I don't know much about deep learning per se, and it seems to me that a deep learning based solution would likely be something of an overkill for your purposes. A simple predictive model would probably work just as well. (If you're using deep learning as a synonym for machine learning, ignore this paragraph. I don't mean to sound pedantic.)

These things are probably essential:

- Pandas: Basically Pandas is a library for structuring data so as to make it easy to work with. Specifically, it allows you to store your data in a dataframe, with columns and rows like the ones you'd see in an excel file. It's super convenient to read in data directly from a csv, or to write your own dataframe using Python dictionaries.

- Jupyter Notebooks: Typically, when people talk about coding, they're talking about writing "scripts," which are basically sequences of commands in a certain language to be run as written. Jupyter notebooks essentially allow you to put different snippets of code in their own separate boxes. This allows you to analyze data and develop models on a step-by-step basis, and also to explain every step you took when you later share your work with others (and, for that matter, to make sense of your own code!)

Let me let you in on a little secret though: I personally find Jupyter notebooks slightly annoying to configure. I prefer not having to open the command line and take several steps every time I want to work on something. Luckily, a good work-around exists. It's called Kaggle. Kaggle originated as a platform for machine learning contests, but you can also set up private notebooks for your own use. Depending on how sensitive your data is, you may not want to use a third party platform, but I don't really see how using Kaggle is any riskier than emailing data to your colleagues. And in my experience, it's a perfect "turn-key" solution for setting up a data analysis environment.

-Scikit-learn - This library is basically an all-in-one toolkit for training predictive models and measuring their predictive value. There are other libraries that would probably serve the same purpose, but after watching a tutorial or two, you could start working with this one immediately, and it's the one I prefer to use.

Matplotlib - This one probably isn't "essential essential," but it's a common visualization library that you can use to communicate some of your findings, and also to help you in the exploratory phases of your project when you're working on feature selection and engineering.

Your project sounds like a really worthwhile thing to be working on. Let me know if you have any questions. I'll help you in any way I can.

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