A Next-Product-To-Buy (or NPTB) model is designed to help retailers and marketers improve the effectiveness of cross-selling product recommendations by predicting the product each customer would be most likely to...
Machine learning (ML) is a branch of artificial intelligence (AI) in which models are created to predict an outcome by learning from patterns present in data. They can automatically improve...
Uplift modeling is a machine learning technique used in marketing and ecommerce to predict which customers are likely to respond to a particular marketing campaign. However, rather than simply predicting...
On-site search in ecommerce has improved massively in recent years, thanks to search systems such as Lucene, Solr, Algolia, and Elastic. Despite on-site search generating massive amounts of revenue for...
When you gain access to a new dataset, chances are, it’s probably not in the format you require for analysis or modeling. The most common problem you’ll encounter is datasets...
Market Basket Analysis, or MBA, is a subset of affinity analysis and has been used in the retail sector for many years. It provides a computational method for identifying common...
Hugging Face Transformers are a collection of State-of-the-Art (SOTA) natural language processing models produced by the Hugging Face group. Basically, Hugging Face take the latest models covered in current natural...
How many times have you struggled to get Python packages like TensorFlow, Keras, or PyTorch working together? How many times have you downloaded code or shared yours with others only...
Although scikit-learn’s machine learning estimator models can be used out-of-the-box with no tuning, you can usually generate further improvements with a little of tweaking. Each estimator class accepts arguments called...
