ordinary-least-squares

There are 0 repository under ordinary-least-squares topic.

marcelomijas / econometrics-cheatsheet
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.
cheatsheet latex econometrics statistics economics ordinary-least-squares ols-regression
Language:TeX 95
fischlerben / Algorithmic-Trading-Project
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.
algorithmic-trading machine-learning linear-regression ordinary-least-squares anova fama-french portfolio-analysis
Language:Jupyter Notebook 25
regularized-linear-regression-deep-dive
wyattowalsh / regularized-linear-regression-deep-dive
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.
regularized-linear-regression lasso-regression ridge-regression elastic-net-regression coordinate-descent optimization python jupyter-notebook ordinary-least-squares regularization regression
Language:Jupyter Notebook 13
shamindras / maars
An R implementation of Models As Approximations
statistics linear-regression ordinary-least-squares r
Language:R 11
ericqu / LinearRegressionKit.jl
Linear Regression for Julia
julia julia-language julia-package linear-regression ordinary-least-squares ridge-regression
Language:Julia 10
dgwozdz / OLS
Set of functions to semi-automatically build and test Ordinary Least Squares (OLS) models in R in parallel.
linear-regression parallelization statistics r statistical-tests ols ordinary-least-squares
Language:R 7
romanwerpachowski / ML
ML++ and cppyml: efficient implementations of selected ML algorithms, with Python bindings.
numerical-methods machine-learning-algorithms machine-learning numerical-computation efficiency decision-trees em-algorithm linear-regression python-bindings efficient-implementations regression cppyml recursive-regression ordinary-least-squares ridge-regression clustering logistic-regression
Language:C++ 5
micts / airbnb-price-prediction
Predictive Analysis of Price on Amsterdam Airbnb Listings Using Ordinary Least Squares.
airbnb price-prediction ordinary-least-squares linear-regression amsterdam predictive-analysis airbnb-listings
Language:R 4
omerfarukeker / Gradient-Descent-Visualisation
Linear line fitting to data and optimising parameters with Gradient Descent algorithm
gradient-descent gradient-descent-algorithm least-squares least-sqaure-method ordinary-least-squares animation
Language:Python 4
bhattbhavesh91 / regression-excercise-ols-ridge
A Regression Exercise covering OLS & Ridge Regression
linear-regression multicollinearity multiple-linear-regression ols-model ordinary-least-squares ridge-regression
Language:Jupyter Notebook 2
gilaniasher / kaggle-house-regression-challenge
Predicting housing prices in Iowa using Python/Pandas/linear regression within SKLearn.
sklearn python pandas machine-learning automated-feature-engineering ordinary-least-squares kaggle
Language:Jupyter Notebook 2
jajokine / Statistics-Analysis
MITx - MicroMasters Program on Statistics and Data Science - Data Analysis: Statistical Modeling and Computation in Applications - First Project
statistics statistical-analysis statistical-models statistical-inference statistical-tests ordinary-least-squares ols-regression-model gradient-descent-algorithm multiple-linear-regression
Language:Jupyter Notebook 2
tboudart / Financial-Markets-Regression-Analysis
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 plots. The plot displaying the residuals against the predicted values indicated multiplicative errors. I, therefore, took the natural log transformation of the dependent variable. The resulting model's R2 was significantly, negatively impacted. After examining scatter plots between the log transformation of market capitalization and the independent variables, I discovered the independent variables also had to be transformed to produce a linear relationship. Using the log transformation of both the dependent and independent variables, I developed models using all the regression techniques mentioned to strike a balance between R2 and producing a parsimonious model. All the models produced similar results, with an R2 of around .80. Since OLS is easiest to explain, had similar residual plots, and the highest R2 of all the models, it was the best model developed.
corrplot dplyr psych amelia leaps car glmnet caret vcd ca stringr tidyr ordinary-least-squares stepwise-regression ridge-regression lasso-regression relaxed-lasso elastic-net-regression regression-analysis r-markdown
2
tboudart / Life-Expectancy-Regression-Analysis-and-Classification
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 regression models were fitted on the entire dataset, along with subsets for developed and developing countries. I tested ordinary least squares, lasso, ridge, and random forest regression models. Random forest regression performed the best on all three datasets and did not overfit the training set. The testing set R2 was .96 for the entire dataset and developing country subset. The developed country subset achieved an R2 of .8. I tested seven different classification algorithms to classify a country as developing or developed. The models obtained testing set balanced accuracies ranging from 86% - 99%. From best to worst, the models included gradient boosting, random forest, Adaptive Boosting (AdaBoost), decision tree, k-nearest neighbors, support-vector machines, and naive Bayes. I tuned all the models' hyperparameters. None of the models overfitted the training set.
pandas numpy seaborn scikit-learn statsmodels ordinary-least-squares ridge-regression lasso-regression random-forest-regression naive-bayes-classifier k-nearest-neighbor-classifier decision-tree-classifier random-forest-classifier support-vector-classifier adaboost-classifier gradient-boosting-classifier jupyter-notebooks python matplotlib-pyplot
Language:Jupyter Notebook 2
academicPapers / dist_lognormal
Artigo submetido ao COBRAC 2018.
lognormal-distribution ordinary-least-squares
Language:HTML 1
aneeshdurai / OLS-Pairs-Trading
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.
ordinary-least-squares pairs-trading
Language:Jupyter Notebook 1
caralifarrell / Beginner-Machine-Learning-in-Python-ChatGPT-Bonus-2023
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.
linear-regression multiple-linear-regression ordinary-least-squares r-squared accuracy-ratio adjusted-r-squared classification clustering confusion-matrix data-science feature-scaling k-means-clustering logistic-regression machine-learning maximum-likelihood python
