Hey, I am Maria, a 3rd year CS from Barcelona.

This year I decided to take ML as one of my subjects because I have always found it really interesting and so far I'm enjoying a lot this subject.
The thing is that next week I am having my midterms 😖 and the professor told us that to assess us he will give us a random problem (either regression, binary classification or multiclass classification) and a dataset. And we will have to clean the dataset, build a ML model using the dataset and evaluate it.
Even though I understand what we are doing in class I am a little bit concerned since we will only have 1 hour to build the model, clean the data, etc. So is there any strategy you guys recommend me in this case? So far we have only seen four different algorithms: linear regression, logistic regression, SVM and decision tree/random forest. What should I do when the dataset is given, which algorithms should I focus on if it has to be done in only 1 hour?
Thanks a lot! 🥰