Predicting Tech Employee Job Satisfaction Using Machine Learning Techniques Sumali J. Conlon1 Lakisha L. Simmons2 Feng Liu3

High-tech industry employees are among the most talented groups of people in the workforce, and are therefore difficult to recruit and retain. We analyze employee reviews submitted by employees from five technology companies. Following the Cross-Industry Standard Process for Data Mining (CRISP-DM) and the data science life cycle process, we use machine learning techniques to analyze employees’ reviews. Our goal is to predict an overall measure of whether employees are satisfied or not, using other information from the reviews, such as employer attitudes towards upper management. We also use predictive analysis to determine which features are more helpful in determining an employee’s overall job satisfaction. Finally, we analyze which prediction algorithm provides the most accurate predictions. We find the percentage of true positives we correctly identify in the holdout sample is 97.4%, while the percentage of true negatives correctly identified is 72.5%.

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Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200904164539 ◽

2020 ◽

Vol 13 ◽

Author(s):

Ritu Khandelwal ◽

Hemlata Goyal ◽

Rajveer Singh Shekhawat

Keyword(s):

Machine Learning ◽

Comparative Analysis ◽

Data Science ◽

Training Data ◽

Machine Learning Techniques ◽

Future Trends ◽

Data Set ◽

Learning Stage ◽

Learning Techniques ◽

Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

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Galactic Air Improves Ancillary Revenues with Dynamic Personalized Pricing

INFORMS Journal on Applied Analytics ◽

10.1287/inte.2021.1105 ◽

2022 ◽

Author(s):

Arinbjörn Kolbeinsson ◽

Naman Shukla ◽

Akhil Gupta ◽

Lavanya Marla ◽

Kartik Yellepeddi

Keyword(s):

Machine Learning ◽

Dynamic Pricing ◽

Machine Learning Techniques ◽

Testing Framework ◽

Customer Group ◽

Industry Standard ◽

Learning Techniques ◽

Start Up ◽

Pricing System ◽

Implementation Period

Ancillaries are a rapidly growing source of revenue for airlines, yet their prices are currently statically determined using rules of thumb and are matched only to the average customer or to customer groups. Offering ancillaries at dynamic and personalized prices based on flight characteristics and customer needs could greatly improve airline revenue and customer satisfaction. Through a start-up (Deepair) that builds and deploys novel machine learning techniques to introduce such dynamically priced ancillaries to airlines, we partnered with a major European airline, Galactic Air (pseudonym), to build models and algorithms for improved pricing. These algorithms recommend dynamic personalized ancillary prices for a stream of features (called context) relating to each shopping session. Our recommended prices are restricted to be lower than the human-curated prices for each customer group. We designed and compared multiple machine learning models and deployed the best-performing ones live on the airline’s booking system in an online A/B testing framework. Over a six-month live implementation period, our dynamic pricing system increased the ancillary revenue per offer by 25% and conversion rate by 15% compared with the industry standard of human-curated rule-based prices.

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Machine Learning Techniques for Internet of Things

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Integrating the Internet of Things Into Software Engineering Practices ◽

10.4018/978-1-5225-7790-4.ch008 ◽

2019 ◽

pp. 160-180

Author(s):

P. Priakanth ◽

S. Gopikrishnan

Keyword(s):

Machine Learning ◽

Data Science ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Independent Learning ◽

Machine Learning Techniques ◽

Analytical Models ◽

Guided Learning ◽

Learning Techniques ◽

Learning Machine

The idea of an intelligent, independent learning machine has fascinated humans for decades. The philosophy behind machine learning is to automate the creation of analytical models in order to enable algorithms to learn continuously with the help of available data. Since IoT will be among the major sources of new data, data science will make a great contribution to make IoT applications more intelligent. Machine learning can be applied in cases where the desired outcome is known (guided learning) or the data is not known beforehand (unguided learning) or the learning is the result of interaction between a model and the environment (reinforcement learning). This chapter answers the questions: How could machine learning algorithms be applied to IoT smart data? What is the taxonomy of machine learning algorithms that can be adopted in IoT? And what are IoT data characteristics in real-world which requires data analytics?

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Machine Learning Techniques for Internet of Things

Research Anthology on Artificial Intelligence Applications in Security ◽

10.4018/978-1-7998-7705-9.ch067 ◽

2021 ◽

pp. 1490-1506

Author(s):

P. Priakanth ◽

S. Gopikrishnan

Keyword(s):

Machine Learning ◽

Data Science ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Independent Learning ◽

Machine Learning Techniques ◽

Analytical Models ◽

Guided Learning ◽

Learning Techniques ◽

Learning Machine

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From machine learning to sustainable taxation: GPS traces of trucks circulating in Belgium

10.5194/egusphere-egu2020-9456 ◽

2020 ◽

Author(s):

Arnaud Adam ◽

Isabelle Thomas

Keyword(s):

