Predictive Modeling

Predictive modelling is a statistical technique which works, in conjunction with machine learning and data mining, to make predictions and forecasts based on existing and historical data. Like the name implies, it is used predict any sort of statistic like the weather patterns or stock market predictions.

Predictive models are known for being efficient and fast-executing, which perform computations in real time, which provide end users with an instant result, to make a decision on-the-spot. For example, a banker would assess an applicant for a credit card or a mortgage, and use predictive modelling application to instantly risk assess and approve an applicant based on that result.

There are 5 different types of Predictive Models:

  • Classification model
  • Clustering model
  • Forecast model
  • Outliers model
  • Time series model

Classification Model

Classification models seek to categorize and draw a conclusion about the data being analyzed, for a particular query or question. Outcomes are given labels which are then applied to datasets. ex. classifying a credit card transaction as "fraudulent" or "authorized".

Clustering Model

Clustering models are similar in their objective as classification models; however, they differ in that cluster models don't use labelled data and analyze groupings of common attributes within a dataset. They also look for patterns, similarities, and outliers within a dataset. ex. used in fields like pattern recognition, bioinformatics, image analysis, etc.

Forecast Model

One of the most commonly used models, forecast models use historical data to make predictions based on the past. It can be applied to both numerical datasets and non-numerical historical data. ex sales projections on previous year's sales

Outliers Model

As the name suggests, this model analyzes outliers and anomalies in datasets, or data points that fall outside a common trend. ex. flagging fraudulent transactions based on abnormal spending habits.

Time Series Model

This model is a a series of data points ordered in time. Time is usually an independent variable, and a chosen metric is compared over time. It's usually seeking to make a forecast for the future. ex. graphs that predict the future spread of Covid-19 over time.

Source: Top 5 Predictive Analytics Models and Algorithms