Text Classification with scikit-learn 📚

Text classification is a fundamental task in natural language processing (NLP) that involves categorizing text into predefined classes. With scikit-learn, you can easily implement this using its powerful machine learning tools. Here's a quick guide to get started:

Steps to Implement Text Classification

1. Data Preparation

   • Collect and preprocess text data (e.g., tokenization, stopword removal)
   • Label your dataset with appropriate categories 📌 Example:

   from sklearn.feature_extraction.text import CountVectorizer
   vectorizer = CountVectorizer()
   X = vectorizer.fit_transform(text_data)
   

2. Model Selection

   • Choose a classifier (e.g., Naive Bayes, SVM, or Logistic Regression)
   • Train the model on your labeled data 📊 Tip: Use TfidfTransformer for better feature weighting

   from sklearn.naive_bayes import MultinomialNB
   model = MultinomialNB()
   model.fit(X, labels)
   

3. Evaluation

   • Test the model with unseen data
   • Calculate accuracy, precision, and recall 📈 Metrics:
   • Accuracy: accuracy_score(y_true, y_pred)
   • F1-Score: f1_score(y_true, y_pred)

Resources for Further Learning

• scikit-learn Documentation for detailed API references
• Text Classification Tutorials to explore advanced techniques
• Machine Learning Concepts for foundational knowledge

Visualize Your Data

For hands-on practice, try the Text Classification Lab to apply these concepts!