The project employs various machine learning algorithms, such as Naive Bayes, Support Vector Machines (SVM), or deep learning models like Recurrent Neural Networks (RNNs) or Transformers, to learn the patterns and characteristics of spam emails. During the training phase, the model optimizes its parameters using appropriate loss functions.
To evaluate the model's performance, the dataset is split into training and testing sets, and metrics such as accuracy, precision, recall, and F1 score are used. Cross-validation techniques may also be applied to ensure the robustness of the classifier.
The final product is a reliable and accurate Email Spam Classifier capable of processing incoming emails and categorizing them as spam or ham. By deploying the classifier in email systems, it can effectively filter out unwanted spam emails, improving user experience and security.
Overall, the "Email Spam Classifier" project demonstrates the power of NLP and machine learning in tackling the challenge of email spam, providing a valuable tool to combat the ever-growing issue of unwanted email communications.
Here I want to make a simple recommender system to gauge the similarity between shows, users and to help me predict whether a user will enjoy a particular movie.