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作者： Walker, Christopher J Texas A&M University

The purpose of this study is to investigate and forecast the impact of hurricane Florence on home prices in North Carolina. Quantitative data was gathered from real estate comparison websites such as Zillow and Redfin, while qualitative data from news reports and FEMA disaster declarations were used to better analyze the impact of hurricane Florence. Using both qualitative and quantitative sources, an analysis was conducted to compare historical home values with home values immediately following hurricane Florence. Using methodologies from past studies, forecasts were made for each impacted zip code that estimate the median home value one year following hurricane Florence to better understand the rate at which different areas recover. Charts and maps were used to visualize historical trends as well as forecasts. By conducting this data analysis study, similar studies on focused on other hurricanes can be used to compare the impact of Hurricane Florence to other hurricane events. Conducting this research serves to enhance the growing literature on the economic impact of hurricanes.

关键词： real estate data data science regression python sklearn scikit-learn home values hurricane florence north carolina disaster economics forecasting predicting forecast predict home prices housing Thesis

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Analýza dat firmy produkující zdravotnické potřeby

Analýza dat firmy produkující zdravotnické potřeby

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作者： Kulhánková, Monika Brno University of Technology

Tato bakalářská práce se zabývá analýzou prodejních dat společnosti, konkrétně klasifikací typu zákazníka podle jeho prodejních dat. Poskytuje teoretick... 详细信息

Tato bakalářská práce se zabývá analýzou prodejních dat společnosti, konkrétně klasifikací typu zákazníka podle jeho prodejních dat. Poskytuje teoretický úvod k získávání znalostí z dat. Popisuje proces klasifikace, metody pro vytváření klasifikátorů a je zde představen model CRISP-DM. V práci jsou popsány poskytnuté datové sady, ze kterých jsou vybrány relevantní atributy. Data jsou předzpracována a použita při tvorbě a testování klasifikačních modelů. Výsledkem práce je porovnání dosažených výsledků.

关键词： Dolování dat Získávání znalostí z dat Analýza dat CRISP-DM Klasifikace Rozhodovací stromy Náhodný les SVM metoda k-nejbližších sousedů Python scikit-learn Data Mining Data Analysis CRISP-DM Classification Decision Trees Random Forest SVM

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EC-KitY: Evolutionary computation tool kit in Python with seamless machine learning integration

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SOFTWAREX 2023年 22卷

作者： Sipper, Moshe Halperin, Tomer Tzruia, Itai Elyasaf, Achiya Ben Gurion Univ Negev Dept Comp Sci IL-8410501 Beer Sheva Israel Ben Gurion Univ Negev Dept Software & Informat Syst Engn IL-8410501 Beer Sheva Israel

EC-KitY is a comprehensive Python library for doing evolutionary computation (EC), licensed under the BSD 3-Clause License, and compatible with scikit-learn. Designed with modern software engineering and machine learning integration in mind, EC-KitY can support all popular EC paradigms, including genetic algorithms, genetic programming, coevolution, evolutionary multi-objective optimization, and more. This paper provides an overview of the package, including the ease of setting up an EC experiment, the architecture, the main features, and a comparison with other libraries. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Evolutionary algorithms Evolutionary computation Genetic programming Machine learning scikit-learn

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A Framework for the Optimization of Complex Cyber-Physical Systems via Directed Acyclic Graph

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SENSORS 2022年第4期22卷 1490-1490页

作者： Castejon-Limas, Manuel Fernandez-Robles, Laura Alaiz-Moreton, Hector Cifuentes-Rodriguez, Jaime Fernandez-Llamas, Camino Univ Leon Dept Mech Comp Sci & Aerosp Engn Leon 24071 Spain

