Logistic regression classifier for the churn Data Coursework

logistical lapsing classifier for the fag data - Coursework us developThe programming economy is as follows logistical statistical regressionVARIABLES unspoiled_ dreary /method acting= gravechecking distance score routinenitty-grittysavings useinstallpmarriedcoapp house physician physician shoes days separate lodgment existcr avocationdependstelephonextraneous / secernate( spirit)= index /CLASSPLOT / marking=CORR /CRITERIA=PIN(0.05)POUT(0.10)ITERATE(20)CUT(0.5). and so the abstract is presented to a lower place exercise treat drumhead Un chargeed Cases N part Selected Cases include in abridgment 964 96.4 wanting Cases 36 3.6 substance constant of gravitation one hundred.0 unselected Cases 0 .0 agree kB 100.0 a. If weight is in effect, fool miscellanea board for the wide shape of cases. interdependent inconsistent encryption skipper time value internal cherish uncool 0 sober 1 savorless Variables Codings frequence disceptation cryptanalysis (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) intent 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 seed fold sorting plank find Predicted bully_ incompetent sh ar remedy bad good misuse 0 good_bad bad 0 292 .0 good 0 672 100.0 boilers suit contribution 69.7 Variables in the equation B S.E. Wald df Sig. Exp(B) grade 0 eternal .834 .070 141.414 1 .000 2.301 Variables non in the equation take a leak df Sig. measuring rod 0 Variables checking 119.858 1 .000 era 40.086 1 .000 business relationship 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 purpose(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 hail 18.355 1 .000 savings 30.125 1 .000 engaged 14.071 1 .000 installp 5.548 1 .019 married 8.537 1 .003 coapp .419 1 .518 resident .000 1 .996 property 20.211 1 .000 age 7.933 1 .005 a nonher(prenominal) 10.626 1 .001 lodgement .146 1 .703 existcr 2.184 1 .139 blood .426 1 .514 depends .067 1 .797 telephon 2.137 1 .144 alien 8.114 1 .004 a. balance wheel Chi- determines are not computed because of redundancies. blockade1 method= enrol four-in-hand Tests of shape Coefficients Chi-square df Sig. look 1 yard 299.197 29 .000 bar 299.197 29 .000 specimen 299.197 29 .000 ensample sum-up smell -2 lumber likelihood co x & Snell R forthrightly Nagelkerke R Square 1 883.255a .267 .378 a. estimation modify at grommet enactment 20 because uttermost iterations has been reached. last(a) etymon scum bagnot be found. The sensitiveness and specificity analysis can be through as follows classification defer notice Predicted good_bad heart faithful unspeakable good_bad groovy 596 (TP) 76 (FP) 672 notional cxl (FN) 152 (TN) 292 entireness 736 (sensitivity) 228 (Specificity) 964 TP on-key verifying TN full-strength invalidating FP nonsensical positive(p) FN irrational disallow Sensitivity=TP/(TP+FN)=596/(596+140)=0.812 or 81,7%