We recently covered the topic of predictive analytics with a video post on the basics of this emerging and impactful area of data analytics. Below we go a step further on the topic, covering predictive modeling and the critical role it plays in designing and building successful predictive analytics initiatives.
According to TechTarget, “Predictive modeling is a mathematical process used to predict future events or outcomes by analyzing patterns in a given set of input data. It is a crucial component of predictive analytics, a type of data analytics which uses current and historical data to forecast activity, behavior, and trends.”
As predictive analytics has evolved, so too have the types of predictive modeling methods that data analytics leaders can deploy. These include:
- Decision Trees: Takes data from multiple sources and maps potential future outcomes in different possible directions or “branches” based on past decisions.
- Time Series Analysis: Analyzes trends based on events that happen over a sequence of time, enabling the prediction of future events and timelines.
- Logistic Regression: Assists in data preparation. As more data is brought in, logistic regression enables algorithms to better sort and classify it, improving the ability to make more accurate predictions for the future.
- Neural Networks: Drawing upon the power of AI and machine learning, neural networks can review massive amounts of data to find deeper connections and correlations between data, uncovering predictions that previously were not possible.
If you’d like to learn more about predictive modeling, watch the TechTarget video below: