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September 10, 2025International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences

Comprehensive Study of Data Imputation Techniques For Machine Learning Models

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Authors

VTVaibhav Tummalapalli

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Overview

This paper reviews imputation techniques for missing data in machine learning, suggesting tools for effective propensity modeling.

Key Points

  • Effective imputation techniques enhance the accuracy of propensity models in machine learning.
  • The study categorizes imputation methods based on data types and missingness scenarios.
  • Tailored strategies for handling missing data can lead to robust and reliable model predictions.
  • Providing practitioners with comprehensive tools can improve data workflows in machine learning.

Cite This Study

Vaibhav Tummalapalli (2025) studied this question.

synapsesocial.com/papers/68c1c24454b1d3bfb60f0345https://doi.org/10.37082/ijirmps.v13.i4.232674
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