Data Cleanup: Roadmap to Successful Statistical Modeling

Authors

Vijaya Chaitanya Palanki

Abstract

The success of statistical modeling in data science leans on the quality and readiness of the underlying data. This paper presents a comprehensive framework for preparing data for statistical modeling, encompassing crucial steps from initial data assessment to final validation. We explore advanced techniques in data cleaning, transformation, and feature engineering, emphasizing the importance of domain knowledge integration and automated data preparation pipelines. The study addresses emerging challenges in handling complex, high-dimensional datasets and provides guidelines for ensuring data reliability, consistency, and relevance for robust statistical analysis.

Keywords

Data preparation, statistical modeling, data cleaning, feature engineering, data quality, machine learning, data science Data preparation, statistical modeling, data cleaning, feature engineering, data quality, machine learning, data science

Data Cleanup: Roadmap to Successful Statistical Modeling. Vijaya Chaitanya Palanki. 2021. IJIRCT, Volume 7, Issue 4. Pages 1-5. https://www.ijirct.org/viewPaper.php?paperId=2410011