Exploratory Data Analysis using Python
Kabita Sahoo1, Abhaya Kumar Samal2, Jitendra Pramanik3, Subhendu Kumar Pani4

1Kabita Sahoo*, Asst. Professor, Dept. of Computer Science, MITS School of Biotechnology, Utkal University, Bhubaneswar, India.
3Abhaya Kumar Samal, Professor, Dept. of Comp. Sc. & Engg., Trident Academy of Technology, Bhubaneswar, India.
3Jitendra Pramanik, Asst. Professor, Centurion University of Technology and Management, Odisha, India.
4Subhendu Kumar Pani, Associate Professor, Orissa Engineering College, Bhubaneswar, India.

Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 4727-4735 | Volume-8 Issue-12, October 2019. | Retrieval Number: L3591081219/2019©BEIESP | DOI: 10.35940/ijitee.L3591.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract:Data need to be analyzed so as to produce good result. Using the result decision can be taken. For example recommendation system, ranking of the page, demand fore casting, prediction of purchase of the product. There are some leading companies where the review of the customer plays a great role to analyze the factor which influences the review rating. We have used exploratory data analysis (EDA) where data interpretations can be done in row and column format. We have used python for data analysis. it is object oriented ,interpreted and interactive programming language. it is open source with rich sets of libraries like pandas, MATplotlib, seaborn etc. We have used different types of charts and various types of parameter to analyze Amazon review data sets which contains the reviews of electronic data items. We have used python programming for the data analysis.
Keywords:  Exploratory Data Analysis (EDA); MATPplotlib, Seaborn, Visualization, Pandas, Jupyter Notebook
Scope of the Article: Data Analysis