Background
In this mini-project the goal is to perform an image analysis prediction challenge. The task is based upon the MNIST dataset (https://en.wikipedia.org/wiki/MNIST_database). The original MNIST contains handwritten numeric digits from 0-9 and the goal is to classify which digit is present in an image.
Here, you will be working with a Modified MNIST dataset that we have constructed. In this modified dataset, the images contain three digits, and the goal is to output the digit in the image with the highest numeric value. Each example is represented as a matrix of pixel intensity values (i.e., the images are grey-scale not color). Examples of this task are shown in Figure 1. Note that this is a supervised classification task: Every image has an associated label (i.e., the digit in the image with the highest numeric value) and your goal is to predict this label.
Figure 1: Example images from the dataset. For example, the target label for the top-left image would be 7, while the target label for the bottom-right image would be 8.
Task
You must design and validate a supervised classification model to perform the Modified MNIST prediction task. There are no restrictions on your model, except that it should be written in Python. As with the previous mini-projects, you must write a report about your approach, so you should develop a coherent validation pipeline and ideally provide justification/motivation for your design decisions. You are free to develop a single model or to use an ensemble; there are no hard restrictions.
