Spam Email Filter

In this project, I undertook the challenge of developing an email spam filter using classification techniques. The primary goal was to create an efficient and accurate model capable of discerning between legitimate emails and spam, enhancing email security and user experience.

Highlights:

• Bayesian Baseline: I created a Bayesian baseline classification model as the initial benchmark. This foundational model laid the groundwork for evaluating the effectiveness of more complex techniques.
• SVM Model: Leveraging Support Vector Machines (SVM), I developed a classification model capable of handling complex email datasets. SVM's ability to find clear boundaries between spam and non-spam emails significantly improved filter accuracy.
• Data Preparation: I preprocessed and prepared the email dataset, addressing issues such as text normalization, feature extraction, and data splitting for training and testing.
• Model Development: The project involved designing, training, and fine-tuning both the Bayesian and SVM models. I optimized hyperparameters and implemented feature engineering techniques to enhance model performance.
• Evaluation and Comparison: Thorough evaluation metrics were applied to measure the effectiveness of both models, including accuracy, precision, recall, and F1-score. Comparative analysis helped identify the strengths and weaknesses of each approach.
• Real-world Applicability: The email spam filter developed in this project has practical implications for email service providers and users, ensuring a more secure and spam-free inbox experience.

Outcomes:

This project successfully produced a email spam filter, leveraging both Bayesian and Support Vector Machines (SVM) models.

---

Full report in PDF