This article is about Data Science
Learning Text Preprocessing in Python – Machine Learning
By NIIT Editorial
Published on 05/06/2021
Generally, a data scientist spends 70 – 80% of his time in cleaning and preprocessing the data because most of the time data is collected from different sources and stored in a raw format making it infeasible for further analysis as most of the machine learning models need information in specific format to execute the aWhat is Preprocessing of Data?lgorithm. That’s why it is important to structure the data as per the combination of your approach and domain.
For achieving better results the data sets should be formatted in a way to execute more than one machine learning algorithm so that it is possible to choose the best out of them.
In this article, you will read about different preprocessing data techniques and their implementation in Python.
What is Preprocessing of Data?
The steps needed to transfer human language to machine-readable format for further processing, or transforming raw data sets into predictable and analyzable format before feeding it to algorithm as per the task is known as preprocessing of data.
There are no fixed steps for preprocessing of data. You need to use steps based on your requirement and dataset. You need to be very careful while choosing preprocessing steps or techniques as it plays an important role in deriving end results.
Understanding Different Text Preprocessing Techniques
For model building preprocessing is performed on text data so that it can be readily accepted and assessed in an algorithm. Some of the preprocessing techniques are:
Text preprocessing is an important step before feeding data into a machine-learning algorithm because most of the algorithm needs to convert human language into machine language for better assessment or else results may vary.
Apart from the techniques shown in the article, many other steps are also used like URL removal, HTML tags, and many more. You must choose the right combination of steps for text preprocessing as per your dataset.
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