This project utilizes the k-means algorithm to analyze small businesses in the United States and identify patterns and opportunities for economic growth. By clustering the businesses based on their economic data, we aim to uncover insights that can drive strategic decision-making. The k-means algorithm partitions the data into clusters, enabling us to identify similarities and differences among the small businesses and provide recommendations for targeted interventions and policies.

This project explores the application of Factor Analysis and Linear Discriminant Analysis for wine classification. The findings reveal underlying characteristics related to citric acid and density, leading to improved accuracy and enhanced sensitivity for the minority class. These techniques offer valuable insights for wine quality assessment.