Data Pipeline

Data Pipeline is the process of transforming the data from the initial one to other form passing it through various stages. In the case of textual data, such as a collection of words or language, data pipelining is essential. This is because we cannot directly apply our statistical formulae or train the model on raw text. Therefore, it becomes necessary to pre-process the text and convert it into a numeric form. This numeric representation is valuable for interpretation, analysis, and model training.

Data analytics involves multiple stages, starting with data collection and followed by data processing. The data prepared for analysis, involving steps like cleaning, transformation, and feature extraction. Finally, the insights derived from the processed data are presented, supporting informed decision-making in Firms and Organizations.

Steps involves for creating pipeline to make data usable for training and modelling is listed below…

1. Data Collection
2. Data Preprocessing (Tokenisation, Stopword removal etc.)
3. Data Stemming
4. Building a vocabulary and Vectorization
5. Classification & Training

“Python provides NLTK library which stands for Natural Language Toolkit, and is powerful enough to work which human language data(text). NLTK provides easy to use interfaces to work over Corpora and lexical resources. Corpus is a collection of text documents and NLTK provides various corpora to provide wide range of language and topics.”

Data pipelining is essential for transforming raw text data into a numeric format suitable for analysis and model training in Natural Language Processing (NLP).

This article outlines a comprehensive preprocessing pipeline, leveraging Python and the NLTK library, to convert textual data into a usable form for training and modeling.