Data Smart: Using Data Science to Transform Information into Insight

this way, clustering is called exploratory data mining, because these clustering techniques help tease out relationships in large datasets that are too hard to identify with an eyeball.

These predictors are often called model features or independent variables, while the thing we're trying to predict “Pregnant (yes/no)?” would be the dependent variable in the sense that its value is dependent on the independent variable data we're pushing into the model.

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

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

This story about Target is just one of many that have peppered the press recently. Watson won Jeopardy!. Netflix offered a million dollar prize to improve its recommendation system. The Obama re-election campaign used artificial intelligence to help direct ground, online, and on the air media and fundraising operations. And then there's Kaggle.com,

Most predictive model performance metrics are based on counts and ratios of four values that come from the predictions on our test set:

One way to strike this balance is to use clustering to create a market segmentation of your customers so that you can market to segments of your base with targeted content, deals, etc.

All you need to do is when you're done typing the formula, instead of pressing Enter, press Ctrl+Shift+Enter. On the Mac, you use Command+Return.

mean big business, more complex measures of prestige, infl uence, and centrality have evolved to account for such bad behavior.