In today’s digital age, owning a mobile phone and receiving frequent communication messages on it is common. However, not all messages are genuine or important, as scammers send out spam messages to trick people into providing their personal information and accessing their accounts. Hence, spam filtering systems are essential to mark such messages as spam based on their content and sender. Developing a spam classification system and evaluating the model using various metrics is crucial to filter such unwanted messages. OpenAI API serves as a major focus of this article to generate embedding vectors for classification. The article uses the Email Spam Classification Dataset to train the Random Forest Classification model using train_test_split. Word embedding, a natural language processing (NLP) technique, maps words to vectors of real numbers, providing dense vector representation that captures semantic and syntactic properties of words. OpenAI offers various modules that help ease daily work and start AI projects.
OpenAI is an artificial intelligence research laboratory that works to develop and promote friendly AI to benefit the broader society. Established in December 2015, OpenAI aims to ensure that AI does not become hostile towards humans but works towards achieving mutual benefits.
In this article, the focus is on developing a spam classification system, with no mention of any specific person or company.
