Advanced Business Analytics, Data Imputation Techniques 229091-D
Laboratorium (LAB) Semestr zimowy 2023/24

Range and scale of advanced business analytics - the place and importance of predictive models

Data management, data quality

Imputation and its importance for business analytics modeling

Missing data patterns, missing data mechanisms and tests for missing data mechanism

Introduction to multiple imputation (MI)

Likelihood-based approach to missing data Expectation-Maximization (EM) algorithm

Quintile regression

Adaptive regression for longitudinal data and quality control. Detecting outliers and shifts in time series using regression techniques.

Mixed models for longitudinal data

Multiple imputation and maximum likelihood - practical examples

Segmentation models

Simple and General Retention Models

Migration model

Lifetime Value Models with Data Mining Approach

ABA case study. Selected examples in R and Python