# Types of Decision Models - Group 4 - 2018/2019, Semester B, Quartile 3

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+ ; Page navigation + # [[PRE2018_3_Group4 | Root]] + # [[Notes - Group 4 - 2018/2019, Semester B, Quartile 3|Notes]] + # [[Initial ideas - Group 4 - 2018/2019, Semester B, Quartile 3|Initial ideas]] + # [[Project setup - Group 4 - 2018/2019, Semester B, Quartile 3|Project setup]] + # [[General problem - Group 4 - 2018/2019, Semester B, Quartile 3|General problem]] + # [[State of the Art - Group 4 - 2018/2019, Semester B, Quartile 3|State of the Art]] + # [[Specific problem - Group 4 - 2018/2019, Semester B, Quartile 3|Specific problem]] + # [[Present situation - Group 4 - 2018/2019, Semester B, Quartile 3|Present situation]] + # [[Drones - Group 4 - 2018/2019, Semester B, Quartile 3|Drones]] + # [[Solutions - Group 4 - 2018/2019, Semester B, Quartile 3|Solutions]] + # [[Airports under a microscope - Group 4 - 2018/2019, Semester B, Quartile 3|Airports under a microscope]] + # [[Recommendation report - Group 4 - 2018/2019, Semester B, Quartile 3|Recommendation report]] + # [[Future - Group 4 - 2018/2019, Semester B, Quartile 3|Future]] + # [[Conclusion - Group 4 - 2018/2019, Semester B, Quartile 3|Conclusion]] + # [[Discussion - Group 4 - 2018/2019, Semester B, Quartile 3|Discussion]] +
= Introduction = = Introduction =

# Introduction

In this section, we will investigate some different approaches for decision models.

# Nearest Neighbour Strategy

NearestNeighbour, short NN, is a mathematical decision model. It is a machine learning decision model, in the sense that existing solutions, often denoted as training data, are used for NN to be able to accurately make predictions about new data such as a user which wants a solution for their airport. This decision model can make the choice which solution fits best to the user. Nearest Neighbour is based on the machine learning strategy KNearestNeighbors </ref "Brilliant">[1]"Brilliant"</ref>.

## Picking variables / attributes

In order for Nearest Neighbour to work