Language:Jupyter Notebook 1
DolbyUUU / regression_algorithm_implementation_python
regression algorithm implementaion from scratch with python (least-squares, regularized LS, L1-regularized LS, robust regression)
python regression regularization l1-regularization l2regularization lasso-regression maximum-likelihood-estimation ordinary-least-squares quantile-regression robust-regresssion
Language:Python 1
gmkoeb / dark-fitter
Fits JxV curves obtained from solar cells operating in the dark and calculates important parameters
ordinary-least-squares python3 solar-cells dark-jxv-measurements fitting-curve fitting
Language:Python 1
MahtabEK / Supervised-learning-Linear-models-and-Loss-functions
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 models produced by the statsmodels package.
supervised-machine-learning linear-models ordinary-least-squares least-absolute-deviation statsmodels rdd loss-functions
Language:Jupyter Notebook 1
MichaelAlexanderBryant / simplyhired-eda
A project where data science job postings are scraped and an exploratory data analysis is performed.
exploratory-data-analysis data-scraping ordinary-least-squares data-cleaning
Language:Jupyter Notebook 1
michstg / Geographically-Weighted-Regression-GWR-Poverty-North-Sumatera
Compared Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) using R programming with interpretation
cross-validation exploratory-data-analysis geographical-data geographically-weighted-regression gwr gwrp maps ordinary-least-squares r-package r-programming regression-models statistical-analysis weighted-regression north-sumatera rasio-kemiskinan
1
MoinDalvs / Simple_Linear_Regression_1
Predicting Delivery Time Using Sorting Time
sklearn sklearn-library ordinary-least-squares ols-regression prediction predictive-modeling simple-linear-regression pandas-dataframe pandas-library numpy matplotlib residuals rmse-score log-transformation data-transformation aic bic f-statistics likelihood
Language:Jupyter Notebook 1
MoinDalvs / Simple_Linear_regression_2
Building a prediction model for Salary hike using Years of Experience
simple-linear-regression scipy-stats ols-regression ordinary-least-squares sklearn-library data-transformation log-transformation prediction-model
Language:Jupyter Notebook 1
PanicMilica / car-price-prediction-python
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.
config github-config machine-learning python prediction-model prediction linear-regression ordinary-least-squares multiple-linear-regression decision-tree
Language:Jupyter Notebook 1
philsaurabh / Tutorials
Tutorials for BSE classes.
pandas numpy statmodels scikit-learn anova-analysis hypothesis-testing linear-regression multiple-linear-regression machine-learning supervised-learning matplotlib seaborn pca-scratch ttest chisquare pvalue principal-component-analysis tvalue ordinary-least-squares rmse-score
Language:Jupyter Notebook 1
ShaikRiyazSandy / Simple-Linear-Regression
Simple Linear Regression
aic bic data-transformation f-statistics likelihood log-transformation matplotlib numpy ols-regression ordinary-least-squares pandas-dataframe pandas-library prediction predictive-modeling residuals rmse-score scipy-stats simple-linear-regression sklearn sklearn-library
Language:Jupyter Notebook 1
Siddharth1989 / WranglingRealEstateData
Wrangled real estate data from multiple sources and file formats, brought it into a single consistent form and analysed the results.
camelot correlation data-wrangling geopandas geopandas-dataframes linear-regression minmaxscaling model-fitting ordinary-least-squares python selenium-python selenium-webdriver web-scrapping-in-python web-scrapping-using-selenium
Language:Jupyter Notebook 1
AdrianKriger / r-spatial-stats
(Geo)spatial Statistics with R (Meuse)
cross-validation interpolation inverse-distance-weighting kriging linear-regression ordinary-least-squares thiessen-polygons variogram
0
AdrianKriger / r-trend-surfaces
Trend Surface Analysis with R (Cape Flats Aquifer)
interpolation-methods ordinary-least-squares terrain-modeling variogram inverse-distance-weighting ordinary-kriging universal-kriging generalized-additive-models generalized-least-squares
0
nekcht / ml-classic-scratch
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.
bayes-classifier classical-machine-learning expectation-maximization implementation-from-scratch k-nearest-neighbors multi-layer-perceptron ols ordinary-least-squares ridge-regression classic-ml-scratch full-bayesian-inference machine-learning-from-scratch
Language:Jupyter Notebook 0
sebastianalamina / ML_2023-2
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.
b-splines k-means-clustering k-medoids lagrange-polynomial-interpolation linear-interpolation mnist newton-polynomial-interpolation newton-raphson taylor-polynomial voronoi dimensionality-reduction k-nearest-neighbours linear-regression logistic-regression principal-component-analysis confusion-matrix ordinary-least-squares sklearn preprocessing tokenizer
Language:Jupyter Notebook 0
shreyansh-2003 / MLR-Gradient-Descent-For-Model-Explainability
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.
gradient-descent linear-regression machine-learning optimization-algorithms boston-housing-price-prediction data-analysis data-visualization explainable-machine-learning multiple-linear-regression-model ordinary-least-squares residual-learning sklearn-library statsmodels
Language:Jupyter Notebook 0
anilesh-prajapati / Probability-and-Statistics-for-Machine-Learning
Probability and Statistics for Machine Learning
cross-entropy gaussian ordinary-least-squares p-values r-squared standard-deviation standard-error uniform variables variance box-and-whisker-plot covariance-and-correlation mean-median-and-mode z-scores
Language:Jupyter Notebook
guilhermedom / pyspark-horsepower-multilinear-regression
PySpark for multiple linear regression on car horsepower using SMOTE for data augmentation.
correlation-matrix data-augmentation generalized-linear-model imblearn linear-regression multilinear-regression ordinary-least-squares pyspark python smote feature-selection
Language:Jupyter Notebook
NajiAboo / MachineLearning
Machine Learning algorithms and models
linear-regression machine-learning ols-regression ordinary-least-square-regression ordinary-least-squares
Language:Jupyter Notebook

