And quantifying the forecast uncertainty by creating prediction intervals is invaluable and often ignored in the forecasting world.

At a simplistic level, you feed a supervised AI algorithm some historical data, purchases at Target for example, and you tell the algorithm, “Hey, these purchases were from pregnant people, and these other purchases were from not-so-pregnant people.”

Your goals should be things like using data to facilitate better targeting, forecasting, pricing, decision-making, reporting, compliance, and so on. In other

Data science is the transformation of data using mathematics and statistics into valuable insights, decisions, and products.

You need to standardize each column so that the values are closer to the same center and spread.

At MailChimp, we retrain our models offline once a quarter, test them, and then promote them into production.

business receives feedback on how their audience is engaging at the individual level through click tracking, online purchases, social sharing, and so on.

You can use it to classify company e-mails, customer support transcripts, AP wire articles, the police blotter, medical documents, movie reviews, whatever!

best model is one that strikes the right balance between functionality and maintainability. If an analytics model is never used, it's worthless.