stepwise-regression

Portfolio Optimization and Quantitative Strategic Asset Allocation in Python

Tools for developing OLS regression models

Automated Backward and Forward Selection On Python

Stepwise Regression-Python

Contains a wide-ranging collection of compressed sensing and feature selection algorithms. Examples include matching pursuit algorithms, forward and backward stepwise regression, sparse Bayesian learning, and basis pursuit.

Automated Bidirectional Stepwise Selection On Python

The effect quantity is decomposed into water pressure component, temperature component and aging component, and the diagram is made automatically. Refer to Prof. Wu Zhongru's Safety Monitoring Theory & It's Appliaction of Hydraulic Structures

An algorithm intended to predict the yield of any crop. Used Agricultural Data sets for building the Step-wise Regression Model. Technology Stack: R language, SQL, Linear Regression library, Plumber library, Swagger API

Model slection with stepwise regression.

Stepwise regression fits a logistic regression model in which the choice of predictive variables is carried out by an automatic forward stepwise procedure.

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.

Integrated Project for Business

Sports Analytics in R (Step-wise Regression and Subset Selection Regression)

Statistical Multivariate Regression Analysis to determine the effects of mortality, economic and social factors on life expectancy.

Data Analysis and Decision Making Project using R

Repo for multiple regression assignments in Quant III for EDUC467.

Forest-Fire-StepwiseRegression The relationships between the ‘Probability of Forest Fire’ in Algeria and its various weather components have been estimated.

Tugas besar analisa data

Sports Analytics in Python

I took a look at the mtcars dataset in R and wanted to do an analysis on this dataset. In 2018