Logistic regression classifier for the churn Data Coursework

Logistic regression classifier for the churn Data - Coursework ExampleThe programming code is as follows LOGISTICREGRESSIONVARIABLESgood_ big / mode=ENTERcheckingdurationhistory adjudicateamountsavingsemployedinstallpmaritalcoappresidentpropertyageotherhousing existcrjobdependstelephon distant /CONTRAST(purpose)=Indicator /CLASSPLOT /PRINT=CORR /CRITERIA=PIN(0.05)POUT(0.10)ITERATE(20)CUT(0.5). Then the analysis is presented below Case Processing Summary Unweighted Cases N Percent Selected Cases Included in Analysis 964 96.4 wanting Cases 36 3.6 Total 1000 100.0 Unselected Cases 0 .0 Total 1000 100.0 a. If weight is in effect, see classification table for the total number of cases. unfree Variable Encoding Original Value Internal Value Bad 0 Good 1 Categorical Variables Codings Frequency Parameter coding (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) purpose 3 1.000 .000 .000 .000 .000 .000 .000 .000 .000 .000 0 225 .000 1.000 .000 .000 .000 .000 .000 .000 .000 .000 1 100 .000 .000 1.000 .000 .000 .000 .000 .000 .000 .000 2 174 .000 .000 .000 1.000 .000 .000 .000 .000 .000 .000 3 268 .000 .000 .000 .000 1.000 .000 .000 .000 .000 .000 4 12 .000 .000 .000 .000 .000 1.000 .000 .000 .000 .000 5 22 .000 .000 .000 .000 .000 .000 1.000 .000 .000 .000 6 47 .000 .000 .000 .000 .000 .000 .000 1.000 .000 .000 8 9 .000 .000 .000 .000 .000 .000 .000 .000 1.000 .000 9 94 .000 .000 .000 .000 .000 .000 .000 .000 .000 1.000 X 10 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 Beginning block Classification Table Observed Predicted good_bad Percentage Correct bad good Step 0 good_bad bad 0 292 .0 good 0 672 100.0 Overall Percentage 69.7 Variables in the equality B S.E. Wald df Sig. Exp(B) Step 0 Constant .834 .070 141.414 1 .000 2.301 Variables not in the Equation Score df Sig. Step 0 Variables checking 119.858 1 .000 duration 40.086 1 .000 History 48.045 1 .000 purpose 39.421 10 .000 purpose(1) 6.926 1 .008 purpose(2) 9.752 1 .002 purpose(3) 9.334 1 .002 purpose(4) .361 1 .548 pu rpose(5) 12.039 1 .001 purpose(6) .053 1 .817 purpose(7) .393 1 .531 purpose(8) 4.846 1 .028 purpose(9) 1.583 1 .208 purpose(10) .694 1 .405 amount 18.355 1 .000 savings 30.125 1 .000 employed 14.071 1 .000 installp 5.548 1 .019 marital 8.537 1 .003 coapp .419 1 .518 resident .000 1 .996 property 20.211 1 .000 age 7.933 1 .005 other 10.626 1 .001 housing .146 1 .703 existcr 2.184 1 .139 job .426 1 .514 depends .067 1 .797 telephon 2.137 1 .144 foreign 8.114 1 .004 a. Residual Chi-Squares are not computed because of redundancies. Block1Method=Enter Omnibus Tests of Model Coefficients Chi-square df Sig. Step 1 Step 299.197 29 .000 Block 299.197 29 .000 Model 299.197 29 .000 Model Summary Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square 1 883.255a .267 .378 a. Estimation terminated at iteration number 20 because maximum iterations has been reached. Final solution cannot be found. The sensitivity and specificity analysis can be through as follows Classification Table Obse rved Predicted good_bad Total Good Bad good_bad Good 596 (TP) 76 (FP) 672 Bad 140 (FN) 152 (TN) 292 Total 736 (Sensitivity) 228 (Specificity) 964 TP true Positive TN True Negative FP False Positive FN False Negative Sensitivity=TP/(TP+FN)=596/(596+140)=0.812 or 81,7%