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8.351a1.312 1.312 0 0 1-1.146 1.954H1.33A1.313 1.313 0 0 1 .183 9.058ZM7 7V3H5v4Zm-1 3a1 1 0 1 0 0-2 1 1 0 0 0 0 2Z"></path> </svg> </span> <span></span> </div> </div> <div data-target="query-builder.screenReaderFeedback" aria-live="polite" aria-atomic="true" class="sr-only"></div> </query-builder></form> <div class="d-flex flex-row color-fg-muted px-3 text-small color-bg-default search-feedback-prompt"> <a target="_blank" href="https://docs.github.com/search-github/github-code-search/understanding-github-code-search-syntax" data-view-component="true" class="Link color-fg-accent text-normal ml-2">Search syntax tips</a> <div class="d-flex flex-1"></div> </div> </div> </div> </div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div class="color-fg-default"> <dialog-helper> <dialog data-target="qbsearch-input.feedbackDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 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class=\"heading-element\" dir=\"auto\"\u003eMachine Learning and Data Science Applications in Industry\u003c/h1\u003e\u003ca id=\"user-content-machine-learning-and-data-science-applications-in-industry\" class=\"anchor\" aria-label=\"Permalink: Machine Learning and Data Science Applications in Industry\" href=\"#machine-learning-and-data-science-applications-in-industry\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🌟 We Are Growing!\u003c/h2\u003e\u003ca id=\"user-content--we-are-growing\" class=\"anchor\" aria-label=\"Permalink: 🌟 We Are Growing!\" href=\"#-we-are-growing\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe're seeking to collaborate with motivated, independent PhD graduates or doctoral students on approximately seven new projects in 2024. If you’re interested in contributing to cutting-edge investment insights and data analysis, please get in touch! This could be in colaboration with a university or as independent study.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://private-user-images.githubusercontent.com/26666267/373640241-da97663a-b63f-4286-94cc-fcd168905109.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.pLc3Ms64VHlsWNOZ92pkT_E2eSMxLqaomowg3hx4wWY\"\u003e\u003cimg src=\"https://private-user-images.githubusercontent.com/26666267/373640241-da97663a-b63f-4286-94cc-fcd168905109.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.pLc3Ms64VHlsWNOZ92pkT_E2eSMxLqaomowg3hx4wWY\" alt=\"image\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🚀 About Sov.ai\u003c/h3\u003e\u003ca id=\"user-content--about-sovai\" class=\"anchor\" aria-label=\"Permalink: 🚀 About Sov.ai\" href=\"#-about-sovai\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSov.ai is at the forefront of integrating advanced machine learning techniques with financial data analysis to revolutionize investment strategies. We are working with \u003cstrong\u003ethree of the top 10\u003c/strong\u003e quantitative hedge funds, and with many mid-sized and boutique firms.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOur platform leverages diverse data sources and innovative algorithms to deliver actionable insights that drive smarter investment decisions.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eBy joining Sov.ai, you'll be part of a dynamic research team dedicated to pushing the boundaries of what's possible in finance through technology. Before expressing your interest, please be aware that the research will be predominantly challenging and experimental in nature.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🔍 Research and Project Opportunities\u003c/h3\u003e\u003ca id=\"user-content--research-and-project-opportunities\" class=\"anchor\" aria-label=\"Permalink: 🔍 Research and Project Opportunities\" href=\"#-research-and-project-opportunities\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe offer a wide range of projects that cater to various interests and expertise within machine learning and finance. Some of the exciting recent projects include:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003ePredictive Modeling with GitHub Logs:\u003c/strong\u003e Develop models to predict market trends and investment opportunities using GitHub activity and developer data.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSatallite Data Analysis:\u003c/strong\u003e Explore non-traditional data sources such as social media sentiment, satellite imagery, or web traffic to enhance financial forecasting.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eData Imputation Techniques:\u003c/strong\u003e Investigate new methods for handling missing or incomplete data to improve the robustness and accuracy of our models.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003ePlease visit \u003ca href=\"https://docs.sov.ai\" rel=\"nofollow\"\u003edocs.sov.ai\u003c/a\u003e for more information on public projects that have made it into the subscription product. If you already have a corporate sponsor, we are also happy to work with them.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🌐 Why Join Sov.ai?\u003c/h3\u003e\u003ca id=\"user-content--why-join-sovai\" class=\"anchor\" aria-label=\"Permalink: 🌐 Why Join Sov.ai?\" href=\"#-why-join-sovai\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eInnovative Environment:\u003c/strong\u003e Engage with the latest technologies and methodologies in machine learning and finance.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCollaborative Team:\u003c/strong\u003e Work alongside a team of experts passionate about driving innovation in investment insights.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eFlexible Projects:\u003c/strong\u003e Tailor your research to align with your interests and expertise, with the freedom to explore new ideas.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eExperienced Researchers:\u003c/strong\u003e Experts previously from NYU, Columbia, Oxford-Man Institute, Alan Turing Institute, and Cambridge.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePost Research:\u003c/strong\u003e Connect with alumni that has moved on to DRW, Citadel Securities, Virtu Financial, Akuna Capital, HRT.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e🤝 How to Apply\u003c/h3\u003e\u003ca id=\"user-content--how-to-apply\" class=\"anchor\" aria-label=\"Permalink: 🤝 How to Apply\" href=\"#-how-to-apply\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you’re excited about leveraging your expertise in machine learning and finance to drive impactful research and projects, we’d love to hear from you! Please reach out to us at \u003ca href=\"mailto:research@sov.ai\"\u003eresearch@sov.ai\u003c/a\u003e with your resume and a brief description of your research interests.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eJoin us in shaping the future of investment insights and making a meaningful impact in the world of finance!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAdmin\u003c/h3\u003e\u003ca id=\"user-content-admin\" class=\"anchor\" aria-label=\"Permalink: Admin\" href=\"#admin\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eHave a look at the newly started \u003ca href=\"https://medium.com/firmai\" rel=\"nofollow\"\u003eFirmAI Medium\u003c/a\u003e publication where we have experts of AI in business, write about their topics of interest.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ePlease add your tools and notebooks to this \u003ca href=\"https://docs.google.com/spreadsheets/d/1pVdV3r4X3k5D1UtKbhMTmjU8mJTZSLAhJzycurgh_o4/edit?usp=sharing\" rel=\"nofollow\"\u003eGoogle Sheet\u003c/a\u003e. Or simply add it to this subreddit, \u003ca href=\"https://www.reddit.com/r/datascienceproject/\" rel=\"nofollow\"\u003er/datascienceproject\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eHighlight in \u003cstrong\u003eYELLOW\u003c/strong\u003e to get your package added, you can also just add it yourself with a \u003cstrong\u003epull request\u003c/strong\u003e.\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/firmai/industry-machine-learning/raw/master/assets/industry.png\"\u003e\u003cimg src=\"https://github.com/firmai/industry-machine-learning/raw/master/assets/industry.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eA curated list of applied machine learning and data science notebooks and libraries accross different industries. The code in this repository is in Python (primarily using jupyter notebooks) unless otherwise stated. The catalogue is inspired by \u003ccode\u003eawesome-machine-learning\u003c/code\u003e. \u003ca href=\"https://www.reddit.com/r/datascienceproject/\" rel=\"nofollow\"\u003er/datascienceproject\u003c/a\u003e is a subreddit where you can share all your data science projects.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003e\u003cstrong\u003eCaution:\u003c/strong\u003e\u003c/em\u003e This is a work in progress, please contribute, especially if you are a subject expert in any of the industries listed below. If you are a \u003cstrong\u003e[analytical, computational, statistical, quantitive]\u003c/strong\u003e researcher/analyst in field \u003cstrong\u003eX\u003c/strong\u003e or a field \u003cstrong\u003eX\u003c/strong\u003e \u003cstrong\u003e[machine learning engineer, data scientist, modeler, programmer]\u003c/strong\u003e then your contribution will be greatly appreciated.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you want to contribute to this list (please do), send me a pull request or contact me \u003ca href=\"https://twitter.com/dereknow\" rel=\"nofollow\"\u003e@dereknow\u003c/a\u003e or on \u003ca href=\"https://www.linkedin.com/in/snowderek/\" rel=\"nofollow\"\u003elinkedin\u003c/a\u003e or get in contact on the website \u003ca href=\"https://www.firmai.org\" rel=\"nofollow\"\u003eFirmAI\u003c/a\u003e.\nAlso, a listed repository should be deprecated if:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRepository's owner explicitly say that \"this library is not maintained\".\u003c/li\u003e\n\u003cli\u003eNot committed for long time (2~3 years).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cbr\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eHelp Needed:\u003c/strong\u003e If there is any contributors out there willing to help first populate and then maintain a Python analytics section \u003cstrong\u003ein any one of the following sub/industries,\u003c/strong\u003e please get in contact with me. Also contact me to add \u003cstrong\u003eadditional industries\u003c/strong\u003e.\u003c/p\u003e\n\u003cbr\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"#accommodation\"\u003eAccommodation \u0026amp; Food\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"#agriculture\"\u003eAgriculture\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"#bankfin\"\u003eBanking \u0026amp; Insurance\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"#biotech\"\u003eBiotechnological \u0026amp; Life Sciences\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"#construction\"\u003eConstruction \u0026amp; Engineering\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"#education\"\u003eEducation \u0026amp; Research\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"#emergency\"\u003eEmergency \u0026amp; Relief\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"#finance\"\u003eFinance\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"#manufacturing\"\u003eManufacturing\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"#public\"\u003eGovernment and Public Works\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"#healthcare\"\u003eHealthcare\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"#media\"\u003eMedia \u0026amp; Publishing\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"#legal\"\u003eJustice, Law and Regulations\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"#miscellaneous\"\u003eMiscellaneous\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"#accounting\"\u003eAccounting\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"#realestate\"\u003eReal Estate, Rental \u0026amp; Leasing\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"#utilities\"\u003eUtilities\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"#wholesale\"\u003eWholesale \u0026amp; Retail\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eIndustry Applications\u003c/h3\u003e\u003ca id=\"user-content-industry-applications\" class=\"anchor\" aria-label=\"Permalink: Industry Applications\" href=\"#industry-applications\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#accommodation\"\u003eAccommodation \u0026amp; Food\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#accommodation-food\"\u003eFood\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#accommodation-rest\"\u003eRestaurant\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#accommodation-acc\"\u003eAccommodation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#accounting\"\u003eAccounting\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#accounting-ml\"\u003eMachine Learning\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#accounting-analytics\"\u003eAnalytics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#accounting-text\"\u003eTextual Analysis\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#accounting-data\"\u003eData\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#accounting-ra\"\u003eResearch and Articles\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#accounting-web\"\u003eWebsites\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#accounting-course\"\u003eCourses\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#agriculture\"\u003eAgriculture\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#agriculture-econ\"\u003eEconomics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#agriculture-dev\"\u003eDevelopment\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#bankfin\"\u003eBanking \u0026amp; Insurance\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#bankfin-cf\"\u003eConsumer Financial\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#bankfin-mo\"\u003eManagement and Operations\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#bankfin-value\"\u003eValuation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#bankfin-fraud\"\u003eFraud\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#bankfin-ir\"\u003eInsurance and Risk\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#bankfin-ph\"\u003ePhysical\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#bankfin-data\"\u003eData\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#biotech\"\u003eBiotechnological \u0026amp; Life Sciences\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#biotech-general\"\u003eGeneral\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#biotech-seq\"\u003eSequencing\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#biotech-chem\"\u003eChemoinformatics