In this task1, you need run the program and plot the accuracy and loss results. Accuracy is perhaps the best-known Machine Learning model validation method used in classification problems. Accuracy tells the percentage of accurate predictions. We calculate it by dividing the number of correct predictions by the total number of predictions. Machines learn by means of a loss function. It's a method of evaluating how well specific algorithm models the given data. If predictions deviate too much from actual results, loss function would cough up a very large number. Task1: Project Deliverables (SUBMISSION) 1. If your program is working, submit the source code of your program. You must demonstrate your program is working during the demonstration in class, and with the test cases. Submit the source code, input file(s), output file(s), and visualizations/results. 2. Please be sure to include thorough comments throughout your program where necessary. The comments should explain the code step-by-step during the demonstration. 3. Provide a detailed description of your algorithm and program flow. Be sure to include a step-by-step description of the tools that you used (compiler, libraries, etc). 4. Zip all files or submit all files at once. This should be in a report format. 5. Prepare a demonstration of your project for the class. This should be done in PowerPoint format, 15-20 slides is required