A review: Data pre-processing and data augmentation techniques

This review paper provides an overview of data pre-processing in Machine learning, focusing on all types of problems while building the machine learning problems. To decrease the dependency on training data and to improve the performance of the machine learning model.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 It deals with two significant issues in the pre-processing process (i).
• 2 Steps to follow to do data analysis with its best approach.
• 3 As raw data are vulnerable to noise, corruption, missing, and inconsistent data, it is necessary to perform pre-processing steps, which is done using classification, clustering, and association and many other pre-processing techniques available.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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