and drug discovery\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#biotech-gene\"\u003eGenomics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#biotech-life\"\u003eLife-sciences\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#construction\"\u003eConstruction \u0026amp; Engineering\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#construction-const\"\u003eConstruction\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#construction-eng\"\u003eEngineering\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#construction-mat\"\u003eMaterial Science\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#economics\"\u003eEconomics\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#economics-general\"\u003eGeneral\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#economics-ml\"\u003eMachine Learning\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#economics-computational\"\u003eComputational\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#education\"\u003eEducation \u0026amp; Research\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#education-student\"\u003eStudent\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#education-school\"\u003eSchool\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#emergency\"\u003eEmergency \u0026amp; Relief\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#emergency-prevent\"\u003ePreventative and Reactive\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#emergency-crime\"\u003eCrime\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#emergency-ambulance\"\u003eAmbulance\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#emergency-disaster\"\u003eDisaster Management\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#finance\"\u003eFinance\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#finance-trade\"\u003eTrading \u0026amp; Investment\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#finance-data\"\u003eData\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#healthcare\"\u003eHealthcare\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#healthcare-general\"\u003eGeneral\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#legal\"\u003eJustice, Law and Regulations\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#legal-tools\"\u003eTools\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#legal-pr\"\u003ePolicy and Regulatory\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#legal-judicial\"\u003eJudicial\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#manufacturing\"\u003eManufacturing\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#manufacturing-general\"\u003eGeneral\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#manufacturing-maintenance\"\u003eMaintenance\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#manufacturing-fail\"\u003eFailure\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#manufacturing-quality\"\u003eQuality\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#media\"\u003eMedia \u0026amp; Publishing\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#media-marketing\"\u003eMarketing\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#miscellaneous\"\u003eMiscellaneous\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#miscellaneous-art\"\u003eArt\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#miscellaneous-tour\"\u003eTourism\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#physics\"\u003ePhysics\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#physics-general\"\u003eGeneral\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#physics-ml\"\u003eMachine Learning\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#public\"\u003eGovernment and Public Works\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#public-social\"\u003eSocial Policies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#public-elect\"\u003eElection Analysis\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#public-dis\"\u003eDisaster Management\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#public-poli\"\u003ePolitics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#public-charity\"\u003eCharities\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#realestate\"\u003eReal Estate, Rental \u0026amp; Leasing\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#realestate-real\"\u003eReal Estate\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#realestate-rental\"\u003eRental \u0026amp; Leasing\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#utilities\"\u003eUtilities\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#utilities-elect\"\u003eElectricity\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#utilities-coal\"\u003eCoal, Oil \u0026amp; Gas\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#utilities-water\"\u003eWater \u0026amp; Pollution\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#utilities-transport\"\u003eTransportation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#wholesale\"\u003eWholesale \u0026amp; Retail\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#wholesale-whole\"\u003eWholesale\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#wholesale-retail\"\u003eRetail\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eML/DS Career Section for Industry Machine Learning\u003c/h2\u003e\u003ca id=\"user-content-mlds-career-section-for-industry-machine-learning\" class=\"anchor\" aria-label=\"Permalink: ML/DS Career Section for Industry Machine Learning\" href=\"#mlds-career-section-for-industry-machine-learning\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSee \u003ca href=\"https://github.com/firmai/data-science-career\"\u003edata-science-career repo\u003c/a\u003e for more.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePlatforms:\u003c/h3\u003e\u003ca id=\"user-content-platforms\" class=\"anchor\" aria-label=\"Permalink: Platforms:\" href=\"#platforms\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://triplebyte.com/a/Nosq7GM/d\" rel=\"nofollow\"\u003eTriplebyte\u003c/a\u003e - Take a quiz. Get offers from multiple top tech companies at once (now have a machine learning track).\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.toptal.com/\" rel=\"nofollow\"\u003eToptal\u003c/a\u003e - Developers seeking to gain entry into the Toptal community are put through a battery of personality and technical tests.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://hired.com/\" rel=\"nofollow\"\u003eHired\u003c/a\u003e - Hired matches employers with qualified candidates through a combination of in-house algorithms and online support.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.kaggle.com/jobs\" rel=\"nofollow\"\u003eKaggle\u003c/a\u003e - Scalable Path is a premium talent matching service.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eReviews:\u003c/h3\u003e\u003ca id=\"user-content-reviews\" class=\"anchor\" aria-label=\"Permalink: Reviews:\" href=\"#reviews\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://www.glassdoor.com/index.htm\" rel=\"nofollow\"\u003eGlassdoor\u003c/a\u003e - Best employee narratives.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.indeed.com/\" rel=\"nofollow\"\u003eIndeed\u003c/a\u003e - Best coverage.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.kununu.com/us\" rel=\"nofollow\"\u003eKununu\u003c/a\u003e - Best well-rounded infromation.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.comparably.com/\" rel=\"nofollow\"\u003eComparably\u003c/a\u003e - Best comparison functionality.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.inhersight.com/\" rel=\"nofollow\"\u003eInHerSight\u003c/a\u003e - Best female-friendly perspective.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-accommodation\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAccommodation \u0026amp; Food\u003c/h2\u003e\u003ca id=\"user-content-accommodation--food\" class=\"anchor\" aria-label=\"Permalink: Accommodation \u0026amp; Food\" href=\"#accommodation--food\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-accommodation-food\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFood\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bschreck/robo-chef\"\u003eRobotChef\u003c/a\u003e - Refining recipes based on user reviews.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Ankushr785/Food-amenities-demand-prediction\"\u003eFood Amenities\u003c/a\u003e - Predicting the demand for food amenities using neural networks\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/catherhuang/FP3-recipe\"\u003eRecipe Cuisine and Rating\u003c/a\u003e - Predict the rating and type of cuisine from a list of ingredients.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/stratospark/food-101-keras\"\u003eFood Classification\u003c/a\u003e - Classification using Keras.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Murgio/Food-Recipe-CNN\"\u003eImage to Recipe\u003c/a\u003e - Translate an image to a recipe using deep learning.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jubins/DeepLearning-Food-Image-Recognition-And-Calorie-Estimation\"\u003eCalorie Estimation\u003c/a\u003e - Estimate calories from photos of food.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Architectshwet/Amazon-Fine-Food-Reviews\"\u003eFine Food Reviews\u003c/a\u003e - Sentiment analysis on Amazon Fine Food Reviews.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-accommodation-rest\"\u003e\u003c/a\u003e\n\u003cstrong\u003eRestaurant\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/nd1/DC_RestaurantViolationForecasting\"\u003eRestaurant Violation\u003c/a\u003e - Food inspection violation forecasting.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/alifier/Restaurant_success_model\"\u003eRestaurant Success\u003c/a\u003e - Predict whether a restaurant is going to fail.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/josephofiowa/dc-michelin-challenge/tree/master/submissions\"\u003ePredict Michelin\u003c/a\u003e - Predict the likelihood that restaurant is a Michelin restaurant.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gzsuyu/Data-Analysis-NYC-Restaurant-Inspection-Data\"\u003eRestaurant Inspection\u003c/a\u003e - An inspection analysis to see if cleanliness is related to rating.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ayeright/sales-forecast-lstm\"\u003eSales\u003c/a\u003e - Restaurant sales forecasting with LSTM.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/anki1909/Recruit-Restaurant-Visitor-Forecasting\"\u003eVisitor Forecasting\u003c/a\u003e - Reservation and visitation number prediction.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/everAspiring/RegressionAnalysis\"\u003eRestaurant Profit\u003c/a\u003e - Restaurant regression analysis.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/klin90/missinglink\"\u003eCompetition\u003c/a\u003e - Restaurant competitiveness analysis.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/nvodoor/RBA\"\u003eBusiness Analysis\u003c/a\u003e - Restaurant business analysis project.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sanatasy/Restaurant_Risk\"\u003eLocation Recommendation\u003c/a\u003e - Restaurant location recommendation tool and analysis.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Lolonon/Restaurant-Analytical-Solution\"\u003eClosure, Rating and Recommendation\u003c/a\u003e - Three prediction tasks using Yelp data.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Myau5x/anti-recommender\"\u003eAnti-recommender\u003c/a\u003e - Find restaurants you don’t want to attend.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bzjin/menus\"\u003eMenu Analysis\u003c/a\u003e - Deeper analysis of restaurants through their menus.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rphaneendra/Menu-Similarity\"\u003eMenu Recommendation\u003c/a\u003e - NLP to recommend restaurants with similar menus.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://gist.github.com/analyticsindiamagazine/f9b2ba171a0eef9ad396ce6f1b83bbbc\"\u003eFood Price\u003c/a\u003e - Predict food cost.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/firmai/interactive-corporate-report\"\u003eAutomated Restaurant Report\u003c/a\u003e - Automated machine learning company report.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-accommodation-acc\"\u003e\u003c/a\u003e\n\u003cstrong\u003eAccommodation\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rochiecuevas/shared_accommodations\"\u003ePeer-to-Peer Housing\u003c/a\u003e - The effect of peer to peer rentals on housing.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SiddheshAcharekar/Liveright\"\u003eRoommate Recommendation\u003c/a\u003e - A system for students seeking roommates.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/nus-usp/room-allocation\"\u003eRoom Allocation\u003c/a\u003e - Room allocation process.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/marcotav/hotels\"\u003eDynamic Pricing\u003c/a\u003e - Hotel dynamic pricing calculations.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Montclair-State-University-Info368/Assignment-6\"\u003eHotel Similarity\u003c/a\u003e - Compare brands that directly compete\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/EliadProject/Hotels-Data-Science\"\u003eHotel Reviews\u003c/a\u003e - Cluster hotel reviews.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/morenobcn/capstone_hotels_arcpy\"\u003ePredict Prices\u003c/a\u003e - Predict hotel room rates.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/morenobcn/hotels_vs_airbnb_proof_of_concept\"\u003eHotels vs Airbnb\u003c/a\u003e - Comparing the two approaches.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/argha48/smarthotels\"\u003eHotel Improvement\u003c/a\u003e - Analyse reviews to suggest hotel improvements.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Hasan330/Order-Cancellation-Prediction-Model\"\u003eOrders\u003c/a\u003e - Order cancellation prediction for hotels.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/danielmachinelearning/HotelSpamDetection\"\u003eFake Reviews\u003c/a\u003e - Identify whether reviews are fake/spam.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/starfoe/Eye-bnb\"\u003eReverse Image Lodging\u003c/a\u003e - Find your preferred lodging by uploading an image.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-accounting\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAccounting\u003c/h2\u003e\u003ca id=\"user-content-accounting\" class=\"anchor\" aria-label=\"Permalink: Accounting\" href=\"#accounting\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-accounting-ml\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMachine Learning\u003c/h4\u003e\u003ca id=\"user-content-machine-learning\" class=\"anchor\" aria-label=\"Permalink: Machine Learning\" href=\"#machine-learning\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/agdgovsg/ml-coa-charging\"\u003eChart of Account Prediction\u003c/a\u003e - Using labeled data to suggest the account name for every transaction.