When documenting a machine learning project, it is important to include the following:
-A clear and concise description of the problem that you are trying to solve. This should include a brief summary of the data that you are using and what type of machine learning algorithm you are using.
-A detailed explanation of your feature engineering process. This should include how you processed and extracted features from your data.
-A description of your model selection process. This should include any cross validation or hyper parameter tuning that you performed.
-A discussion of your results. This should include any metrics that you used to evaluate your model’s performance as well as any insights that you gained from your analysis.