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Identifying Spam Emails
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Course Content

Identifying Spam Emails

Identifying Spam Emails

IntroductionImport DataColumns AdjustmentsPreliminary AnalysisPlot the Target DistributionRemove StopwordsUsing .loc[ ] Train-Test SplitData PreprocessingModelingPutting All Together

bookData Preprocessing

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.

Task

  1. Import the CountVectorizer class.
  2. Initialize it and store the instance in the count_vectorizer variable.
  3. Fit it to the training data (X_train) using the correct method.
  4. Create the document term matrix using the .transform() method.
  5. Transform the resulting matrix into an array using the .toarray() method.

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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.

Task

  1. Import the CountVectorizer class.
  2. Initialize it and store the instance in the count_vectorizer variable.
  3. Fit it to the training data (X_train) using the correct method.
  4. Create the document term matrix using the .transform() method.
  5. Transform the resulting matrix into an array using the .toarray() method.

Mark tasks as Completed
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 9
AVAILABLE TO ULTIMATE ONLY
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