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	Raw data processing and sampling Advanced anomaly identification and handling (missing values, wrong numbers, numeric outliers) Detection and resolution of mistyped values Preliminary analysis of distribution Merging categories for both numeric and categorical variables Advanced algorithm to reduce dataset size when keeping same data distributions
	Portfolio data preparation Automatic and manual sampling Good/Bad records marking Visual "Good" and "Bad" borrowers representation Graphical statistics Easy column and records inclusion/exclusion Portfolio data preparation, filtering and re-assignment Separate statistics for training and out-of-sample validation subsets Portfolio data import from MS Excel, CSV and SAS data files
	Variables selection Goods/Bads distributions Bad Rate and WOE for each category Logical Trend - Easy analysis of WOE Single-click automatic binning with any of 3 algorithms Simple and flexible manual binning with drag-n-drop Binning and distribution visualization Information values for each variable Separate statistics for training and out-of-sample validation subsets
	Scorecard modeling Forward stepwise and backward stepwise regression Auto adjustment for prior probabilities Auto scaling by odds Score points range selection Category encoding method selection (1-of-N or WOE) Scoring points for every category can be manually changed Visual cut-off point definition Visual score bands definition
	Scorecard monitoring & validation ROC curve K-S curve Strategy curve Marginal event vs Score Probability Distribution Score Points Distribution Risk Segments Approval vs Bad Rate Chi-squared Scorecard Stability Test Export to MS Excel
	Reject Inference (RI) Automatic Reject Inference Load rejects from CSV or MS Excel file Bad Rate comparison for accepts and rejects Reject Inference with Fuzzy Augmentation Comparison of scorecards before and after RI RI can be reverted with a single mouse click

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