Distributed-Naive-Bayes-Classifier

Overview

This project implements a Naive Bayes Classifier from scratch for categorical datasets, using various approaches including single-threaded, multi-threaded, and distributed computing. The classifier predicts class labels based on input features and supports efficient parallel computation.

Key Features:

File 1: Multi-threaded Naive Bayes Classifier

Uses Python's multiprocessing and threading libraries to distribute computation across multiple threads and processes. Demonstrates parameter estimation using counters and logical operations for efficient computations.

File 2: Distributed Naive Bayes Classifier (MPI)

Leverages mpi4py for distributed computation. Splits data across multiple processes to compute probabilities independently and aggregates the results using MPI communication.

File 3: Single-threaded Naive Bayes Classifier

A basic implementation of the Naive Bayes algorithm. Provides a clean and simple approach to training and predicting class labels for categorical datasets.

Installation Steps

1. Install necessary packages

   sudo apt-get -y update && sudo apt-get -y install python3 git python3-pip python3-venv libmpich-dev

2. Create virtual environment

   python3 -m venv /venv

3. Install pip packages

   /venv/bin/pip install numpy pandas mpi4py

4. Clone the repository

   git clone https://github.com/AvinashSubhash/Distributed-Naive-Bayes-Classifier.git /NBC

5. Run the bash script

   ./Output.sh