Implement Reservoir Computing models for time series classification, clustering, forecasting, and much more!

Discrete, Gaussian, and Heterogenous HMM models full implemented in Python. Missing data, Model Selection Criteria (AIC/BIC), and Semi-Supervised training supported. Easily extendable with other types of probablistic models.

MLimputer: Missing Data Imputation Framework for Machine Learning

Numerical data imputation methods for extremely missing data contexts

Machine learning project clustering countries based on socio-economic & demographic indicators using K-Means, iterative imputation & feature scaling.

Cleans and validates raw data against predefined rules

Implementation of Missing Imputation algorithms for Incomplete tabular data with PyTorch.

Real-time imputation of missing environmental sensor data for fault-tolerant edge computing.

Comparison of Image Inpainting Techniques for Medical Images

Research code for the paper "CFMI: Flow Matching for Missing Data Imputation".

My Data Cleaning Library

Fairness-Machine Learning in the Context of Missing Data Imputation

Large Language Models for Data Imputation: A prompt engineering approach

Codebase for Missing Data Imputation under Green Artificial Intelligence Constraints

AI-powered survey data cleaning, imputation, estimation, and reporting

Data Manipulation of Biopic Dataset

Codebase for evaluating the fairness of Missing Data Imputation strategies

Adversarial Machine Learning Applied to Missing Data Imputation