Applying Linear regression for car price prediction and key variable identification

Explored data using data visualisation and exploratory data analysis. Used Logistic Regression to create a basic prediction model. Improved model using recursive feature elimination.

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.

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.

Class to perform cross validation and draw ROC curves for Test and Training data

Fraud Detection on Credit Transaction using Data Cleaning, Variable Creation, Feature Selection, ML Model Exploration and Selection

Analysis of the Underlying Dynamics in the Stock Market: Stock Price of Southwest Airlines and Its Relationship with Other Stocks in the Market

Predict prices of diamond data in ggplot2

GEV-SSVS regression

In this study we seek to predict employee attrition with KNN clustering and Naive Bayes, and to predict employee salary using multiple linear regression

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

R/C code for Bayesian variable selection for Dirichlet-multinomial regression models. Accompany paper: Wadsworth et al. (2016). An Integrative Bayesian Dirichlet-Multinomial Regression Model for the Analysis of Taxonomic Abundances in Microbiome data. BMC Bioinformatics 18:94.

Code and data of the paper "A nonparametric test of independence between two random variables of any kind"

Business case (Python): Finding opportunities for real-estate investments on an European capital

ASIGNACION de valores a variables en JAVA