In this project, 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. You need to use the machine learning model classify the image of a user's face into one of two categories: real or fake. To begin this process, you downloaded a real and fake face. https://www.kaggle.com/datasets/ciplab/real-and-fake-face-detection Also, make sure to include your OWN images as well for the testing. This is a dataset containing 2041 images. We downloaded the dataset from Real and Fake Face Detection Kaggle. You will use these images to train and test the model. Once the images have been downloaded, you resize and save them to a folder in the C drive to be used later. This is a Programming Project you are going to use exiting source code or write your own python program to detect the real and fake face in given dataset. You will need use the following algorithms: 1. Naive Bayes 2. K-Nearest Neighbor 3. Decision tree 4. Logistic regression