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.

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.

An R implementation of Models As Approximations

Linear Regression for Julia

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

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

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

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

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…

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

A Regression Exercise covering OLS & Ridge Regression

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…

Simple Linear Regression

A project where data science job postings are scraped and an exploratory data analysis is performed.

In the following research, we will analyze the effects of pairs trading (multiple companies across multiple industries) excluding the profitability of such strategies. Rather, we will analyze various risk measures across all different pairings of stocks within their own respective industry across multiple industries.

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…

Compared Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) using R programming with interpretation

The goal of the project was to predict the price based on the given attributes of the car. It was done in Python, using Machine Learning techniques like Simple Linear Regression, Multiple Linear Regression and Decision tree.

An introduction into the world of machine learning with a comprehensive Udemy online course, designed for beginners, to learn Python programming fundamentals and gain valuable insights into the practical applications of machine learning.

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…