Predictive analytics in data science for business intelligence solutions

Wazurkar, Parth; Bhadoria, Robin Singh; Bajpai, Dhananjai

doi:10.1109/csnt.2017.8418568

Cited by 40 publications

(21 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The movement carried out by firefly i because of the attraction to j with a brighter light intensity, will change its position according to equation (3).…”

Section: Firefly Algorithm -Adaptive Search Proceduresmentioning

confidence: 99%

“…Missing data is a general weakness that can influence the consequences of the prediction system to be ineffective [1][2][3]. Ignoring the missing data has an impact on the results of the analysis [4][5][6][7][8][9], learning outcomes, predictive results [10] and potentially weakens the validity of the results and conclusions [8,9] and leads to estimation of biased parameters [7,[11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Class Center-Based Firefly Algorithm for Handling Missing Data

Nugroho

Utama

Surendro

2020

Preprint

View full text Add to dashboard Cite

Estimating missing data in a dataset is a significant advance during the data cleaning stage. Improper data handling can make inaccurate results when conducting data analysis. Most of the research about missing data estimation is irrespective of the correlation between attributes. However, an adaptive search procedure helps find the estimates of the missing data when correlations between attributes are considered in the process. Firefly Algorithm (FA) implements an adaptive search procedure in the imputation of the missing data by finding the estimated value that is closest to the value in other data known. Therefore, this study proposes a class center-based adaptive approach model for missing data by considering the attribute correlation in the imputation process (C3-FA). Based on the experiment, the general result find that the class center-based firefly algorithm is an efficient technique for getting the actual value in handling the missing data. This can be seen on the value of Pearson correlation coefficient (r) that close to 1 and the root mean squared error (RMSE) value is generally closer to 0. In addition, the proposed method can maintain the true distribution of data values. This is indicated by the Kolmogorov–Smirnov test that value of DKS for most of the attributes in the dataset is generally closer to 0. Also, the results of the accuracy evaluation using three classifiers, showed that the proposed method produces good accuracy.

show abstract

“…The movement carried out by firefly i because of the attraction to j with a brighter light intensity, will change its position according to equation (3).…”

Section: Firefly Algorithm -Adaptive Search Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Class Center-Based Firefly Algorithm for Handling Missing Data

Nugroho

Utama

Surendro

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Agile BI helps to realise the return on investment sooner as decision-makers can get value quicker. This dynamic way of working helps the business to evolve and adapt to changes quicker (Wazurkar, Bhadoria & Bajpai 2017).…”

Section: Factors Influencing the Quality Of Decision-makingmentioning

confidence: 99%

Factors Influencing the Quality of Decision-Making Using Business Intelligence in a Metal Rolling Plant in KwaZulu-Natal

Chummun¹,

Singh²

2019

J. Rev. Global Econ.

View full text Add to dashboard Cite

The current study sought to investigate the factors which influence the quality of decision-making using business intelligence (BI) in a metal rolling plant in KwaZulu-Natal. Specifically, the study was focused on information quality, system quality and BI service quality. The study used a self-administered survey sent out to participants having sufficient report runs which made up the population of the study. The collected data came from different levels of employees, namely; managers (47%) and non-managers (53%) with varying levels of BI experience, and the data was imported into SPSS for analysis. The results showed that information quality had a positive significant impact on the quality of decision-making; system quality had a positive significant impact on the quality of decision-making; and BI service quality had a positive significant impact on the quality of decision-making. Multiple linear regression analysis was conducted to determine the strength of these variances in influencing decision-making. It was found that the three variables explained 65.7% of the variance in the quality of decision-making. Overall, the study found that high quality information, coupled with a high-quality system and good BI service, leads to a higher quality of decision-making, and that the impact of BI on decision-making is positive. The study recommends that the company implement data quality management focusing on data cleansing, it should also implement more sophisticated analysis techniques to get insights and have strategies to upskill both technical and business workers.

show abstract

“…BI is related as a technology used to analyze the business data regarding to the decision making process based on prediction analysis and provided the data enhancement process followed by an analytical technology which is widely used in several aspects such as market intelligence, e-commerce, e-health etc. [1,2]. Currently, researchers have been accomplishing studies and concluding that BI affords two things which are process and product.…”

Section: Introductionmentioning

confidence: 99%

“…Product defines as information about empowering organizations to predict the service, customer satisfaction, competitor, marketplace, technology, etc. These two important things become a necessary part in market research and have a powerful analysis result to predict the market demand and customer satisfaction [1,3].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis for Customer Review: Case Study of GO-JEK Expansion

Prananda

Thalib

2020

JISEBI

View full text Add to dashboard Cite

Background: Market prediction is an important thing that needs to be analyzed deeply. Business intelligence becomes an important analysis procedure for analyzing the market demand and satisfaction. Since business intelligence needs a deep analysis, sentiment analysis becomes a powerful algorithm for analyzing customer review regarding to the business intelligence analysis.Objective: In this study, we perform a sentiment analysis for identifying the business intelligence analysis in GO-JEK.Methods: We use Twitter posts collected from the Twint library which consists of 3111 tweets. Since the dataset did not provide a ground truth, we perform Microsoft Text Analytic for determining positive, neutral, and negative sentiment. Before applying Microsoft Text Analytic, we conduct a pre-processing step to remove the unwanted data such as duplicate tweets, image, website address, etc.Results: According to the Microsoft Text Analytic, the results are 666 positive sentiment numbers, 2055 neutral sentiment numbers, and 127 negative sentiment numbers.Conclusion: According to these results, we conclude that most GO-JEK customers are satisfied with the GO-JEK services. In this research, we also develop classification model to predict the sentiment analysis of new data. We use some classifier algorithms such as Decision Tree, Naïve Bayes, Support Vector Machine and Neural Network. In the result, the system shows that the decision tree provides the best performance.

show abstract

Predictive analytics in data science for business intelligence solutions

Cited by 40 publications

References 11 publications

Class Center-Based Firefly Algorithm for Handling Missing Data

Class Center-Based Firefly Algorithm for Handling Missing Data

Factors Influencing the Quality of Decision-Making Using Business Intelligence in a Metal Rolling Plant in KwaZulu-Natal

Sentiment Analysis for Customer Review: Case Study of GO-JEK Expansion

Contact Info

Product

Resources

About