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/GitiHubi/deepAI/blob/master/GTC_2018_CoLab.ipynb\"\u003eAccounting Anomalies\u003c/a\u003e - Using deep-learning frameworks to identify accounting anomalies.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rameshcalamur/fin-stmt-anom\"\u003eFinancial Statement Anomalies\u003c/a\u003e - Detecting anomalies before filing, using R.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.firmai.org/documents/Aged%20Debtors/\" rel=\"nofollow\"\u003eUseful Life Prediction (FirmAI)\u003c/a\u003e - Predict the useful life of assets using sensor observations and feature engineering.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Niels-Peter/XBRL-AI\"\u003eAI Applied to XBRL\u003c/a\u003e - Standardized representation of XBRL into AI and Machine learning.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-accounting-analytics\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAnalytics\u003c/h4\u003e\u003ca id=\"user-content-analytics\" class=\"anchor\" aria-label=\"Permalink: Analytics\" href=\"#analytics\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mschermann/forensic_accounting\"\u003eForensic Accounting\u003c/a\u003e - Collection of case studies on forensic accounting using data analysis. On the lookout for more data to practise forensic accounting, \u003cem\u003eplease get in \u003ca href=\"https://github.com/mschermann/\"\u003etouch\u003c/a\u003e\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.firmai.org/documents/General%20Ledger/\" rel=\"nofollow\"\u003eGeneral Ledger (FirmAI)\u003c/a\u003e - Data processing over a general ledger as exported through an accounting system.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.firmai.org/documents/Bullet-Graph-Article/\" rel=\"nofollow\"\u003eBullet Graph (FirmAI)\u003c/a\u003e - Bullet graph visualisation helpful for tracking sales, commission and other performance.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.firmai.org/documents/Aged%20Debtors/\" rel=\"nofollow\"\u003eAged Debtors (FirmAI)\u003c/a\u003e - Example analysis to invetigate aged debtors.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/CharlesHoffmanCPA/charleshoffmanCPA.github.io\"\u003eAutomated FS XBRL\u003c/a\u003e - XML Language, however, possibly port analysis into Python.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-accounting-text\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTextual Analysis\u003c/h4\u003e\u003ca id=\"user-content-textual-analysis\" class=\"anchor\" aria-label=\"Permalink: Textual Analysis\" href=\"#textual-analysis\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/EricHe98/Financial-Statements-Text-Analysis\"\u003eFinancial Sentiment Analysis\u003c/a\u003e - Sentiment, distance and proportion analysis for trading signals.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TiesdeKok/Python_NLP_Tutorial/blob/master/NLP_Notebook.ipynb\"\u003eExtensive NLP\u003c/a\u003e - Comprehensive NLP techniques for accounting research.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-accounting-data\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eData, Parsing and APIs\u003c/h4\u003e\u003ca id=\"user-content-data-parsing-and-apis\" class=\"anchor\" aria-label=\"Permalink: Data, Parsing and APIs\" href=\"#data-parsing-and-apis\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/TiesdeKok/UW_Python_Camp/blob/master/Materials/Session_5/EDGAR_walkthrough.ipynb\"\u003eEDGAR\u003c/a\u003e - A walk-through in how to obtain EDGAR data.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gaulinmp/pyedgar\"\u003ePyEDGAR\u003c/a\u003e - A library for downloading, caching, and accessing EDGAR filings.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://social-metrics.org/sox/\" rel=\"nofollow\"\u003eIRS\u003c/a\u003e - Acessing and parsing IRS filings.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://raw.rutgers.edu/Corporate%20Financial%20Data.html\" rel=\"nofollow\"\u003eFinancial Corporate\u003c/a\u003e - Rutgers corporate financial datasets.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://raw.rutgers.edu/Non-Financial%20Corporate%20Data.html\" rel=\"nofollow\"\u003eNon-financial Corporate\u003c/a\u003e - Rutgers non-financial corporate dataset.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/danshorstein/python4cpas/blob/master/03_parsing_pdf_files/AR%20Aging%20-%20working.ipynb\"\u003ePDF Parsing\u003c/a\u003e - Extracting useful data from PDF documents.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/danshorstein/ficpa_article\"\u003ePDF Tabel to Excel\u003c/a\u003e - How to output an excel file from a PDF.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-accounting-ra\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eResearch And Articles\u003c/h4\u003e\u003ca id=\"user-content-research-and-articles\" class=\"anchor\" aria-label=\"Permalink: Research And Articles\" href=\"#research-and-articles\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"http://social-metrics.org/accountinganalytics/\" rel=\"nofollow\"\u003eUnderstanding Accounting Analytics\u003c/a\u003e - An article that tackles the importance of accounting analytics.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.vlfeat.org/\" rel=\"nofollow\"\u003eVLFeat\u003c/a\u003e - VLFeat is an open and portable library of computer vision algorithms, which has Matlab toolbox.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-accounting-web\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWebsites\u003c/h4\u003e\u003ca id=\"user-content-websites\" class=\"anchor\" aria-label=\"Permalink: Websites\" href=\"#websites\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"http://raw.rutgers.edu/\" rel=\"nofollow\"\u003eRutgers Raw\u003c/a\u003e - Good digital accounting research from Rutgers.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-accounting-course\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCourses\u003c/h4\u003e\u003ca id=\"user-content-courses\" class=\"anchor\" aria-label=\"Permalink: Courses\" href=\"#courses\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://www.youtube.com/playlist?list=PLauepKFT6DK8TaNaq_SqZW4LIDJhCkZe2\" rel=\"nofollow\"\u003eComputer Augmented Accounting\u003c/a\u003e - A video series from Rutgers University looking at the use of computation to improve accounting.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.youtube.com/playlist?list=PLauepKFT6DK8_Xun584UQPPsg1grYkWw0\" rel=\"nofollow\"\u003eAccounting in a Digital Era\u003c/a\u003e - Another series by Rutgers investigating the effects the digital age will have on accounting.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-agriculture\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAgriculture\u003c/h2\u003e\u003ca id=\"user-content-agriculture\" class=\"anchor\" aria-label=\"Permalink: Agriculture\" href=\"#agriculture\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-agriculture-econ\"\u003e\u003c/a\u003e\n\u003cstrong\u003eEconomics\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/deadskull7/Agricultural-Price-Prediction-and-Visualization-on-Android-App\"\u003ePrices\u003c/a\u003e - Agricultural price prediction.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Vipul115/Statistical-Time-Series-Analysis-on-Agricultural-Commodity-Prices\"\u003ePrices 2\u003c/a\u003e - Agricultural price prediction.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/DFS-UCU/UkrainianAgriculture\"\u003eYield\u003c/a\u003e - Agricultural analysis looking at crop yields in Ukraine.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/vicelab/slaer\"\u003eRecovery\u003c/a\u003e - Strategic land use for agriculture and ecosystem recovery\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gumballhead/mpr\"\u003eMPR\u003c/a\u003e - Mandatory Price Reporting data from the USDA's Agricultural Marketing Service.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-agriculture-dev\"\u003e\u003c/a\u003e\n\u003cstrong\u003eDevelopment\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/chrieke/InstanceSegmentation_Sentinel2\"\u003eSegmentation\u003c/a\u003e - Agricultural field parcel segmentation using satellite images.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jfzhang95/LSTM-water-table-depth-prediction\"\u003eWater Table\u003c/a\u003e - Predicting water table depth in agricultural areas.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/surajmall/Agriculture-Assistant/tree/master/models\"\u003eAssistant\u003c/a\u003e - Notebooks from agricultural assistant.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/tecoevo/agriculture\"\u003eEco-evolutionary\u003c/a\u003e - Eco-evolutionary dynamics.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gauravmunjal13/Agriculture\"\u003eDiseases\u003c/a\u003e - Identification of crop diseases and pests using Deep Learning framework from the images.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/divyam3897/agriculture\"\u003eIrrigation and Pest Prediction\u003c/a\u003e - Analyse irrigation and predict pest likelihood.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-bankfin\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBanking \u0026amp; Insurance\u003c/h2\u003e\u003ca id=\"user-content-banking--insurance\" class=\"anchor\" aria-label=\"Permalink: Banking \u0026amp; Insurance\" href=\"#banking--insurance\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-bankfin-cv\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eConsumer Finance\u003c/h4\u003e\u003ca id=\"user-content-consumer-finance\" class=\"anchor\" aria-label=\"Permalink: Consumer Finance\" href=\"#consumer-finance\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Paresh3189/Bankruptcy-Prediction-Growth-Modelling\"\u003eLoan Acceptance\u003c/a\u003e - Classification and time-series analysis for loan acceptance.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Featuretools/predict-loan-repayment\"\u003ePredict Loan Repayment\u003c/a\u003e - Predict whether a loan will be repaid using automated feature engineering.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/RealRadOne/Gyani-The-Loan-Eligibility-Predictor\"\u003eLoan Eligibility Ranking\u003c/a\u003e - System to help the banks check if a customer is eligible for a given loan.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.firmai.org/documents/Aggregator/#each-time-step-takes-30-seconds\" rel=\"nofollow\"\u003eHome Credit Default (FirmAI)\u003c/a\u003e - Predict home credit default.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/abuchowdhury/Mortgage_Bank_Loan_Analtsics/blob/master/Mortgage%20Bank%20Loan%20Analytics.ipynb\"\u003eMortgage Analytics\u003c/a\u003e - Extensive mortgage loan analytics.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/IBM-Cloud-DevFest-2018/Data-Science-for-Banking/blob/master/02-CreditCardApprovalModel/CreditCardApprovalModel.ipynb\"\u003eCredit Approval\u003c/a\u003e - A system for credit card approval.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Brett777/Predict-Risk\"\u003eLoan Risk\u003c/a\u003e - Predictive model to help to reduce charge-offs and losses of loans.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.firmai.org/documents/Amortization%20Schedule/\" rel=\"nofollow\"\u003eAmortisation Schedule (FirmAI)\u003c/a\u003e - Simple amortisation schedule in python for personal use.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-bankfin-mo\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eManagement and Operation\u003c/h4\u003e\u003ca id=\"user-content-management-and-operation\" class=\"anchor\" aria-label=\"Permalink: Management and Operation\" href=\"#management-and-operation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/am-aditya/Artificial-Intelligence-for-Banking/blob/master/03_ipy_notebooks/clv_prediction.ipynb\"\u003eCredit Card\u003c/a\u003e - Estimate the CLV of credit card customers.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/am-aditya/Artificial-Intelligence-for-Banking/blob/master/01_code/01_02_clv_survival/Survival_Analysis.py\"\u003eSurvival Analysis\u003c/a\u003e - Perform a survival analysis of customers.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/am-aditya/Artificial-Intelligence-for-Banking/blob/master/01_code/01_02_clv_survival/Customer_NextTransaction_Prediction.py\"\u003eNext Transaction\u003c/a\u003e - Deep learning model to predict the transaction amount and days to next transaction.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/am-aditya/Artificial-Intelligence-for-Banking/blob/master/01_code/01_02_clv_survival/Customer_NextTransaction_Prediction.py\"\u003eCredit Card Churn\u003c/a\u003e - Predicting credit card customer churn.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sekhansen/mpc_minutes_demo/blob/master/information_retrieval.ipynb\"\u003eBank of England Minutes\u003c/a\u003e - Textual analysis over bank minutes.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/kaumaron/Data_Science/tree/master/CEO_Compensation\"\u003eCEO\u003c/a\u003e - Analysis of CEO compensation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-bankfin-value\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eValuation\u003c/h4\u003e\u003ca id=\"user-content-valuation\" class=\"anchor\" aria-label=\"Permalink: Valuation\" href=\"#valuation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/eswar3/Zillow-prediction-models\"\u003eZillow Prediction\u003c/a\u003e - Zillow valuation prediction as performed on Kaggle.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/denadai2/real-estate-neighborhood-prediction\"\u003eReal Estate\u003c/a\u003e - Predicting real estate prices from the urban environment.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/exercises/P1-UsedVehiclePricePrediction.ipynb\" rel=\"nofollow\"\u003eUsed Car\u003c/a\u003e - Used vehicle price prediction.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-bankfin-fraud\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFraud\u003c/h4\u003e\u003ca id=\"user-content-fraud\" class=\"anchor\" aria-label=\"Permalink: Fraud\" href=\"#fraud\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/KSpiliop/Fraud_Detection\"\u003eXGBoost\u003c/a\u003e - Fraud Detection by tuning XGBoost hyper-parameters with Simulated Annealing\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/longtng/frauddetectionproject/blob/master/A%20Consideration%20Point%20of%20%20Fraud%20Detection%20in%20Bank%20Loans%20Project%20Code.ipynb\"\u003eFraud Detection Loan in R\u003c/a\u003e - Fraud detection in bank loans.