https://github.com/samarthmule/chatbot

This project implements a generic chatbot using Natural Language Processing (NLP) and Machine Learning techniques. The chatbot is designed to classify user input into predefined intents and provide context-aware responses. The solution is scalable, interactive, and suitable for various domains.
https://github.com/samarthmule/chatbot

chatbot internship machine-learning machine-learning-algorithms nlp nltk project-repository python python3 scikit-learn streamlit

Last synced: 2 months ago
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This project implements a generic chatbot using Natural Language Processing (NLP) and Machine Learning techniques. The chatbot is designed to classify user input into predefined intents and provide context-aware responses. The solution is scalable, interactive, and suitable for various domains.

• Host: GitHub
• URL: https://github.com/samarthmule/chatbot
• Owner: SamarthMule
• Created: 2025-01-04T11:03:47.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2025-01-04T16:59:12.000Z (over 1 year ago)
• Last Synced: 2025-12-28T08:24:07.989Z (6 months ago)
• Topics: chatbot, internship, machine-learning, machine-learning-algorithms, nlp, nltk, project-repository, python, python3, scikit-learn, streamlit
• Language: Jupyter Notebook
• Homepage:
• Size: 4.85 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Security: SECURITY.md

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README

          
# Generic Chatbot Using NLP and Machine Learning

This project implements a generic chatbot using Natural Language Processing (NLP) and Machine Learning techniques. The chatbot is designed to classify user input into predefined intents and provide context-aware responses. The solution is scalable, interactive, and suitable for various domains.

---

## **Features**

- Intent classification using machine learning models.

- Preprocessing techniques like tokenization, stopword removal, and TF-IDF vectorization.

- Supports Logistic Regression, Random Forest, and SVM models.

- User-friendly web-based interface built with Streamlit.

- Scalable and adaptable for different use cases.

---

## **Technologies Used**

- **Programming Language**: Python

- **Libraries**:

  - `nltk`: For text preprocessing (tokenization, stopword removal).

  - `scikit-learn`: For machine learning models and evaluation metrics.

  - `streamlit`: For creating the chatbot's web interface.

  - `numpy` and `pandas`: For data manipulation and analysis.

---

## **Project Structure**

```plaintext

project-directory/

│

├── app.py                  # Main Streamlit app for chatbot

├── intents.json            # Dataset containing intents, patterns, and responses

├── chatbot_model.pkl       # Trained machine learning model

├── vectorizer.pkl          # TF-IDF vectorizer

├── requirements.txt        # List of dependencies

├── README.md               # Project documentation

└── .gitignore              # Ignored files for Git