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rajia7315
May 11, 2022
In Welcome to the Forum
Over the past few months, we have been working with a company called Static (a data science company from Brazil) to design features for predictive algorithms. One of the first considerations when working with predictive algorithms is choosing relevant data to train on. We Phone Number List decided quite naively to put together a list of web page features that we thought might offer some value. Our goal was simply to see if, from the features available, we could come close to predicting a web page's rank in Google. We learned early in this process that we had to put blinders on inaccessible data and hope for the best with what we had. What follows is an analysis of the data we collected, how Phone Number List we collected it, and useful correlations derived from the data. Data An initial problem was that we needed to access ranking data to get enough search engine results page (SERP) results to provide a useful training package. Fortunately, Get Phone Number List Stat has made this very easy. With Get Stat, we simply loaded keyword combinations in the top 25 service industries with the location of the top 200 cities (by size) in the United States. This resulted in 5,000 unique search terms (eg, “Charlotte Accountant” from Charlotte, NC). Our company, Consult webs, focuses on legal marketing, but we wanted the model to be more universal. After loading all 5,000 terms and waiting a day, we got around 500,000 search results that we could use to Phone Number List build our dataset. After finding it so simple, we collected the rest of the data. I had built several crawlers with Node.js, So I decided to create a feature extraction mechanism on top of the pre-existing work. Fortunately, Node.js is a great ecosystem for this type of work. Below I list Phone Number List several libraries that make Node wonderful for data collection: Ayleen Tetani - This is a node API for a third-party service that performs sentiment analysis, text mining, summarization, concept/keyword extraction, and named entity recognition (NER). Natural - A great natural language processing toolkit for node. This ignores what's available on Python, but was surprisingly useful for our purposes. Text Statistics - Useful for Phone Number List getting data on sentence length, reading level,
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