Build a basic spam email filter based on machine learning using ARM Assembler

Build a basic spam email filter based on machine learning using ARM Assembler. You will be using an existing machine learning open-source library provided as part of this lab. The bulk of the work, on your part, is to dynamically allocate the arrays needed to read in the email contents and process the email contents to prepare it for the SVM training and testing code. Your code will read in an email message in some standard format (we will determine that standard) and will classify whether that email is a spam or non-spam email. There are databases containing example spam and non-spam emails that you can download and use to train your machine learning filter to make this determination. More about that database will be provided. The basic steps in determining whether a text file containing an email is a spam or non-spam is as followed: 1. Prepare the text data by reading in the file 2. Create the word dictionary to count the words and their frequency 3. Extract the feature from the dictionary created and prepare the data to input into the machine-learning function (whether it is for training or for testing) 4. Pick a machine-learning algorithm to train on your features from step 3 5. Finally, you will test your code using sample emails to see how well your software works. Please check the attached description document for more details.