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https://github.com/ragunathjeyakumar/support-agent-chatbot-for-cdp-how-to-questions

🚀 Support Agent Chatbot for CDP "How-to" Questions "An AI-powered chatbot designed to assist users with CDP-related 'How-to' queries using NLP and Machine Learning. Built with Flask, Python, and NLP techniques to provide automated responses and enhance customer support."
https://github.com/ragunathjeyakumar/support-agent-chatbot-for-cdp-how-to-questions

cdp chatbot customer-support flask ml nlp

Last synced: 7 months ago
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🚀 Support Agent Chatbot for CDP "How-to" Questions "An AI-powered chatbot designed to assist users with CDP-related 'How-to' queries using NLP and Machine Learning. Built with Flask, Python, and NLP techniques to provide automated responses and enhance customer support."

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# Building a Support Agent Chatbot for CDP "How-to" Questions

## Overview
This project is a chatbot designed to assist users with "How-to" questions related to CDP (Customer Data Platform). The chatbot leverages machine learning and NLP (Natural Language Processing) techniques to provide automated responses, reducing the workload on human support agents.

## Features
- NLP-based question-answering
- Machine learning model for understanding queries
- Predefined knowledge base for CDP-related queries
- Easy-to-use interface

### Output
![Image](https://github.com/user-attachments/assets/b6a12598-5549-4733-ba58-88cec3242faf)

### Prerequisites
Ensure you have the following installed:
- Python 3.x
- pip (Python package manager)
- Flask (for the web interface)
- Required Python libraries (listed in `requirements.txt`)

### Steps
1. Clone the repository:
```sh
git clone https://github.com/RagunathJeyakumar/Support-Agent-Chatbot-for-CDP-How-to-Questions.git
cd Support-Agent-Chatbot-for-CDP-How-to-Questions
```
2. Install dependencies:
```sh
pip install -r requirements.txt
```
3. Run the chatbot server:
```sh
py app.py
```
4. Open the application in your browser at `http://localhost:5000`

## Usage
- Ask the chatbot CDP-related "How-to" questions.
- The chatbot processes the question and provides a response.
- If the chatbot cannot answer, it provides suggestions or alternative queries.

## Technologies Used
- **Python**: Backend development
- **Flask**: Web framework
- **NLTK / SpaCy**: Natural Language Processing
- **Machine Learning**: Training models for understanding queries
- **HTML, CSS, JavaScript**: Frontend UI