Topic: ordinary-least-squares Goto Github
Some thing interesting about ordinary-least-squares
Some thing interesting about ordinary-least-squares
ordinary-least-squares,Artigo submetido ao COBRAC 2018.
Organization: academicpapers
Home Page: https://academicpapers.github.io/dist_lognormal/
ordinary-least-squares,Linear Regression, Statistical Machine Learning and Neural Networks
User: adames-ouro
ordinary-least-squares,(Geo)spatial Statistics with R (Meuse)
User: adriankriger
Home Page: https://adriankriger.github.io/r-spatial-stats/
ordinary-least-squares,Trend Surface Analysis with R (Cape Flats Aquifer)
User: adriankriger
Home Page: https://adriankriger.github.io/r-trend-surfaces/
ordinary-least-squares,Implemented ordinary least squares regression from scratch in python by computing root mean square error and coefficient estimates
User: akshay-madar
ordinary-least-squares,Gentle yet comprehensive introduction to regression
User: ammopy
ordinary-least-squares,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.
User: aneeshdurai
ordinary-least-squares,Probability and Statistics for Machine Learning
User: anilesh-prajapati
ordinary-least-squares,A Regression Exercise covering OLS & Ridge Regression
User: bhattbhavesh91
ordinary-least-squares,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.
User: caralifarrell
Home Page: https://www.udemy.com/certificate/UC-630f29d7-9e0e-416a-9404-711893eb5759/
ordinary-least-squares,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.
User: dataspherex
ordinary-least-squares,Set of functions to semi-automatically build and test Ordinary Least Squares (OLS) models in R in parallel.
User: dgwozdz
ordinary-least-squares,regression algorithm implementaion from scratch with python (least-squares, regularized LS, L1-regularized LS, robust regression)
User: dolbyuuu
ordinary-least-squares,Basic Functions and algorithms of Statistics used in Data Analysis and data-science
Organization: douxesprit
ordinary-least-squares,Linear Regression for Julia
User: ericqu
ordinary-least-squares,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.
User: fischlerben
ordinary-least-squares,Predicting housing prices in Iowa using Python/Pandas/linear regression within SKLearn.
User: gilaniasher
ordinary-least-squares,Fits JxV curves obtained from solar cells operating in the dark and calculates important parameters
User: gmkoeb
ordinary-least-squares,PySpark for multiple linear regression on car horsepower using SMOTE for data augmentation.
User: guilhermedom
ordinary-least-squares,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 forces, whether they have kids, etc. This data is used to get an estimate of the standard wages equation of women using the Ordinary Least Square (OLS) method a.k.a. Linear Regression. The education level, years of experience, race, ethnicity are the independent variables (predictors) that drive the behavior of the dependent variable - income.
User: hetashah27
ordinary-least-squares,Ordinary Least Squares and Normal Equations to Estimate Linear Regression Coefficients/Parameters
User: jairiidriss
ordinary-least-squares,MITx - MicroMasters Program on Statistics and Data Science - Data Analysis: Statistical Modeling and Computation in Applications - First Project
User: jajokine
ordinary-least-squares,Ejercicio de regresiones por distintos métodos (Mejor Selección de Conjuntos, Selección de pasos hacia adelante, Ridge, LASSO, Elastic Net, Componentes Principales, Mínimos Cuadrados Parciales, etc.)
User: jvf368
ordinary-least-squares,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.
User: mahtabek
ordinary-least-squares,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.
User: marcelomijas
ordinary-least-squares,Ordinary Least Squares problem, guide, and solver
User: markstock
ordinary-least-squares,A project where data science job postings are scraped and an exploratory data analysis is performed.
User: michaelalexanderbryant
ordinary-least-squares,Compared Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) using R programming with interpretation
User: michstg
ordinary-least-squares,Predictive Analysis of Price on Amsterdam Airbnb Listings Using Ordinary Least Squares.
User: micts
ordinary-least-squares,Predicting Delivery Time Using Sorting Time
User: moindalvs
ordinary-least-squares,Building a prediction model for Salary hike using Years of Experience
User: moindalvs
ordinary-least-squares,Machine Learning algorithms and models
User: najiaboo
ordinary-least-squares,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.
User: nekcht
ordinary-least-squares,Linear line fitting to data and optimising parameters with Gradient Descent algorithm
User: omerfarukeker
ordinary-least-squares,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.
User: panicmilica
Home Page: https://github.com/PanicMilica
ordinary-least-squares,Tutorials for BSE classes.
User: philsaurabh
Home Page: https://github.com/philsaurabh/Tutorials
ordinary-least-squares,Causal Inference Case Studies
User: psanghal
ordinary-least-squares,ML++ and cppyml: efficient implementations of selected ML algorithms, with Python bindings.
User: romanwerpachowski
ordinary-least-squares,Simple Linear Regression
User: shaikriyazsandy
ordinary-least-squares,An R implementation of Models As Approximations
User: shamindras
Home Page: https://shamindras.github.io/maars/
ordinary-least-squares,Algorithms from scratch to know how the algorithms work.
User: sharika-anjum
ordinary-least-squares,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.
User: shreyansh-2003
ordinary-least-squares,Wrangled real estate data from multiple sources and file formats, brought it into a single consistent form and analysed the results.
User: siddharth1989
ordinary-least-squares,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.
User: tboudart
ordinary-least-squares,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.
User: tboudart
ordinary-least-squares,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.
User: tboudart
ordinary-least-squares,🐟 Statistical analysis of fish dimensions and weights implemented into linear regression (Ordinary Least Squares) predictive model
User: tylerrussin
Home Page: https://fish-market-dataset-analysis.herokuapp.com/
ordinary-least-squares,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.
User: wyattowalsh
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