Email Spam Classifier

A machine learning project to classify emails into spam and non-spam categories. This project utilizes various text preprocessing techniques and machine learning algorithms to accurately filter out unwanted emails.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Dataset
• Results
• Contributing
• License
• Acknowledgements
• Contact

Introduction

Email spam is a common problem that can clutter inboxes and pose security risks. This project aims to solve this issue by building a robust classifier that differentiates between spam and legitimate emails. The approach involves:

• Preprocessing email text (tokenization, cleaning, etc.)
• Extracting features using techniques such as TF-IDF
• Training a classification model (e.g., Naive Bayes, SVM) on a labeled dataset
• Evaluating model performance using standard metrics

Features

• Data Preprocessing: Clean and tokenize email content.
• Feature Extraction: Utilize TF-IDF vectorization for text features.
• Model Training: Implement classifiers such as Naive Bayes or SVM.
• Performance Evaluation: Metrics include accuracy, precision, recall, and F1-score.
• Prediction: Easily classify new emails using the trained model.

Installation

Clone the repository to your local machine:

git clone https://github.com/adilshamim8/email-spam-classifier.git
cd email-spam-classifier

Install the required dependencies using pip:

pip install -r requirements.txt

Usage

1. Prepare the Dataset:
   Ensure your dataset is formatted correctly (e.g., CSV file with labels for spam and non-spam emails).

2. Preprocess the Data:
   Run the preprocessing script to clean and tokenize email text.

   python preprocess.py

3. Train the Model:
   Train your classifier using the training script.

   python train.py

4. Evaluate the Model:
   Evaluate model performance on the test set.

   python evaluate.py

5. Make Predictions:
   Use the prediction script to classify new emails.

   python predict.py --input "Your email text here..."

Dataset

The project is designed to work with publicly available email datasets (e.g., the SpamAssassin Public Corpus) or your own custom dataset. Make sure the dataset is in the required format before running the scripts.

Results

After training, the classifier typically achieves competitive performance. For example, you might see an accuracy of around X%, with detailed metrics provided in the results/ folder. Feel free to update this section with your own experimental results.

Contributing

Contributions are welcome! If you'd like to improve the project, please fork the repository and submit a pull request. For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgements

• SpamAssassin Public Corpus for providing the dataset.
• Open-source libraries such as scikit-learn, NumPy, and Pandas.
• Inspiration from various email filtering research projects.

Contact

For any questions or suggestions, feel free to contact me at [[email protected]].