R package for economic experts panel survey data

Strategizing to maximize Customer Retention in Telecom Company

R package for controlled multiple imputation of ordinal or binary responses with missing data in clinical study

This project demonstrates data cleaning on the Nashville Housing dataset (2013–2016) using R and packages like tidyverse, lubridate, and janitor. Key steps included standardizing column names, handling missing values, formatting dates, cleaning text fields, and identifying outliers. The cleaned dataset is now ready for analysis and modeling to unco

Apply unsupervised learning techniques to identify customers segments.

Detecting errors and anomalies in structured data using automation

Using visualization to put to the test some intuitive insights between energy consumption and human development.

This is the repository of The Glasgow Geographic Data Science Centre (GGDS) Website

Comparison of clustering methods for determining the operational states of a wastewater treatment plant (BSc project in Statistics) 🔧 🚰 🔄 ♻️ 💦

Materials for the 4 Questions discussed on February 16th, 2017

Predictive Modeling with Missing Data

MSc thesis 'Missing the Point: Non-Convergence in Iterative Imputation Algorithms' by Hanne Oberman

Tutorials illustrating the use of baseline information to conduct more efficient randomized trials

FIMUS imputes numerical and categorical missing values by using a data set’s existing patterns including co-appearances of attribute values, correlations among the attributes and similarity of values belonging to an attribute.

{shinymice} is an R package for interactive evaluation of incomplete data by Hanne Oberman, guided by Gerko Vink and Stef van Buuren.

Frontend of mpa

metaSEM package