Ajateenijatelt kogutud andmete visualiseerimine veebiliideses ja enneaegselt reservi minemise põhjuste ennustamine
Date
2018
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Abstract
Eesti Kaitsevägi viib igal aastal ajateenijate seas läbi küsitlusi, mille käigus kogutakse erinevat informatsiooni nii ajateenistuse kui ajateenijate kohta. Kogutud andmestikku on varasemalt juba uuritud, kuid mitte automaatsete meetoditega. Antud töö põhineb 2016. aasta juuli- ja oktoobrikuu kutsealuste peal läbi viidud küsitluste andmetel. Käesoleva töö üheks eesmärgiks oli uurida, milline osa kogutud informatsioonist on oluline määramaks, kas ajateenija arvatakse enne teenistuse normaalaja lõppu reservi. Seda tehti kasutadesmasinõppe meetodeid, mille puhul on võimalik treenitud mudelis kasutatud tunnuste olulisust mõõta. Töö teiseks eesmärgiks oli luua prototüüp veebiliidesest, mis võimaldaks kasutajal mugavalt ja kiirelt saada ülevaade erinevate väeosade probleemidest ja väeosade arengust läbi aja. Töö tulemusena loodi viis erinevat masinõppe mudelit ja analüüsiti nende poolt oluliseks peetud tunnuseid. Samuti loodi kirjeldatud veebiliidese prototüüp ja kirjeldati selle kasutusvõimalusi. Nii mudelite treenimisel kui veebiliidese loomiselpeeti silmas seda, et neid oleks võimalik kasutada Eesti Kaitseväe töös ka tulevikus kui andmeid juurde kogutakse.
Estonian Defense Forces regularly carry out surveys among people who are going through military service and collect diverse information about the service as well as the people themselves. The resulting data set has previously already been investigated, but not using automatic methods. This thesis is based on a data set consisting of the answers from people recruited in July and October of 2016. The first purpose of this thesis was to identify which part of the collected information is important in detecting, whether the recruit will be relieved from duty before the standard end date of the service. This was performed using machine learning methods, that have a way of interpreting the importance of a feature used in the created model. Another purpose of this thesis was to create a prototype of a web interface allowing easy and fast overview of potentialproblems in different military units as well as track their developments through time.As a result of this thesis five different machine learning models were designed and their most important features analyzed. Also, the aforementioned prototype of a web interface was created and the utility possibilities were described. In the process of making these models and the web interface, emphasis was put on future compatibility, so that the created tools could be used by the Estonian Defense Forces after additional information has been collected.
Estonian Defense Forces regularly carry out surveys among people who are going through military service and collect diverse information about the service as well as the people themselves. The resulting data set has previously already been investigated, but not using automatic methods. This thesis is based on a data set consisting of the answers from people recruited in July and October of 2016. The first purpose of this thesis was to identify which part of the collected information is important in detecting, whether the recruit will be relieved from duty before the standard end date of the service. This was performed using machine learning methods, that have a way of interpreting the importance of a feature used in the created model. Another purpose of this thesis was to create a prototype of a web interface allowing easy and fast overview of potentialproblems in different military units as well as track their developments through time.As a result of this thesis five different machine learning models were designed and their most important features analyzed. Also, the aforementioned prototype of a web interface was created and the utility possibilities were described. In the process of making these models and the web interface, emphasis was put on future compatibility, so that the created tools could be used by the Estonian Defense Forces after additional information has been collected.