A project using Spotify data to assign archetypes to users using PCA

Materiales de las clases prácticas de AID y Aprendizaje Automático

Unsupervised machine learning project 2: Alcanzando los objetivos de desarrollo sostenible, uso de NLP para clasificación de documentos.

Understanding photothermal interactions can help expand production range and increase genetic diversity of lentil (Lens culinaris Medik.)

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

In this repository we perform Principal component analysis ( PCA ) on swiss dataset & t-distributed Stochastic Neighbor Embedding (t-sne) on optdigits dataset.

Investigate personnel elements influencing organizational dynamics by looking at HR analytics data using python and advanced machine learning models. Forecast employment status, estimate the period of termination, and maximize performance and satisfaction initiatives.

A formula interface for model-first PCA; PCA for the people!

Language: R. Study, Exploratory Data Analytics and Data Visualizations about stationarity in data scientists roles applying the following techniques: PCA, Factor Analysis, Clustering, KMeans and Hierarchical Clustering.

Application of PCA and K-means algorithms using R on FIFA19 data set.

This repository includes my data sciences projects while working at the Sustainable Transitions Lab at Dartmouth as a First-Year Research in Engineering Intern.

This dataset from "ShufersalML" captures customer order history, aiming to predict future purchases using Python. It involves interconnected files that detail customer orders over time. The goal is to build a predictive model leveraging past order patterns to anticipate which products a user is likely to include in their next order.

GitHub repo for customer data analysis to drive personalized marketing strategies and enhance engagement, loyalty, and revenue

'Live fuel moisture and shoot water potential exhibit contrasting relationships with leaf-level flammability thresholds during laboratory flammability tests', by Indra Boving, Joe Celebrezze, Aaron Ramirez, Ryan Salladay, Leander Love-Anderegg and Max Moritz

LinguaNet is a language identification model built on DNNs using Python and TensorFlow. It utilizes character n-grams for accurate language classification. With an 89.2% accuracy, LinguaNet effectively identifies and differentiates languages. The repository includes model details, visualization of learned features, and implementation code.

Facilitating deep phenotyping by automating the screening of case studies & the comparison of patient similarities (through clustering). This can be used to get an understanding of the underlying pathophysiology for a rare genetic disorder.

Customer Segmentation done by Clustering, with dimensional reduction with PCA

Process Analytics Course 2023 delivered by Dr. Sal Garcia @ The Sargent Center

Dimension Reduction in R