The client needed to analyse the patterns of customer that lead to buying a product by them. In this the main task was to find out the the average transaction value + If there was a significant difference between spend in different store types.
- First of all we dig in the the customer data-set that contained various parameters related to customer background and buying details.
- Followed by feature selection to extract important features and then trained our model with supervised machine learning algorithm to find the probability of a user clicking on a particular content on website.
We created a production system to deliver the most relevant content card to the user on the website.