Customer segmentation is a fundamental application of unsupervised learning in marketing analytics. This report employs the K-means clustering algorithm to segment customers of a retail mall based on demographic and behavioral variables—namely, age, annual income, and spending score. The objective is to derive actionable customer profiles to inform targeted marketing strategies.

Market Basket Analysis (MBA) is a fundamental data mining technique widely used in retail and e-commerce to discover relationships between products that are frequently purchased together. This knowledge enables retailers to optimize product placement, design effective promotional campaigns, and enhance customer experience through personalized recommendations. Association rules mining, particularly using the Apriori algorithm, provides a systematic approach to identify such patterns by analyzing transaction data. The rules generated take the form “if {item A} is purchased, then {item B} is also likely to be purchased,” quantified by metrics such as support, confidence, and lift. This study analyzes a dataset of supermarket transactions containing 22 common grocery items. The objective is to extract meaningful association rules that can provide actionable insights for retail decision-making.

Monthly oceanographic report

Dimention reduction of Countries by Mental Health and GDP in 2024