This Email Spam Detection project was a part of my data science internship at Oasis Infobyte. The primary objective was to develop a robust system to identify and filter out spam emails, enhancing email security and user experience.
-
Data Preprocessing: The project involved data cleaning and transformation to prepare email datasets for machine learning analysis.
-
Machine Learning Algorithms: We implemented various machine learning algorithms, such as Naive Bayes, Support Vector Machines, and Random Forest, to classify emails into spam and non-spam categories.
-
Model Evaluation: Extensive model evaluation was conducted, including metrics like accuracy, precision, recall, and F1-score, to ensure the effectiveness of the spam detection system.
We used a labeled dataset of emails, containing both spam and non-spam examples, to train and test our models.
The SVM model achieved an accuracy of 0.9713, with a precision of 0.97 for "ham" and 1.00 for "spam," recall of 1.00 for "ham" and 0.76 for "spam," and F1-scores of 0.98 for "ham" and 0.87 for "spam." These metrics demonstrate the strong performance of the SVM model in classifying emails into "ham" (non-spam) and "spam" categories.
In the future, I plan to explore deep learning techniques and further enhance the project's accuracy and robustness. Your contributions and feedback are highly encouraged to make this project even more effective.