Data Analytics for Electricity Revenue Forecasting by using Linear Regression and Classification Method

2021 ◽  
Author(s):  
Tapanee Treeratanaporn ◽  
Pattarachai Rochananak ◽  
Chamnan Srichaikij
Keyword(s):  
Linear Regression ◽  
Data Analytics ◽  
Classification Method ◽  
Revenue Forecasting

Descriptive and Predictive Analytical Methods for Big Data

Web Services ◽  
2019 ◽  
pp. 314-331 ◽  
Author(s):  
Sema A. Kalaian ◽  
Rafa M. Kasim ◽  
Nabeel R. Kasim
Keyword(s):  
Big Data ◽  
Standard Deviation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Knowledge Discovery ◽  
Data Visualization ◽  
Analytical Methods ◽  
Data Analytics ◽  
Enterprise Performance ◽  
Analytical Tools

Data analytics and modeling are powerful analytical tools for knowledge discovery through examining and capturing the complex and hidden relationships and patterns among the quantitative variables in the existing massive structured Big Data in efforts to predict future enterprise performance. The main purpose of this chapter is to present a conceptual and practical overview of some of the basic and advanced analytical tools for analyzing structured Big Data. The chapter covers descriptive and predictive analytical methods. Descriptive analytical tools such as mean, median, mode, variance, standard deviation, and data visualization methods (e.g., histograms, line charts) are covered. Predictive analytical tools for analyzing Big Data such as correlation, simple- and multiple- linear regression are also covered in the chapter.

scholarly journals Oxidation States of Binary Oxides from Data Analytics of the Electronic Structure

2018 ◽  
Author(s):  
Sergei Posysaev ◽  
Olga Miroshnichenko ◽  
Matti Alatalo ◽  
Duy Le ◽  
Talat S. Rahman
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Linear Regression ◽  
Data Analytics ◽  
Learning Algorithm ◽  
Mixed Valence ◽  
Oxidation States ◽  
Machine Learning Algorithm ◽  
Binary Oxides ◽  
Mixed Valence Compounds

<p>A connection between the oxidation state (OS) and Bader charge has been missing so far. To our knowledge, all previous work tried to connect OS with Bader charges only with few compounds. The aim of this work was to find a dependency between OS and Bader charge, using <a>a large number of compounds from an open database</a>. We show that a <a>correlation indeed exists between OSs and Bader charges</a> using the simplest machine learning algorithm, linear regression. The applicability of determining OS by Bader charges in mixed-valence compounds and surfaces is considered.</p>

scholarly journals Motion Detection and Prediction Using Machine Learning Algorithm

2020 ◽  
pp. 220-226
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Logistic Regression ◽  
Linear Regression ◽  
21St Century ◽  
Motion Detection ◽  
Data Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Processing Data

Machine learning is a branch of Artificial Intelligence which is gaining importance in the 21st century with increasing processing speeds and miniaturization of sensors, the applications of Artificial Intelligence and cognitive technologies are growing rapidly. An array of ultrasonic sensors i.e., HCSR-04 is placed at different directions, collecting data for a particularinterval of a period during a particular day. The acquired sensor values are subjected to pre-processing, data analytics, and visualization. The prepared data is now split into test and train. A prediction model is designed using logistic regression and linear regression and checked for accuracy, F1 score, and precision compared.

scholarly journals Oxidation States of Binary Oxides from Data Analytics of the Electronic Structure

2018 ◽  
Author(s):  
Sergei Posysaev ◽  
Olga Miroshnichenko ◽  
Matti Alatalo ◽  
Duy Le ◽  
Talat S. Rahman
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Linear Regression ◽  
Data Analytics ◽  
Learning Algorithm ◽  
Mixed Valence ◽  
Oxidation States ◽  
Machine Learning Algorithm ◽  
Binary Oxides ◽  
Mixed Valence Compounds

<p>A connection between the oxidation state (OS) and Bader charge has been missing so far. To our knowledge, all previous work tried to connect OS with Bader charges only with few compounds. The aim of this work was to find a dependency between OS and Bader charge, using <a>a large number of compounds from an open database</a>. We show that a <a>correlation indeed exists between OSs and Bader charges</a> using the simplest machine learning algorithm, linear regression. The applicability of determining OS by Bader charges in mixed-valence compounds and surfaces is considered.</p>

Descriptive and Predictive Analytical Methods for Big Data

2016 ◽  
pp. 66-83
Author(s):  
Sema A. Kalaian ◽  
Rafa M. Kasim ◽  
Nabeel R. Kasim
Keyword(s):  
Big Data ◽  
Standard Deviation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Knowledge Discovery ◽  
Data Visualization ◽  
Analytical Methods ◽  
Data Analytics ◽  
Enterprise Performance ◽  
Analytical Tools

Data analytics and modeling are powerful analytical tools for knowledge discovery through examining and capturing the complex and hidden relationships and patterns among the quantitative variables in the existing massive structured Big Data in efforts to predict future enterprise performance. The main purpose of this chapter is to present a conceptual and practical overview of some of the basic and advanced analytical tools for analyzing structured Big Data. The chapter covers descriptive and predictive analytical methods. Descriptive analytical tools such as mean, median, mode, variance, standard deviation, and data visualization methods (e.g., histograms, line charts) are covered. Predictive analytical tools for analyzing Big Data such as correlation, simple- and multiple- linear regression are also covered in the chapter.

scholarly journals Data Analytics Enforced Content-Based Fitness Recommender System for Individuals without Allergies and Ailments

2017 ◽  
Vol 2 (1) ◽  
pp. 11-16
Author(s):  
James Roy Vicente Aquino ◽  
Lovely Joy Mariano Barrientos ◽  
Madeline Castro Coloma ◽  
Larissa Vivien Santos Lacar ◽  
James Patrick Apostol Acang
Keyword(s):  
Linear Regression ◽  
Recommender System ◽  
Data Analytics ◽  
Good Health ◽  
Daily Activities ◽  
Healthy Living ◽  
Unhealthy Lifestyle ◽  
Passive Monitoring ◽  
Poor Diet ◽  
The Right

The society these days is bounded with inactive and unhealthy lifestyle.  According to the recent studies, there is a rapid increase of individuals suffering and dying from diseases caused by poor diet and unhealthy lifestyle. In view of this, knowing the right kind of food to eat, and the appropriate exercise is essential in achieving good health.  Being fit relates to good health, and healthy living makes life longer and happier. The objective of this study is to develop a system that will recommend its users the appropriate exercise and diet for their daily activities.  Exercise and diet plans were gathered and were validated by experts to ensure utmost level of effectiveness and safety among users.  Linear regression with passive monitoring of user activities was used to estimate and forecast future fitness states of the users.

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