3.51 3212 77.70 53 112 2.71 3324 80.41 54 143 3.46 3467 83.87 55 125 3.02 3592 86.89 56 135 3.27 3727 90.15 57 132 3.19 3859 93.35 58 143 3.46 4002 96.81 59 132 3.19 4134 100.00 Experience distribution Personal Experience Frequency Percent Cumulative Frequency Cumulative Percent 2 85 2.06 85 2.06 3 129 3.12 214 5.18 4 113 2.73 327 7.91 5 146 3.53 473 11.44 6 119 2.88 592 14.32 7 121 2.93 713 17.25 8 119 2.88 832 20.13 9 147 3.56 979 23.68 10 118 2.85 1097 26.54 11 116 2.81 1213 29.34 12 102 2.47 1315 31.81 13 117 2.83 1432 34.64 14 127 3.07 1559 37.71 15 119 2.88 1678 40.59 16 127 3.07 1805 43.66 17 125 3.02 1930 46.69 18 137 3.31 2067 50.00 19 135 3.27 2202 53.27 20 148 3.58 2350 56.85 21 113 2.73 2463 59.58 22 124 3.00 2587 62.58 23 144 3.48 2731 66.06 24 131 3.17 2862 69.23 25 142 3.43 3004 72.67 26 134 3.24 3138 75.91 27 125 3.02 3263 78.93 28 138 3.34 3401 82.27 29 124 3.00 3525 85.27 30 123 2.98 3648 88.24 31 99 2.39 3747 90.64 32 150 3.63 3897 94.27 33 109 2.64 4006 96.90 34 70 1.69 4076 98.60 35 58 1.40 4134 100.00 Personal Loan Personal_Loan Frequency Percent Cumulative Frequency Cumulative Percent 0 3738 90.42 3738 90.42 1 396 9.58 4134 100.00

It is observed that only 9.5% of customers have availed personal loan from 4134 customers.

Further age group analysis of this 9.5% customers will help us in understanding the impact. Similarly Experience needs to be understood for better credit decision

LOG

1 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK;

71 proc sort data=new;

72 by id;run;

NOTE: There were 4134 observations read from the data set WORK.NEW.

NOTE: The data set WORK.NEW has 4134 observations and 14 variables.

NOTE: PROCEDURE SORT used (Total process time):

real time 0.00 seconds

user cpu time 0.01 seconds

system cpu time 0.00 seconds

memory 2268.87k

OS Memory 31416.00k

Timestamp 11/03/2019 07:13:04 PM

Step Count 144 Switch Count 2

Page Faults 0

Page Reclaims 286

Page Swaps 0

Voluntary Context Switches 10

Involuntary Context Switches 0

Block Input Operations 0

Block Output Operations 1032

73 proc freq data= new;

74 tables age experience Personal_Loan;

75 run;

NOTE: There were 4134 observations read from the data set WORK.NEW.

NOTE: PROCEDURE FREQ used (Total process time):

real time 0.09 seconds

user cpu time 0.09 seconds

system cpu time 0.01 seconds

memory 3355.25k

OS Memory 32172.00k

Timestamp 11/03/2019 07:13:04 PM

Step Count 145 Switch Count 2

Page Faults 0

Page Reclaims 500

Page Swaps 0

Voluntary Context Switches 9

Involuntary Context Switches 0

Block Input Operations 0

Block Output Operations 304

77 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK;

User: agrisampath0

DATA Step Statements

There are no Errors in the code

0 notes

agrisampath-blog · 6 years

Text

UP SELLING OF PERSONAL LOANS TO LIABILITY CUSTOMERS

Preliminarily all variables are included to enhance the scope of study. Impact of other variables such as average credit card spending and mortgage value will be analysed

LITERATURE REVIEW

https://www.sciencedirect.com/science/article/abs/pii/016781169190030B

https://www.sciencedirect.com/science/article/pii/S0263237307000163

HYPOTHESIS: Income and Age have impact on Credit Decisions and up-selling

SAS CODE

libname mydata '/home/agrisampath0/my_courses '; proc import datafile= '/home/agrisampath0/Wesleyian University/Bank_Personal_Loan_Modelling.xlsx' out=mydata.loanmodelling dbms=xlsx replace;sheet='Data'; getnames=yes;run; data new; set mydata.loanmodelling; label age='Age Distribution' Experience=" Experience distribution" Personal Loan= "PL Availment"; rename 'personal loan'n= Personal_Loan 'zip Code'n= Zip_Code 'Securities Account'n =Securities_Account 'CD Account'n = CD_Account; if age<60; if experience>1; run; proc sort data=new; by id;run; proc freq data= new; tables age experience Personal_Loan; run;

0 notes