MiniBatchKMeans, PCA, and t-SNE for Exploratory Data Analysis | Data Science Tutorial || AI || EDA |
Source Code :- https://github.com/sangramdhurve/EDA/...
we will explore three powerful techniques for exploratory data analysis (EDA) in data science: MiniBatchKMeans, PCA (Principal Component Analysis), and t-SNE (t-Distributed Stochastic Neighbor Embedding). We will start with an introduction to EDA and then dive into the details of these techniques. We will cover topics such as data preprocessing, feature scaling, dimensionality reduction, and data visualization. We will also demonstrate how to use MiniBatchKMeans for clustering, PCA for reducing dimensionality, and t-SNE for visualizing high-dimensional data in two or three dimensions. By the end of this video, you will have a solid understanding of how these techniques can be applied to EDA in data science.
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