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.
- Introduction
- Why This Project
- Dataset
- Features
- Models Implemented
- Evaluation Metrics
- Setup and Installation
- Demo
- Contributing
- Challenges Faced
- Lessons Learned
- License
- Contact
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.
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.
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.
- 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.
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.
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.
To run this project locally, follow these steps:
-
Clone the repository:
git clone https://github.com/Md-Emon-Hasan/ML-Project-Email-Spam-Classifier.git
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Navigate to the project directory:
cd ML-Project-Email-Spam-Classifier -
Install the required dependencies:
pip install -r requirements.txt
-
Run the notebooks or scripts as per your requirements.
Explore the live demo of the project here
Contributions to enhance or expand the project are welcome! Here's how you can contribute:
-
Fork the repository.
-
Create a new branch:
git checkout -b feature/new-feature
-
Make your changes:
- Implement new features, improve model performance, or enhance documentation.
-
Commit your changes:
git commit -am 'Add a new feature or update' -
Push to the branch:
git push origin feature/new-feature
-
Submit a pull request.
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.
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.
This project is licensed under the Apache License 2.0. See the LICENSE file for more details.
- Email: iconicemon01@gmail.com
- WhatsApp: +8801834363533
- GitHub: Md-Emon-Hasan
- LinkedIn: Md Emon Hasan
- Facebook: Md Emon Hasan
Feel free to reach out for any questions or feedback regarding the project!
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