October 3, 2025AIOpen Access

Data Preprocessing and Feature Engineering for Data Mining: Techniques, Tools, and Best Practices

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This review analyzes data preprocessing methods and their impact on model accuracy in data mining, suggesting best practices.

Data preprocessing significantly improves the accuracy and interpretability of data mining results, enhancing analytical outcomes.
Comprehensive techniques such as data cleaning, normalisation, and dimensionality reduction are vital for effective data preparation.
Automated methods for feature engineering streamline processes, integrating state-of-the-art tools into large-scale data pipelines.
Emerging issues in data mining require ethical considerations for fairness and interpretability, indicating new directions for research.

Koukaras et al. (2025) studied this question.

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