GitHub topics: ordinary-least-squares
shamindras/maars
An R implementation of Models As Approximations
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msikorski93/Linear-Regression-From-Scratch
Different solutions of linear and polynomial regression made from scratch.
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DolbyUUU/regression_algorithm_implementation_python
regression algorithm implementaion from scratch with python (least-squares, regularized LS, L1-regularized LS, robust regression)
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SrotoshiGhosh/Bayesian-Inference-and-MCMC
Various codes centered around problems in Bayesian inference, Bayesian Linear Regression, and Bayesian Non-Linear Parameter Estimation using the application of various Markov Chain Monte Carlo Algorithms
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Hadley-Dixon/CancerMortality
Multivariate least squares regression model that predicts cancer mortality rates for US counties
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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.
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NajiAboo/MachineLearning
Machine Learning algorithms and models
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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.
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anilesh-prajapati/Probability-and-Statistics-for-Machine-Learning
Probability and Statistics for Machine Learning
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ShaikRiyazSandy/Simple-Linear-Regression
Simple Linear Regression
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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.
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DataSphereX/Logistic-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.
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omerfarukeker/Gradient-Descent-Visualisation
Linear line fitting to data and optimising parameters with Gradient Descent algorithm
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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.
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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.
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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.
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romanwerpachowski/ML
ML++ and cppyml: efficient implementations of selected ML algorithms, with Python bindings.
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AdrianKriger/r-spatial-stats
(Geo)spatial Statistics with R
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AdrianKriger/r-trend-surfaces
Trend Surface Analysis with R (Cape Flats Aquifer)
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AmMoPy/Simple_Multiple_Bayesian_Linear_Regression
Gentle yet comprehensive introduction to regression
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Siddharth1989/WranglingRealEstateData
Wrangled real estate data from multiple sources and file formats, brought it into a single consistent form and analysed the results.
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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.
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ericqu/LinearRegressionKit.jl
Linear Regression for Julia
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jajokine/Statistics-Analysis
MITx - MicroMasters Program on Statistics and Data Science - Data Analysis: Statistical Modeling and Computation in Applications - First Project
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MoinDalvs/Simple_Linear_regression_2
Building a prediction model for Salary hike using Years of Experience
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gilaniasher/kaggle-house-regression-challenge
Predicting housing prices in Iowa using Python/Pandas/linear regression within SKLearn.
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guilhermedom/pyspark-horsepower-multilinear-regression
PySpark for multiple linear regression on car horsepower using SMOTE for data augmentation.
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micts/airbnb-price-prediction
Predictive Analysis of Price on Amsterdam Airbnb Listings Using Ordinary Least Squares.
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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.
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academicPapers/dist_lognormal
Artigo submetido ao COBRAC 2018.
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MichaelAlexanderBryant/simplyhired-eda
A project where data science job postings are scraped and an exploratory data analysis is performed.
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psanghal/causal_inference
Causal Inference Case Studies
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MoinDalvs/Simple_Linear_Regression_1
Predicting Delivery Time Using Sorting Time
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gmkoeb/dark-fitter
Fits JxV curves obtained from solar cells operating in the dark and calculates important parameters
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markstock/estOLS
Ordinary Least Squares problem, guide, and solver
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tylerrussin/Fish-Dimensions-Regression-Analysis
🐟 Statistical analysis of fish dimensions and weights implemented into linear regression (Ordinary Least Squares) predictive model
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philsaurabh/Tutorials
Tutorials for BSE classes.
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tboudart/Chicago-Crime-Regression-Analysis
As part of a group project, I developed separate regression models using R to predict the daily number of batteries and robberies in Chicago using four different datasets. I tested interactive and second-order terms and used stepwise feature selection to find the best model with the given data. I tested several potential models using cross-validation and chose the model that minimized the cross-validation errors while striking a balance with the model's simplicity. I checked the residual assumptions and both models exhibit autocorrelation as indicated by rejecting the null hypothesis of the Durbin-Watson Test. If I had more time, I would try using an ARMA model instead of multiple regression.
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bhattbhavesh91/regression-excercise-ols-ridge
A Regression Exercise covering OLS & Ridge Regression
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dgwozdz/OLS
Set of functions to semi-automatically build and test Ordinary Least Squares (OLS) models in R in parallel.
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sharika-anjum/Machine-Learning-algorithms
Algorithms from scratch to know how the algorithms work.
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