Low-hanging Fruit of Predictive Analytics . . and Then the Rest

Most of the predictive power of predictive analytics comes after the all-important data clean-up and the application of a 1st-year uni stats method: Multiple Linear Regression (MLR). We’ll talk about what that is in another post, but for now, just appreciate that MLR is a really straight-forward technique that allows us to see how ‘smooth-changing’, ‘one-directional’ variables can combine to predict your desired output variable.

And most of the time that MLR method will get you to within about 10-30% of the best prediction. It means that at Antara PA we can get you a quick answer on whether your data is predictive.

After that, you can decide whether we apply the next level of tools in the armoury: machine learning methods like Artificial Neural Networks (ANN), Decision Trees (DT) and Support Vector Machines (SVM). These need a real analytics expertise and will improve your prediction by 10-30%.

Take home message: we can rapidly tell you whether your data is predictive. But usually you’ll want to go the next step. If your prediction is really working, why throw away 10-30% of your customers?

To contact us click here.

About Paul Pallaghy

I'm a PhD physicist & technologist with track records in computational physicist, AI, bioinformatics, structural biology, marine biology, outbreak epidemiology and multiple tech, biotech & agtech startups.
This entry was posted in Data clean up, low-hanging fruit, Machine learning, Multiple linear regression, Predictive analytics, Uncategorized. Bookmark the permalink.

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