From google drive, obtain the Hawaii-Mar dataset that contains hourly weather data in the month of Mar for Hawaii for approximately 40 years. Take the help of your mentors to read the format of the dataset. Use this dataset to build a logistic regression model that predicts cloud cover 6 hours in the future using current weather measurements. Evaluate your model with a receiver operating characteristic curve, and find the area under the curve. For the evaluation, you will have to make sure that the training and test data are not the same. It is up to you to figure out how to manage this. Test your model on weather data from Stockholm (again obtain the dataset from koa). How good or bad is the model trained in Hawaii?
Build a neural network with one neuron (with an appropriate choice of a loss function, regularization, and activation) that mimics
You are welcome to use either pytorch or any wrapper around tensorflow. I would encourage you to use pytorch (just a lot simpler down the line). But if you are already established with keras or another wrapper, stick with it.
You do not need to turn in the optional parts yet—you can do so if you already know about perceptrons and support vector machines. Optimize the neural network weights using Stochastic Gradient Descent, and see how closely you can match the optimal fit obtained by corresponding closed-form/convex-optimized solutions.