https://github.com/mohammed-majid/logistic-binary-email-classification

Binary Classification of spam/ham emails
https://github.com/mohammed-majid/logistic-binary-email-classification

binary-classification logistic-regression scikit-learn

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Binary Classification of spam/ham emails

• Host: GitHub
• URL: https://github.com/mohammed-majid/logistic-binary-email-classification
• Owner: Mohammed-Majid
• Created: 2024-05-30T07:59:38.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-30T08:12:52.000Z (8 months ago)
• Last Synced: 2024-11-16T12:12:45.173Z (2 months ago)
• Topics: binary-classification, logistic-regression, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 21.5 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Email Binary Classification using Logistic Regression

This repository contains code for a binary classification task: predicting whether an email is spam (1) or ham (0) using logistic regression.

## Overview

The main script email_classification.py demonstrates the process of:

- Pre-processing: Loading the dataset, checking for missing values, and splitting it into training and testing sets.

- Feature Extraction: Utilizing TF-IDF Vectorization to convert text data into numerical features.

- Modeling: Training a logistic regression model on the extracted features.

- Evaluation: Assessing the model's performance on both training and testing data, including accuracy, confusion matrix, and classification report.

- Prediction: Accepting user input (email text) and predicting whether it's spam or ham.

## Usage

- Clone the Repository

- Install Dependencies: Make sure you have the necessary dependencies installed. You can install them using pip:

```

pip install numpy pandas scikit-learn seaborn

```

- Run the Script

- Input Email: When prompted, enter an email text to classify it as spam or ham.

## Dataset

The dataset email_classification.csv contains email texts labeled as spam or ham.

## Requirements

- Python 3.x

- numpy

- pandas

- scikit-learn

- seaborn

  

## Project Structure

- Model.ipynb: The file that contains the script.

- email_classification.csv: The dataset used to train and test the model.