Master of Science in Business Analytics Candidate, Northeastern University, D’Amore-McKim School of Business
𝗗𝗮𝘆 18: 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 🚀 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗦𝗰𝗵𝗼𝗼𝗹: 𝗛𝗮𝗿𝗻𝗲𝘀𝘀𝗶𝗻𝗴 𝗣𝗖𝗔 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 🚀 In data analysis, more isn’t always better—large datasets with many variables can obscure patterns and increase processing time. That’s where dimensionality reduction techniques like Principal Component Analysis (PCA) come into play. In the Data Mining and Machine Learning for Business course, I explored how PCA transforms complex datasets into simpler, actionable representations without losing critical information. 𝗞𝗲𝘆 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗶𝗻 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻: • 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗮𝗹 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗣𝗖𝗔) : PCA identifies the most significant features in a dataset, projecting them onto new axes to minimize redundancy. 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: Reducing noise in high-dimensional datasets, improving the accuracy of clustering and classification tasks. Simplifying visualizations for multi-dimensional data, enabling more intuitive insights. • 𝗚𝗲𝗼𝗺𝗲𝘁𝗿𝗶𝗰 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗣𝗖𝗔 : Understanding eigenvalues and eigenvectors helped me grasp how PCA retains variance while discarding irrelevant dimensions. By focusing on principal components, I could distill complex relationships into a manageable format for analysis. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 : Customer Segmentation: Streamlined analysis by reducing hundreds of behavioral variables to a handful of meaningful components. Supply Chain Optimization: Simplified operational data to identify key drivers of inefficiency and cost reduction. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀: Dimensionality reduction is a cornerstone of efficient data analysis. It ensures that decision-makers focus on the most relevant insights, reducing noise and improving interpretability. PCA, in particular, empowers analysts to tackle high-dimensional data challenges with precision and clarity. In the next post, I’ll bring together the course’s key techniques—regression, clustering, association rules, and dimensionality reduction—to highlight how they collectively solve real-world business problems. 𝗙𝗲𝗹𝗹𝗼𝘄 𝗮𝗻𝗮𝗹𝘆𝘀𝘁𝘀: 𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝘆𝗼𝘂𝗿 𝗴𝗼-𝘁𝗼 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 𝗳𝗼𝗿 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀? #BusinessAnalytics #DataMining #MachineLearning #PCA #DimensionalityReduction #DAmoreMcKim #CareerJourney
