A literature review exploring how missing data was handled across the pipeline of commonly used UK clinical prediction models

EDI uses two layers/steps of imputation namely the Early-Imputation step and the Advanced-Imputation step.

Using nationally representative demographic and health survey data, measles vaccine utilization has been classified, and its underlying factors are identified through an ensemble machine learning approach.

Repository containing the full R code analyzing the impact of missing data handling techniques (listwise deletion, single imputation by median\mode and proximity - based imputation) on Random Forests predictive performance

Statistical imputation for missing values in machine learning

Develop classification strategies and preprocess data with pandas to prepare for predicative modeling.

FSE+Attention model for particle identification in ALICE (Pb-Pb Run 3) at CERN. State-of-the-art detector masking with 92.8% accuracy using JAX/Flax. Handles missing detector data through Feature Set Embedding + Multi-head Attention. Production-ready with Focal Loss, class weighting, and two-tier model persistence.

Contributing to Open Source code

Explaining three common methods for advanced quantitative data analysis and their implementation in R.

Implementation of experiments for paper titled "Sufficient identification conditions and semiparametric estimation under missing not at random mechanisms"

Data imputation with collaborative filtering and latent factor models for wind farms time series data

Basic neural matrix factorization for missing data imputation

Extracted local and global temperature data and analyzed trends

Analyze Seattle AirBnB homes data.

Data preprocessing methods explained with sample dataset

Handling missing data , outliers, duplicate rows, inconsistent columns names, untidy data

Methods for making inferences about partially matched samples

BVVU testing system for the loss of log records