Can I not call my project a "Machine Learning" project if it doesn't involve a prediction at the end? I'm doing an analysis on some data involved with the NBA right now, and I'm nearing the end of the project but I need to decide if I want to include an official prediction for future data or not. 

From what I've seen online, the steps to a Machine Learning project is Gathering Data, Data Prep, Observe Data, Train Model, Evaluate Model, then Predict (on unknown data).

The first 5 steps seem to all the same as a data analysis but a "data analysis project" would focus a lot more on the observe data part and derive conclusions from that. What I did was follow all the steps, derive conclusions from the data and graphs I've observed, I ran some models on the data as well to prove some correlation between the data, and to prove that a trained model can predict data.

However, at the end I don't think a prediction of the future is necessary for my project. If I don't include this "predicting" part of my analysis, can I not technically call that a "Machine Learning" project? Because all I did was look at and analyze data and graphs and derive a conclusion from that rather than concluding with a "with our trained model we can predict these trends"?

This is a big vague so let me know if I can clarify anything, thanks!
Could you talk a bit more about your model training and evaluation? 

If you are viewing correlations, it sounds like exploratory data analysis to me.

Typically, I believe a machine learning model will be trying to predict a data point/variable. This could be in the form of classification or regression (i.e. did you do one of these?)

Just curious, why is it important to label as a machine learning project? If a class project, maybe you would be interested in your model predicting points, points per game, or win/loss ratio. Or maybe based on your model, you would want to be clustering players into different segments.

Good luck!