DATA WRANGLING AND PREPROCESSING FOR DATA SCIENCE Article Swipe
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· 2025
· Open Access
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· DOI: https://doi.org/10.58532/nbennuraith4
· OA: W4411840263
Data preprocessing forms the critical foundation of effective data science workflows, transforming raw, unstructured data into reliable inputs for analysis and modeling. This chapter emphasizes the pivotal role of preprocessing in addressing pervasive data quality challenges such as missing values, outliers, and inconsistent formatting, which collectively impact over 80% of real-world datasets [1]. Key techniques include robust missing value imputation strategies (mean, median, and advanced methods like MICE), outlier detection using inter quartile ranges (IQR) and clustering algorithms, and feature engineering to derive meaningful predictors. Practical implementation is demonstrated through industry-standard tools: Python’s Pandas for automated data cleaning, R’s dplyr for structured transformations, and Open Refine for non-programmatic data wrangling. These tools enable reproducible preprocessing pipelines that maintain data integrity while optimizing datasets for machine learning applications. The chapter also highlights how systematic preprocessing reduces computational overhead by 30- 50% and improves model accuracy by addressing biases inherent in raw data [2]. By integrating theoretical principles with handson examples, this section equips practitioners to handle heterogeneous data sources, ensure compatibility across analytical platforms, and build trustworthy data products.
