To study what factors and how they would impact the landing distance of a commercial flight
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Updated
May 21, 2020 - R
To study what factors and how they would impact the landing distance of a commercial flight
This is a development version of DMRnet — Delete or Merge Regressors Algorithms for Linear and Logistic Model Selection and High-Dimensional Data.
This repository contains various classification models.
The aim of this project is to develop a machine learning model to predict the levels of CO in the air using historical datasets containing atmospheric variables. The project makes use of variables selection, decision trees, and cross-validation techniques to ensure robustness and model accuracy.
Predict and prevent customer churn in the telecom industry with this project. Leverage advanced analytics and ML on a diverse dataset to build a robust classification model. Gain a deep understanding of customer behavior and identify key factors influencing churn. Clone the repository, explore insights, and enhance customer retention startegies.
Applying Linear regression for car price prediction and key variable identification
The MCB for variable selection identifies two nested models (upper and lower confidence bound models) containing the true model at a given confidence level.
Explored data using data visualisation and exploratory data analysis. Used Logistic Regression to create a basic prediction model. Improved model using recursive feature elimination.
All independent variables do not have the similar impact on dependent variable. Here we will try to find the independent varibles that have most significant impact on dependent variable to make the ML algorithm fast and accurate by utilizing RFE.
This package was the result of master thesis that is seen at link https://tede2.uepg.br/jspui/handle/prefix/152 and in the article https://doi.org/10.5335/rbca.2015.3727.
Code and tutorials for implementing the GlObal And Local Score
R package for Non-local Prior Based Iterative Variable Selection for Genome-Wide Association Studies, or Other High-Dimensional Data
Variable selection with Sliced Inverse Regression (SIR) thresholded
Analysis of the Underlying Dynamics in the Stock Market: Stock Price of Southwest Airlines and Its Relationship with Other Stocks in the Market
All independent variables do not have the similar impact on dependent variable. Here we will try to find the independent varibles that have most significant impact on dependent variable to make the ML algorithm fast and accurate by utilizing LASSO.
Predict prices of diamond data in ggplot2
In this study we seek to predict employee attrition with KNN clustering and Naive Bayes, and to predict employee salary using multiple linear regression
Variable selection for heterogeneous populations using the vennLasso penalty
A wrapper of the mathjs.org (https://mathjs.org/) JavaScript library for Android to evaluate math expressions.
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