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Data Mining Techniques
  • Introduction to Text Mining Applications
    • Text Mining in Marketing
    • Text Mining in Healthcare
    • Text Mining in Finance
    • Solution Comparison
    • Conclusion
  • Introduction to Data Mining
    • Getting Started
    • Preprocessing text data
      • Activity Preprocessing for Text Mining
  • Feature Extraction
  • Text Classification
    • Naive Bayes Algorithm, Support Vector, Decision Tree Algorithm
  • Scalable Pattern Discovery
    • Advantages Limitations of Techniques
    • Algorithms for Pattern Discovery
  • Techniques for Data Mining
  • Pattern Evaluation Metrics
  • Database Management System
    • Retrieving specific data using SELECT statements
      • Activity
    • Identify the tables that need to be joined in the SQL query
    • Determine the common column(s) between the tables
    • Retrieving data from multiple tables using JOIN
  • How to Use Join Query
  • Some useful Queries
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  1. Introduction to Data Mining
  2. Preprocessing text data

Activity Preprocessing for Text Mining

  • Introduction to text preprocessing

  • Text cleaning techniques (removing stop words, stemming, etc.)

  • Text normalization techniques (lowercasing, tokenization, etc.)

  • Hands-on exercise: Preprocessing a dataset using Python's NLTK library

To do: Preprocess a sample text dataset for text mining using Python's NLTK library to improve your understanding of text mining fundamentals.

Scoring Criteria:

  1. Proper implementation of text cleaning techniques (removing stop words, stemming, etc.)

  2. Correct application of text normalization techniques (lowercasing, tokenization, etc.)

  3. Successfully preprocess the sample dataset using Python's NLTK library.

Step-by-step plan:

  1. Choose a sample text document or dataset to work with. It can be a collection of tweets, news articles, or any other text-based dataset.

  2. Import necessary libraries in Python, such as NLTK, pandas, and re (regular expressions).

  3. Read the dataset into a pandas DataFrame and inspect its structure.

  4. Apply lowercasing to the text using the str.lower() method in pandas.

  5. Tokenize the text using NLTK's word_tokenize() function.

  6. Remove stop words from the tokenized words using NLTK's stopwords.words('english') list.

  7. Apply stemming to the tokenized words using NLTK's PorterStemmer or any other appropriate stemming algorithm.

  8. Reconstruct the cleaned and preprocessed text back into the dataset.

  9. Save the preprocessed dataset as a new file for further analysis.

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Last updated 1 year ago