## Model Performance Assessment Statistics – Concordance: Steps to Calculate

Binary Decision Outcome is one of most common analytics problem being solved in the industry. Some examples where binary predictive models are used.. Some of the common Model Performance Statistics which are used assessing performance of a Binary Predictive Models are: Confusion Matrix ROC Chart Concordance % Gini KS Lift Chart Gain Table and Chart ... Read more

## Data Science: Profile Screening Model for Mid-Management Roles

Business Context: The client was an executive search firm. It has built a candidate database with over a million candidate profiles.  The client wanted to leverage the candidate database for smart candidate selection and recruitment process. For this project, the aim was to build a predictive model which will help in identifying a list of ... Read more

## Scenarios: Binary Predictive Models

A long list of business decisions are of binary in nature and we will list a few of such scenarios. When a decision variable (also referred as target variable, Response variable or dependent variable) is binary (takes only two value), a long list of supervised statistical and machine learning algorithms can be used. Some of ... Read more

## Logistic Regression using R: German Credit Example

Logistic Regression is one of the oldest  and widely used Statistical/Machine Learning techniques for Binary Decision Variable scenarios. In the previous blog, we have explained the overall steps to build a predictive model using Logistic Regression.   Also, if you are interested to understand Binary Model Performance Statistics, you can read a detailed blog on Model ... Read more

## Predictive Modelling Technique - Logistic Regression - Interpret Output - Part 2

MAXIMUM LIKELIHOOD AND ODDS RATIO Analysis of Maximum Likelihood Estimates Parameters in logistic regression are estimated using Maximum Likelihood Estimation (MLE) functions.  The significance of individual exploratory variable parameters is assessed using Wald Chi Square test. Parameter:  Intercept and exploratory variables used in a logistic model, the weight of these are estimated using MLE DF: ... Read more

## Predictive Modelling Technique - Logistic Regression - Interpret Output

Originally published on RamG Data Analytics & Insights (www.ramganalytics.com) In the previous blog, we  elaborated on Why and How to learn Predictive Modelling? One of the commonly used statistical techniques is Logistic Regression.  In this blog focus is to understand logistic regression out. We are using SAS for executing logistics regression but similar results & ... Read more

## Credit Score: What is it and how is it developed?

When a customer applies for a credit facility (e.g. credit card, personal loan, car/vehicle loan, home/mortgage loan, home equity etc.) at a bank, the bank evaluates credit worthiness of the customer.  What do we mean by credit worthiness? It is assessment by the bank that the customer would be able to meet his/her financial obligations. ... Read more

## Model Performance: R code and Explanations

In the last blog, we have described model performance statistics. Considering interest and questions from users, we are describing R functions in a bit more details. Model Building and Calculating Predicted Probability In this, we have taken cross sell data. The name of data frame is termCrossSell. Dependent variable in this dataset is target. Using ... Read more

## Steps to prepare data for Predictive Modeling using R

Introduction to Predictive Model Predictive Modeling is an approach to build an statistical relationship between a response variable and a set of independent variables using a data sample (called development sample). The model so developed will be used for predicting values of Response Variable on a new data. Predictive Models are used across functional areas ... Read more

## Predictive Modeling: Customer Attrition and Retention Scenario for Financial Services

Author: Ram and Amitayu   It is well established now that retaining an existing customer is a lot easier than acquiring a new customer. Typically, acquiring a new credit card customer cost \$50-\$80. Surprising though, a lot of banks and financial services organizations still focus a lot more on customer acquisition programs. It could be ... Read more