scholarly journals Systematic Approach to Perform Task Centric Exploratory Data Analysis with Case study

2021 ◽  
Vol 10 (3) ◽  
pp. 1920-1927
Keyword(s):  
Data Analysis ◽  
Systematic Approach ◽  
Exploratory Data Analysis ◽  
Data Organization ◽  
Interactive Programming ◽  
Different Types ◽  
Exploratory Data ◽  
Hidden Patterns ◽  
Visualization Techniques

Exploratorydata analysis is a method to summarize main characteristics of data, and also to understand data more deeply using visualization techniques. This paper focuses on defining systematic approach in the form of well-defined sequence of steps to explore data in various aspects. Every organization produces lot of data. Organization needs to analyze this data very carefully to extract hidden patterns in the data. Task Centric EDA[2]produces actionable insights as outcome to improve business process.This uses Pythonprogramming language and Jupyter Notebook for data analysis. Python is an object oriented and interactive programming language, which contains rich sets of libraries likepandas, MATplotlib, seaborn[10]etc. We have used different types of charts and various types of parameters to analyze retail dataset and to improve sales using precision marketing.

scholarly journals Exploratory Data Analysis using Python

2019 ◽  
Vol 8 (12) ◽  
pp. 4727-4735
Keyword(s):  
Data Analysis ◽  
Recommendation System ◽  
Exploratory Data Analysis ◽  
Object Oriented ◽  
Data Sets ◽  
Great Role ◽  
Interactive Programming ◽  
Different Types ◽  
Exploratory Data ◽  
Python Programming

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

Selecting Robust Kinetic Models on Noisy Data Using the Bootstrap

2007 ◽  
Vol 5 (1) ◽  
Author(s):  
Cayle J Sharrock ◽  
Roelof Coetzer
Keyword(s):  
Systematic Approach ◽  
Exploratory Data Analysis ◽  
Noisy Data ◽  
Water Gas Shift ◽  
Industrial Case Study ◽  
Analysis Techniques ◽  
Exploratory Data ◽  
Shift Reaction ◽  
Water Gas

A systematic approach to identifying a robust kinetic model fitted on noisy data is presented. The bootstrap coupled with Monte-Carlo simulations and exploratory data analysis techniques are employed to evaluate candidate model formulations to given sets of experimental data. The approach is applied in an industrial case study in determining the most practical rate expression for the water-gas shift reaction over a cobalt Fischer-Tropsch catalyst.

scholarly journals A Sensor-Based Methodology to Differentiate Pure and Mixed White Tequilas Based on Fused Infrared Spectra and Multivariate Data Treatment

Chemosensors ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 47
Author(s):  
Christian Hazael Pérez-Beltrán ◽  
Víctor M. Zúñiga-Arroyo ◽  
José M. Andrade ◽  
Luis Cuadros-Rodríguez ◽  
Guadalupe Pérez-Caballero ◽  
...  
Keyword(s):  
Data Analysis ◽  
Ir Spectra ◽  
Multivariate Data Analysis ◽  
Exploratory Data Analysis ◽  
Multivariate Data ◽  
Simple Application ◽  
Infrared Measurements ◽  
Exploratory Data ◽  
Pls Discriminant Analysis

Mexican Tequila is one of the most demanded import spirits in Europe. Its fast-raising worldwide request makes counterfeiting a profitable activity affecting both consumers and legal distillers. In this paper, a sensor-based methodology based on a combination of infrared measurements (IR) and multivariate data analysis (MVA) is presented. The case study is about differentiating two categories of white Tequila: pure Tequila (or ‘100% agave’) and mixed Tequila (or simply, Tequila). The IR spectra were treated and fused with a low-level approach. Exploratory data analysis was performed using PCA and partial least squares (PLS), whilst the authentication analyses were carried out with PLS-discriminant analysis (DA) and soft independent modeling for class analogy (SIMCA) models. Results demonstrated that data fusion of IR spectra enhanced the outcomes of the authentication models capable of differentiating pure from mixed Tequilas. In fact, PLS-DA presented the best results which correctly classified all fifteen commercial validation samples. The methodology thus presented is fast, cheap, and of simple application in the Tequila industry.

scholarly journals Big Data Analytics: A Spotify Case Study

2021 ◽  
Vol 9 (10) ◽  
pp. 1823-1829
Author(s):  
Suraj Ingle
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Customer Satisfaction ◽  
Customer Loyalty ◽  
Data Analytics ◽  
Exploratory Data Analysis ◽  
Big Data Analytics ◽  
Exploratory Data ◽  
Insight Into

Abstract: By developing products that are in line with consumer needs, anticipating their profitability and manufacturing them, Big Data has opened up a lot of possibilities for building customer loyalty and commercial business by proactively engaging and comprehensively streamlining offers across all customer touch points. The use of big data to determine the best, most efficient ways to engage and interact with their customers will be discussed in this paper. An insight into how Spotify intends to provide music lovers additional ways to find their favourite songs, interact with artists, and improve Spotify recommendations has been provided. Keywords: Big Data, Data Analytics, Customer Satisfaction, Exploratory Data Analysis

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