dataset on restaurants to perform market basket analysis

Product review datasets are great for improving your Natural Language Processing skills, particularly sentiment analysis.

Retailrocket recommender system dataset.

eCommerce behavior data from multi category store.For example, the pages they viewed, items they searched for, or products they added to their basket.Ĭlick-stream datasets can be used in several ways, including predicting whether customers will purchase during their visit, Learning to Rank models which seek to examine search relevance and define the optimum order for search results, response models to predict who will purchase on a subsequent visit based on recent online activity, and in-visit recommender systems. Marketing Promotion Campaign Uplift ModellingĬlick-stream datasets include details on the actions customers performed when visiting a website.Marketing response datasets can be used for: marketing response models, customer propensity models, uplift models, and marketing targeting models, such as those to identify Marketing Qualified Leads (MQLs). Usually they’re based on the response to a single campaign, but sometimes they come in the form of a data set comprising a built-in marketing test and control, which can be really useful in modeling.

Marketing response datasets contain the details on whether customers responded to a marketing campaign or not.

Brazilian E-Commerce Public Dataset by Olist.

Transactional item datasets can be used in a variety of models including: customer churn models, time series forecasting models, customer segmentation models, customer clustering models, product recommender systems, Market Basket Analysis, and Next-Product-To-Buy or NPTB models. Transactional item datasets include a row for each item a customer purchased, so can be used to construct secondary datasets on customers and products. To make the data set hunting process a little easier, I’ve compiled a selection of some of the most useful, interesting, or most widely used datasets for creating models in the ecommerce, retail, and marketing sector. It can, therefore, be quite a time-consuming process to sift through all the dataset repositories in order to find the right one for the job. However, the datasets aren’t always curated, aren’t always free of charge, vary in quality, and might not include the right features for the model you’re aiming to build. There are various places you can track these down (see below). If you read research papers on machine learning, you’ll notice that many researchers use the same standard datasets so other data scientists can reproduce their work or try and improve upon it.

Dataset on restaurants to perform market basket analysis