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Michaels72/AML-Due-Diligence/blob/master/AML_Finance_DD.ipynb\"\u003eAML Finance Due Diligence\u003c/a\u003e - Search news articles to do finance AML DD.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/am-aditya/Artificial-Intelligence-for-Banking/blob/master/03_ipy_notebooks/fraud_detection.ipynb\"\u003eCredit Card Fraud\u003c/a\u003e - Detecting credit card fraud.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-bankfin-ir\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInsurance and Risk\u003c/h4\u003e\u003ca id=\"user-content-insurance-and-risk\" class=\"anchor\" aria-label=\"Permalink: Insurance and Risk\" href=\"#insurance-and-risk\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/neokt/car-damage-detective\"\u003eCar Damage Detective\u003c/a\u003e - Assessing car damage with convolution neural networks for a personal auto \u003cem\u003eclaims.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/roshank1605A04/Insurance-Claim-Prediction/blob/master/InsuranceClaim.ipynb\"\u003eMedical Insurance Claims\u003c/a\u003e - Predicting medical insurance claims.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/slegroux/claimdenial/blob/master/Claim%20Denial.ipynb\"\u003eClaim Denial\u003c/a\u003e - Predicting insurance claim denial\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rshea3/alpha-insurance\"\u003eClaim Fraud\u003c/a\u003e - Predictive models to determine which automobile claims are fraudulent.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/dchannah/fraudhacker\"\u003eClaims Anomalies\u003c/a\u003e - Anomaly detection system for medical insurance claims data.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/JSchelldorfer/ActuarialDataScience\"\u003eActuarial Sciences (R)\u003c/a\u003e - A range of actuarial tools in R.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Shomona/Bank-Failure-Prediction/blob/master/Bank.ipynb\"\u003eBank Failure\u003c/a\u003e - Predicting bank failure.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/andrey-lukyanov/Risk-Management\"\u003eRisk Management\u003c/a\u003e - Finance risk engagement course resources.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hamaadshah/market_risk_gan_keras\"\u003eVaR GaN\u003c/a\u003e - Estimate Value-at-Risk for market risk management using Keras and TensorFlow.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SaiBiswas/Bank-Grievance-Compliance-Management/blob/master/The%20Main%20File.ipynb\"\u003eCompliance\u003c/a\u003e - Bank Grievance Compliance Management.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/apbecker/Systemic_Risk/blob/master/Generalized.ipynb\"\u003eStress Testing\u003c/a\u003e - ECB stress testing.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/kaitai/stress-testing-with-jupyter/blob/master/Playing%20with%20financial%20data%20and%20Python%203.ipynb\"\u003eStress Testing Techniques\u003c/a\u003e - A notebook with various stress testing exercises.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/arcadynovosyolov/reverse_stress_testing/blob/master/reverse_stress_testing.ipynb\"\u003eReverse Stress Test\u003c/a\u003e - Given a portfolio and a predefined loss size, determine which factors stress (scenarios) would lead to that loss\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/VankatPetr/BoE_stress_test/blob/master/BoE_stress_test_5Y_cummulative_imparment_charge.ipynb\"\u003eBoE stress test\u003c/a\u003e- Stress test results and plotting.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hkacmaz/Bankin_Recovery/blob/master/Banking_Recovery.ipynb\"\u003eRecovery\u003c/a\u003e - Recovery of money owed.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mick-zhang/Quality-Control-for-Banking-using-LDA-and-LDA-Mallet\"\u003eQuality Control\u003c/a\u003e - Quality control for banking using LDA\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-bankfin-ph\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePhysical\u003c/h4\u003e\u003ca id=\"user-content-physical\" class=\"anchor\" aria-label=\"Permalink: Physical\" href=\"#physical\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/apoorv-goel/Bank-Note-Authentication-Using-DNN-Tensorflow-Classifier-and-RandomForest\"\u003eBank Note Fraud Detection\u003c/a\u003e - Bank Note Authentication Using DNN Tensorflow Classifier and RandomForest.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ShreyaGupta08/InfosysHack\"\u003eATM Surveillance\u003c/a\u003e - ATM Surveillance in banks use case.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-biotech\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBiotechnological \u0026amp; Life Sciences\u003c/h2\u003e\u003ca id=\"user-content-biotechnological--life-sciences\" class=\"anchor\" aria-label=\"Permalink: Biotechnological \u0026amp; Life Sciences\" href=\"#biotechnological--life-sciences\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-biotech-general\"\u003e\u003c/a\u003e\n\u003cstrong\u003eGeneral\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/burkesquires/python_biologist\"\u003eProgramming\u003c/a\u003e - Python Programming for Biologists\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://colab.research.google.com/drive/17E4h5aAOioh5DiTo7MZg4hpL6Z_0FyWr\" rel=\"nofollow\"\u003eIntroduction DL\u003c/a\u003e - A Primer on Deep Learning in Genomics\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/talmo/leap\"\u003ePose\u003c/a\u003e - Estimating animal poses using DL.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/greenelab/SPRINT_gan\"\u003ePrivacy\u003c/a\u003e - Privacy preserving NNs for clinical data sharing.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004845\" rel=\"nofollow\"\u003ePopulation Genetics\u003c/a\u003e - DL for population genetic inference.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ricket-sjtu/bioinformatics\"\u003eBioinformatics Course\u003c/a\u003e - Course materials for Computational \u003cem\u003eBiology\u003c/em\u003eand Bioinformatics\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/waldronlab/AppStatBio\"\u003eApplied Stats\u003c/a\u003e - Applied Statistics for High-Throughput \u003cem\u003eBiology\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mingzhangyang/Mybiotools\"\u003eScripts\u003c/a\u003e - Python scripts for biologists.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mitmedialab/Evolutron\"\u003eMolecular NN\u003c/a\u003e - A mini-framework to build and train neural networks for molecular \u003cem\u003ebiology\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hallba/WritingSimulators\"\u003eSystems Biology Simulations\u003c/a\u003e - Systems \u003cem\u003ebiology\u003c/em\u003e practical on writing simulators with F# and Z3\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jrieke/lstm-biology\"\u003eCell Movement\u003c/a\u003e - LSTM to predict biological cell movement.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/deepchem/deepchem\"\u003eDeepchem\u003c/a\u003e - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-biotech-seq\"\u003e\u003c/a\u003e\n\u003cstrong\u003eSequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ehsanasgari/Deep-Proteomics\"\u003eDNA, RNA and Protein Sequencing\u003c/a\u003e - Anew representation for biological sequences using DL.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/budach/pysster\"\u003eCNN Sequencing\u003c/a\u003e - A toolbox for learning motifs from DNA/RNA sequence data using convolutional neural networks\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hussius/deeplearning-biology\"\u003eNLP Sequencing\u003c/a\u003e - Language transfer learning model for genomics\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-biotech-chem\"\u003e\u003c/a\u003e\n\u003cstrong\u003eChemoinformatics and drug discovery\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/HIPS/neural-fingerprint\"\u003eNovel Molecules\u003c/a\u003e - A convolutional net that can learn features.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/aspuru-guzik-group/chemical_vae\"\u003eAutomating Chemical Design\u003c/a\u003e - Generate new molecules for efficient exploration.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gablg1/ORGAN\"\u003eGAN drug Discovery\u003c/a\u003e - A method that combines generative models with reinforcement learning.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/MarcusOlivecrona/REINVENT\"\u003eRL\u003c/a\u003e - generating compounds predicted to be active against a biological target.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/deepchem/deepchem\"\u003eOne-shot learning\u003c/a\u003e - Python library that aims to make the use of machine-learning in drug discovery straightforward and convenient.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-biotech-gene\"\u003e\u003c/a\u003e\n\u003cstrong\u003eGenomics\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ucsd-ccbb/jupyter-genomics\"\u003eJupyter Genomics\u003c/a\u003e - Collection of computation biology and bioinformatics notebooks.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/google/deepvariant\"\u003eVariant calling\u003c/a\u003e - Correctly identify variations from the reference genome in an individual's DNA.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mila-iqia/gene-graph-conv\"\u003eGene Expression Graphs\u003c/a\u003e - Using convolutions on an image.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/greenelab/adage\"\u003eAutoencoding Expression\u003c/a\u003e - Extracting relevant patterns from large sets of gene expression data\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/uci-cbcl/D-GEX\"\u003eGene Expression Inference\u003c/a\u003e - Predict the expression of specified target genes from a panel of about 1,000 pre-selected “landmark genes”.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/widdowquinn/Teaching-EMBL-Plant-Path-Genomics\"\u003ePlant Genomics\u003c/a\u003e - Presentation and example material for \u003cem\u003ePlant\u003c/em\u003e and Pathogen Genomics\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-biotech-life\"\u003e\u003c/a\u003e\n\u003cstrong\u003eLife-sciences\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/viritaromero/Plant-diseases-classifier\"\u003ePlants Disease\u003c/a\u003e - App that detects diseases in \u003cem\u003eplants\u003c/em\u003e using a deep learning model.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AayushG159/Plant-Leaf-Identification\"\u003eLeaf Identification\u003c/a\u003e - Identification of \u003cem\u003eplants\u003c/em\u003e through \u003cem\u003eplant\u003c/em\u003e leaves on the basis of their shape, color and texture.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/openalea/eartrack\"\u003eCrop Analysis\u003c/a\u003e - An imaging library to detect and track future position of ears on maize \u003cem\u003eplants\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mfsatya/PlantSeedlings-Classification\"\u003eSeedlings\u003c/a\u003e - \u003cem\u003ePlant\u003c/em\u003e Seedlings Classification from kaggle competition\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Planteome/ontology-of-plant-stress\"\u003ePlant Stress\u003c/a\u003e - An ontology containing plant stresses; biotic and abiotic.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sacul-git/hierarpy\"\u003eAnimal Hierarchy\u003c/a\u003e - Package for calculating \u003cem\u003eanimal\u003c/em\u003e dominance hierarchies.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/A7med01/Deep-learning-for-Animal-Identification\"\u003eAnimal Identification\u003c/a\u003e - Deep learning for animal identification.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NomaanAhmed/BigData_AnimalSpeciesAnalysis\"\u003eSpecies\u003c/a\u003e - Big Data analysis of different species of \u003cem\u003eanimals\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/timsainb/AVGN\"\u003eAnimal Vocalisations\u003c/a\u003e - A generative network for animal vocalizations\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hardmaru/estool\"\u003eEvolutionary\u003c/a\u003e - Evolution Strategies Tool\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/OGGM/oggm-edu\"\u003eGlaciers\u003c/a\u003e - Educational material about glaciers.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-construction\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eConstruction \u0026amp; Engineering\u003c/h2\u003e\u003ca id=\"user-content-construction--engineering\" class=\"anchor\" aria-label=\"Permalink: Construction \u0026amp; Engineering\" href=\"#construction--engineering\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-construction-con\"\u003e\u003c/a\u003e\n\u003cstrong\u003eConstruction\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/carolineh101/deep-learning-architecture\"\u003eDL Architecture\u003c/a\u003e - Deep learning classifier and image generator for building architecture.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/damontallen/Construction-materials\"\u003eConstruction Materials\u003c/a\u003e - A course on construction materials.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/dariusmehri/Social-Network-Bad-Actor-Risk-Tool\"\u003eBad Actor Risk Model\u003c/a\u003e - Risk model to improve construction related building safety\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/dariusmehri/Tracking-Inspectors-with-Euclidean-Distance-Algorithm\"\u003eInspectors\u003c/a\u003e - Determine the assigned inspections.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/dariusmehri/Social-Network-Analysis-to-Expose-Corruption\"\u003eCorrupt Social Interactions\u003c/a\u003e - Uncover potential corrupt social interactions between an industry member and the staff at the DOB\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/dariusmehri/Risk-Screening-Tool-to-Predict-Accidents-at-Construction-Sites\"\u003eRisk Construction\u003c/a\u003e - Identify high risk construction.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/dariusmehri/Algorithm-for-Finding-Buildings-with-Facade-Risk\"\u003eFacade Risk\u003c/a\u003e - A risk model to predict unsafe facades.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/dariusmehri/Predicting-Staff-Levels-for-Front-line-Workers\"\u003eStaff Levels\u003c/a\u003e - Predicting staff levels for front line workers.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/dariusmehri/Topic-Modeling-and-Analysis-of-Building-Related-Injuries\"\u003eInjuries\u003c/a\u003e - Building related injuries topic modelling.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/dariusmehri/Predictive-Analysis-of-Building-Violations\"\u003eBuilding Violations\u003c/a\u003e - Predictive analysis of building violations.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/dariusmehri/Inspection-Productivity-Analysis-and-Visualization-with-Tableau\"\u003eProductivity\u003c/a\u003e - Productivity analysis and inspection with Tableau.