https://github.com/udacity-machinelearning-internship/spamclassifier

Naive Bayes theorem applying using sklearn for Spam classifier.
https://github.com/udacity-machinelearning-internship/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/udacity-machinelearning-internship/spamclassifier
• Owner: Udacity-MachineLearning-Internship
• Created: 2024-05-14T10:32:58.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-17T03:42:35.000Z (about 1 year ago)
• Last Synced: 2025-01-21T08:24:13.076Z (5 months ago)
• Topics: jupyter-notebook, machine-learning, naive-bayes-classifier, python, sklearn, supervised-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 40 KB
• Stars: 0
• 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/)

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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.