In this tutorial, we use the binary logistic regression for the construction of the gain chart. We show also that the variable selection is really useful in the context of dealing with large number of predictive variables.

We use a real/realistic dataset from a website (see Reference below). It contains 2158 examples and 200 predictive attributes. The objective variable is a response variable indicating whether or not a consumer responded to a direct mail campaign for a specific product.

**Keywords**: scoring, marketing campaign, logistic regression, feature selection, backward, forward, gain chart, lift curve

**Components**: Supervised learning, Binary logistic regression, Select examples, Scoring, Lift curve, Forward-logit, Backward-logit

**Tutorial**: en_Tanagra_Variable_Selection_Binary_Logistic_Regression.pdf

**Dataset**: dataset_scoring_bank.xls

**References**:

Statistical Society of Canada, "Data Mining - Case Studies - 2000"