The primary aim of this project is to build an automated system that can accurately classify emails based on their content. In the modern digital age, email remains a dominant form of communication, often resulting in users being overwhelmed by large volumes of messages. Manually sorting these emails is inefficient and prone to errors. This project addresses this problem by applying NLP and supervised learning to automate the classification process. By the end of the project, students are expected to have developed a fully functional email classification system that can categorize incoming emails into relevant groups. They will also gain practical experience in handling text data, applying machine learning algorithms, evaluating model performance, and developing user-friendly interfaces. The outcome is a deployable model that improves email handling while demonstrating the application of AI in real-world scenarios.
To complete the project successfully, students will follow a well-defined sequence of tasks over a twelve-week timeline. Initially, they will learn the foundational concepts of machine learning and natural language processing, which form the backbone of the classification system. They will then acquire or collect relevant datasets containing email content for training and testing the model. Students will install necessary tools such as Python, Anaconda Navigator, or use online environments like Google Colab to begin development.
Subsequent weeks will involve learning and utilizing libraries such as scikit-learn, TensorFlow, and NLTK for processing textual data and training machine learning models. They will create a framework for the email classifier, build and train the model using various data samples, and then evaluate its performance using a separate test dataset. Model accuracy will be enhanced through optimization techniques including feature engineering and hyperparameter tuning. In the final phases, students will integrate all components into a functional system, develop an interface for displaying classified emails, conduct testing, prepare thorough documentation, and present their final solution. Additional expectations include maintaining a standard coding and documentation style, dividing tasks effectively among team members, and ensuring all work complies with academic integrity standards.
