MR. Filter

Welcome to the Spam Classifier project! This machine learning application is designed to classify emails as either spam or non-spam (ham) using various machine learning algorithms.

Overview

The Spam Classifier is a machine learning model built using Python and popular libraries like scikit-learn and pandas. It analyzes the content and features of emails to predict whether they are spam or legitimate. This project aims to provide users with a reliable tool for automatically filtering out unwanted spam emails from their inbox.

Features

• Classification: The classifier accurately predicts whether an email is spam or non-spam based on its content and features.
• Customization: Users can customize and fine-tune the model by adjusting parameters and features to suit their specific requirements.
• Performance Metrics: The project provides evaluation metrics such as accuracy, precision, recall, and F1-score to assess the performance of the classifier.
• Data Visualization: Visualizations are provided to help users understand the distribution of spam and non-spam emails in the dataset and the performance of the classifier.

Getting Started

To get started with the Spam Classifier project, follow these steps:

1. Clone this repository to your local machine.
2. Install the required dependencies by running pip install -r requirements.txt.
3. Prepare your dataset: Ensure you have a labeled dataset of emails (spam and non-spam) in a suitable format (e.g., CSV, Excel).
4. Train the model: Use the provided scripts or notebooks to preprocess the data, train the classifier, and evaluate its performance.
5. Test the model: Once trained, test the classifier on new email data to assess its accuracy and performance.
6. Customize and fine-tune: Experiment with different algorithms, features, and parameters to optimize the classifier for your specific use case.

Dataset

The Spam Classifier project uses a publicly available dataset of labeled emails for training and evaluation. You can find more information about the dataset and its source in https://www.kaggle.com/.

Contributing

Contributions to the Spam Classifier project are welcome! Whether you want to report a bug, suggest a feature, or contribute code improvements, please feel free to submit a pull request.

License

This project is licensed under the MIT License.

Acknowledgments

Special thanks to the scikit-learn and pandas communities for their invaluable support and contributions to the development of this project.