Automated Email Classification Machine Learning Model
This project presents a machine learning model designed to classify emails automatically. The model utilizes several libraries and techniques for effective classification and performance evaluation.
- NLTK (Natural Language Toolkit): Utilized for data preprocessing tasks such as tokenization, stemming, and stop words removal.
- Tf-idf (Term Frequency-Inverse Document Frequency): Employed to convert textual data into numerical form, capturing the importance of terms in documents.
- Support Vector Machine (SVM) and Naive Bayes: Implemented to compare and evaluate the performance of two different classification algorithms.
The model's performance is evaluated using two key metrics:
- Accuracy: Measures the overall correctness of the classification.
- F1-Score: Provides a balance between precision and recall, particularly useful for imbalanced class distributions.
- The test data is visualized to gain insights into the distribution of classes and potential patterns.
- The accuracy and F1-score are computed for both SVM and Naive Bayes models.
- Performance comparison is conducted to determine the superior performing model.
This repository includes:
- Detailed documentation on the project setup, data preprocessing, model implementation, and evaluation.
- Instructions for reproducing the results and running the classification model.
- Visualization of test data distribution and model performance metrics.
- Discussion on the implications and potential enhancements. Acknowledgments: We acknowledge the contributions of the open-source community and the developers of NLTK, scikit-learn, and other libraries used in this project.