Simple ML Chatbot

Overview

This repository contains a lightweight machine learning (ML) chatbot built in Python. The chatbot is trained on a small intents dataset, learning to classify user inputs and generate appropriate responses. While simple, it demonstrates the fundamentals of designing, training, and evaluating conversational AI models.

Features

• Intent classification: Trains a model to recognize user intents from text.
• Model training: Includes an epochs-based training loop with loss tracking.
• Prediction: Generates responses based on predicted intent.
• Notebook format: Implemented in a Jupyter Notebook (ChatBot.ipynb) for clarity and reproducibility.

Why This Matters

This project showcases my ability to:

• Translate product ideas into working ML prototypes.
• Structure and train simple Natural Language Processing (NLP) models.
• Document and share projects in a reproducible way.
• Consider next steps for scaling from demo to production.

Tech Stack

• Python
• TensorFlow / Keras (for training)
• Numpy, Scikit-learn
• Jupyter Notebook

Repository Structure

simple-ml-chatbot/
│
├── README.md            # Project overview
├── requirements.txt     # Dependencies
│
├── notebooks/           # Jupyter notebooks
│   └── ChatBot.ipynb
│
├── data/                # Training data (e.g., intents.json)
│   └── README.md
│
├── models/              # Saved model files after training
│   └── README.md
│
└── src/                 # Source code scripts
    └── README.md

Setup

1. Clone the repository:

   git clone https://github.com/juanocampo400/simple-ml-chatbot.git
   cd simple-ml-chatbot

2. Install dependencies:

   pip install -r requirements.txt

3. Download NLTK resources (run this once inside Python):

   import nltk
   nltk.download('punkt')
   nltk.download('wordnet')

4. Open the notebook:

   jupyter notebook ChatBot.ipynb

Future Enhancements

• Expand training data for better coverage.
• Add word embeddings for improved text understanding.
• Build a web app interface with Flask or FastAPI.
• Explore transformer-based models (e.g., BERT).

Notes

This is not a production system. It is intended as a demonstration of ML applied to conversational AI.