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How To Use Python To Test SEO Theories (And Why You Should)

Learn how to test your SEO theories using Python. Discover the steps required to pre-test search engine rank factors and validate implementation sitewide. When working on sites with traffic, there is as much to lose as there is to gain from implementing SEO recommendations The downside risk of an SEO implementation gone wrong can be mitigated using machine learning models to pre-test search engine rank factors. Pre-testing aside, split testing is the most reliable way to validate SEO theories before making the call to roll out the implementation sitewide or not. We will go through the steps required on how you would use Python to test your SEO theories. Choose Rank Positions One of the challenges of testing SEO theories is the large sample sizes required to make the test conclusions statistically valid. Split tests – popularized by Will Critchlow of SearchPilot – favor traffic-based metrics such as clicks, which is fine if your company is enterprise-level or has copious traffic. If you...
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What is machine learning (ML)?

Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy.

How does machine learning work?

UC Berkeley (link resides outside ibm.com) breaks out the learning system of a machine learning algorithm into three main parts.

  1. A Decision Process: In general, machine learning algorithms are used to make a prediction or classification. Based on some input data, which can be labeled or unlabeled, your algorithm will produce an estimate about a pattern in the data.
  2. An Error Function: An error function evaluates the prediction of the model. If there are known examples, an error function can make a comparison to assess the accuracy of the model.
  3. A Model Optimization Process: If the model can fit better to the data points in the training set, then weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will repeat this iterative “evaluate and optimize” process, updating weights autonomously until a threshold of accuracy has been met. 

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