Assumptions:
-> Binary logistic regression requires the dependent variable to be binary.
-> For a binary regression, the factor level 1 of the dependent variable should represent the desired outcome.
-> Only the meaningful variables should be included.
-> The independent variables should be independent of each other. That is, the model should have little or no multicollinearity.
-> The independent variables are linearly related to the log odds.
-> Logistic regression requires quite large sample sizes.
Objective:
The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).
Our prediction will be based on the customer’s
1> job
2> marital status
3> whether he/she has credit in default
4> whether he/she has a housing loan
5> whether he/she has a personal loan
6> outcome of the previous marketing campaigns.
Output:
