PYTHON3/linux : prediction from a file of a current alert escalation list

This is a job wichi must be written in python 3, running on Linux, last kernel (5.0)

On an automatical integrated electronical system, every 5 minutes, 12 channels provide the number of 12 alert escalations. A file in .csv format, contains all the alerts, separated by a comma ",". Those alerts comes from an array between 1 to 28.

When a list of 12 alert occur, there are solved one by one. the list of the 12 most recent alerts is written in the last line of the [login to view URL] file

Those numbers corresponding to one specific alert, are not aleatory, means, when a number is shown, there is an high probability it will be repeated next time. It happens after a while, when an alert is closed, it disapear for the futur result

Because it takes too much time to solve all the alerts in a glance, I need to predict at least, which 7 alert numbers are most likely to occur in the next 5 minutes.

For this project, you should propose at least 4 different algorythms,

i.e Short Term memory , Nearest neighbour, Forest .... Naive ... .may be EPSO

For each algorithm you propose, the algorithm should display the list of the 7 most probable alert numbers. At the same time, you should estimate the prediction efficiency of this algorithm on the proposed result and display it

Do not propose MLP., it does not work properly

Python AI (Artificial Intelligence) HW/SW Genetic Algorithms Algoritmi

Projektin tunnus: #32099988

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