Greetings! Welcome to Venky Rao's blog on Statistics, Predictive Analytics, Geospatial Analytics and Visualization. This blog aims to de-mystify statistics and predictive analytics for the layman.
Introduction to Predictive Analytics (video blog)
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I recently presented at an event in Nashville introducing Predictive Analytics to the audience and demonstrating some live applications. Here is the video: http://vimeo.com/80917063
There appears to be a lot of confusion between the terms Artificial Intelligence (AI), Machine Learning (ML), Neural Networks (NN) and Deep Learning (DL). Based on research from various popular blogs and articles, here is my attempt at a simple visual explanation:
Lift and Gain Charts are a useful way of visualizing how good a predictive model is. In SPSS, a typical gain chart appears as follows: In today's post, we will attempt to understand the logic behind generating a gain chart and then discuss how gain and lift charts are interpreted. To do this, we will use the example of a direct mailing company. Let us assume that based on experience, the company knows that the average response rate on its direct mail campaigns is 10%. Let us further make the following assumptions: * Cost per ad mailed = $1 * Return per response = $50 Additionally, let us assume that the company mails out ads in lots of 10,000. Based on these assumptions, if the company mails out 100,000 ads, a table summarizing the results it would obtain from this campaign is provided below: Now let us assume that the company uses SPSS Modeler to develop a predictive model using data from previous campaigns. "Response / No Response" is identified as the &
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