Multiple econometrics cheat sheets with a complete and summarize review 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.

Algorithms from scratch to know how the algorithms work.

Linear Regression for Julia

An R implementation of Models As Approximations

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

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…

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…

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.

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.

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…

Predicting Delivery Time Using Sorting Time

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

Tutorials for BSE classes.

You will have to build a logistic regression model and interpret the result. Make sure you partition the data set by allocating 70% -for training data and 30% -for validating the results.

(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