Using Natural Language Processing for Spam Detection in Emails

Through the above, we have successfully fit a bi-directional LSTM model on our email data, and detected 125 of 1114 emails as spam. Since the percentage of spam in data is often low, Measuring the model's performance by accuracy alone is not recommended. We need to evaluate it using other performance metrics as well, which we'll look at below. Precision and recall are the two most widely used performance metrics for a classification problem to get a better understanding of the problem. Precision is the fraction of the relevant instances from all the retrieved instances.