Numerical data imputation methods for extremely missing data contexts

mlim: single and multiple imputation with automated machine learning

Different imputation technique with example

PyTorch implementation of a modified Denoising Autoencoder for improved imputation performance (Bachelor Thesis Project)

This was the hackathon project organized by IIT G in collaboration with ai planet. Where we had to create a ML model to predict whether an activity on the website can be considered a Neptune attack or not. This is what I'd done and got 100% score.

Visualization and Imputation of Missing Values

Project, hours, users and clients management application for the company Hodeia Digital (Bilbao)

Predicitng a timely diagnosis in metastatic cancer patients. Data cleaning, feature engineering and hyperparams tuning of classification model ensemble

Machine Learning - This is a hands-on Machine Learning endeavor showcasing data preprocessing, feature engineering, and model deployment using Amazon SageMaker, aimed at advancing proficiency in ML workflows.

Imputation of zeros, nondetects and missing data in compositional data sets

A workaround to missing values using machine learning imputation techniques

MLimputer: Missing Data Imputation Framework for Supervised Machine Learning

Python package for missing-data imputation with deep learning

Predicting if a NBA rookie player will last at least 5 years in the league

A proactive approach to maintenance called predictive maintenance employs data and analysis to spot possible issues before they cause an asset to fail. This can lessen the likelihood of expensive repairs and unforeseen downtime. One of the most significant uses of predictive maintenance is the remaining useful life prediction of water pumps.

Example code for the handbook "Comparative effectiveness and personalized medicine using real-world data"

A simulation study looking at which combinations of missing data handling methods across a prediction model's pipeline are compatible, and which ones lead to bias.

Exploratory Data Analysis - Telecom Customer Churn

Cleaning data using decision tree and k-nn techniques

Presentation slides for a talk about missing data