Machine Learning ◽

Point Of View ◽

Machine Learning Techniques ◽

Spatial Effects ◽

Space And Time ◽

Learning Techniques ◽

Other Information ◽

Gps Traces ◽

The Individual ◽

Taxation System

Transport geography has always been characterized by a lack of accurate data, leading to surveys often based on samples that are spatially not representative. However, the current deluge of data collected through sensors promises to overpass this scarcity of data. We here consider one example: since April 1st 2016, a GPS tracker is mandatory within each truck circulating in Belgium for kilometre taxes. Every 30 seconds, this tracker collects the position of the truck (as well as some other information such as speed or direction), leading to an individual taxation of trucks. This contribution uses a one-week exhaustive database containing the totality of trucks circulating in Belgium, in order to understand transport fluxes within the country, as well as the spatial effects of the taxation on the circulation of trucks.Machine learning techniques are applied on over 270 million of GPS points to detect stops of trucks, leading to transform GPS sequences into a complete Origin-Destination matrix. Using machine learning allows to accurately classify stops that are different in nature (leisure stop, (un-)loading areas, or congested roads). Based on this matrix, we firstly propose an overview of the daily traffic, as well as an evaluation of the number of stops made in every Belgian place. Secondly, GPS sequences and stops are combined, leading to characterise sub-trajectories of each truck (first/last miles and transit) by their fiscal debit. This individual characterisation, as well as its variation in space and time, are here discussed: is the individual taxation system always efficient in space and time?This contribution helps to better understand the circulation of trucks in Belgium, the places where they stopped, as well as the importance of their locations in a fiscal point of view. What are the potential modifications of the trucks routes that would lead to a more sustainable kilometre taxation? This contribution illustrates that combining big-data and machine learning open new roads for accurately measuring and modelling transportation.

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Introduction to Computational Psychometrics: Towards a Principled Integration of Data Science and Machine Learning Techniques into Psychometrics

Methodology of Educational Measurement and Assessment - Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessment ◽

10.1007/978-3-030-74394-9_1 ◽

2021 ◽

pp. 1-6

Author(s):

Alina A. von Davier ◽

Robert J. Mislevy ◽

Jiangang Hao

Keyword(s):

Machine Learning ◽

Data Science ◽

Machine Learning Techniques ◽

Learning Techniques

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Application of Machine Learning Techniques As a Means of Mooring Integrity Monitoring

Volume 3: Structures, Safety, and Reliability ◽

10.1115/omae2019-96411 ◽

2019 ◽

Author(s):

Jonathan M. Gumley ◽

Hayden Marcollo ◽

Stuart Wales ◽

Andrew E. Potts ◽

Christopher J. Carra

Keyword(s):

Machine Learning ◽

Data Science ◽

Single Point ◽

Original System ◽

Training Data ◽

Machine Learning Techniques ◽

Mooring Line ◽

Artificial Noise ◽

Data Set ◽

Learning Techniques

Abstract There is growing importance in the offshore floating production sector to develop reliable and robust means of continuously monitoring the integrity of mooring systems for FPSOs and FPUs, particularly in light of the upcoming introduction of API-RP-2MIM. Here, the limitations of the current range of monitoring techniques are discussed, including well established technologies such as load cells, sonar, or visual inspection, within the context of the growing mainstream acceptance of data science and machine learning. Due to the large fleet of floating production platforms currently in service, there is a need for a readily deployable solution that can be retrofitted to existing platforms to passively monitor the performance of floating assets on their moorings, for which machine learning based systems have particular advantages. An earlier investigation conducted in 2016 on a shallow water, single point moored FPSO employed host facility data from in-service field measurements before and after a single mooring line failure event. This paper presents how the same machine learning techniques were applied to a deep water, semi taut, spread moored system where there was no host facility data available, therefore requiring a calibrated hydrodynamic numerical model to be used as the basis for the training data set. The machine learning techniques applied to both real and synthetically generated data were successful in replicating the response of the original system, even with the latter subjected to different variations of artificial noise. Furthermore, utilizing a probability-based approach, it was demonstrated that replicating the response of the underlying system was a powerful technique for predicting changes in the mooring system.

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Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques

10.4018/978-1-7998-8455-2.ch008 ◽

2022 ◽

pp. 209-232

Author(s):

Xiang Li ◽

Jingxi Liao ◽

Tianchuan Gao

Keyword(s):

Machine Learning ◽

Principal Component Analysis ◽

Data Science ◽

Principal Component ◽

Machine Learning Techniques ◽

Classification Models ◽

Performance Measurements ◽

Learning Techniques ◽

Source Data ◽

Bed And Breakfast

Machine learning is a broad field that contains multiple fields of discipline including mathematics, computer science, and data science. Some of the concepts, like deep neural networks, can be complicated and difficult to explain in several words. This chapter focuses on essential methods like classification from supervised learning, clustering, and dimensionality reduction that can be easily interpreted and explained in an acceptable way for beginners. In this chapter, data for Airbnb (Air Bed and Breakfast) listings in London are used as the source data to study the effect of each machine learning technique. By using the K-means clustering, principal component analysis (PCA), random forest, and other methods to help build classification models from the features, it is able to predict the classification results and provide some performance measurements to test the model.

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Data Analytics and Modeling in IoT-Fog Environment for Resource Constrained IoT-Applications - A Review

Recent Advances in Computer Science and Communications ◽

10.2174/2666255814666210715161630 ◽

2021 ◽

Vol 14 ◽

Author(s):

Omar Farooq ◽

Parminder Singh

Keyword(s):

Machine Learning ◽

Data Science ◽

Data Classification ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Resource Constrained ◽

Learning Techniques ◽

Iot Devices ◽

The Right ◽

Continuous Use

Introduction: The emergence of the concepts like Big Data, Data Science, Machine Learning (ML), and the Internet of Things (IoT) has added the potential of research in today's world. The continuous use of IoT devices, sensors, etc. that collect data continuously puts tremendous pressure on the existing IoT network. Materials and Methods: This resource-constrained IoT environment is flooded with data acquired from millions of IoT nodes deployed at the device level. The limited resources of the IoT Network have driven the researchers towards data Management. This paper focuses on data classification at the device level, edge/fog level, and cloud level using machine learning techniques. Results: The data coming from different devices is vast and is of variety. Therefore, it becomes essential to choose the right approach for classification and analysis. It will help optimize the data at the device edge/fog level to better the network's performance in the future. Conclusion: This paper presents data classification, machine learning approaches, and a proposed mathematical model for the IoT environment.

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