Predictive Modeling

Artificial intelligence has a growing number of individual functions that all fall under the umbrella of AI. Machine learning, data analytics, predictive analytics, and predictive modeling are all subsets of AI that all function together. Think of it as sort of an assembly line process by which intelligence is produced. Predictive modeling is essentially the final outcome of all the other processes leading up to it. Here is an overview of predictive modeling: what it is, where it comes from and the value it has to offer.

What is Predictive Modeling?

Imagine you designed a program that could analyze dozens of different data sets to determine specific erosion patterns at the Grand Canyon. To start with, you designed a bot that would scour various governmental and NGO databases to find which organizations were taking measurements that would benefit your study. The program could analyze satellite images taken of the Grand Canyon every day to determine exactly how much soil erodes or is piling up every day and then compile that with information from sensors placed on the floor of the canyon under the Colorado river to measure daily depth. The program could compile that data with daily measurements of water lines to determine annual ebbs and flows and the impact they have on the canyon itself.

Now imagine you were able to go back and gather historic weather and other geographical data to determine which events had what kind of impact on the canyon. Eventually, you would be able to compile enough data to make a remarkably accurate model of the Grand Canyon on almost every day of the year going back several decades. Now imagine you took that same data and used it to create a 3D rending of what the Grand Canyon will look like 30 years from now. That is predictive modeling. Predictive modeling is using data analysis to evaluate current patterns and trends, which then allows for enough predictive analytics to be generated to create a full-scale rendering of a future event. Predictive modeling provides a full-scale picture of the data generated by predictive analytics.

How is Predictive Modeling Used?

Imagine you tracked the traffic patterns in the area of the new SoFi stadium following both wins and losses of the LA Rams. Statistical analysis may conclude that traffic is 12% heavier at the end of the game after a loss than after a win. It may also determine that 30% of the tickets for outbound flights at nearby LAX are canceled or rescheduled for the next day following a win and are used as planned following a loss. It can look at other outcomes as well, such as public intoxication rates or under what conditions or circumstances fights may be most likely to break out.

Using that data, predictive analytics could then predict specific outcomes based on the outcome of the game. Statistical modeling, however, can show the ripple effect of the outcomes of statistical analysis. The truth is, any large sporting event like a football game can involve up to 75,000 fans or more. That many people suddenly exiting one location can, in turn, have a huge impact on a city. Predictive modeling can take the outcomes of data analysis and create a stunningly accurate picture of the ripple effects of what is most likely to happen following any major event. It can even predict the most likely ripple effects that can be caused by a much more minor event in conjunction with a number of other minor events. In other words, predictive modeling can be used to determine what types of events are likely to cause exactly the kind of "perfect storm" of circumstances that government officials and those tasked with safety often live most in fear of.

Using those models, however, officials and other entities can also create any number of emergency or even routine plans to deal with the most likely outcomes of any event. In fact, thanks to predictive modeling, they don't just have to plan for the most likely outcomes but they can even be better prepared for any outcome. For instance, predictive modeling may help the local police force to determine that they will need 12% more personnel following a loss to a rival team than they do following a loss to any other team and they need more personnel patrolling the streets than the area around the stadium following a win versus a loss. Connected scheduling systems can then use the data generated by predictive modeling to ensure the right amount of personnel is available in the right places.

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