Textbook for IND5003 course
Implied Risk Premia Analysis with GARCH, Covariance, and PCA - Interactive Streamlit Demo
Multivariate & hierarchical analysis of climate change effects on pear production using Ecotron experimental data.
An end-to-end Data Science project predicting Human Development (HDI) using R. Features automated ETL (World Bank API), advanced EDA (PCA, Preston Curve), and a comparative analysis of Linear Regression vs. Random Forest models to uncover non-linear economic drivers.
Transversal project in R: Biostatistical análisis applied to genetic expression
HER2-Amplified vs Non-Amplified Breast Cancer Transcriptome Analysis (TCGA BRCA): Differential Expression, Pathway Enrichment, and Lasso-Cox Survival Stratification
'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
Materiales de las clases prácticas de AID y Aprendizaje Automático
Contains a collection of my experimentations, explorations, and data analysis of random datasets
Jupyter notebook with demonstration of PCA on a high-dimensional genomic data set.
This Model Classifies patient's diabetes level/severity in depth using 200k Samples from patients past patterns.
Customer segmentation analysis using unsupervised learning on German demographics data (Bertelsmann Arvato Analytics). The project applies data preprocessing, PCA for dimensionality reduction, and KMeans clustering to identify customer groups that are over-represented compared to the general population.
Exploration of PCA and K-Means clustering on the UCI Flags dataset, with EDA, dimensionality reduction, and interactive visualizations. This project was done in Python.
Understanding photothermal interactions can help expand production range and increase genetic diversity of lentil (Lens culinaris Medik.)
This repository serves as a collection of my work and learning in machine learning while my internship in Cellual-Technologies, including algorithm explanations, data preprocessing workflows, and two projects.
R package for High dimensional data analysis and integration with O2PLS!
A machine learning project that classifies facial emotions (happy vs. neutral) using Principal Component Analysis for feature extraction and Support Vector Machine for classification.
Built an unsupervised Machine Learning pipeline to detect anomalies in Bitcoin transactions by selecting 19 key features from 700.
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.
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