Natural Language Processing (NLP)

Under this Notebook, I have added on how to deal with Text Classification using NLP.

Converts to lower case.
Removal of Punctuations.
Splits Whitespaces.
Using Stemming.
Use TD-IDF to calculate the importance of each word in the document.
Apply ML models such as Logistic Regression, Support Vector Classifier, XGBoost and calculte their Accuracy Scores.
"CountVectorizer: It is a popular tool in Natural Language Processing that converts a collection of text documents to a matrix of token counts. By representing each document as a vector of word counts, we can apply machine learning algorithms to the text data.

This simple yet powerful tool is widely used for tasks like sentiment analysis, topic modeling, and text classification. Its ability to handle large datasets and generate meaningful insights from unstructured text data makes it an essential part of any NLP project.

Whether you're an experienced data scientist or just getting started in the field, CountVectorizer is definitely a tool worth exploring. Have you used CountVectorizer in your projects? Share your experiences in the comments below!"