R code for estimation of latent class logit model with aggregate data
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Updated
Jul 3, 2017 - R
R code for estimation of latent class logit model with aggregate data
Trabajo de Fin de Máster KSchool
Replicates the Monte Carlo analysis of Sun & Ishihara (2017)
Evaluate the Buy Online Pick-up in Store (BOPS) strategy with a real-world dataset
This project is the Incident driven contract conversion modelling based on Logit probability.There were two datasets one with conversion information and other past incidence information of a company that provides professional services. The proposed model helps business leaders to take informed decisions about chances of contract renewals based o…
This project goes over data science theories and data preprocessing, leveraging the Titanic data set provided by Kaggle. The goal is to determine which passengers will likely survive or perish the monumental tragedy. The binary classification problem is addressed using two methods, each with three machine learning algorithms. The first approach …
Modelo predictivo de clasificación de fraudes financieros como proyecto final de Análitica de Negocios en ESEN.
Generación de reportes para los mejores modelos Logit obtenidos con el paquete glmulti
Go binary for shipping things from Elasticsearch to Splunk (with support for Logit)
Master Degree Coursework: Econometrics II
This Rmarkdown document is a brief description on Logistical Regression. Typos and changes welcomed via git pull requests
ML models for HR classification problem. For more information please visit the link: https://datahack.analyticsvidhya.com/contest/wns-analytics-hackathon-2018-1/
This project is about to classify if an event is terrorist or other forms of crime as per the Global Standards.
Data science project pro DSBI Academy VSE 2019/2020
Hand-made R Functions Mainly for Statistical Data Analysis
Étude de marché sur les personnes âgées qui souscrivent une assurance complémentaire aux États-Unis en réalisant des modèles probit et logit pour identifier les variables importantes.
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