I am very new to the machine learning field and have been practicing logistic regression on few sample data sets. I have built a Model using the logistic regression algorithm. Few of the coefficients have a p-value of more than 0.05 (which is the alpha I am considering).
R-Code for building the model and a summary of the model is given below.
model.bank.1 <- glm(y~., data=bankfull, family ="binomial")
Now, before considering AIC, Residual/Null Deviance, confusion matrix and ROC for evaluating. I have observed that the p-values for some of the independent variables is more than 0.05(age has very high p-value, what does this mean?). In such a case, what should be done? Should i straight away remove all such predictors from my model ? Is there any way by which i can make p-value of these predictors less than 0.05 ?
What are the things needed to be checked before going on to evaluate the model using the AIC,Deviance, Confusion Matrix and ROC measures ?
Edit 1: I've tried standardizing the numerical columns, but there is no change in the model at all.