🛒 Smart Market Basket Chatbot

A conversational AI chatbot that provides intelligent product recommendations based on market basket analysis using the Apriori algorithm. Built with Streamlit and powered by association rules mined from real-world transaction data.

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🚀 Features

• 💬 Conversational chatbot interface
• 🧠 NLP-based intent detection (e.g., "I need tea", "I want bread")
• 📈 Apriori-based market basket analysis for recommendations
• 🧺 Real transaction data support
• 🔁 Reset chat functionality
• 🧠 Similarity matching with fallback response
• ⚡ Built with Streamlit for quick deployment

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📦 Installation

git clone https://github.com/yourusername/market-basket-chatbot.git
cd market-basket-chatbot
pip install -r requirements.txt

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▶️ Run the App

streamlit run app.py

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🧠 How It Works

1. User Input — The chatbot interprets phrases like:

   • "I need tea"
   • "Do you have cookies?"
   • "Give me bread"

2. NLP Matching — Uses string similarity to map input to known products.

3. Apriori Rules — Applies association rules to suggest items often bought together.

4. Bot Response — Returns top product recommendations in conversational style.

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📂 Project Structure

📁 market-basket-chatbot
├── app.py                 # Streamlit frontend
├── apriori.py             # Apriori algorithm & rule generation
├── chatbot.py             # NLP matching and suggestion logic
├── data/
│   └── transactions.csv   # Input data (real-world transactions)
├── requirements.txt       # Python dependencies
└── README.md              # This file

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📊 Example

🧑 You: I need tea
🤖 Bot: People who bought tea also often bought: sugar, cookies, bread.

🧑 You: i want soap
🤖 Bot: I didn’t quite get that. Try something like I need milk, I want bread.

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📘 Requirements

• Python 3.8+
• Streamlit

Install all dependencies via:

pip install -r requirements.txt

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