Click 'Create modeling Project' to set up a project for modeling process. Fill in project name and details Start your modeling adventure by clicking 'Data management' to import your raw data The raw data should be in TXT or CSV formats. Once you have uploaded the file. It will come up from the file list. Get a preview by selecting a file from the list and translate into sample or layout. Press next step when all is done. Define a target variable (Dependent variable). And define a sample weight (Sweight) if the modeling sample is a subsample of the total universe. provide GainsChart of how do you want to define samples for model development and validation. Click next when this step complete. Note that you will not be able to go back and modify samples when you confirm this step. Univariate analysis before binning. You can examine the distributions of variables,remove outlier, handle missing values, drop unwanted variable and create new variables in this step. This is a variable transformation step to bin the variables values into clusters that capture the implicit relationship between individual variables and the target by just one click. This is a variable transformation step to bin the variables values into clusters that capture the implicit relationship between individual variables and the target by just one click. In Coarse Classing, you can either use the advance option (adjusting parameters of the binning algorithm) or combine groups according to business sense to maximize the information value and KS for a variable. Put in variables one by one starting from the highest IV and most stable KS value across development and validation samples. Continue the fitting iteration until all variables which has positive marginal contributions to the model are included. A full set of evaluation reports (validation table, KS Chart, Gains Chart,Lift Chart and more) are available by clicks. A scoring code of the final model is available for model implementation.