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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
Task: To predict the percentage of a student based on the number of study hours.
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
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…
Predictive Analysis of Price on Amsterdam Airbnb Listings Using Ordinary Least Squares.
Compared Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) using R programming with interpretation
Regression algorithm implementaion from scratch with python (OLS, LASSO, Ridge, robust regression)
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…
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.
PySpark for multiple linear regression on car horsepower using SMOTE for data augmentation.
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.
Simple Linear Regression
Algorithms from scratch to know how the algorithms work.
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