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:
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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.
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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.
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Data Preparation: I preprocessed and prepared the email
dataset, addressing issues such as text normalization, feature
extraction, and data splitting for training and testing.
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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.
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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.
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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