Skip to content

Latest commit

 

History

History
40 lines (24 loc) · 1.51 KB

README.md

File metadata and controls

40 lines (24 loc) · 1.51 KB
Raw

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