Exploratory Data Analysis using Python

TLDR

Data analysis is crucial for informed decision‑making across domains such as recommendation, ranking, forecasting, and purchase prediction, with customer reviews often driving key insights. The authors conducted exploratory data analysis in Python, leveraging libraries like pandas, Matplotlib, and seaborn to visualize and interpret Amazon electronic product review datasets through diverse charts and parameters.

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.