ML Chatbot

A simple Machine Learning + NLP based chatbot prototype built in Python. This project demonstrates how a chatbot can answer user queries using a QnA dataset and basic NLP techniques, wrapped with a minimal web interface.

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📌 Features

• Text‑based chatbot using a custom QnA dataset (CSV)
• Basic NLP preprocessing and similarity matching
• Simple web interface (HTML, CSS, JavaScript)
• Modular Python code structure
• Easy to extend with better ML models or vector search

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🗂 Project Structure

ML-chatbot/
│
├── appChat.py              # Main application entry point
├── modelSourceCode.py      # NLP / ML logic for chatbot responses
├── prototype.py            # Experimental / testing script
├── QnA.csv                 # Question–Answer dataset
│
├── templates/              # HTML templates
│   └── index.html
│
├── static/                 # CSS & JavaScript files
│   ├── style.css
│   └── script.js
│
├── requirements.txt        # Python dependencies
└── README.md               # Project documentation

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⚙️ How It Works (High‑Level)

1. User enters a question through the UI.
2. The input text is preprocessed (cleaning, tokenization, etc.).
3. The chatbot compares the input with questions stored in QnA.csv.
4. The most relevant answer is selected and returned to the user.

This is a rule / similarity‑based ML approach, not a generative LLM.

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🚀 Installation & Setup

1️⃣ Clone the repository

git clone https://github.com/Adarsh-V-101/ML-chatbot.git
cd ML-chatbot

2️⃣ Create a virtual environment (recommended)

python -m venv venv
source venv/bin/activate      # On Linux / macOS
venv\Scripts\activate         # On Windows

3️⃣ Install dependencies

pip install -r requirements.txt

4️⃣ Run the application

python app.py

Open your browser and visit the local server URL shown in the terminal.

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🧪 Example Use Case

User: What is machine learning? Bot: Machine learning is a subset of AI that allows systems to learn from data and improve over time.

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This project is ideal for:

• Understanding chatbot fundamentals
• Practicing NLP pipelines
• Learning ML‑driven information retrieval
• Building portfolio‑ready ML prototypes

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Adarsh Vishwakarma GitHub: https://github.com/Adarsh-V-101

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This project is open‑source and free to use for educational purposes.