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1. Supervised-Learning-Algorithms-diabetic-data Supervised-Learning-Algorithms-diabetic-data Public

   A comprehensive application of supervised learning methods on the Diabetes Dataset, including Linear, Ridge, Lasso, ElasticNet, SVR, KNN, Random Forest, Gradient Boosting, XGBoost, AdaBoost, Bayesi…

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2. Baseline-Correction Baseline-Correction Public

   correct baseline for spectroscopy data with polynomial subtraction and asymmetric least square method

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3. unsupervised_learning_MNIST unsupervised_learning_MNIST Public

   This repository applies various unsupervised learning methods to the MNIST dataset of handwritten digits. Techniques include k-Means, Hierarchical Clustering, DBSCAN, GMM, PCA, t-SNE, Autoencoders,…

   Jupyter Notebook