Sentiment Analysis is widely used across industries to track how customers react to various stimuli, like in social media algorithms. With the rise of advanced machine learning algorithms, we can analyze many different types of data to make informed decisions on business strategy. One such recent breakthrough is the use of video and audio data to classify human emotions, and this project covers a detailed approach to doing so without any complex neural networks. No matter how experienced you are, I’m sure you will find this project very interesting!

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This project leverages the power of Higher-Order Discriminant Analysis (HODA) and Support Vector Machines (SVM) to classify celebrity photos. And yes, this project does not use any neural networks! Facial recognition technology has become a crucial tool in modern society due to its wide-ranging applications in security, healthcare, social media, and entertainment. By enabling machines to identify and verify individuals based on their facial features, it enhances security systems, simplifies user authentication processes, and offers personalized user experiences.

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Customer churn is a core component of marketing analytics and marketing-focused data science. Analyzing customer churn is crucial for shaping business strategy as it provides insights into why customers leave, allowing businesses to identify and address underlying issues, improve customer retention, and enhance overall customer satisfaction. By understanding the factors that contribute to churn, companies can implement targeted interventions, optimize their products or services, and tailor marketing efforts to retain existing customers and attract new ones. Did this pique your interest? Then let’s dive right in!

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