Data exploration from: An experiment in trying to predict Google rankings

Introduction Over the last few months, we have been working with a company named Statec (a data science company from Brazil) to engineer features for predictive algorithms. One of the initial considerations in working with predictive algorithms is picking relevant data to train them on. We set out quite naively to put together a list of webpage features that we thought may offer some value. Our goal was simply to see if from available features, we could get close to predicting the rank of a webpage in Google. We learned soon into this process that we had to put blinders on to data that was unreachable and hope for the best with what we had. The following is an analysis of the data we collected, how we collected it and useful correlations derived from the data. The data One initial problem was that we needed to gain access to ranking data for enough search engine results page (SERP) results to provide a Search Engine Land Source

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