Mathematical modeling and data-driven methodologies are frequently required to optimize industrial processes in the context of Cyber-Physical Systems (CPS). This paper introduces the PipeGraph software library, an open-source python toolbox for easing the creation of machine learning models by using Directed Acyclic Graph (DAG)-like implementations that can be used for CPS. scikit-learn's Pipeline is a very useful tool to bind a sequence of transformers and a final estimator in a single unit capable of working itself as an estimator. It sequentially assembles several steps that can be cross-validated together while setting different parameters. Steps encapsulation secures the experiment from data leakage during the training phase. The scientific goal of PipeGraph is to extend the concept of Pipeline by using a graph structure that can handle scikit-learn's objects in DAG layouts. It allows performing diverse operations, instead of only transformations, following the topological ordering of the steps in the graph;it provides access to all the data generated along the intermediate steps;and it is compatible with GridSearchCV function to tune the hyperparameters of the steps. It is also not limited to (X,y) entries. Moreover, it has been proposed as part of the scikit-learn-contrib supported project, and is fully compatible with scikit-learn. Documentation and unitary tests are publicly available together with the source code. Two case studies are analyzed in which PipeGraph proves to be essential in improving CPS modeling and optimization: the first is about the optimization of a heat exchange management system, and the second deals with the detection of anomalies in manufacturing processes.

关键词： Cyber-Physical Systems Lean Manufacturing Directed Acyclic Graphs scikit-learn pipegraph machine learning models

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ANALYZING TERRIFIC TRAFFIC IN URBAN AREAS: A SMALL STEP TOWARDS BRINGING ORDER INTO CITY ROADS

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COMPUTING AND INFORMATICS 2022年第2期41卷 571-589页

作者： Hossain, Sakhawat Islam, Mosaddekul Rahman, Ashikur Bangladesh Univ Engn & Technol BUET Dept Comp Sci & Engn Dhaka 1000 Bangladesh

Accurate travel time information enables travellers to plan their journey more wisely and efficiently. This in turn, lessens traffic congestion and improves people???s travel experience, particularly in urban areas. Open-source traffic data available from different sources and Google Map API have raised opportunities for analyzing and predicting the traffic more accurately. The purpose of this research work is to analyze bus or car travel time data and showcase different insight and aspect of a society from its traffic pattern. Google Distance Matrix API, Python programming language and machine learning algorithms have been applied in this study to automatically extract, analyze, and visualize traffic data and showcase analysis methodology to improve people???s travel experience in Dhaka City and the City of New York. In particular, we apply data analytics to develop an oracle that will give answers to different queries about traffic, such as least congested period and/or least congested route within a day/week/month etc., which in turn would enable people to make informed decisions for travel arrangements. The experimental results and detailed analyses show that there exists a wide fluctuation of travel time during the day in both cities. Furthermore, unlike other works, we accomplish various socio-cultural aspects and behaviour from traffic patterns in those two cities, perform the accessibility analysis and provide recommendations for further research.

关键词： Traveling time prediction crowdsourced data Google distance matrix API polynomial regression scikit-learn

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System Identification Techniques for Soft Sensors and Multiphase Flow Metering

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IFAC-PapersOnLine 2024年第15期58卷 538-543页

作者： Pedro H.C. Paulo Felipe C. Pereira Helon V.H. Ayala Petroleo Brasileiro S/A Rio de Janeiro Brazil Department of Mechanical Engineering Pontifical Catholic University of Rio de Janeiro Rio de Janeiro Brazil Pontifícia Universidade Católica do Paraná PR Brasil

Production rates estimation and forecasting is a complex problem in multiphase flow systems, such as oil wells. In the oil and gas industry, this task is normally performed by first principles simulation models, which are costly to maintain and adjust. An alternative to this process could be black-box models based on historical data to perform this task. This work proposes to perform an extensive evaluation of existing regressors to create a black-box system identification model to predict oil well liquid flow rates. Improvement strategies such as the creation of error models, time series decomposition, and the use of current-time data are also evaluated. The final results demonstrated the importance of pressure and temperature data for the well flow rate determination and identified the use of current-time data as the best tested technique to improve the performance of the final model.

