customer segmentation and effective cross selling money makes the world go round and in the current ecosystem of data intensive business practicesit is safe to claim that data also makes the world go round very important skill set for data scientists is to match the technical aspects of analytics with its business valuei its monetary value this can be done in variety of ways and is very much dependent on the type of business and the data available in the earlier we covered problems that can be framed as business problems (leveraging the crisp-dm modeland linked to revenue generation in this we will directly focus on two very important problems that can directly have positive impact on the revenue streams of businesses and establishments particularly from the retail domain this is also unique in the way that we address different paradigm of machine learning algorithm altogetherfocusing more on tasks pertaining to pattern recognition and unsupervised learning in this first we digress from our usual technical focus and try to gather some business and domain knowledge this knowledge is quite importantas often this is the stumbling block for many data scientists in scenarios where perfectly developed machine learning solution is not productionalized due to lack of focus in the actual value obtained from the solution based on business demands firm grasp on the underlying business (and monetarymotivation helps the data scientists with defining the value aspect of their solutions and hence ensuring that they are deployed and contribute to generation of realizable value for their employers for achieving this objectivewe will start with retail transactions based dataset sourced from uci machine learning repository (use this dataset to target two fairly simple but important problems the detailed codenotebooksand datasets used in this are available in the respective directory for in the github repository at customer segmentationcustomer segmentation is the problem of uncovering information about firm' customer basebased on their interactions with the business in most cases this interaction is in terms of their purchase behavior and patterns we explore some of the ways in which this can be used market basket analysismarket basket analysis is method to gain insights into granular behavior of customers this is helpful in devising strategies which uncovers deeper understanding of purchase decisions taken by the customers this is interesting as lot of times even the customer will be unaware of such biases or trends in their purchasing behavior (cdipanjan sarkarraghav bali and tushar sharma sarkar et al practical machine learning with python