- 🔭 I am an Assistant Manager/Senior Data Scientist at DISR. I use cutting-edge Ai techniques and Microsoft Azure Cognitive Services
- 🌱 I've completed my Master of Data Science at the University of Sydney
- 🥅 2023 In-progress: Microsoft Azure Ai-102 AI Enginer Certification
- 💬 Ask me about Azure, DevOps, Python, R, QGIS/ArcMap
- ⚡ Fun fact: I enjoy building high-performance computers and playing eSports
PyImpuyte is a Python application that brings together a range of supervised machine learning algorithms in a customer-centric way that generates synthetic data to mitigate against sparse matrices.By adding PyImpuyte to their toolkit, practitioners can preserve their data and avoid losing valuable information that results from dropping observations with missing values.
- Interfaces with
scikit-learnto provide a customer-centric and efficient way to perform imputation using machine learning algorithms. - Support for numerous imputation strategies and performance metrics.
- Conference Paper - Sharpening the BLADE: Missing Data Imputation Using Supervised Machine Learning: AI2019: Advances in Artificial Intelligence.
@inbook{inbook,
author = {Suresh, Marcus and Taib, Ronnie and Zhao, Yanchang and Jin, Warren},
year = {2019},
month = {11},
pages = {215-227},
title = {Sharpening the BLADE: Missing Data Imputation Using Supervised Machine Learning},
isbn = {978-3-030-35287-5},
doi = {10.1007/978-3-030-35288-2_18}
}- Python Package: PyImpuyte.
@misc{Suresh2020_PyImpuyte,
title={PyImpuyte},
author={Suresh, Marcus et al.},
year={2020},
howpublished={\url{https://bitbucket.csiro.au/projects/DDE/repos/pyimpuyte}},
}