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-construction-eng\"\u003e\u003c/a\u003e\n\u003cstrong\u003eEngineering:\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ritchie46/anaStruct\"\u003eStructural Analysis\u003c/a\u003e - 2D Structural Analysis in Python.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/buddyd16/Structural-Engineering\"\u003eStructural Engineering\u003c/a\u003e - Structural engineering modules.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/JorgeDeLosSantos/nusa\"\u003eNusa\u003c/a\u003e - Structural analysis using the finite element method.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/BrianChevalier/StructPy\"\u003eStructPy\u003c/a\u003e - Structural Analysis Library for Python based on the direct stiffness method\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/albiboni/AileronSimulation\"\u003eAileron\u003c/a\u003e - Structural analysis of the aileron of a Boeing 737\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/vibrationtoolbox/vibration_toolbox\"\u003eVibration\u003c/a\u003e - Educational vibration programs.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ebrahimraeyat/Civil\"\u003eCivil\u003c/a\u003e - Collection of civil engineering tools in FreeCAD\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/manuvarkey/GEstimator\"\u003eGEstimator\u003c/a\u003e - Simple civil estimation software\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Gunnstein/fatpack\"\u003eFatpack\u003c/a\u003e - Functions and classes for fatigue analysis of data series.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/yajnab/pySteel\"\u003ePysteel\u003c/a\u003e - Automated design of different steel structure\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/davidsteinar/structural-uncertainty\"\u003eStructural Uncertainty\u003c/a\u003e - Quantifying structural uncertainty with deep learning.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jellespijker/pymech\"\u003ePymech\u003c/a\u003e - A Python module for mechanical engineers\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AlvaroMenduina/Jupyter_Notebooks/tree/master/Introduction_Aerospace_Engineering\"\u003eAerospace Engineering\u003c/a\u003e - Astrodynamics and Statistics\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/psi4/psi4numpy\"\u003eInteractive Quantum Chemistry\u003c/a\u003e - Combining Psi4 and Numpy for education and development.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/CAChemE/learn\"\u003eChemical and Process Engineering\u003c/a\u003e - Various resources.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/iurisegtovich/PyTherm-applied-thermodynamics\"\u003ePyTherm\u003c/a\u003e - Applied Thermodynamics\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/kshitizkhanal7/Aerogami\"\u003eAerogami\u003c/a\u003e - Aerodynamics using planes.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/geoscixyz/em-apps\"\u003eElectro geophysics\u003c/a\u003e - Interactive applications for electromagnetics in geophysics\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mdeff/pygsp_tutorial_graphsip\"\u003eGraph Signal\u003c/a\u003e - Graph signal processing tutorial.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/DocVaughan/MCHE485---Mechanical-Vibrations\"\u003eMechanical Vibrations\u003c/a\u003e - Mechanical Vibrations at the Univsersity of Louisiana.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/OpenChemE/CHBE356\"\u003eProcess Dynamics\u003c/a\u003e - Process Dynamics and Control\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rdbraatz/data-driven-prediction-of-battery-cycle-life-before-capacity-degradation\"\u003eBattery Life Cycle\u003c/a\u003e - Data driven prediction of batter life cycle.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/DTUWindEnergy/Python4WindEnergy\"\u003eWind Energy\u003c/a\u003e - Python for wind energy\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/openeemeter/eemeter/blob/master/scripts/tutorial.ipynb\"\u003eEnergy Use\u003c/a\u003e - Standard methods for calculating normalized metered energy consumption\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/HitarthiShah/Radiation-Data-Analysis\"\u003eNuclear Radiation\u003c/a\u003e - How people are affected by radiations emitted by nuclear power plants\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-construction-mat\"\u003e\u003c/a\u003e\n\u003cstrong\u003eMaterial Science\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/materialsproject/pymatgen/\"\u003ePython Materials Genomics\u003c/a\u003e - Robust material analysis code used in a well-established project.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/dchannah/materials_mining\"\u003eMaterials Mining\u003c/a\u003e - Scripts for simulations and analysis of materials.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/materialsproject/emmet\"\u003eEmmet\u003c/a\u003e - Build databases of material properties.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/materialsvirtuallab/megnet\"\u003eMegnet\u003c/a\u003e - Graph networks as a ML framework for Molecules and Crystals\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hackingmaterials/atomate\"\u003eAtomate\u003c/a\u003e - Pre-built workflows for computational material science.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Mehranov/UnderstandingAndPredictingPropertyMaintenanceFines/blob/master/Assignment4_complete.ipynb\"\u003eBylaws Compliance\u003c/a\u003e - Predicting property fines.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sierraporta/asphalt_binder\"\u003eAsphalt Binder\u003c/a\u003e - Construction materials, free energy and chemical composition of asphalt binder.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hbutsuak95/Quality-Optimization-of-Steel\"\u003eSteel\u003c/a\u003e - Optimisation of steel.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/tilde-lab/awesome-materials-informatics\"\u003eAwesome Materials Informatics\u003c/a\u003e - Curated list of known efforts in materials informatics.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-economics\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eEconomics\u003c/h2\u003e\u003ca id=\"user-content-economics\" class=\"anchor\" aria-label=\"Permalink: Economics\" href=\"#economics\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-economics-general\"\u003e\u003c/a\u003e\n\u003cstrong\u003eGeneral\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/tradingeconomics/tradingeconomics\"\u003eTrading Economics API\u003c/a\u003e - Information for 196 countries.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jhconning/Dev-II/tree/master/notebooks\"\u003eDevelopment Economics\u003c/a\u003e - Development microeconomics are written mostly as interactive jupyter notebooks\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/lnsongxf/Applied_Computational_Economics_and_Finance/blob/master/Chapter05.ipynb\"\u003eApplied Econ \u0026amp; Fin\u003c/a\u003e - Applied Computational Economics and Finance\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jlperla/ECON407_2018\"\u003eMacroeconomics\u003c/a\u003e - Topics in macroeconomics with notebook examples.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-economics-ml\"\u003e\u003c/a\u003e\n\u003cstrong\u003eMachine Learning\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/microsoft/EconML\"\u003eEconML\u003c/a\u003e - Automated Learning and Intelligence for Causation and \u003cem\u003eEconomics.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/saisrivatsan/deep-opt-auctions\"\u003eAuctions\u003c/a\u003e - Optimal auctions using deep learning.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-economics-comp\"\u003e\u003c/a\u003e\n\u003cstrong\u003eComputational\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jstac/quantecon_nyu_2016\"\u003eQuant Econ\u003c/a\u003e - Quantitative economics course by NYU\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/zhentaoshi/econ5170\"\u003eComputational\u003c/a\u003e - Computational methods in economics.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/QuantEcon/columbia_mini_course\"\u003eComputational 2\u003c/a\u003e - Small course in computational economics.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jstac/econometrics/tree/master/notebooks\"\u003eEconometric Theory\u003c/a\u003e - Notebooks of A Primer on Econometric theory.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-education\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eEducation \u0026amp; Research\u003c/h2\u003e\u003ca id=\"user-content-education--research\" class=\"anchor\" aria-label=\"Permalink: Education \u0026amp; Research\" href=\"#education--research\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-education-student\"\u003e\u003c/a\u003e\n\u003cstrong\u003eStudent\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/roshank1605A04/Education-Process-Mining\"\u003eStudent Performance\u003c/a\u003e - Mining student performance using machine learning.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/janzaib-masood/Educational-Data-Mining\"\u003eStudent Performance 2\u003c/a\u003e - Student exam performance.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/RohithYogi/Student-Performance-Prediction\"\u003eStudent Performance 3\u003c/a\u003e - Student achievement in secondary education.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/roshank1605A04/Students-Performance-Analytics\"\u003eStudent Performance 4\u003c/a\u003e - Students Performance Evaluation using Feature Engineering\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/eloyekunle/student_intervention/blob/master/student_intervention.ipynb\"\u003eStudent Intervention\u003c/a\u003e - Building a student intervention system.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/arrahman17/Learning-Analytics-Project-\"\u003eStudent Enrolment\u003c/a\u003e - Student enrolment and performance analysis.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/janzaib-masood/Educational-Data-Mining\"\u003eAcademic Performance\u003c/a\u003e - Explore the demographic and family features that have an impact a student's academic performance.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/kaumaron/Data_Science/tree/master/Grade_Analysis\"\u003eGrade Analysis\u003c/a\u003e - Student achievement analysis.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-education-school\"\u003e\u003c/a\u003e\n\u003cstrong\u003eSchool\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/nprapps/school-choice\"\u003eSchool Choice\u003c/a\u003e - Data analysis for education's school choice.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/tullyvelte/SchoolPerformanceDataAnalysis\"\u003eSchool Budgets and Priorities\u003c/a\u003e - Helping the school board and mayor make strategic decisions regarding future school budgets and priorities\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bradleyrobinson/School-Performance\"\u003eSchool Performance\u003c/a\u003e - Data analysis practice using data from data.utah.gov on school performance.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/vtyeh/pandas-challenge\"\u003eSchool Performance 2\u003c/a\u003e - Using pandas to analyze school and student performance within a district\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/benattix/philly-schools\"\u003eSchool Performance 3\u003c/a\u003e - Philadelphia School Performance\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/adrianakopf/NJPublicSchools\"\u003eSchool Performance 4\u003c/a\u003e - NJ School Performance\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/whugue/school-closure\"\u003eSchool Closure\u003c/a\u003e - Identify schools at risk for closure by performance and other characteristics.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/datacamp/course-resources-ml-with-experts-budgets/blob/master/notebooks/1.0-full-model.ipynb\"\u003eSchool Budgets\u003c/a\u003e - Tools and techniques for school budgeting.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/nymarya/school-budgets-for-education/tree/master/notebooks\"\u003eSchool Budgets\u003c/a\u003e - Same as a above, datacamp.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/JonathanREB/Budget_SchoolsAnalysis/blob/master/PyCitySchools_starter.ipynb\"\u003ePyCity\u003c/a\u003e - School analysis.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/1davegalloway/SchoolDistrictAnalysis\"\u003ePyCity 2\u003c/a\u003e - School budget vs school results.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jinsonfernandez/NLP_School-Budget-Project\"\u003eBudget NLP\u003c/a\u003e - NLP classification for budget resources.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/DivyaMadhu/School-Budget-Prediction\"\u003eBudget NLP 2\u003c/a\u003e - Further classification exercise.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sushant2811/SchoolBudgetData/blob/master/SchoolBudgetData.ipynb\"\u003eBudget NLP 3\u003c/a\u003e - Budget classification.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/kaumaron/Data_Science/tree/master/Education\"\u003eSurvey Analysis\u003c/a\u003e - Education survey analysis.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-emergency\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eEmergency \u0026amp; Police\u003c/h2\u003e\u003ca id=\"user-content-emergency--police\" class=\"anchor\" aria-label=\"Permalink: Emergency \u0026amp; Police\" href=\"#emergency--police\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-emergency-prevent\"\u003e\u003c/a\u003e\n\u003cstrong\u003ePreventative and Reactive\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/aeronetlab/emergency-mapping\"\u003eEmergency Mapping\u003c/a\u003e - Detection of destroyed houses in California\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/roshetty/Supporting-Emergency-Room-Decision-Making-with-Relevant-Scientific-Literature\"\u003eEmergency Room\u003c/a\u003e - Supporting em\u003cem\u003eergency r\u003c/em\u003eoom decision making\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mesgarpour/T-CARER\"\u003eEmergency Readmission\u003c/a\u003e - Adjusted Risk of \u003cem\u003eEmergency\u003c/em\u003e Readmission.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/LeadingIndiaAI/Forest-Fire-Detection-through-UAV-imagery-using-CNNs\"\u003eForest Fire\u003c/a\u003e - Forest fire detection through UAV imagery using CNNs\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sky-t/hack-or-emergency-response\"\u003eEmergency Response\u003c/a\u003e - Emergency response analysis.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bayesimpact/bayeshack-transportation-ems\"\u003eEmergency Transportation\u003c/a\u003e - Transportation prompt on \u003cem\u003eemergency\u003c/em\u003e services\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jamesypeng/Smarter-Emergency-Dispatch\"\u003eEmergency Dispatch\u003c/a\u003e - Reducing response times with predictive modeling, optimization, and automation\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/analystiu/LICT-Project-Emergency-911-Calls\"\u003eEmergency Calls\u003c/a\u003e - Emergency calls analysis project.