By Meta S. Brown

Info mining is instantly turning into indispensable to making price and company momentum. the facility to notice unseen styles hidden within the numbers exhaustively generated via day by day operations permits savvy decision-makers to take advantage of each software at their disposal within the pursuit of higher company. via developing versions and checking out even if styles delay, it truly is attainable to find new intelligence that may switch your business's complete paradigm for a extra winning final result. info Mining for Dummies indicates you why it does not take an information scientist to realize this virtue, and empowers usual company humans to begin shaping a method correct to their business's wishes. during this booklet, you will study the hows and whys of mining to the depths of your information, and the way to make the case for heavier funding into information mining services.

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Many calls for service have been closed. ✓ The property is marketed for lease or sale. ✓ The property is in foreclosure. Although you have good reasons to believe that each of these factors is important, no one has yet confirmed their value by building and testing a predictive model. You’d like to investigate each of them — and others as well. But you don’t have adequate data for some, and the others will all require effort for data preparation. Your goal for this project is not to develop the greatest possible model, but to use the data to demonstrate that at least one variable has value for predicting changes in property ownership.

Figure 2-15: Filtering out cases with incomplete data. To identify property owners who don’t live in, or very close to, their properties, you want to compare the owner’s home zip code to the zip code of the property. You have data for each of those, but some challenges exist for comparing the two. Some of the zip codes are recorded as five digits; others are in longer formats. So you’ll need to get all the zip codes into a consistent format before you can create an indicator variable for properties whose owners are not local.

Later today, you’ll begin exploring the data. Perhaps you’ll begin to build a model that you’ll continue to refine and improve in the days to come. And of course, you’ll document all your work as you go. It’s just another day in the life of a data miner. This chapter shows you how it’s done. Starting Your Day Off Right You’ve had a good night’s sleep, and now you wake up early for a little exercise and a good breakfast. This has little to do with data mining, but it is a nice way to start your day.

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