Econometrics cheat sheets with a concise review of the subject, going from the basics of an econometric model to the solution of the most popular problems.

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.

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

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

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

MITx - MicroMasters Program on Statistics and Data Science - Data Analysis: Statistical Modeling and Computation in Applications - First Project

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.