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Gusani, Renato (2023) A Comprehensive Time Series Analysis on 7 Decades of Space Travel Data: Data Analysis Report. Undergraduate thesis, Dublin, National College of Ireland.
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Abstract
The purpose of this project is to look at data about space exploration and technological advancements in space travel. The study begins with a historical overview of space exploration, followed by an update on NLP LDA on rocket and missions. The report then describes the data used in the analysis, which includes information about previous and current space missions, as well as technology used for these missions.
This study will make use of the SpaceX API [1] and the NASA APOD API [2] in addition to the "Space Missions from 1957 to 2022" data set. I'll use a wrapper for the SpaceX API to get information on the company's most recent launches and missions. The NASA APOD API will have its own website page, displaying the "Astronomy Picture of the Day" with an explanation and image. These APIs will supplement the "Space Missions from 1957 to 2022" data set [3] by providing additional insights and actual data on space exploration and missions.
The procedure for this study entails using data pre-processing methods to clean and organize the data, as well as various statistical and machine learning methods to analyze the data. The analysis is concerned with detecting trends and patterns in the data, as well as investigating the relationships between various variables.
According to the analysis' findings, the number of space missions has increased significantly in recent years, as has the technology used for these missions. The analysis also reveals that certain variables, such as mission cost and number of countries involved in the mission, have strong relationships.
The report concludes by discussing the project's benefits and limitations, as well as future research recommendations. Overall, this report is informative about the current state of space exploration and technological advances used in space travel.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Supervisors: | Name Email Moldovan, Arghir-Nicolae UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > TL Motor vehicles. Aeronautics. Astronautics Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Bachelor of Science (Honours) in Computing |
| Depositing User: | Tamara Malone |
| Date Deposited: | 16 Jan 2024 16:26 |
| Last Modified: | 16 Jan 2024 16:26 |
| URI: | https://norma.ncirl.ie/id/eprint/6922 |
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