How to create a Google rank checker tool using Python

Import the packages First, open a Jupyter notebook and install my EcommerceTools Python package. EcommerceTools includes a range of modules for performing a range of ecommerce and marketing tasks, including...

How to use the Feefo API for ecommerce competitor analysis

Most ecommerce websites use review platforms, such as Feefo, Trustpilot, and Google Reviews, to allow customers to give feedback on their service and the products they sell. The reviews help...

How to compare time periods using the Google Search Console API

One common task you’ll perform in Google Search Console is to compare the data from two different time periods to see how impressions, clicks, click-through rate (CTR), or average position...

How to create a Google Service Account client secrets JSON key

The Google Cloud Platform offers a variety of ways for users, or applications, to authenticate themselves in order to gain access to data. For Python developers, one of the most...

A quick guide to the RFM model for data scientists

The RFM model is probably one of the best known and most widely used customer segmentation models by data driven marketers. It’s used for both measuring customer value and predicting...

How to run time-based SEO tests using Python

One of the problems with search engine optimisation or SEO is that search engine algorithms are essentially black boxes. They analyse so many on-page and off-page factors, and use multiple...

How to create content recommendations using TF IDF

After work, when I’m not learning about data science, practising data science, or writing about data science, I like to browse classic car auction sites looking for cars I can’t...

How to create a contractual churn model in scikit-learn

A growing proportion of what we buy regularly is purchased via a subscription, or some other kind of contract. Most people have contracts for their internet, mobile phone, car insurance,...

How to avoid model overfitting with early stopping rounds

One issue with the more sophisticated algorithms, such as Extreme Gradient Boosting, is that they can overfit to the data. This basically means that the model picks up the idiosyncrasies...