(Geo)spatial Statistics with R (Meuse)

This repository contains a comprehensive implementation of gradient descent for linear regression, including visualizations and comparisons with ordinary least squares (OLS) regression. It also includes an additional implementation for multiple linear regression using gradient descent.

Gentle yet comprehensive introduction to regression

Algorithms from scratch to know how the algorithms work.

Implemented ordinary least squares regression from scratch in python by computing root mean square error and coefficient estimates

Machine Learning algorithms and models

Ordinary Least Squares problem, guide, and solver

Trabajos presentados como parte del curso de Reconocimiento de Patrones y Aprendizaje Automatizado, impartido por el profesor Sergio Hernández López durante el semestre 2023-2 en la Facultad de Ciencias, UNAM.

As part of a group project, I developed separate regression models using R to predict the daily number of batteries and robberies in Chicago using four different datasets. I tested interactive and second-order terms and used stepwise feature selection to find the best model with the given data. I tested several potential models using cross-valid…

Basic Functions and algorithms of Statistics used in Data Analysis and data-science

Data about 5,634 married women (out of which 3,286 are reported being in the labor force) is taken from the Wooldridge Current Population Survey (CPS91) Database for Wage/Income analysis. There are 24 variables that give information about married women, their husbands, their demographics, if they belong to any unions, or are a part of labor forc…

Causal Inference Case Studies

Probability and Statistics for Machine Learning

PySpark for multiple linear regression on car horsepower using SMOTE for data augmentation.

Fits JxV curves obtained from solar cells operating in the dark and calculates important parameters

Ordinary Least Squares and Normal Equations to Estimate Linear Regression Coefficients/Parameters

Ejercicio de regresiones por distintos métodos (Mejor Selección de Conjuntos, Selección de pasos hacia adelante, Ridge, LASSO, Elastic Net, Componentes Principales, Mínimos Cuadrados Parciales, etc.)

🐟 Statistical analysis of fish dimensions and weights implemented into linear regression (Ordinary Least Squares) predictive model

Trend Surface Analysis with R (Cape Flats Aquifer)

Ordinary Least Squares, Ridge Regression, Expectation Maximization, Full Bayesian Inference, Bayes Classifiers, kNN, and MLP core algorithms from scratch. Some auxiliary functions are also used.