Data Cleaning in Machine Learning: Best Practices and Methods

Machine learning is all about training and feeding data to algorithms to perform various compute intensive tasks. However, businesses typically face challenges in feeding the right data to machine learning algorithms or cleaning of irrelevant and error-prone data. In other words, when it comes to utilizing ML data, most of the time is spent on cleaning data sets or creating a dataset that is free of errors. Setting up a quality plan, filling missing values, removing rows, reducing data size are some of the best practices used for data cleaning in Machine Learning.

Reading Time: 4 minutes
Read the article   [responsivevoice_button buttontext='Hear the article' voice='US English Female']


Smishad Thomas

Smishad Thomas is the Customer Experience Manager at eInfochips. He has over 10 years of experience into customer service and marketing. Smishad has completed his Masters in English Literature along with a degree in Corporate Communication.