VII. Data Science and Technology in Finance

The intuition behind the Support Vector Machine Algorithm
Advanced algorithms enable computers to identify patterns, make decisions, and even predict the future based on data. Among these powerful tools, the Support Vector Machine (SVM) is notable for its effectiveness, especially in the field of classification. But how does it work? Let's demystify this algorithm, starting with one of its fundamental concepts: the hyperplane. Imagine you're at a park and you observe a wide, open field with various types of flowers scattered all around.

The intuition behind K-Means clustering
K-Means clustering: think of it as organizing a room full of strangers into smaller friend groups based on shared interests. Similarly, K-Means groups data points based on their similarities. In finance, this helps in classifying customers, investments, or market trends. By uncovering hidden patterns, K-Means offers valuable insights, guiding better decisions in portfolio management and customer service. A handy tool for making sense of vast data!

In finance, Supervised Machine Learning is like teaching a computer with a guide. You provide specific examples with correct answers, and it learns patterns for future predictions. It's a matching game with clear outcomes. Conversely, Unsupervised Machine Learning lets the computer explore data independently, finding hidden patterns or groupings. Think of giving a computer financial indicators without outcomes; it uncovers


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Registered Training Organization No. 24280185328 

Contact: Florian CAMPUZAN Phone: 0680319332 

© 2023 FINANCE TUTORING, All Rights Reserved.