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Scalable Prediction Models for Airbnb Listing in Spark Big Data Cluster using GPU-accelerated RAPIDS
Samyuktha Muralidharan,Savita Yadav,허정우,이상훈,우종욱 한국정보통신학회 2022 Journal of information and communication convergen Vol.20 No.2
We aim to build predictive models for Airbnb’s prices using a GPU-accelerated RAPIDS in a big data cluster. The Airbnb Listings datasets are used for the predictive analysis. Several machine-learning algorithms have been adopted to build models that predict the price of Airbnb listings. We compare the results of traditional and big data approaches to machine learning for price prediction and discuss the performance of the models. We built big data models using Databricks Spark Cluster, a distributed parallel computing system. Furthermore, we implemented models using multiple GPUs using RAPIDS in the spark cluster. The model was developed using the XGBoost algorithm, whereas other models were developed using traditional central processing unit (CPU)-based algorithms. This study compared all models in terms of accuracy metrics and computing time. We observed that the XGBoost model with RAPIDS using GPUs had the highest accuracy and computing time.
Predictive Analysis for Airbnb Listing Rating using scalable Big Data platform
Savita Yadav,Samyuktha Muralidharan,Jongwook Woo 한국경영정보학회 2021 한국경영정보학회 학술대회논문집 Vol.2021 No.11
This paper aims to build predictive models for Airbnb Rating using the Big Data systems, which is distributed parallel computing systems. We use Machine Learning algorithms to build models to predict a rating of the Airbnb listing. The Airbnb ratings can help hosts improve the listing and the hospitality to gain more potential customers. On the other hand, the guests can make a decision based on the ratings that previous guests provided. It is essential to understand customer experience and its role in forming customer rating behavior. The overall ratings provided by customers are reflections of their experiences with a product or service. We use Two-Class Classification models to predict if the listing has a high or low rating based on the features of the listing. We compare the results and the performance of rating prediction models. The comparison is illustrated in terms of the accuracy metrics and computing time.
Samuel, T.S. Arun,Balamurugan, N.B.,Niranjana, T.,Samyuktha, B. The Korean Institute of Electrical Engineers 2014 Journal of Electrical Engineering & Technology Vol.9 No.2
In this paper, a new two dimensional (2D) analytical modeling and simulation for a surrounding gate tunnel field effect transistor (TFET) is proposed. The Parabolic approximation technique is used to solve the 2-D Poisson equation with suitable boundary conditions and analytical expressions for surface potential and electric field are derived. This electric field distribution is further used to calculate the tunneling generation rate and thus we numerically extract the tunneling current. The results show a significant improvement in on-current characteristics while short channel effects are greatly reduced. Effectiveness of the proposed model has been confirmed by comparing the analytical results with the TCAD simulation results.
T.S.Arun Samuel,N.B.Balamurugan,T.Niranjana,B.Samyuktha 대한전기학회 2014 Journal of Electrical Engineering & Technology Vol.9 No.2
In this paper, a new two dimensional (2D) analytical modeling and simulation for a surrounding gate tunnel field effect transistor (TFET) is proposed. The Parabolic approximation technique is used to solve the 2-D Poisson equation with suitable boundary conditions and analytical expressions for surface potential and electric field are derived. This electric field distribution is further used to calculate the tunneling generation rate and thus we numerically extract the tunneling current. The results show a significant improvement in on-current characteristics while short channel effects are greatly reduced. Effectiveness of the proposed model has been confirmed by comparing the analytical results with the TCAD simulation results.