Using descriptive analytics, review the data and provide major insights into data.
Report any p reprocessing that you would like to apply before developing the model with justification.
Divide the data randomly into training (70%) and validation (30%) partitions, and develop classification models using Logistic regression using cutoff of 0.5. Report the confusion matrix and the net profit for the validation data. Evaluate the model performance using appropriate metrics.
Using You den's index, identify the optimum cutoff and report the associated performance metrics