# 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.
