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https://github.com/barasedih11/spamclassifier

Naive Bayes theorem applying using sklearn for Spam classifier.
https://github.com/barasedih11/spamclassifier

jupyter-notebook machine-learning naive-bayes-classifier python sklearn supervised-learning

Last synced: 3 months ago
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Naive Bayes theorem applying using sklearn for Spam classifier.

Host: GitHub
URL: https://github.com/barasedih11/spamclassifier
Owner: BaraSedih11
Created: 2024-05-14T10:32:58.000Z (9 months ago)
Default Branch: main
Last Pushed: 2024-05-17T03:42:35.000Z (9 months ago)
Last Synced: 2024-05-17T04:36:44.660Z (9 months ago)
Topics: jupyter-notebook, machine-learning, naive-bayes-classifier, python, sklearn, supervised-learning
Language: Jupyter Notebook
Homepage:
Size: 38.1 KB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

![Spam_Classifier](https://github.com/BaraSedih11/SpamClassifier/assets/98843912/c1334184-7a07-4e73-ac38-cd15c732f226)

![GitHub repo size](https://img.shields.io/github/repo-size/BaraSedih11/SpamClassifier) ![GitHub repo file count (file type)](https://img.shields.io/github/directory-file-count/BaraSedih11/SpamClassifier) [![Python Version](https://img.shields.io/badge/python-3.8-blue)](https://www.python.org/downloads/release/python-380/)
[![Pip Version](https://img.shields.io/badge/pip-21.0-orange)](https://pypi.org/project/pip/21.0/)
![GitHub last commit (branch)](https://img.shields.io/github/last-commit/BaraSedih11/SpamClassifier/main)
[![Version](https://img.shields.io/badge/version-v1.0.0-blue)](https://github.com/BaraSedih11/SpamClassifier/releases/tag/v1.0.0)
[![Contributors](https://img.shields.io/github/contributors/BaraSedih11/SpamClassifier)](https://github.com/BaraSedih11/SpamClassifier/graphs/contributors)
![GitHub pull requests](https://img.shields.io/github/issues-pr-raw/BaraSedih11/SpamClassifier)

This repository contains a simple spam classifier built using machine learning techniques.

## Introduction

The Spam Classifier is a project aimed at detecting spam messages using natural language processing (NLP) techniques. It utilizes a dataset of labeled messages to train a machine learning model to distinguish between spam and non-spam messages.

## Features

- Utilizes popular machine learning libraries such as scikit-learn for model training.
- Implements various NLP techniques such as tokenization, TF-IDF vectorization, and classification algorithms.
- Provides easy-to-use scripts for training the model and evaluating its performance.

## Contents

- `Bayesian_Inference.ipynb`: Jupyter Notebook containing the implementation of polynomial regression using Python.
- `README.md`: This file providing an overview of the repository.

## Requirements
To run the code in the Jupyter Notebook, you need to have Python installed on your system along with the following libraries:

* NumPy
* pandas
* scikit-learn
* matplotlib
You can install these libraries using pip:

```bash
pip install numpy pandas scikit-learn matplotlib
```

## Usage

1. Clone this repository to your local machine:

```bash
git clone https://github.com/BaraSedih11/SpamClassifier.git
```

2. Navigate to the repository directory:

```bash
cd SpamClassifier
```

3. Open and run the Jupyter Notebook `Bayesian_Inference.ipynb` using Jupyter Notebook or JupyterLab.

4. Follow along with the code and comments in the notebook to understand how polynomial regression is implemented using Python.

## Acknowledgements

- [scikit-learn](https://scikit-learn.org/): The scikit-learn library for machine learning in Python.
- [NumPy](https://numpy.org/): The NumPy library for numerical computing in Python.
- [pandas](https://pandas.pydata.org/): The pandas library for data manipulation and analysis in Python.
- [matplotlib](https://matplotlib.org/): The matplotlib library for data visualization in Python.