Predictive Analysis of Price on Amsterdam Airbnb Listings Using Ordinary Least Squares.

Set of functions to semi-automatically build and test Ordinary Least Squares (OLS) models in R in parallel.

Artigo submetido ao COBRAC 2018.

Linear line fitting to data and optimising parameters with Gradient Descent algorithm

A Regression Exercise covering OLS & Ridge Regression

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

Predicting housing prices in Iowa using Python/Pandas/linear regression within SKLearn.

Algorithms from scratch to know how the algorithms work.

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…

In this project, I have worked with some data on possums. It is a relatively small data set, but it's a good size to try with ordinary least squares (OLS) and least absolute deviation (LAD), and to gain experience with supervised learning. I have written my own methods to fir both OLS and LAD models, and then at the end compared them to the mode…

Explanations and Python implementations of Ordinary Least Squares regression, Ridge regression, Lasso regression (solved via Coordinate Descent), and Elastic Net regression (also solved via Coordinate Descent) applied to assess wine quality given numerous numerical features. Additional data analysis and visualization in Python is included.

Algorithmic Trading project that examines the Fama-French 3-Factor Model and the Fama-French 5-Factor Model in predicting portfolio returns. The respective factors are used as features in a Machine Learning model and portfolio results are evaluated and compared.

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…

My role in this group project was to perform regression analysis on quarterly financial data to predict a company's market capitalization. I used R to develop ordinary least squares (OLS), stepwise, ridge, lasso, relaxed lasso, and elastic net regression models. I first used stepwise and OLS regression to develop a model and examine its residual…

I contributed to a group project using the Life Expectancy (WHO) dataset from Kaggle where I performed regression analysis to predict life expectancy and classification to classify countries as developed or developing. The project was completed in Python using the pandas, Matplotlib, NumPy, seaborn, scikit-learn, and statsmodels libraries. The r…

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.)

An R implementation of Models As Approximations

Tutorials for BSE classes.

ML++ and cppyml: efficient implementations of selected ML algorithms, with Python bindings.