Categorical Data: Need, Encoding, Selection of Encoding Method and Its Emergence in Machine Learning Models—A Practical Review Study on Heart Disease Prediction Dataset Using Pearson Correlation-Reference-Cited by-同舟云学术

Categorical Data: Need, Encoding, Selection of Encoding Method and Its Emergence in Machine Learning Models—A Practical Review Study on Heart Disease Prediction Dataset Using Pearson Correlation

Published:2023 Issue: Volume: Page:369-382
ISSN:2367-3370
Container-title:Proceedings of International Conference on Data Science and Applications
language:
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Author:

Kosaraju Nishoak,Sankepally Sainath Reddy,Mallikharjuna Rao K.

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-6631-6_26

Reference20 articles.

1. https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)

2. https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/

3. Dahouda MK, Joe I (2021) A deep-learned embedding technique for categorical features encoding. IEEE Access 9:114381–114391. https://doi.org/10.1109/ACCESS.2021.3104357

4. Gnat S (2021) Impact of categorical variables encoding on property mass valuation. Procedia Comput Sci 192:3542–3550

5. Von Eye A, Clogg CC (eds) (1996) Categorical variables in developmental research: methods of analysis. Elsevier

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