Predictive modeling is a statistical technique that uses machine learning and data mining in predicting and forecasting the likely future outcomes using historical and existing data. A Predictive model will make assumptions based on what has happened in the past and what is likely to happen now. Most predictive models work fast and often complete their calculations in real time. That’s why banks and retailers can, for example, calculate the risk of an online mortgage or credit card application and accept or decline the request almost instantly based on that prediction.

Types of Predictive Models

Predictive analytics tools use a variety of vetted models and algorithms that can be applied to a wide spread of use cases. 

These predictive analytics models include:

1.) Classification model 
2.) Clustering model 
3.) Forecast model
4.) Outliers model
5.) Time series model

 

Common Predictive Algorithms

Predictive algorithms use  machine learning or deep learning. Both are subsets of artificial intelligence (AI). Machine learning (ML) involves structured data, such as spreadsheet or machine data. Deep learning (DL) deals with unstructured data such as video, audio, text, social media posts and images—essentially the stuff that humans communicate with that are not numbers or metric reads.

 

Some of the more common predictive algorithms are:

1.) Random Forest: 
2.) Generalized Linear Model (GLM) for Two Values: 
3.) Gradient Boosted Model: 
4.) K-Means:
5.) Prophet: 

Benefits of Predictive Modeling

Reduce time, effort and costs in forecasting business outcomes. 

 

Examples of specific types of forecasting that can benefit businesses include demand forecasting, headcount planning, churn analysis, external factors, competitive analysis, fleet and IT hardware maintenance and financial risks.

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Challenges of Predictive Modeling

It’s essential to keep predictive analytics focused on producing useful business insights because not everything this technology digs up is useful. Some mined information is of value only in satisfying a curious mind and has few or no business implications. Getting side-tracked is a distraction few businesses can afford.

Limitations of Predictive Modeling
Errors in data labeling: 
Shortage of massive data sets needed to train machine learning: 
Generalizability of learning, or rather lack thereof: 
Bias in data and algorithms: 

Predictive Modeling Usage in Platforms

planning, forecasting and budgeting features may provide a statistical model engine to rapidly model multiple scenarios that deal with changing market conditions.

predict potentially late deliveries, purchase or sales orders and other risks or impacts.
Alternate suppliers can also be represented on the dashboard