Create Anomaly Detection Machine Learning with Python

Need Python Machine Learning Expert to learns the data distribution during normal every-day execution and signals when that output is anomalous with respect to the past.

Most importantly can great good GUI and amazing graphs

This is my proposal

Work Scope
Milestone 1. Create python program to collect the data from snort and other analyzers and then merge all the datas to a jason format. (API Endpoints)
Milestone 2. Add a feature in the program that read a jason file, get and show the unlabelled data, then a user can label the data manually.
Milestone 3. Train and test the data using ML. Get the best classifier and hyperparameters according to the testing accuracy.
Milestone 4. From the best classifier model obtained above, modify the python program by embedding the prediction after a new data coming.
Milestone 5. Create separated program that will update the classifier model after N new datas are labelled or every certain period e.g. re-train should be done twice a day or maybe once an hour. According to the rate of incoming data and the speed of training.