`CountVectorizer` is a **feature extraction** tool in Natural Language Processing (NLP) that converts a collection of text documents into a matrix of **token counts**. 

It begins by tokenizing the input text, building a **vocabulary** of known words. It then counts the occurrences of each word in the text and constructs a matrix where each row represents a **document**, and each column represents a **word** from the vocabulary.

This matrix can be used as input for various **machine learning** models to perform text classification, sentiment analysis, and other NLP tasks. Additionally, `CountVectorizer` can be configured to include preprocessing steps such as removing stopwords and performing stemming or lemmatization.

In this project, we are going to classify spam emails according to their content.

In this project, we are going to classify spam email according to their content.

Data Preprocessing

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Data Preprocessing

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