Sports Shop Customer Support Chatbot

This repository contains a simple Customer Support Chatbot built using basic AI techniques in Python. The chatbot is designed to assist customers of a sports shop with their queries, such as store information, product availability, discounts, delivery details, and more. This project uses a CSV file for storing FAQs and their corresponding responses, which is processed using Python and NLP libraries.

Table of Contents

• Features
• Requirements
• Installation
• Usage
• Project-structure
• Data Format
• How It Works
• Future Enhancements
• License]

Features

• Automates customer support for a sports shop with predefined questions and answers.
• Uses a CSV file to store customer queries and responses.
• Handles basic product inquiries, store hours, delivery, return policies, and more.
• Easily extendable with more data and questions.

Requirements

Before running the project, ensure you have the following installed:

• Python 3.x
• Google Colab (or local Jupyter environment)
• Required Python libraries:
  • pandas
  • nltk

You can install the required libraries using:

pip install pandas nltk

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/sports-shop-chatbot.git
   cd sports-shop-chatbot

2. Upload the CSV file: In Google Colab or your local Jupyter environment, upload the CSV file containing the FAQs (sports_shop_chatbot_data.csv).

3. Install necessary libraries:

   !pip install pandas nltk

4. Run the chatbot script in your environment.

Usage

1. Load the CSV data: The CSV file (sports_shop_chatbot_data.csv) is loaded into the Colab notebook, and it contains the pre-defined questions and answers for the chatbot.

2. Start Chatting: Once the notebook is run, you can start interacting with the chatbot. The chatbot matches user queries with questions from the CSV file and returns the appropriate answer.

   Example interaction:

   You: What are your working hours?
   Chatbot: We are open from Monday to Saturday, 9 AM to 7 PM, and on Sundays from 11 AM to 5 PM.
   

3. Exit the Chat: Type thanks to end the conversation.

Project Structure

├── sports_shop_chatbot_data.csv  # CSV file with FAQs and responses
├── chatbot.ipynb                 # Jupyter/Colab notebook containing the chatbot code
├── README.md                     # Project documentation

CSV File Format

The CSV file contains the following columns:

• Category: The category of the query (e.g., Store Info, Delivery, Products).
• Question: The customer query that the chatbot will respond to.
• Alternative Phrasing: Variations of the query to ensure the chatbot recognizes different ways of asking the same question.
• Answer: The predefined response for the given question.
• Tags: Keywords related to the query for better intent recognition.

Example CSV data:

Category,Question,Alternative Phrasing,Answer,Tags
Store Info,What are your working hours?,When are you open?,We are open from Monday to Saturday, 9 AM to 7 PM, and on Sundays from 11 AM to 5 PM.,hours,working,schedule
Delivery,Do you offer home delivery?,Can you deliver to my home?,Yes, we offer home delivery across the country. Free delivery on orders above $50!,delivery,shipping,home

How It Works

1. Load Data: The chatbot reads the CSV file and creates a dictionary of questions and answers.
2. Text Matching: The chatbot uses simple text matching techniques with tokenization to find the most relevant answer to the customer’s question.
3. Response Generation: Once a match is found, the chatbot responds with the predefined answer. If no match is found, it returns a fallback message.

Future Enhancements

Some ideas for improving the chatbot:

• Natural Language Processing: Implement more advanced NLP techniques to improve question matching and intent recognition.
• Machine Learning: Use ML models like BERT or GPT for better understanding and responding to queries.
• Database Integration: Instead of CSV, use a database to store questions and answers, making it more scalable.
• Additional Features: Implement additional features such as live agent handoff, order tracking integration, and personalized recommendations.

License

This project is NOT licensed and is Free to use.