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/tanoybhattacharya/911-Data-Analysis\"\u003eCalls Data Analysis\u003c/a\u003e - 911 data analysis.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/amunategui/Leak-At-Chemical-Factory-RL\"\u003eEmergency Response\u003c/a\u003e - Chemical factory RL.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-emergency-crime\"\u003e\u003c/a\u003e\n\u003cstrong\u003eCrime\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/datadesk/lapd-crime-classification-analysis\"\u003eCrime Classification\u003c/a\u003e - Times analysis of serious assaults misclassified by LAPD.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/chicago-justice-project/article-tagging\"\u003eArticle Tagging\u003c/a\u003e - Natural Language Processing of Chicago news article\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/chrisPiemonte/crime-analysis\"\u003eCrime Analysis\u003c/a\u003e - Association Rule Mining from Spatial Data for \u003cem\u003eCrime\u003c/em\u003e Analysis\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/search?o=desc\u0026amp;q=crime+language%3A%22Jupyter+Notebook%22+NOT+%22taxi%22+NOT+%22baseline%22\u0026amp;s=stars\u0026amp;type=Repositories\"\u003eChicago Crimes\u003c/a\u003e - Exploring public Chicago \u003cem\u003ecrimes\u003c/em\u003e data set in Python\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pedrohserrano/graph-analytics-nederlands\"\u003eGraph Analytics\u003c/a\u003e - The Hague Crimes.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/vikram-bhati/PAASBAAN-crime-prediction\"\u003eCrime Prediction\u003c/a\u003e - \u003cem\u003eCrime\u003c/em\u003e classification, analysis \u0026amp; prediction in Indore city.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/tina31726/Crime-Prediction\"\u003eCrime Prediction\u003c/a\u003e - Developed predictive models for \u003cem\u003ecrime\u003c/em\u003e rate.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/felzek/Crime-Review-Data-Analysis\"\u003eCrime Review\u003c/a\u003e - Crime review data analysis.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/benjaminsingleton/crime-trends\"\u003eCrime Trends\u003c/a\u003e - The \u003cem\u003eCrime\u003c/em\u003e Trends Analysis Tool analyses \u003cem\u003ecrime\u003c/em\u003e trends and surfaces problematic \u003cem\u003ecrime\u003c/em\u003e conditions\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/cmenguy/crime-analytics\"\u003eCrime Analytics\u003c/a\u003e - Analysis of \u003cem\u003ecrime\u003c/em\u003e data in Seattle and San Francisco.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-emergency-ambulance\"\u003e\u003c/a\u003e\n\u003cstrong\u003eAmbulance:\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/kaiareyes/ambulance\"\u003eAmbulance Analysis\u003c/a\u003e - An investigation of Local Government Area ambulance time variation in Victoria.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ankitkariryaa/ambulanceSiteLocation\"\u003eSite Location\u003c/a\u003e - Ambulance site locations.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/DimaStoyanov/Ambulance-Dispatching\"\u003eDispatching\u003c/a\u003e - Applying game theory and discrete event simulation to find optimal solution for ambulance dispatching\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/scngo/SD-ambulance-allocation\"\u003eAmbulance Allocation\u003c/a\u003e - Time series analysis of ambulance dispatches in the City of San Diego.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/nonsignificantp/ambulance-response-time\"\u003eResponse Time\u003c/a\u003e - An analysis on the improvements of ambulance response time.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/aditink/EMSRouting\"\u003eOptimal Routing\u003c/a\u003e - Project to find optimal routing of ambulances in Ithaca.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ArpitVora/Maryland_Crash\"\u003eCrash Analysis\u003c/a\u003e - Predicting the probability of accidents on a given segment on a given time.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-emergency-disaster\"\u003e\u003c/a\u003e\n\u003cstrong\u003eDisaster Management\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Polichinel/Master_Thesis\"\u003eConflict Prediction\u003c/a\u003e - Notebooks on conflict prediction.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Polichinel/Master_Thesis\"\u003eBurglary Prediction\u003c/a\u003e - Spatio-Temporal Modelling for burglary prediction.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ab-bh/Disease-Outbreak-Prediction/blob/master/Disease%20Outbreak%20Prediction.ipynb\"\u003ePredicting Disease Outbreak\u003c/a\u003e - Machine Learning implementation based on multiple classifier algorithm implementations.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/leportella/federal-road-accidents\"\u003eRoad accident prediction\u003c/a\u003e - Prediction on type of victims on federal road accidents in Brazil.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rajaswa/Disaster-Management-\"\u003eText Mining\u003c/a\u003e - Disaster Management using Text mining.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/paultopia/concrete_NLP_tutorial/blob/master/NLP_notebook.ipynb\"\u003eTwitter and disasters\u003c/a\u003e - Try to correctly predict whether tweets that are about disasters.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/arijitsaha/FloodRisk\"\u003eFlood Risk\u003c/a\u003e - Impact of catastrophic flood events.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Senkichi/The_Catastrophe_Coefficient\"\u003eFire Prediction\u003c/a\u003e - We used 4 different algorithms to predict the likelihood of future fires.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-finance\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFinance\u003c/h2\u003e\u003ca id=\"user-content-finance\" class=\"anchor\" aria-label=\"Permalink: Finance\" href=\"#finance\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-finance-trading\"\u003e\u003c/a\u003e\n\u003cstrong\u003eTrading and Investment\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFor \u003cstrong\u003emore\u003c/strong\u003e see \u003ca href=\"https://github.com/firmai/financial-machine-learning\"\u003efinancial-machine-learning\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eFor \u003cstrong\u003easset management\u003c/strong\u003e see \u003ca href=\"https://github.com/firmai/machine-learning-asset-management\"\u003efinancial-machine-learning\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/DLColumbia/DL_forFinance\"\u003eDeep Portfolio\u003c/a\u003e - Deep learning for finance Predict volume of bonds.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/borisbanushev/stockpredictionai/blob/master/readme2.md\"\u003eAI Trading\u003c/a\u003e - Modern AI trading techniques.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ishank011/gs-quantify-bond-prediction\"\u003eCorporate Bonds\u003c/a\u003e - Predicting the buying and selling volume of the corporate bonds.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/chenbowen184/Computational_Finance\"\u003eSimulation\u003c/a\u003e - Investigating simulations as part of computational finance.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SeanMcOwen/FinanceAndPython.com-ClusteringIndustries\"\u003eIndustry Clustering\u003c/a\u003e - Project to cluster industries according to financial attributes.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/MiyainNYC/Financial-Modeling/tree/master/codes\"\u003eFinancial Modeling\u003c/a\u003e - HFT trading and implied volatility modeling.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://inseaddataanalytics.github.io/INSEADAnalytics/ExerciseSet2.html\" rel=\"nofollow\"\u003eTrend Following\u003c/a\u003e - A futures trend following portfolio investment strategy.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/MAydogdu/TextualAnalysis\"\u003eFinancial Statement Sentiment\u003c/a\u003e - Extracting sentiment from financial statements using neural networks.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/chenbowen184/Data_Science_in_Applied_Corporate_Finance\"\u003eApplied Corporate Finance\u003c/a\u003e - Studies the empirical behaviors in stock market.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sarachmax/MarketCrashes_Prediction/blob/master/LPPL_Comparasion.ipynb\"\u003eMarket Crash Prediction\u003c/a\u003e - Predicting market crashes using an LPPL model.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/chenbowen184/Research_Documents_Curation_with_NLP\"\u003eNLP Finance Papers\u003c/a\u003e - Curating quantitative finance papers using machine learning.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/imhgchoi/Corr_Prediction_ARIMA_LSTM_Hybrid\"\u003eARIMA-LTSM Hybrid\u003c/a\u003e - Hybrid model to predict future price correlation coefficients of two assets\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SeanMcOwen/FinanceAndPython.com-Investments\"\u003eBasic Investments\u003c/a\u003e - Basic investment tools in python.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SeanMcOwen/FinanceAndPython.com-Derivatives\"\u003eBasic Derivatives\u003c/a\u003e - Basic forward contracts and hedging.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SeanMcOwen/FinanceAndPython.com-BasicFinance\"\u003eBasic Finance\u003c/a\u003e - Source code notebooks basic finance applications.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jjakimoto/finance_ml\"\u003eAdvanced Pricing ML\u003c/a\u003e - Additional implementation of Advances in Financial Machine Learning (Book)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/aluo417/Financial-Engineering-Projects\"\u003eOptions and Regression\u003c/a\u003e - Financial engineering project for option pricing techniques.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/LongOnly/Quantitative-Notebooks\"\u003eQuant Notebooks\u003c/a\u003e - Educational notebooks on quant finance, algorithmic trading and investment strategy.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bukosabino/financial-forecasting-challenge-gresearch\"\u003eForecasting Challenge\u003c/a\u003e - Financial forecasting challenge by G-Research (Hedge Fund)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/firmai?after=Y3Vyc29yOnYyOpK5MjAxOS0wNS0wMlQwNToyMzoyMSswMTowMM4KBjIV\u0026amp;tab=stars\"\u003eXGboost\u003c/a\u003e - A trading algorithm using XgBoost\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rawillis98/alpaca\"\u003eResearch Paper Trading\u003c/a\u003e - A strategy implementation based on a paper using Alpaca Markets.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/arcadynovosyolov/finance\"\u003eVarious\u003c/a\u003e - Options, Allocation, Simulation\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/joelowj/Machine-Learning-and-Reinforcement-Learning-in-Finance\"\u003eML \u0026amp; RL NYU\u003c/a\u003e - Machine Learning and Reinforcement Learning in Finance.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-finance-data\"\u003e\u003c/a\u003e\n\u003cstrong\u003eData\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mbravidor/PyDSout\"\u003eDatastream\u003c/a\u003e - Datastrem from Thomson Reuters accessible through Python.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://twopirllc\" rel=\"nofollow\"\u003eAlphaVantage\u003c/a\u003e - API wrapper to simplify the process of acquiring free financial data.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/duncangh/FSA\"\u003eFSA\u003c/a\u003e- A project to transfer SEC Edgar Filings’ financial data to custom financial statement analysis models.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/tradeasystems/tradeasystems_connector\"\u003eTradeConnector\u003c/a\u003e - A layer to connect with market data providers.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/healthgradient/sec_employee_information_extraction\"\u003eEmployee Count SEC Filings\u003c/a\u003e - Extraction to get the exact employee count values for companies from SEC filings.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/healthgradient/sec-doc-info-extraction/blob/master/classify_sections_containing_relevant_information.ipynb\"\u003eSEC Parsing\u003c/a\u003e - NLP to find and extract specific information from long, unstructured documents\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/LexPredict/openedgar\"\u003eOpen Edgar\u003c/a\u003e - OpenEDGAR (openedgar.io)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://www.ratingshistory.info/\" rel=\"nofollow\"\u003eRating Industries\u003c/a\u003e - Histories from multiple agencies converted to CSV format\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePersonal Papers\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3371902\" rel=\"nofollow\"\u003eFinancial Machine Learning Regulation\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3420490\" rel=\"nofollow\"\u003ePredicting Restaurant Facility Closures\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3420889\" rel=\"nofollow\"\u003ePredicting Corporate Bankruptcies\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3420722\" rel=\"nofollow\"\u003ePredicting Earnings Surprises\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3420952\" rel=\"nofollow\"\u003eMachine Learning in Asset Management\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-healtcare\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHealthcare\u003c/h2\u003e\u003ca id=\"user-content-healthcare\" class=\"anchor\" aria-label=\"Permalink: Healthcare\" href=\"#healthcare\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-healtcare-general\"\u003e\u003c/a\u003e\n\u003cstrong\u003eGeneral\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pzivich/zEpid\"\u003ezEpid\u003c/a\u003e - Epidemiology analysis package.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pzivich/Python-for-Epidemiologists\"\u003ePython For Epidemiologists\u003c/a\u003e - Tutorial to introduce epidemiology analysis in Python.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/rjhere/Prescription-compliance-prediction\"\u003ePrescription Compliance\u003c/a\u003e - An analysis of prescription and medical compliance\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/alistairwallace97/olympian-biotech\"\u003eRespiratory Disease\u003c/a\u003e - Tracking respiratory diseases in Olympic athletes\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/callysto/curriculum-notebooks/blob/master/Humanities/BubonicPlague/bubonic-plague-and-SIR-model.ipynb\"\u003eBubonic Plague\u003c/a\u003e - Bubonic plague and SIR model.