Predictive modeling is an arithmetic and analytical method using data mining and machine learning to predict future outcomes for businesses.
Moreover, the use of data mining and machine learning involved in predictive modeling is for utilizing the data from the past and present to predict future outcomes. Furthermore, predictive modeling is a synonym for Predictive Analytics.
The Need for Predictive Modeling
Most organizations today drive success through foreseeing the business factors like financial blueprints, operations, product demand, and supply, etc. However, it is not only just predicting the outcomes, but also, about the preciseness of the predictions involved in business processes and prospects.
In addition to this, the Insurance industry has always depended on the most upon predictive modeling for forecasting. Also, predictive modeling helps to cater to the needs for generating accurate predictions and figuring out appropriate premiums. Alternatively, predictive modeling assists insurance companies in targeting clientage, economic conditions, figuring out fraudulent claims, etc.
Moreover, predictive modeling is capable of generating rapid responses through the use of its statistical analysis in real-time. For instance, banks use predictive modeling for credit cards. Banks today can instantly reject applications based on real-time analysis of the credit score and history of the applicant.
Models Used in Predictive Modeling Process
The predictive modeling or analytics process inculcates the use of various models in the model development stage. Predictive models are classified as per the analysis that needs to be performed for a predictive model. Also, entails statistical methods to Manœuvre an accurate response.
The statistical methods involved in the predictive model differ from data mining. In Data Mining we work upon building numerous relationships, trends, and patterns for businesses. However, Statistical approaches inculcate specific relationships between different business variables set for the predictions. Some of these models include;
In predictive models’ variables are set for prediction. Regression modeling arithmetically relates a relationship between an independent (Predictor) variable and a dependent (Predicted) variable.
Furthermore, the dependent variable is used for the prediction as a variable for prediction and the independent variables are the explanatory variables provided in the sample data or the data set. Moreover, through the involvement of causation, regression models predefine a specific relationship far from correlation.
Regression Techniques Used in Regression Models;
Linear regression involves a specific relationship between a predictor variable with a predicted variable with the use of pre-defined relationships. It is also known as ordinary least squares (OLS).
Partial or stepwise regression follows up after linear regression to correlate between independent and dependent variables after figuring out the aftereffects of the other variables within the model. Moreover, they necessarily don’t use pre-defined relationships.
Logit or Probit Regression
Logit or probit models bifurcate outcomes, unlike the other models. They are used for creating discrete responses from the group of variables coined in the dataset. This type of model is most commonly used in fraudulent claims.
The regression Spines method is used by combining the data of different regression models, thus, dividing the data piece by piece and dependent upon the distribution of the variables. Mathematically, generating smooth curves through complicated datasets.
Some advanced techniques for predictive modeling include the use of neural networks for predictive analytics.
Advanced Models are coined as more advanced than the regression method, because firstly, through the use of neural networks, they are cable of working on far more variables at the same time. Secondly, neural networks are referred to as nonlinear approaches for predictive modeling, completely divergent and free from specified dimensions.
The forecast model uses the data contained with numerical values from the past assumptions collected from the organization’s database.
It works upon numeric value predictions. For instance, a car company predicts how many cars can they sell in a week, or a call center predicts the total number of calls for the day. For this, the historical data would be used for analysis.