From the above definition, we can easily sense that data can be in mainly four different forms, Numerical, Textual, Visual or Audio. But this raw form of data can not be used directly for building machine learning models.

In this blog, we will do hands-on on all these preprocessing techniques. We will use different datasets for demonstration and briefly discuss the intuition behind the methods.

Time series data is found everywhere, and to perform the time series analysis, we must preprocess the data first. Time Series preprocessing techniques have a significant influence on data modelling accuracy.

Computers only understand numbers, not text. So we need to convert our text into vectors using vector encoding.

Text data pre-processing ensures optimal results when executed properly. Fortunately, Python has excellent support of NLP libraries such as NLTK, spaCy, and Gensim to ease our text analysis.

Big organizations in data science and machine learning domains record many attributes/properties to ensure they do not lose any critical information.