Applied Data Science and Analytics | Blog-Post

Running head: APPLIED DATA SCIENCE AND ANALYTICS
APPLIED DATA SCIENCE AND ANALYTICS
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APPLIED DATA SCIENCE AND ANALYTICS2
The blog post tries to create an understanding of a data science problem created by
Kaggle which challenges users (https://medium.com/kaggle-blog/instacart-market-basket-
analysis-feda2700cded ) to predict which grocery stores will be purchased by an Instacart
customer and when. The post then takes the reader behind the working of the solution by one
of the 2nd place holder of solving this problem.
Retail is a vast industry with big companies opening chains across different countries.
As the growth of a retail chain expands from a modest size to cross country and even an inter
country network the prospect of information regarding its operations becomes enormous and
ripe for using to make informed recommendations for strategic decision making.
It tries to know in advance when a stock of a particular product bought by a consumer
will end and the consumer will purchase the product again. The difference between predicting
which products a customer might want to buy that uses a recommendation algorithm and this
particular case is that this problem relies on understanding temporal behavioural patterns.
Whereas Netflix might be fine assuming you want to watch another movie similar to the one
you just watched, it’s less clear that you’ll want to reorder a fresh batch of almond butter or
toilet paper if you bought them yesterday.
The goal of the problem is to correctly predict grocery reorders. The data provided has a
user’s purchase history and the aim of the problem is to predict which of their previously
purchased product they will reorder.
The problem was solved by a Japanese data scientist Kazuki Onodera and he used a mixture
of gradient boosted tree models, complex feature engineering, and a special modelling of the
competition’s F1 evaluation metric to solve the problem.