关键词： system identification scikit-learn oil gas production forecast digital twin

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A Data Analysis Framework for Earth System Simulation within an <i>In-Situ</i>Infrastructure

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Journal of Computer and Communications 2017年第14期5卷 76-85页

作者： D. Wang X. Luo F. Yuan N. Podhorszki Environmental Science Division Oak Ridge National Laboratory Oak Ridge TN USA Department of Computer Science and Electric Engineering University of Tennessee Knoxville TN USA Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge TN USA

This paper presents a generic procedure to implement a scalable and high performance data analysis framework for large-scale scientific simulation within an in-situ infrastructure. It demonstrates a unique capability for global Earth system simulations using advanced computing technologies (i.e., automated code analysis and instrumentation), in-situ infrastructure (i.e., ADIOS) and big data analysis engines (i.e., scikit-learn). This paper also includes a useful case that analyzes a globe Earth System simulations with the integration of scalable in-situ infrastructure and advanced data processing package. The in-situ data analysis framework can provides new insights on scientific discoveries in multiscale modeling paradigms.

关键词： In-Situ Data Analysis Source Code Analysis Data Staging ADIOS Earth System Model Machine learning scikit-learn E3SM

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sklvq: scikit learning vector quantization

The Journal of Machine Learning Research

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The Journal of Machine learning Research 2021年第1期22卷 10499-10504页

作者： Rick Van Veen Michael Biehl Gert-Jan De Vries Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen The Netherlands Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen The Netherlands and SMQB Institute of Metabolism and Systems Research College of Medical and Dental Sciences Birmingham UK Philips Research Healthcare Eindhoven The Netherlands

The sklvq package is an open-source Python implementation of a set of learning vector quantization (LVQ) algorithms. In addition to providing the core functionality for the GLVQ, GMLVQ, and LGMLVQ algorithms, sklvq is distinctive by putting emphasis on its modular and customizable design. Not only resulting in a feature-rich implementation for users but enabling easy extensions of the algorithms for researchers. The theory behind this design is described in this paper. To facilitate adoptions and inspire future contributions, sklvq is publicly available on Github (under the BSD license) and can be installed through the Python package index (PyPI). Next to being well-covered by automated testing to ensure code quality, it is accompanied by detailed online documentation. The documentation covers usage examples and provides an in-depth API including theory and scientific references.

关键词： python scikit-learn learning vector quantization matrix relevance learning

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metric-learn: metric learning algorithms in Python

The Journal of Machine Learning Research

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The Journal of Machine learning Research 2020年第1期21卷 5447-5452页

作者： William De Vazelhes C. J. Carey Yuan Tang Nathalie Vauquier Aurélien Bellet Paris Research Center Huawei Technologies Boulogne-Billancourt France Google LLC New York NY Ant Group Sunnyvale CA Magnet Team INRIA Lille Nord Europe Villeneuve d'Ascq France

metric-learn is an open source Python package implementing supervised and weakly-supervised distance metric learning algorithms. As part of scikit-learn-contrib, it provides a unified interface compatible with scikit-learn which allows to easily perform cross-validation, model selection, and pipelining with other machine learning estimators. metric-learn is thoroughly tested and available on PyPi under the MIT license.

关键词： machine learning python metric learning scikit-learn

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GraKeL: a graph kernel library in Python

The Journal of Machine Learning Research

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The Journal of Machine learning Research 2020年第1期21卷 1993-1997页

作者： Giannis Siglidis Giannis Nikolentzos Stratis Limnios Christos Giatsidis Konstantinos Skianis Michalis Vazirgiannis LIP6 UPMC Université Paris 6 Sorbonne Universités Paris France LIX École Polytechnique Palaiseau France LIX École Polytechnique Palaiseau France and Department of Informatics Athens University of Economics and Business Athens Greece

The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promising approach to this problem. There are now many kernels, each focusing on different structural aspects of graphs. Here, we present GraKeL, a library that unifies several graph kernels into a common framework. The library is written in Python and adheres to the scikit-learn interface. It is simple to use and can be naturally combined with scikit-learn's modules to build a complete machine learning pipeline for tasks such as graph classification and clustering. The code is BSD licensed and is available at: https://***/ysig/GraKeL.

关键词： graph similarity graph kernels scikit-learn Python

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