K-means, Spectral clustering, PCA, and Kernel PCA
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Updated
Jan 10, 2023 - Jupyter Notebook
K-means, Spectral clustering, PCA, and Kernel PCA
Winning one of the DACON competition
Implementation of supervised and unsupervised Machine Learning algorithms in python from scratch!
Analyzing and overcoming the curse of dimensionality and exploring various gradient descent techniques with implementations in R
This repository implements customer segmentation techniques to analyze credit card user behavior and identify distinct customer groups. By leveraging Python libraries like pandas, Scipy and scikit-learn.
Project on Non-Linear Dimensionality Reduction - ENSAE ParisTech
This repository explores the interplay between dimensionality reduction techniques and classification algorithms in the realm of breast cancer diagnosis. Leveraging the Breast Cancer Wisconsin dataset, it assesses the impact of various methods, including PCA, Kernel PCA, LLE, UMAP, and Supervised UMAP, on the performance of a Decision Tree.
Implementation of PCA and Kernel PCA algorithms from scratch with practical examples, including datasets and image processing tasks like compression and denoising.
Subnational Cholera Analysis in Yemen
📃 Exploration of Nonlinear Component Analysis as a Kernel Eigenvalue Problem
This repository is dedicated to the lab activities of the course of Unsupervised Learning @Units
Complete Tutorial Guide with Code for learning ML
This repository contains the Python code my blog post Image denoising techniques: A comparison of PCA, kernel PCA, autoencoder, and CNN. See post for more details and results.
Applying NLP methods and kernel PCA on news dataset to build a clustering model
UML dimensionality reduction and clustering models for predicting if a banknote is genuine or not based on the dataset from OpenML containing wavelet analysis results for genuine and forged banknotes - practical exercise. (Python 3)
Machine Learning assignments from coursework.
Houses a series of projects I worked on for a course in Data Mining that I took in my Ph.D. Data Science program at UTEP in the Fall of 2022. Covers areas such as Regularized Logistic Regression, Optimization, Kernel Methods, PageRank, Kernel PCA, Association Rule Mining, Anomaly Detection, Parametric/Nonparametric Nonlinear Regression, etc.
Notes, homework and project for PSU's STAT 672 Winter 2020
Unsupervised machine learning algorithm. Classical and kernel methods for non-linearly seperable data.
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