Predictive Analytics summary

As our ability to use technology for predictive purposes gets better and more sophisticated, an important question arises: How many spoilers do you want to be given about your life? And, more importantly, how many lives are you willing to spoil?

But it’s more than just the question of knowing about your future; a more pressing concern with predictive analytics, and the data mining that goes hand-in-hand with it, is privacy.

These days data is a valuable and essential business commodity, and we’re producing more of it every single day. In predictive analytics, the philosophy is: the more data, the better – as long as it’s well balanced.

This means you have to be selective in the data you use, and add equal amounts of each type.

As we've seen, predictive analytics benefits from machine learning, so that, over time, a model can get even more precise in its predictions.

But there’s another very important benefit of machine learning, which is its ability to recognize disguised risks, or “microrisks.”

Like aspiring artists and entrepreneurs, predictive analytics has benefited from crowdsourcing. By opening itself up to the collective mind of the public, PA has been able to reap the rewards of ensemble modeling.

The ensemble model is a combination predictive model, and it was developed through the kind of competition and cooperation that crowdsourcing contests promote.

The increased power of ensemble models has been put to use for some fascinatingly complex projects, including the ability of machines to process natural language.

One of the biggest challenges in any project involving computational linguistics is picking up on all the subtle nuances in human speech.

Do you hate getting spam from mobile-phone companies and creditors? The good news is that advancements in predictive modeling now let companies know which people are receptive to unprompted ads and which ones are best left alone.

No company wants to annoy its target audience into avoiding its product or service, so there’s a need to be subtly persuasive, which is where the art of predictive analytics is headed.