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Baum-Welch Hidden Markov Model (HMM) Dashboard

Name: Rohan Shyam

Roll Number: TCR24CS056

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🚀 Project Overview

An interactive dashboard for analyzing Hidden Markov Models using the Baum-Welch algorithm. This project allows users to input observation sequences and visualize the optimization of Transition (A) and Emission (B) matrices.

🛠️ Tech Stack

• Backend: Django, Django REST Framework, NumPy
• Frontend: Next.js, React, Tailwind CSS
• Environment: Developed and tested on Fedora Linux

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💻 Local Execution Instructions

1. Prerequisites

Ensure you have Python 3.11+ and Node.js installed on your machine.

git clone https://github.com/rohan-shyam/Baum-Welch-Algorithm/
cd Baum-Welch-Algorithm
python3 -m venv .venv
source .venv/bin/activate
pip install -r server/requirements.txt

3. Navigate to the server directory:

cd server

4. Run the server:

python manage.py runserver

*The API will be live at http://127.0.0.1:8000*

3. Frontend Setup

1. Open a new terminal.
2. Navigate to the client directory:

cd client

3. Install dependencies and run the development server:

npm install
npm run dev

*Access the dashboard at http://localhost:3000*

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⚙️ Core Logic: Baum-Welch Algorithm

The algorithm performs iterative expectation-maximization to find the maximum likelihood estimates of HMM parameters.

1. Expectation (E-step): Calculates forward () and backward () probabilities.
2. Maximization (M-step): Updates the A, B, and matrices based on the calculated probabilities.

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Screenshots