Data Analytics for the Identification of Fake Reviews Using Supervised Learning

Clustering techniques typically group similar instances underlying individual attributes by supposing that similar instances have similar attributes characteristic. On contrary, clustering similar instances given a specific behavior is framed through supervised learning. For instance, which fashion products have similar behavior in term of sales. Unfortunately, conventional clustering methods cannot tackle this case, since they handle attributes by a same manner. In fact, conventional clustering approaches do not consider any response, and moreover they assume attributes act by the same importance. However, clustering instances with respect to responses leads to a better data analytics. In this research, the authors introduce an approach for the goal supervised clustering and show its advantage in terms of data analytics as well as prediction. To verify the feasibility and the performance of this approach the authors conducted several experiments on a real dataset derived from an apparel industry.

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Data Analytics Implemented over E-commerce Data to Evaluate Performance of Supervised Learning Approaches in Relation to Customer Behavior

Advances in Intelligent Systems and Computing - Soft Computing for Problem Solving ◽

10.1007/978-981-15-0035-0_22 ◽

2019 ◽

pp. 285-293

Author(s):

Kailash Hambarde ◽

Gökhan Silahtaroğlu ◽

Santosh Khamitkar ◽

Parag Bhalchandra ◽

Husen Shaikh ◽

...

Keyword(s):

Supervised Learning ◽

Data Analytics ◽

Customer Behavior ◽

Learning Approaches

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Data Classification Using Decision Trees J48 Algorithm for Text Mining of Business Data

Lahore Garrison University Research Journal of Computer Science and Information Technology ◽

10.54692/lgurjcsit.2021.0502210 ◽

2021 ◽

Vol 5 (2) ◽

pp. 73-81

Author(s):

Asif Yaseen

Keyword(s):

Text Mining ◽

Customer Satisfaction ◽

Decision Tree ◽

Decision Trees ◽

Supervised Learning ◽

Data Analytics ◽

Training Model ◽

Decision Tree Classification ◽

Class Labels ◽

Financial Transactions

The business industry is generating a lot of data on daily business deals and financial transactions. These businesses are generating intensive-data like they need customer satisfaction on top priority, fulfilling their needs, etc. In every step, Data is being produced. This Data has a great value that is hidden from regular users. Data analytics is used to unhide those values. In our project, we are using a business-related dataset that contains strings and their class (0 or 1). 0 or 1 denotes the positive or negative string labels. To analyze this data, we are using a decision tree classification algorithm (J48 exceptionally) to perform text mining (classification) on our target dataset. Text mining comes under supervised learning (type). In-text mining, generally, we use two datasets. One is used to train the model, and the second dataset is used to predict the missing class labels in the second dataset based on this training model generated using the first dataset.

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Modeling and optimizing the performance of PVC/PVB ultrafiltration membranes using supervised learning approaches

RSC Advances ◽

10.1039/c5ra24654g ◽

2016 ◽

Vol 6 (33) ◽

pp. 28038-28046 ◽

Cited By ~ 4

Author(s):

Lina Chi ◽

Jie Wang ◽

Tianshu Chu ◽

Yingjia Qian ◽

Zhenjiang Yu ◽

...

Keyword(s):

Supervised Learning ◽

Vinyl Chloride ◽

Data Analytics ◽

Phase Inversion ◽

Polyvinyl Butyral ◽

Learning Approaches ◽

Ultrafiltration Membranes ◽

Systematic Data ◽

Poly Vinyl Chloride ◽

Wet Phase Inversion

A systematic data analytics framework is developed based on supervised learning (SL), which is used to optimize poly(vinyl chloride) (PVC) and polyvinyl butyral (PVB) blend ultrafiltration membranes fabricated via dry/wet phase inversion.

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Big Data Analytics: The Next Big Thing

The Management Accountant Journal ◽

10.33516/maj.v54i5.20-24p ◽

2019 ◽

Vol 54 (5) ◽

pp. 20

Author(s):

Dheeraj Kumar Pradhan

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

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Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

WiSt - Wirtschaftswissenschaftliches Studium ◽

10.15358/0340-1650-2020-5-11 ◽

2020 ◽

Vol 49 (5) ◽

pp. 11-17

Author(s):

Thomas Wrona ◽

Pauline Reinecke

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

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