Text classification is a fundamental task in Natural Language Processing (NLP) that involves categorizing text into predefined classes. This tutorial will guide you through building a text classification model using Python and popular machine learning libraries.

🚀 Steps to Build a Text Classification Model

  1. Data Preparation

    • Collect a labeled dataset (e.g., movie reviews, spam detection)
    • Preprocess text: tokenization, stopword removal, stemming
    • Convert text to numerical features using techniques like TF-IDF or word embeddings
    Data_Preprocessing_Steps

  2. Model Selection

    • Choose between traditional ML models (e.g., Naive Bayes, SVM) or deep learning approaches (e.g., RNN, Transformers)
    • For NLP tasks, BERT and other pre-trained models are highly effective
    Model_Selection_Options

  3. Training & Evaluation

    • Split data into training/validation/test sets
    • Train your model and evaluate performance using metrics like accuracy, F1-score
    • Fine-tune hyperparameters for better results
    Model_Training_Process

  4. Deployment

    • Save trained model using joblib or pickle
    • Create a simple API endpoint with Flask/Django for real-time predictions
    Model_Deployment_Flow

📚 Recommended Learning Path

For deeper understanding of NLP concepts:
Explore NLP Fundamentals

🧪 Example Code Snippet

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline

text_clf = Pipeline([
    ('tfidf', TfidfVectorizer()),
    ('clf', MultinomialNB())
])

text_clf.fit(training_data, training_labels)
Python_Code_Example