A review: Data pre-processing and data augmentation techniques

🤖 Plain-English Summary

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.

🔑 Key Findings

• It deals with two significant issues in the pre-processing process (i).
• Steps to follow to do data analysis with its best approach.
• 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.

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