This project analyzes tumor cell data from 550 patients using Python. It involves data cleaning, exploratory analysis, feature engineering, and machine learning to classify tumors as malignant or benign. Techniques include PCA, logistic regression, and k-fold cross-validation to ensure model accuracy and reliability.

This repository contains a Python implementation of Principal Component Analysis (PCA) for dimensionality reduction and variance analysis. PCA is a powerful statistical technique used to identify patterns in data by transforming it into a set of orthogonal (uncorrelated) components, ranked by the amount of variance they explain.

Increase processing efficiency via principal components analysis

Analytics Professional Project Work

This repository contains functions/codes related to different methods of machine learning for classification and clustering in python.

Training machine learning models through supervised learning, utilizing PCA for visualization, and employing the k-means clustering algorithm for unsupervised learning on unlabelled data.

Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language

Faces recognition project using Support Vector Machines (SVM) and Principal Component Analysis (PCA). It utilizes the Labeled Faces in the Wild (LFW) dataset, employs dimensionality reduction with PCA, and fine‑tunes SVM hyperparameters using RandomizedSearchCV.

Slides, exercises, and exams for my course "Statistical Learning with R" (Ecole Normale Supérieure Paris-Saclay, 2023)

Fast Best-Subset Selection Library

ML assignment to implement Principal Component Analysis and Linear Regression.

Discovering Universal Geometry in Embeddings with ICA

An overview of my understanding of PCA for dimensionality reduction and Logistic Regression for model training and evaluation.

Practice Assignments for Data Science Coursework

A general purpose Snakemake workflow to perform unsupervised analyses (dimensionality reduction & cluster analysis) and visualizations of high-dimensional data.

Unsupervised learning! Using Principal component analysis for dimension reduction and Clustering analysis.

Explore the H1B Visa data from 2014 through a comprehensive multivariate analysis.

Comparative analysis of different feature extraction techniques for hyperspectral image classification.

pca: A Python Package for Principal Component Analysis.

GreeDS algorithm from Pairet etal 2020. Re-implemented to be independent from MAYONNAISE