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.
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Updated
Sep 19, 2024 - TeX
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.
Machine Learning algorithms and models
The practical works (TP) of SD-TSIA204 - Statistics: linear models course at Télécom Paris.
Simple Linear Regression
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.
Ordinary Least Squares, Ridge Regression, Expectation Maximization, Full Bayesian Inference, Bayes Classifiers, kNN, and MLP core algorithms from scratch. Some auxiliary functions are also used.
Ordinary Least Squares and Normal Equations to Estimate Linear Regression Coefficients/Parameters
(Geo)spatial Statistics with R (Meuse)
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.
Trend Surface Analysis with R (Cape Flats Aquifer)
Trabajos presentados como parte del curso de Reconocimiento de Patrones y Aprendizaje Automatizado, impartido por el profesor Sergio Hernández López durante el semestre 2023-2 en la Facultad de Ciencias, UNAM.
Probability and Statistics for Machine Learning
Gentle yet comprehensive introduction to regression
Wrangled real estate data from multiple sources and file formats, brought it into a single consistent form and analysed the results.
Linear Regression for Julia
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.
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.
regression algorithm implementaion from scratch with python (least-squares, regularized LS, L1-regularized LS, robust regression)
Compared Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) using R programming with interpretation
PySpark for multiple linear regression on car horsepower using SMOTE for data augmentation.
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