https://github.com/andrewsy1004/logistic-regression-spam-classifier

This project implements a spam email classifier using Logistic Regression.
https://github.com/andrewsy1004/logistic-regression-spam-classifier

numpy pandas scikit-learn

Last synced: 27 days ago
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This project implements a spam email classifier using Logistic Regression.

• Host: GitHub
• URL: https://github.com/andrewsy1004/logistic-regression-spam-classifier
• Owner: Andrewsy1004
• Created: 2024-12-15T16:15:43.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-12-15T16:23:00.000Z (about 1 month ago)
• Last Synced: 2024-12-15T17:26:52.667Z (about 1 month ago)
• Topics: numpy, pandas, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 205 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 📬 Logistic Regression Spam Classifier

This project implements a **Spam Email Classifier** using **Logistic Regression**, trained on a dataset of SMS messages. The model distinguishes between **ham** (non-spam) and **spam** messages. This project demonstrates how to process text data, apply machine learning, and evaluate model performance. 

## 🚀 Features:

- **Text Preprocessing**: The text data is cleaned and transformed using **TF-IDF Vectorization**, which converts the raw text into numerical feature vectors.

- **Model Training**: A **Logistic Regression** model is trained to classify SMS messages as either "ham" or "spam".

- **Model Evaluation**: Performance metrics such as **accuracy**, **precision**, **recall**, and **F1-score** are used to evaluate the model's effectiveness.

## 📊 Steps:

1. **Data Preprocessing**:

   - The dataset is cleaned by removing stop words and converting all text to lowercase.

   - The text is transformed into numerical features using the **TF-IDF** vectorizer.

   

2. **Training**:

   - The **Logistic Regression** model is trained on the processed data.

   

3. **Evaluation**:

   - The model is evaluated on both training and test datasets using multiple performance metrics (accuracy, precision, recall, F1-score).

   

## 📋 Dependencies:

- `pandas`: For data manipulation and handling.

- `numpy`: For numerical operations.

- `scikit-learn`: For machine learning models, including logistic regression and vectorization.