Trying to build a predictive model for attrition prediction at service desk/call center.
Have daily data on the following parameters:
1.Call quality - QTM (0-100%), 2.No. of calls - Calls(Number) 3.Attendance 4.Customer feedback(1/0) Q1,Q2 (0-100%) for both, agents who left the job and for the ones who are still there, for a duration of 6 months.
Aim: to predict agents tendency/probability of staying/leaving based on his/her daily performance.
Doubts i have, 1. how should i use the data to train the model(logistic regression)
should it be trained based on the avg of the parameters taken over a duration of 6 months.
**if so can we test the daily metrics based on a model which is trained using mean of the parameters for 6 months.
this is my first attempt at making a predictive model,i have gone thru various case studies/models such as the titanic survival model using logistic regression,Wisconsin DEWS model.
I decided to model using the weekly aggregates of the the two populations(attrites and Non-attrites).
The Data Set (approx 5 months data,with weekly aggregates of the two populations i.e Attrites and Non-Attrites.) AW1 : Week1 Aggregates of the performance metrics for Attrites NAW1: Week1 Aggregates of the performance metrics for Non-Attrites
Post this i ran a logistic Regression on 80% of this data-set and kept aside the other 20% for testing. Results of the logistic regression:
and then i used the predict function on the 20% of the data which contained 3 data points for both attrites and Non-attrites,so to be 100% accurate the model should have predicted 3 as attrites and 3 as Non-attrites but the correct prediction is 5/6 that is one wrong prediction out of 6.
Please help me in interpreting the meaning of the results of the model all the z values are zero im not sure what that signifies.
Googled a little regarding the z values = 0 issue and came across some posts on stackoveflow that suggested using "bayesglm" instead of "glm" did that and the results are good at the first look but being a newbie in the field i would like you to guide me with respect to the statistical significance of the issue and is the model really as good as the results of the "bayesglm" or is it just by fluke.
the model gives a 100% accurate prediction on the test set now 6/6.