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-legal\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eJustics, Law \u0026amp; Regulations\u003c/h2\u003e\u003ca id=\"user-content-justics-law--regulations\" class=\"anchor\" aria-label=\"Permalink: Justics, Law \u0026amp; Regulations\" href=\"#justics-law--regulations\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-legal-tools\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTools\u003c/h4\u003e\u003ca id=\"user-content-tools\" class=\"anchor\" aria-label=\"Permalink: Tools\" href=\"#tools\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/LexPredict/lexpredict-contraxsuite\"\u003eLexPredict\u003c/a\u003e - Software package and library.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/davidawad/lobe\"\u003eAI Para-legal\u003c/a\u003e - Lobe is the world's first AI paralegal.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hockeyjudson/Legal-Entity-Detection/blob/master/Dataset_conv.ipynb\"\u003eLegal Entity Detection\u003c/a\u003e - NER For Legal Documents.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Law-AI/summarization\"\u003eLegal Case Summarisation\u003c/a\u003e - Implementation of different summarisation algorithms applied to legal case judgements.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/GirrajMaheshwari/Web-scrapping-/blob/master/Google_scholar%2BExtract%2Bcase%2Bdocument.ipynb\"\u003eLegal Documents Google Scholar\u003c/a\u003e - Using Google scholar to extract cases programatically.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/akarazeev/LegalTech\"\u003eChat Bot\u003c/a\u003e - Chat-bot and email notifications.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/propublica/congress-api-docs\"\u003eCongress API\u003c/a\u003e - ProPublica congress API access.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/toningega/Data_Generator\"\u003eData Generator GDPR\u003c/a\u003e - Dummy data generator for GDPR compliance\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ICLRandD/Blackstone\"\u003eBlackstone\u003c/a\u003e - spaCy pipeline and model for NLP on unstructured legal text.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-legal-pr\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePolicy and Regulatory\u003c/h4\u003e\u003ca id=\"user-content-policy-and-regulatory\" class=\"anchor\" aria-label=\"Permalink: Policy and Regulatory\" href=\"#policy-and-regulatory\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/erickjtorres/AI_LegalDoc_Hackathon\"\u003eGDPR scores\u003c/a\u003e - Predicting GDPR Scores for Legal Documents.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/siddhantmaharana/text-analysis-on-FINRA-docs\"\u003eDriving Factors FINRA\u003c/a\u003e - Identify the driving factors that influence the FINRA arbitration decisions.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/davidsontheath/bias_corrected_estimators/blob/master/bias_corrected_estimators.ipynb\"\u003eSecurities Bias Correction\u003c/a\u003e - Bias-Corrected Estimation of Price Impact in Securities Litigation.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/anshu3769/FirmEmbeddings\"\u003ePublic Firm to Legal Decision\u003c/a\u003e - Embed public firms based on their reaction to legal decisions.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Kevin-McIsaac/Nightlife\"\u003eNight Life Regulation\u003c/a\u003e - Australian nightlife and its regulation and policing\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ProximaDas/nlp-govt-regulations\"\u003eComments\u003c/a\u003e - Public comments on government regulations.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/philxchen/Clustering-Canadian-regulations\"\u003eClustering\u003c/a\u003e - Clustering Canadian regulations.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ds-modules/EEP-147\"\u003eEnvironment\u003c/a\u003e - Regulation of Energy and the Environment\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/vsub21/systemic-risk-dashboard\"\u003eRisk\u003c/a\u003e - Systematic risk of various financial regulations.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/raymond180/FINRA_TRACE\"\u003eFINRA Compliance\u003c/a\u003e - Topic modelling on compliance.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-legal-judicial\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eJudicial Applied\u003c/h4\u003e\u003ca id=\"user-content-judicial-applied\" class=\"anchor\" aria-label=\"Permalink: Judicial Applied\" href=\"#judicial-applied\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/davidmasse/US-supreme-court-prediction\"\u003eSupreme Court Prediction\u003c/a\u003e - Predicting the ideological direction of Supreme Court decisions: ensemble vs. unified case-based model.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AccelAI/AI-Law-Minicourse/tree/master/Supreme_Court_Topic_Modeling\"\u003eSupreme Court Topic Modeling\u003c/a\u003e - Multiple steps necessary to implement topic modeling on supreme court decisions.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/GirrajMaheshwari/Legal-Analytics-project---Court-misclassification\"\u003eJudge Opinion\u003c/a\u003e - Using text mining and machine learning to analyze judges’ opinions for a particular concern.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/whs2k/GPO-AI\"\u003eML Law Matching\u003c/a\u003e - A machine learning law match maker.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/brightmart/sentiment_analysis_fine_grain\"\u003eBert Multi-label Classification\u003c/a\u003e - Fine Grained Sentiment Analysis from AI.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.youtube.com/channel/UC5UHm2J9pbEZmWl97z_0hZw\" rel=\"nofollow\"\u003eSome Computational AI Course\u003c/a\u003e - Video series Law MIT.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3371902\" rel=\"nofollow\"\u003eFinancial Machine Learning Regulation (Paper)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-manufacturing\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eManufacturing\u003c/h2\u003e\u003ca id=\"user-content-manufacturing\" class=\"anchor\" aria-label=\"Permalink: Manufacturing\" href=\"#manufacturing\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-manufacturing-general\"\u003e\u003c/a\u003e\n\u003cstrong\u003eGeneral\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Danila89/kaggle_mercedes\"\u003eGreen Manufacturing\u003c/a\u003e - Mercedes-Benz Greener \u003cem\u003eManufacturing\u003c/em\u003e competition on Kaggle.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Meena-Mani/SECOM_class_imbalance\"\u003eSemiconductor Manufacturing\u003c/a\u003e - Semicondutor \u003cem\u003emanufacturing\u003c/em\u003e process line data analysis.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/usnistgov/modelmeth\"\u003eSmart Manufacturing\u003c/a\u003e - Shared work of a modelling Methodology.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/han-yan-ds/Kaggle-Bosch\"\u003eBosch Manufacturing\u003c/a\u003e - Bosch manufacturing project, Kaggle.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-manufacturing-maintenance\"\u003e\u003c/a\u003e\n\u003cstrong\u003eMaintenance\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Azure/lstms_for_predictive_maintenance\"\u003ePredictive Maintenance\u003c/a\u003e 1 - Predict remaining useful life of aircraft engines\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Samimust/predictive-maintenance\"\u003ePredictive Maintenance 2\u003c/a\u003e - Time-To-Failure (TTF) or Remaining Useful Life (RUL)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/m-hoff/maintsim\"\u003eManufacturing Maintenance\u003c/a\u003e - Simulation of maintenance in \u003cem\u003emanufacturing\u003c/em\u003e systems.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-manufacturing-fail\"\u003e\u003c/a\u003e\n\u003cstrong\u003eFailure\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/IBM/iot-predictive-analytics\"\u003ePredictive Analytics\u003c/a\u003e - Method for Predicting failures in Equipment using Sensor data.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/roshank1605A04/SECOM-Detecting-Defected-Items\"\u003eDetecting Defects\u003c/a\u003e - Anomaly detection for defective semiconductors\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jorgehas/smart-defect-inspection\"\u003eDefect Detection\u003c/a\u003e - Smart defect detection for pill manufacturing.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/aayushmudgal/Reducing-Manufacturing-Failures\"\u003eManufacturing Failures\u003c/a\u003e - Reducing manufacturing failures.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mohan-mj/Manufacturing-Line-I4.0\"\u003eManufacturing Anomalies\u003c/a\u003e - Intelligent anomaly detection for \u003cem\u003emanufacturing\u003c/em\u003e line.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-manufacturing-quality\"\u003e\u003c/a\u003e\n\u003cstrong\u003eQuality\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/buzz11/productionFailures\"\u003eQuality Control\u003c/a\u003e - Bosh failure of quality control.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/limberc/tianchi-IMQF\"\u003eManufacturing Quality\u003c/a\u003e - Intelligent \u003cem\u003eManufacturing\u003c/em\u003e Quality Forecast\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/trentwoodbury/ManufacturingAuctionRegression\"\u003eAuto Manufacturing\u003c/a\u003e - Regression Case Study Project on \u003cem\u003eManufacturing\u003c/em\u003e Auction Sale Data.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-media\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMedia \u0026amp; Publishing\u003c/h2\u003e\u003ca id=\"user-content-media--publishing\" class=\"anchor\" aria-label=\"Permalink: Media \u0026amp; Publishing\" href=\"#media--publishing\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-media-marketing\"\u003e\u003c/a\u003e\n\u003cstrong\u003eMarketing\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/andrei-rizoiu/hip-popularity\"\u003eVideo Popularity\u003c/a\u003e - HIP model for predicting the popularity of videos.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hathix/youtube-transcriber\"\u003eYouTube transcriber\u003c/a\u003e - Automatically transcribe YouTube videos.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/byukan/Marketing-Data-Science\"\u003eMarketing Analytics\u003c/a\u003e - Marketing analytics case studies.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ikatsov/algorithmic-examples\"\u003eAlgorithmic Marketing\u003c/a\u003e - Models from Introduction to Algorithmic Marketing book\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/HowardNTUST/Marketing-Data-Science-Application\"\u003eMarketing Scripts\u003c/a\u003e - Marketing data science applications.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mikhailklassen/Mining-the-Social-Web-3rd-Edition/tree/master/notebooks\"\u003eSocial Mining\u003c/a\u003e - Mining the social web.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-miscellaneous\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMiscellaneous\u003c/h2\u003e\u003ca id=\"user-content-miscellaneous\" class=\"anchor\" aria-label=\"Permalink: Miscellaneous\" href=\"#miscellaneous\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-miscellaneous-art\"\u003e\u003c/a\u003e\n\u003cstrong\u003eArt\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ivan-bilan/Painting_Forensics\"\u003ePainting Forensics\u003c/a\u003e - Analysing paintings to find out their year of creation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-miscellaneous-tour\"\u003e\u003c/a\u003e\n\u003cstrong\u003eTourism\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/xiaofei6677/TourismFlickrMiner\"\u003eFlickr\u003c/a\u003e - Metadata mining tool for tourism research.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/khanhnamle1994/fashion-recommendation\"\u003eFashion\u003c/a\u003e \u003cstrong\u003e-\u003c/strong\u003e A clothing retrieval and visual recommendation model for fashion images\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-physics\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePhysics\u003c/h2\u003e\u003ca id=\"user-content-physics\" class=\"anchor\" aria-label=\"Permalink: Physics\" href=\"#physics\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-physics-general\"\u003e\u003c/a\u003e\n\u003cstrong\u003eGeneral\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/fvisconti/gammas_machine_learning\"\u003eGamma-hadron Reconstruction\u003c/a\u003e - Tools used in Gamma-ray ground based astronomy.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/callysto/curriculum-notebooks/tree/master/Physics\"\u003eCurriculum\u003c/a\u003e - Newtonian notebooks.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/higgsfield/interaction_network_pytorch\"\u003eInteraction Networks\u003c/a\u003e - Interaction Networks for Learning about Objects, Relations and \u003cem\u003ePhysics.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hep-lbdl/adversarial-jets\"\u003eParticle Physics\u003c/a\u003e - Training, generation, and analysis code for learning Particle \u003cem\u003ePhysics\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ernestyalumni/CompPhys\"\u003eComputational Physics\u003c/a\u003e - A computational physics repository.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/robmarkcole/Useful-python-for-medical-physics\"\u003eMedical Physics\u003c/a\u003e - Useful python for medical physics.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pymedphys/pymedphys\"\u003eMedical Physics 2\u003c/a\u003e - A common, core Python package for Medical \u003cem\u003ePhysics\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/FPAL-Stanford-University/FloATPy\"\u003eFlow Physics\u003c/a\u003e - Flow \u003cem\u003ePhysics\u003c/em\u003e and Aeroacoustics Toolbox with Python\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-physics-ml\"\u003e\u003c/a\u003e\n\u003cstrong\u003eMachine Learning\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/dkirkby/MachineLearningStatistics\"\u003ePhysics ML and Stats\u003c/a\u003e - Machine learning and statistics for physicists\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/arogozhnikov/hep_ml\"\u003eHigh Energy\u003c/a\u003e - Machine Learning for High Energy \u003cem\u003ePhysics\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hep-lbdl/CaloGAN\"\u003eHigh Energy GAN\u003c/a\u003e - Generative Adversarial Networks for High Energy \u003cem\u003ePhysics.