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:

pip install numpy pandas scikit-learn matplotlib

Usage

1. Clone this repository to your local machine:

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

2. Navigate to the repository directory:

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: The scikit-learn library for machine learning in Python.
• NumPy: The NumPy library for numerical computing in Python.
• pandas: The pandas library for data manipulation and analysis in Python.
• matplotlib: The matplotlib library for data visualization in Python.