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203.2.3 Multiple Logistic Regression

LAB: Multiple Logistic Regression

    1. Import Dataset: Fiberbits/Fiberbits.csv
  • Active_cust variable indicates whether the customer is active or already left the network.
    1. Build a model to predict the chance of attrition for a given customer using all the features.
    1. How good is your model?
    1. Import Dataset: Fiberbits/Fiberbits.csv
Fiberbits <- read.csv("C:\\Amrita\\Datavedi\\Fiberbits\\Fiberbits.csv")
    1. Build a model to predict the chance of attrition for a given customer using all the features.
Fiberbits_model_1<-glm(active_cust~.,family=binomial(),data=Fiberbits)
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
summary(Fiberbits_model_1)
## 
## Call:
## glm(formula = active_cust ~ ., family = binomial(), data = Fiberbits)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -8.4904  -0.8752   0.4055   0.7619   2.9465  
## 
## Coefficients:
##                              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                -1.761e+01  3.008e-01  -58.54   <2e-16 ***
## income                      1.710e-03  8.213e-05   20.82   <2e-16 ***
## months_on_network           2.880e-02  1.005e-03   28.65   <2e-16 ***
## Num_complaints             -6.865e-01  3.010e-02  -22.81   <2e-16 ***
## number_plan_changes        -1.896e-01  7.603e-03  -24.94   <2e-16 ***
## relocated                  -3.163e+00  3.957e-02  -79.93   <2e-16 ***
## monthly_bill               -2.198e-03  1.571e-04  -13.99   <2e-16 ***
## technical_issues_per_month -3.904e-01  7.152e-03  -54.58   <2e-16 ***
## Speed_test_result           2.222e-01  2.378e-03   93.44   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 136149  on 99999  degrees of freedom
## Residual deviance:  98359  on 99991  degrees of freedom
## AIC: 98377
## 
## Number of Fisher Scoring iterations: 8

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