By Meta S. Brown
Info mining is instantly turning into crucial to making worth and company momentum. the facility to observe unseen styles hidden within the numbers exhaustively generated via day by day operations permits savvy decision-makers to use each instrument at their disposal within the pursuit of higher company. by way of growing types and trying out no matter if styles delay, it really is attainable to find new intelligence which may swap your business's whole paradigm for a extra winning consequence. facts Mining for Dummies exhibits you why it does not take a knowledge scientist to achieve this virtue, and empowers usual company humans to begin shaping a technique appropriate to their business's wishes. during this booklet, you are going to examine the hows and whys of mining to the depths of your information, and the way to make the case for heavier funding into facts mining features.
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Additional info for Data Mining For Dummies
In this model, you only see two bars in the chart, but lift charts for more complex models often have many bars. The greatest-confidence group is always the first bar on the left, and each subsequent bar has the next-greatest confidence. 5 percent of them, 549 cases, will be true changes in property ownership. The line through the bars shows that choosing 909 cases at random would only turn up about 280 true property changes. So the model is nearly doubling your effectiveness at predicting true property changes.
In theory, the public agency that shared the data can fill in those blanks. But you imagine phoning the property assessor’s office and explaining the problem, perhaps many times over, looking for someone who understands it and is willing to help. When you reach that person, you have no assurance that willingness to help will translate into success in correcting the flaws in the data. You think that you can do more productive things with that time and decide to live without those 30 cases. Then some cases have fewer than ten digits in their property codes.
You find that ✓ A few of the fields in the data from public sources simply don’t match the documentation provided (public data isn’t always perfect data). ✓ Additional notes are available to explain how some of the derived fields were created. ✓ Some of the undocumented data was obtained by web scraping (using specialized software to automatically extract information from websites), and you can’t find any dependable documentation for it. You update your notes about the data, revising them with additional documentation.