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This Python notebook demonstrates the application of Support Vector Machines (SVM) for classification tasks on the MNIST dataset. The notebook covers data preprocessing, hyperparameter tuning, and dimensionality reduction using PCA.
This project is an implementation of Principal Component Analysis (PCA) in Python. PCA is a technique for dimensionality reduction and data visualization that aims to find the most important underlying patterns in a dataset.
Implementing PCA (Principal Component Analysis) from scratch for Dimensionality Reduction which is Reducing the number of input variables for a predictive model
It is a subset of variables from a study carried out in 1988 in different regions of the world to predict the risk of suffering a heart-related disease.