Exploratory Data Analysis using Python

Sahoo*, Kabita; Samal, Abhaya Kumar; Pramanik, Jitendra; Pani, Subhendu Kumar

doi:10.35940/ijitee.l3591.1081219

Cited by 81 publications

(46 citation statements)

References 2 publications

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“…To identify the road type factor, we aggregated and averaged the perceived scores of all points by road type. Then, we used box plot analysis (Sahoo et al, 2019) to analyze the performance of perception scores of different road types for different models. Combined with the characteristics of the rich regional distribution of the studied cities, the applicability of different models was compared and analyzed.…”

Section: Methodsmentioning

confidence: 99%

Understanding urban wealth perception using a hybrid dataset and ranking‐scoring framework

Liu

Guo

2022

Transactions in GIS

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Research on urban perception using street view images has developed rapidly. Global urban perception datasets commonly used in local urban perception research may be inaccurate because of the differences in the characteristics across cities. Building a new local urban perception dataset requires a practical and efficient solution to construct high‐availability datasets and a corresponding urban perception framework. In this study, we developed a simple and effective ranking‐scoring framework containing two stages. First, we constructed a hybrid dataset in a 1:1 ratio, based on the global and local datasets. Next, we built a ranking‐scoring network that uses deep learning models to obtain the features of images and a learning‐to‐rank approach to determine perceptual scores. To validate the effectiveness of our method, we examined the perception of urban wealth in Wuhan, China. Our framework showed high accuracy when the hybrid dataset was used. Furthermore, we analyzed the differences between the spatial distribution of the perception results of the hybrid model and the global model in the study area. In addition, we analyzed driving factors for wealth perception, such as visual elements and road types. The results verified that the hybrid dataset is more suitable for local urban perception. Our framework could assist researchers in conducting fast and accurate urban perception of local cities, using a hybrid dataset and ranking‐scoring, thus enabling urban planners to provide more consideration to users' perceptions during the planning process without dwelling upon additional burdens.

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Section: Methodsmentioning

confidence: 99%

Understanding urban wealth perception using a hybrid dataset and ranking‐scoring framework

Liu

Guo

2022

Transactions in GIS

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“…The first quartile is symbolized by Q1, known as the lower quartile, Q2 denotes the second quartile as the middle quartile, and Q3, the upper quartile, symbolizes the third quartile. Therefore, IQR is the upper quartile minus the lower quartile or the difference between the upper and lower quartile, with the formula [5]:…”

Section: Data Cleaningmentioning

confidence: 99%

Data Exploratory Analysis and Feature Selection of Low-Speed Wind Tunnel Data for Predicting Force and Moment of Aircraft

Hidiyanto¹,

Leksono²,

Atmaja³

et al. 2023

Majalah Ilmiah Pengkajian Industri

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This paper discusses exploratory data analysis (EDA) and feature selection of aircraft test results in Indonesia's low-speed wind tunnels (ILST). First, we briefly explain input and output parameters and data processing to make readable and higher accurate data. Then, we used feature selection using embedded and random forest methods to find parameters that most affect the force coefficient of aircraft. The research activities carried out in this study are to review literature from either scientific journals, the internet, or books and interview with an engineer who tests aircraft models at ILST. Then create a program for processing data from test results, such as data extraction, data cleaning, exploratory data analysis, and feature selection with python. After applying the feature selection method, we found that all the methods show similar results and have succeeded in separating the powerful features from the weak ones with a significant score difference. We decide to use the Random Forest method. The three most strongest features in the coefficient of an aircraft model in the ILST test (CL, CD, CM25, CYAW, CROLL and CY) are the following: for CL are ALFA (0.984), T0 (0.008), P0 (0.004), on for CD is are ALFA (0.965), T0 (0.009), RE (0.007), in CM25 are ALFA (0.416), P0 (0.285), T0 (0.168), in CYAW are BETA (0.44), T0 (0.141), ALFA (0.141), in CROLL is BETA (0.79), ALFA (0.091), P0 (0.036), and in CY are BETA (0.842), ALFA (0.114) and T0 (0.014). The results of this paper can be used to help build a model for the coefficient of aircraft design using machine learning based on the data from the ILST test more effectively and efficiently. Keyword: Machine Learning, Feature Selection, Exploratory Data Analysis, Aircraft Modeling.

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“…Perangkat-perangkat yang dimaksud ialah sebagaimana pada tabel 3 di bawah ini: Membantu penulis dalam memprapharase artikel yang telah diketahui smilaritynya, sehingga persentase maksimum similarity tidak terlampaui Sesuai dengan penjelasan narasumber, pemanfaatan beberapa perangkat di atas akan membantu setiap peneliti dalam menyelesaikan pekerjaannya sehingga menghasilkan artikel ilmiah. Penjelasan narasumber yang dijelaskan di atas relevan dengan berbagai kesimpulan para ahli yang menegaskan bahwa pemanfaatan berbagai perangkat adalah hal yang tidak bisa dipisahkan dengan kesuksesan penyelesaian pekerjaan akademik di masa teknologi informasi (Fiona Willtsher, 2002;Zamawe, 2015;Ishak & Bakar, 2012;Lakshmi, 2018;Sahoo et al, 2019;Cao et al, 2021;Alilli & Krstev, 2019. Untuk mengatur sitasi dalam artikel yang ditulis, penulis juga mesti memanfaatkan management sitasi seperti mendeley, zotero ataupun endnote, pernyataan ini relevan dengan pernyataan para ahli yang mengatakan bahwa tanpa menggunakan management sitasi maka human error dalam menulis rujukan rentan terjadi (Sungur & Seyhan, 2013;Penders, 2018).…”

Section: Simulasiunclassified

Peningkatan Pemahaman Dosen Terhadap Strategi Penulisan Dan Publikasi Artikel

et al. 2022

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Produktifitas dosen dalam menulis dan menerbitkan artikel ilmiah pada journal bagian yang fundamental untuk membangun reputasi diri dan lembaga pendidikan tinggi. Namun dalam kenyataan, dosen pada berbagai perguruan tinggi masih banyak yang belum memahami strategi penulisan dan publikasi artikel pada Journal. Oleh karenanya, kegiatan ini bertujuan untuk memberikan pelatihan kepada dosen dalam menulis dan publikasi ilmiah pada journal. Kegiatan ini dilakukanmelalui workshop dengan langkah-langkah penjelasan konseptual, diskusi dan simulasi. Hasil kegiatan ialah dari sejumlah peserta yang mengikuti kegiatan pada awalnya hanya dua orang yang memiliki pemahaman terkait langkah penulisan dan proses pengiriman naskah artikel ke journal yang ditargetkan, namun setelah diadakannya pelatihan semua peserta telah memiliki kemampuan untuk mengirimkan artikel ke jurnal, kegiatan ini juga menghasilkan peserta yang memiliki berbagai akun yang erat kaitannya dengan peningkatan nilai publikasi, seperti google scholar, research gate, Orcid ID, publon ID.

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Exploratory Data Analysis using Python

Cited by 81 publications

References 2 publications

Understanding urban wealth perception using a hybrid dataset and ranking‐scoring framework

Understanding urban wealth perception using a hybrid dataset and ranking‐scoring framework

Data Exploratory Analysis and Feature Selection of Low-Speed Wind Tunnel Data for Predicting Force and Moment of Aircraft

Peningkatan Pemahaman Dosen Terhadap Strategi Penulisan Dan Publikasi Artikel

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