Predictive Modeling

- Overview

Predictive modeling is a mathematical technique that combines machine learning and AI with historical data to accurately predict future outcomes. It's a key part of predictive analytics, a type of data analytics that uses current and historical data to predict trends, activity, and behavior. 

Predictive modeling uses machine learning algorithms to identify correlations, trends, and statistical patterns in datasets. These models analyze large amounts of historical data to make accurate predictions and estimations about future events. 

Predictive modeling has been around for decades, but it's only recently been considered a subset of artificial intelligence. It can help teams improve their KPIs by taking a data-driven approach to decision-making. 

Predictive AI can help anticipate user behavior based on past activity. For example, in healthcare, predictive AI can help forecast potential future health conditions based on a person's medical history. 

Some examples of predictive modeling include: 

• Binary prediction: When the question asked has two possible answers, such as yes/no, true/false, on-time/late, or go/no-go.
• Machine learning algorithms: The most popular options for predicting values, identifying similarities, and discovering unusual data patterns. These include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, linear regression, logistic regression, and decision trees.