Predictive Analysis

           Predictive Analysis is the process of making predictions about future or unknown events using a variety of techniques from statistics, modelling, machine learning, and data mining that analyse current and historical facts. Using historical data, a predictive model can identify risks and opportunities for us. Relationship between various factors can be captured which helps in the assessment of risk or potential associated with a particular set of conditions, guiding decision making.

           Generally, the term predictive analytics is used to mean predictive modelling, "scoring" data with predictive models, and forecasting. However, people are increasingly using the term to refer to related analytical disciplines, such as descriptive modelling and decision modelling or optimization. These disciplines also involve rigorous data analysis, and are widely used in business for segmentation and decision making but have different purposes and the statistical techniques underlying them vary.

           Predictive models analyse past performance to assess how likely a customer is to exhibit a specific behaviour in order to improve marketing effectiveness. Predictive models often perform calculations during live transactions in order to guide a decision.  

           Descriptive models quantify relationships in data in a way that is often used to classify customers or prospects into groups. Unlike predictive models that focus on predicting a single customer behaviour (such as credit risk), descriptive models identify many different relationships between customers or products.

           Decision models describe the relationship between all the elements of a decision — the known data (including results of predictive models), the decision, and the forecast results of the decision — in order to predict the results of decisions involving many variables.

Predictive analytics has many applications. Some of them are

• ·         Analytical Customer Relationship Management
• ·         Clinical Decision Support Systems
• ·         Collection Analytics
• ·         Cross-sell
• ·         Customer retention
• ·         Direct marketing
• ·         Fraud detection
• ·         Portfolio, product or economy level prediction
• ·         Risk management
• ·         Underwriting

The approaches and techniques used to conduct predictive analytics can broadly be grouped into regression techniques and machine learning techniques.

• ·         Regression techniques
• ·         Linear regression models
• ·         Discrete choice models
• ·         Logistic regression
• ·         Multinomial logistic regression
• ·         Probit regression
• ·         Logit vs probit
• ·         Time series models
• ·         Survival or duration analysis
• ·         Classification of regression trees
• ·         Multivariate adaptive regression splines
• ·         Machine learning techniques
• ·         Neural networks
• ·         Multilayer perceptron
• ·         Radial basis functions