ordinary-least-squares

marcelomijas / econometrics-cheatsheet

fischlerben / Algorithmic-Trading-Project

wyattowalsh / regularized-linear-regression-deep-dive

shamindras / maars

ericqu / LinearRegressionKit.jl

dgwozdz / OLS

romanwerpachowski / ML

micts / airbnb-price-prediction

omerfarukeker / Gradient-Descent-Visualisation

bhattbhavesh91 / regression-excercise-ols-ridge

gilaniasher / kaggle-house-regression-challenge

jajokine / Statistics-Analysis

tboudart / Financial-Markets-Regression-Analysis

tboudart / Life-Expectancy-Regression-Analysis-and-Classification

academicPapers / dist_lognormal

aneeshdurai / OLS-Pairs-Trading

caralifarrell / Beginner-Machine-Learning-in-Python-ChatGPT-Bonus-2023

DolbyUUU / regression_algorithm_implementation_python

gmkoeb / dark-fitter

MahtabEK / Supervised-learning-Linear-models-and-Loss-functions

MichaelAlexanderBryant / simplyhired-eda

michstg / Geographically-Weighted-Regression-GWR-Poverty-North-Sumatera

MoinDalvs / Simple_Linear_Regression_1

MoinDalvs / Simple_Linear_regression_2

PanicMilica / car-price-prediction-python

philsaurabh / Tutorials

ShaikRiyazSandy / Simple-Linear-Regression

Siddharth1989 / WranglingRealEstateData

AdrianKriger / r-spatial-stats

AdrianKriger / r-trend-surfaces

nekcht / ml-classic-scratch

sebastianalamina / ML_2023-2

shreyansh-2003 / MLR-Gradient-Descent-For-Model-Explainability

anilesh-prajapati / Probability-and-Statistics-for-Machine-Learning

guilhermedom / pyspark-horsepower-multilinear-regression

NajiAboo / MachineLearning