Cite this DOI

Dr. Ayesha Banu, D. A. B. (2022). Graphical Exploratory Data Analysis (GEDA): A Case Study on Employee Attrition. *Journal of Science & Technology*, *7*(9), 1–11. https://doi.org/10.46243/jst.2022.v7.i09.pp01-11

@article{drayeshabanu2022graphical,
  author    = {Dr. Ayesha Banu, Dr. Ayesha Banu},
  title     = {{Graphical Exploratory Data Analysis (GEDA): A Case Study on Employee Attrition}},
  journal   = {Journal of Science \& Technology},
  year      = {2022},
  month     = {may},
  volume    = {7},
  number    = {9},
  pages     = {1--11},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2022.v7.i09.pp01-11},
  url       = {https://doi.org/10.46243/jst.2022.v7.i09.pp01-11},
  language  = {en},
  abstract  = {Exploratory Data Analysis (EDA) popularly performs some preliminary investigations on the dataset to understand its content and structure. EDA is a mandatory step in the complete process of data analysis, since its mandatory to analyze the data in order to produce good results and in turn help in decision making. There are several Graphical EDA techniques which not only analyze the data but also present the results in graphical form. This paper uses the Python programming language for both data analysis and visualization of results. The rich set of python libraries including pandas, numpy, matplotlib, seaborn etc greatly supports the process of GEDA. This paper works on the “Employee Performance and Attrition” dataset to analyze and extract potential information and present results in graphical form}
}

TY  - JOUR
TI  - Graphical Exploratory Data Analysis (GEDA): A Case Study on Employee Attrition
AU  - Dr. Ayesha Banu, Dr. Ayesha Banu
JO  - Journal of Science & Technology
PY  - 2022
DA  - 2022/05/11/
VL  - 7
IS  - 9
SP  - 1
EP  - 11
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Exploratory Data Analysis (EDA) popularly performs some preliminary investigations on the dataset to understand its content and structure. EDA is a mandatory step in the complete process of data analysis, since its mandatory to analyze the data in order to produce good results and in turn help in decision making. There are several Graphical EDA techniques which not only analyze the data but also present the results in graphical form. This paper uses the Python programming language for both data analysis and visualization of results. The rich set of python libraries including pandas, numpy, matplotlib, seaborn etc greatly supports the process of GEDA. This paper works on the “Employee Performance and Attrition” dataset to analyze and extract potential information and present results in graphical form
DO  - 10.46243/jst.2022.v7.i09.pp01-11
UR  - https://doi.org/10.46243/jst.2022.v7.i09.pp01-11
ER  -

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    "URL": "https://doi.org/10.46243/jst.2022.v7.i09.pp01-11",
    "title": "Graphical Exploratory Data Analysis (GEDA): A Case Study on Employee Attrition",
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    "language": "en",
    "abstract": "Exploratory Data Analysis (EDA) popularly performs some preliminary investigations on the dataset to understand its content and structure. EDA is a mandatory step in the complete process of data analysis, since its mandatory to analyze the data in order to produce good results and in turn help in decision making. There are several Graphical EDA techniques which not only analyze the data but also present the results in graphical form. This paper uses the Python programming language for both data analysis and visualization of results. The rich set of python libraries including pandas, numpy, matplotlib, seaborn etc greatly supports the process of GEDA. This paper works on the “Employee Performance and Attrition” dataset to analyze and extract potential information and present results in graphical form",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2022.v7.i09.pp01-11 gives all four in one JSON answer.