Principal Component Analysis (PCA) is a fundamental technique for dimensionality reduction. This report applies PCA to a well-known wine dataset containing the chemical analysis of 13 constituents for wines derived from three different cultivars. The objective is to reduce the dimensionality of the data, visualize the underlying structure, and investigate whether the chemical profiles alone can distinguish the wine types.

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.