Naive Bayes Implementation

This repository contains a Jupyter Notebook that demonstrates the implementation of the Naive Bayes algorithm for classification tasks. The Naive Bayes classifier is a simple yet effective probabilistic classifier based on Bayes' theorem, assuming independence between features.

Table of Contents

• Overview
• Features
• Installation
• Usage
• Dependencies
• Contributing
• License

Overview

The Naive Bayes algorithm is widely used for text classification, spam detection, and other tasks that require a probabilistic approach to classification. This notebook provides a step-by-step guide to understanding and implementing the Naive Bayes algorithm from scratch.

Features

• Theory and Intuition: Detailed explanation of the Naive Bayes theorem.
• Implementation: A Python-based implementation of the Naive Bayes classifier.
• Examples: Application of the Naive Bayes classifier on example datasets.
• Evaluation: Metrics and methods to evaluate the performance of the classifier.

Installation

To run this notebook, you'll need to clone this repository and install the required dependencies.

1. Clone the repository:

   git clone https://github.com/alihassanml/naive-bayes-implementation.git
   cd naive-bayes-implementation
   

2. Install the necessary Python packages:

   pip install -r requirements.txt
   

Usage

1. Open the Jupyter Notebook:

   jupyter notebook Naive\ Bayes\ Theorem.ipynb
   

2. Follow the instructions in the notebook to understand and run the Naive Bayes implementation.

Dependencies

Make sure you have the following Python libraries installed:

• numpy
• pandas
• scikit-learn
• matplotlib
• seaborn

These can be installed using the following command:

pip install numpy pandas scikit-learn matplotlib seaborn

Contributing

Contributions are welcome! Please feel free to submit a Pull Request or open an issue to improve this repository.

License

This project is licensed under the MIT License. See the LICENSE file for details.