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Identifying Spam Emails
course content

Course Content

Identifying Spam Emails

Identifying Spam Emails

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

bookRemove Stopwords

Removing stopwords is a common preprocessing step in natural language processing (NLP) tasks. Stopwords are words frequently used in a language, such as 'a', 'an', 'the', 'and', 'or', etc., and are considered of little value in text analysis because they carry minimal meaning on their own.

There are several reasons why removing stopwords is important:

  • Reducing dataset size: Stopwords occupy much space in the text, increasing the dataset's size. Removing them reduces the dataset's size, making it more manageable for further processing.
  • Improving processing efficiency: Common stopwords can slow down text analysis algorithms by constituting a large proportion of the text, thus making processing more computationally expensive.
  • Minimizing noise: Stopwords add noise to text analysis, obscuring meaningful insights. Eliminating them helps clarify patterns or topics in the text.
  • Reducing bias: In analyses based on word frequency, stopwords can bias the results. By removing them, the focus shifts to more meaningful words, yielding more accurate outcomes.

Task

  1. Import the nltk library.
  2. Correctly import the word_tokenize() function.
  3. Correctly import the stopwords module.
  4. Load English stopwords.
  5. Correctly apply a lambda function to the 'text' column of the df DataFrame.

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Removing stopwords is a common preprocessing step in natural language processing (NLP) tasks. Stopwords are words frequently used in a language, such as 'a', 'an', 'the', 'and', 'or', etc., and are considered of little value in text analysis because they carry minimal meaning on their own.

There are several reasons why removing stopwords is important:

  • Reducing dataset size: Stopwords occupy much space in the text, increasing the dataset's size. Removing them reduces the dataset's size, making it more manageable for further processing.
  • Improving processing efficiency: Common stopwords can slow down text analysis algorithms by constituting a large proportion of the text, thus making processing more computationally expensive.
  • Minimizing noise: Stopwords add noise to text analysis, obscuring meaningful insights. Eliminating them helps clarify patterns or topics in the text.
  • Reducing bias: In analyses based on word frequency, stopwords can bias the results. By removing them, the focus shifts to more meaningful words, yielding more accurate outcomes.

Task

  1. Import the nltk library.
  2. Correctly import the word_tokenize() function.
  3. Correctly import the stopwords module.
  4. Load English stopwords.
  5. Correctly apply a lambda function to the 'text' column of the df DataFrame.

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 6
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