https://github.com/jeus0522/ml-project-email-spam-classifier
ML project focused on email spam classification, demonstrating data preprocessing, model training, and evaluation using Python and scikit-learn.
https://github.com/jeus0522/ml-project-email-spam-classifier
data-science eda email-classification machine-learning-projects nlp spam-classification text-classification
Last synced: 2 months ago
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ML project focused on email spam classification, demonstrating data preprocessing, model training, and evaluation using Python and scikit-learn.
- Host: GitHub
- URL: https://github.com/jeus0522/ml-project-email-spam-classifier
- Owner: jeus0522
- License: apache-2.0
- Created: 2024-08-07T00:07:59.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2024-08-07T00:35:52.000Z (almost 2 years ago)
- Last Synced: 2025-02-03T22:37:37.163Z (over 1 year ago)
- Topics: data-science, eda, email-classification, machine-learning-projects, nlp, spam-classification, text-classification
- Language: Jupyter Notebook
- Homepage: https://email-sms-classifier-by-emon.onrender.com/
- Size: 2.64 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Machine Learning Project: Email Spam Classifier
Welcome to the **Email Spam Classifier** machine learning project repository! This project focuses on classifying emails as spam or non-spam (ham) using various machine learning techniques.
## 📋 Contents
- [Introduction](#introduction)
- [Why This Project](#why-this-project)
- [Dataset](#dataset)
- [Features](#features)
- [Models Implemented](#models-implemented)
- [Evaluation Metrics](#evaluation-metrics)
- [Setup and Installation](#setup-and-installation)
- [Demo](#demo)
- [Contributing](#contributing)
- [Challenges Faced](#challenges-faced)
- [Lessons Learned](#lessons-learned)
- [License](#license)
- [Contact](#contact)
---
## 📖 Introduction
This repository contains a machine learning project focused on classifying emails as spam or non-spam using supervised learning techniques. It includes data preprocessing, model development, evaluation, and deployment aspects of the project.
---
## 🎯 Why This Project
The primary motivation behind creating this project is to tackle the issue of email spam, which remains a significant problem affecting users' productivity and security. By accurately classifying emails, this project aims to improve email filtering systems.
---
## 📊 Dataset
The dataset used for this project contains a collection of emails labeled as spam or ham. It includes various features extracted from the emails, such as text content, subject line, sender information, etc.
---
## 🌟 Features
- **Data Preprocessing:** Cleaned and transformed dataset for machine learning model compatibility.
- **Model Development:** Trained multiple machine learning models to classify emails as spam or ham.
- **Model Evaluation:** Evaluated models using appropriate metrics to ensure accuracy and reliability.
- **Deployment:** Implemented a simple web-based or command-line application for classifying new emails.
---
## 🧠 Models Implemented
Several machine learning models were implemented and evaluated:
- Naive Bayes Classifier
- Support Vector Machine (SVM)
- Random Forest Classifier
- Logistic Regression
- others
Each model's performance was compared based on metrics such as accuracy, precision, recall, and F1-score.
---
## 📊 Evaluation Metrics
The models were evaluated using the following metrics:
- **Accuracy:** Overall correctness of the predictions.
- **Precision:** Proportion of true positives among all positive predictions.
- **Recall:** Proportion of true positives identified correctly.
- **F1-score:** Harmonic mean of precision and recall, providing a balance between the two metrics.
---
## 🚀 Setup and Installation
To run this project locally, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/Md-Emon-Hasan/ML-Project-Email-Spam-Classifier.git
```
2. Navigate to the project directory:
```bash
cd ML-Project-Email-Spam-Classifier
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
4. Run the notebooks or scripts as per your requirements.
---
## 🌐 Demo
Explore the live demo of the project [here](https://email-sms-classifier-by-emon.onrender.com)
---
## 🤝 Contributing
Contributions to enhance or expand the project are welcome! Here's how you can contribute:
1. **Fork the repository.**
2. **Create a new branch:**
```bash
git checkout -b feature/new-feature
```
3. **Make your changes:**
- Implement new features, improve model performance, or enhance documentation.
4. **Commit your changes:**
```bash
git commit -am 'Add a new feature or update'
```
5. **Push to the branch:**
```bash
git push origin feature/new-feature
```
6. **Submit a pull request.**
---
## 🛠️ Challenges Faced
During the development of this project, the following challenges were encountered:
- Handling text data preprocessing, including tokenization and feature extraction.
- Dealing with class imbalance in the dataset.
- Optimizing model performance and scalability.
---
## 📚 Lessons Learned
Key lessons learned from this project include:
- Importance of feature selection and engineering in text classification tasks.
- Evaluation and selection of appropriate metrics based on project goals.
- Deployment considerations for machine learning models in real-world applications.
---
## 📄 License
This project is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for more details.
---
## 📬 Contact
- **Email:** [iconicemon01@gmail.com](mailto:iconicemon01@gmail.com)
- **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)
- **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)
- **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)
- **Facebook:** [Md Emon Hasan](https://www.facebook.com/mdemon.hasan2001/)
Feel free to reach out for any questions or feedback regarding the project!
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