A hybrid chatbot combining TF-IDF vectorization with intent classification for intelligent response generation.
- NLP Libraries: NLTK, scikit-learn
- ML Algorithm: TF-IDF Vectorization + Cosine Similarity
- Classification: Intent Recognition Model
- Language: Python 3.x
- TF-IDF based text similarity matching
- Intent classification for query understanding
- Hybrid response generation (retrieval + rule-based)
- Customizable intent patterns
User Input → Preprocessing → TF-IDF Vectorization ↓ Intent Classification ↓ Response Selection (Cosine Similarity) ↓ Bot Response
# Clone the repository
git clone https://github.com/MohdSarar/Chatbot-hybrid.git
cd Chatbot-hybrid
# Install dependencies
pip install nltk scikit-learn numpy
# Download NLTK data
python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords')"python chatbot.py| Component | Method |
|---|---|
| Text Vectorization | TF-IDF |
| Similarity Measure | Cosine Similarity |
| Intent Detection | Classification Model |
Mohammed ABUSARAR - GitHub