Repo for project done as part of the Data Analytics ( UE19CS312 ) course at PES University

Predicting the european soccer championships matches with ordinal regression, using qualifier data

This research includes the analyses of my Master's thesis on women's educational attainment and intimate partner violence in Mexico

Water quality analysis of web-scraped data from EWG, and US Census to identify underserved communities at risk

Deep learning pipeline for knee osteoarthritis severity assessment from X-rays using ordinal classification.

Ordinal Logistic Regression with ElasticNet Regularization using Multi-Assay Epigenomics Data from CHDI NeuroLINCS Consortium.

Descriptive and Statistical Analysis on the Cancer Patients dataset

Predict customer star-ratings (1-5) for Olist e-commerce orders using numeric features and spaCy FastText text embeddings; compares linear, multinomial-logistic, and ordinal-logistic models with balanced training data and clear evaluation visuals.

Assignments from Microeconometrics

Utilización de técnicas multivariantes para el estudio del aprendizaje de la mejora de la accesibilidad en el subtitulado de vídeos

Analyze how maternal age, income, and education influenced postnatal depression levels during the COVID-19 pandemic using statistical modeling.

Power Link Functions in Modeling Dependent Ordinal Data

R package for controlled multiple imputation of ordinal or binary responses with missing data in clinical study

This project analyzes individuals' propensity to protest in Hong Kong, using World Values Survey Wave 7 data for various countries in Asia..

Covid stay days EDA and modeling with neural networks and random forests. (Bachelor's thesis)

Ordinal Regression. Mixed Models. Penalized Splines. Causal Inference. Random Effects. Trabajos Prácticos de la materia Intr. al Modelado Estadístico para Ciencias de Datos. 1C2025 FCEN

Coursework, Stata code, and notes for PBHS 32700: Biostatistical Methods (Spring 2024, University of Chicago). Topics include contingency tables, logistic regression, Poisson and negative binomial models, and survival analysis using Kaplan-Meier, Cox, and parametric models. The course emphasizes categorical and time-to-event analysis using Stata.

I used data from US.GOV regarding a survey that was carried out in Philadelphia in 2017 to see both how BikePGH donor-members, and Pittsburgh residents at large, feel about about sharing the road with AVs as a bicyclist and/or as a pedestrian.

A scalable ordinal regression model for classifying Python code snippets by readability (1-5)