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/GiggleLiu/marburg\"\u003eNeural Networks\u003c/a\u003e - P\u003cem\u003ehysics\u003c/em\u003e meets neural networks\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-public\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGovernment and Public Works\u003c/h2\u003e\u003ca id=\"user-content-government-and-public-works\" class=\"anchor\" aria-label=\"Permalink: Government and Public Works\" href=\"#government-and-public-works\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-public-social\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSocial Policies\u003c/h4\u003e\u003ca id=\"user-content-social-policies\" class=\"anchor\" aria-label=\"Permalink: Social Policies\" href=\"#social-policies\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/dssg/triage\"\u003eTriage\u003c/a\u003e - General Purpose Risk Modeling and Prediction Toolkit for Policy and Social Good Problems.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/worldbank/ML-classification-algorithms-poverty/tree/master/notebooks\"\u003eWorld Bank Poverty I\u003c/a\u003e - A comparative assessment of machine learning classification algorithms applied to poverty prediction.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/avsolatorio/world-bank-pover-t-tests-solution\"\u003eWorld Bank Poverty II\u003c/a\u003e - Repository for the World Bank Pover-t Test Competition Solution Overseas Company Land Ownership .\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Global-Witness/overseas-companies-land-ownership/blob/master/overseas_companies_land_ownership_analysis.ipynb\"\u003eOverseas Company Land Ownership\u003c/a\u003e - Identifying foreign ownership in the UK.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/MAydogdu/ConsumerFinancialProtectionBureau/blob/master/CFPB_Complaints_2017September.ipynb\"\u003eCFPB\u003c/a\u003e - Consumer Finances Protection Bureau complaints analysis.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/tslindner/Effects-of-Cannabis-Legalization\"\u003eCannabis Legalisation Effect\u003c/a\u003e - Effects of cannabis legalization on crime.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/dmodjeska/barnet_transactions/blob/master/Barnet_Transactions_Analysis.ipynb\"\u003ePublic Credit Card\u003c/a\u003e - Identification of potential fraud for council credit cards. \u003ca href=\"https://open.barnet.gov.uk/dataset/corporate-credit-card-transaction-2016-17\" rel=\"nofollow\"\u003eData\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/shayanray/GlassBox/tree/master/mlPredictor\"\u003eRecidivism Prediction\u003c/a\u003e - Transparency and audibility to recidivism risk assessment\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Featuretools/predict-household-poverty\"\u003eHousehold Poverty\u003c/a\u003e - Predict poverty in households in Costa Rica.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ancilcrayton/nlp_public_policy\"\u003eNLP Public Policy\u003c/a\u003e - An example of an NLP use-case in public policy.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/roshank1605A04/World-Food-Production\"\u003eWorld Food Production\u003c/a\u003e - Comparing Top food and feed Producers around the globe.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/DataScienceForGood/TaxationInequality\"\u003eTax Inequality\u003c/a\u003e - Data project around taxation and inequality in Basel Stadt.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/austinbrian/sheriffs\"\u003eSheriff Compliance\u003c/a\u003e - Compliance to ICE requests.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/MengchuanFu/Suspecious-Apps-Detection\"\u003eApps Detection\u003c/a\u003e - Suspicious app detection for kids.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/farkhondehm/Social-Assistance\"\u003eSocial Assistance\u003c/a\u003e - Trending information on social assistance\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/abjer/sds/tree/master/material\"\u003eComputational Social Science\u003c/a\u003e - Social data science summer school course.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bhaveshgoyal/safeLiquor\"\u003eLiquor and Crime\u003c/a\u003e - Effect of liquor licenses issued on the crime rate.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/austinpetsalive/distemper-outbreak\"\u003eAnimal Placement Kennels\u003c/a\u003e - Optimising animal placement in shelters.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ryanschaub/The-U.S.-Mexican-Border-Wall-and-Staffing-A-Statistical-Approach-\"\u003eStaffing Wall\u003c/a\u003e - Independent exploration project on U.S. Mexican Border wall\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/zischwartz/workerfatalities\"\u003eWorker Fatalities\u003c/a\u003e - Worker Fatalities and Catastrophes Map from OSHA data\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-public-charity\"\u003e\u003c/a\u003e\n\u003cstrong\u003eCharities\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/johnfwhitesell/CensusPull/blob/master/Census_ACS5_Pull.ipynb\"\u003eCensus Data API\u003c/a\u003e - Pull variables from the 5-year American Community Survey.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/datakind/datadive-gates92y-proj3-form990\"\u003ePhilantropic Giving\u003c/a\u003e - Work done by numerous DataKind volunteers on harnessing Form 990 data\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Chris-Manna/charity_recommender\"\u003eCharity Recommender\u003c/a\u003e - NYC \u003cem\u003eCharity\u003c/em\u003e Collaborative Recommender System on an Implicit DataSet.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gouravaich/finding-donors-for-charity\"\u003eDonor Identification\u003c/a\u003e - A machine learning project in which we need to find donors for \u003cem\u003echarity.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/staceb/charities_in_the_united_states\"\u003eUS Charities\u003c/a\u003e - Charity exploration and machine learning.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/LauraChen/02-Metis-Web-Scraping\"\u003eCharity Effectiveness\u003c/a\u003e - Scraping online data about \u003cem\u003echarities\u003c/em\u003e to understand effectiveness\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-public-election\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eElection Analysis\u003c/h4\u003e\u003ca id=\"user-content-election-analysis\" class=\"anchor\" aria-label=\"Permalink: Election Analysis\" href=\"#election-analysis\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/1jinwoo/DeepWave/blob/master/DR_Random_Forest.ipynb\"\u003eElection Analysis\u003c/a\u003e - Election Analysis and Prediction Models\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Akesari12/LS123_Data_Prediction_Law_Spring-2019/blob/master/labs/OLS%20for%20Causal%20Inference/OLS_Causal_Inference_solution.ipynb\"\u003eAmerican Election Causal\u003c/a\u003e - Using ANES data with causal inference models.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sfbrigade/datasci-campaign-finance/blob/master/notebooks/ML%20Campaign%20Finance%20and%20Election%20Results%20Example.ipynb\"\u003eCampaign Finance and Election Results\u003c/a\u003e - Investigating the relation between campaign finance and subsequent election results.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/nealmcb/pr_voting_methods\"\u003eVoting System\u003c/a\u003e - Proportional representation voting methods.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/austinbrian/portfolio/blob/master/tax_votes/president_counties.ipynb\"\u003ePresident Vote\u003c/a\u003e - Vote by income level analysis..\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-public-politics\"\u003e\u003c/a\u003e\n\u003cstrong\u003ePolitics\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/kaumaron/Data_Science/tree/master/Congressional_Partisanship\"\u003eCongressional politics\u003c/a\u003e - House and senate congressional partisanship.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/okfn-brasil/perfil-politico\"\u003ePolitico\u003c/a\u003e - A platform for profiling public figures in Brazilian \u003cem\u003epolitics.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ParticipaPY/politic-bots\"\u003eBots\u003c/a\u003e - Tools and algorithms to analyze Paraguayan Tweets in times of election\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/PrincetonUniversity/gerrymandertests\"\u003eGerrymander tests\u003c/a\u003e - Lots of metrics for quantifying gerrymandering.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/JulianMar11/SentimentPoliticalCompass\"\u003eSentiment\u003c/a\u003e - Analyse newspapers with respect to their \u003cem\u003epolitical\u003c/em\u003e conviction using entity sentiments of party representatives.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/muntisa/Deep-Politics\"\u003eDL Politics\u003c/a\u003e - Prediction of Spanish \u003cem\u003ePolitical\u003c/em\u003e Affinity with Deep Neural Nets: Socialist vs People's Party\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/edmundooo/more-money-more-problems\"\u003ePAC Money\u003c/a\u003e - Effects of PAC money on US \u003cem\u003epolitics\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/abhiagar90/power_networks\"\u003ePower Networks\u003c/a\u003e - Constructing a watchdog for Indian corporate and \u003cem\u003epolitical\u003c/em\u003e networks\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/philippschmalen/Project_tsds\"\u003eElite\u003c/a\u003e - Political elite in the US.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/kkirchhoff01/DebateAnalysis\"\u003eDebate Analysis\u003c/a\u003e - Program to analyze \u003cem\u003epolitical\u003c/em\u003e debates.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/davidjwiner/political_affiliation_prediction\"\u003ePolitical Affiliation\u003c/a\u003e - Political affiliation prediction using twitter metadata.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/philiplbean/facebook_political_ads\"\u003ePolitical Ads\u003c/a\u003e - Investigation into Facebook \u003cem\u003ePolitical\u003c/em\u003e Ads and Targeting\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pgromano/Political-Identity-Analysis\"\u003ePolitical Identity\u003c/a\u003e - Multi-axial \u003cem\u003epolitical\u003c/em\u003e model.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/kmunger/YT_descriptive\"\u003eYT Politics\u003c/a\u003e - Mapping \u003cem\u003ePolitics\u003c/em\u003e on YouTube\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/albertwebson/Political-Vector-Projector\"\u003ePolitical Ideology\u003c/a\u003e - Unsupervised learning of \u003cem\u003epolitical\u003c/em\u003e ideology by word vector projections\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-realestate\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eReal Estate, Rental \u0026amp; Leasing\u003c/h2\u003e\u003ca id=\"user-content-real-estate-rental--leasing\" class=\"anchor\" aria-label=\"Permalink: Real Estate, Rental \u0026amp; Leasing\" href=\"#real-estate-rental--leasing\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-realestate-real\"\u003e\u003c/a\u003e\n\u003cstrong\u003eReal Estate\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/GretelDePaepe/FindingDonuts\"\u003eFinding Donuts\u003c/a\u003e - Finding real estate opportunities by predicting transforming neighbourhoods.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/denadai2/real-estate-neighborhood-prediction\"\u003eNeighbourhood\u003c/a\u003e - Predicting real estate prices from the urban environment.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Sardhendu/PropertyClassification\"\u003eReal Estate Classification\u003c/a\u003e - Classifying the type of property given Real Estate, satellite and Street view Images\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hyattsaleh15/RealStateRecommender\"\u003eRecommender\u003c/a\u003e - This tools aims to recommend a user the top 5 real estate properties that matches their search.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Shreyas3108/house-price-prediction\"\u003eHouse Price\u003c/a\u003e - Predicting \u003cem\u003ehouse\u003c/em\u003e prices using Linear Regression and GBR\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/girishkuniyal/Predict-housing-prices-in-Portland\"\u003eHouse Price Portland\u003c/a\u003e - Predict housing prices in Portland.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/eswar3/Zillow-prediction-models\"\u003eZillow Prediction\u003c/a\u003e - Zillow valuation prediction as performed on Kaggle.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/denadai2/real-estate-neighborhood-prediction\"\u003eReal Estate\u003c/a\u003e - Predicting real estate prices from the urban environment.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-realestate-rent\"\u003e\u003c/a\u003e\n\u003cstrong\u003eRental \u0026amp; Leasing\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ual/rental-listings\"\u003eAnalysing Rentals\u003c/a\u003e - Analyzing and visualizing rental listings data.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mratsim/Apartment-Interest-Prediction\"\u003eInterest Prediction\u003c/a\u003e - Predict people interest in renting specific NYC apartments.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/5x12/pwc_europe_data_analytics_hackathon\"\u003eHousing Uni vs Non-Uni\u003c/a\u003e - The effect on university lodging after the GFC.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Featuretools/predict-household-poverty\"\u003ePredict Household Poverty\u003c/a\u003e - Predict the poverty of households in Costa Rica using automated feature engineering.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://inseaddataanalytics.github.io/INSEADAnalytics/groupprojects/AirbnbReport2016Jan.html\" rel=\"nofollow\"\u003eAirbnb public analytics competition\u003c/a\u003e: - Now strategic management.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-utilities\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUtilities\u003c/h2\u003e\u003ca id=\"user-content-utilities\" class=\"anchor\" aria-label=\"Permalink: Utilities\" href=\"#utilities\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-utilities-elec\"\u003e\u003c/a\u003e\n\u003cstrong\u003eElectricity\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/luqmanhakim/research-on-sp-wholesale/blob/master/research-on-sp-wholesale-plan.ipynb\"\u003eElectricity Price\u003c/a\u003e - Electricity price comparison Singapore.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/richardddli/state_electricity_rates\"\u003eElectricity-Coal Correlation\u003c/a\u003e - Determining the correlation between state electricity rates and coal generation over the past decade.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/datadesk/california-electricity-capacity-analysis\"\u003eElectricity Capacity\u003c/a\u003e - A Los Angeles Times analysis of California's costly power glut.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/PyPSA/WHOBS\"\u003eElectricity Systems\u003c/a\u003e - Optimal Wind+Hydrogen+Other+Battery+Solar (WHOBS) \u003cem\u003eelectricity\u003c/em\u003e systems for European countries.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pipette/Electricity-load-disaggregation\"\u003eLoad Disaggregation\u003c/a\u003e - Smart meter load disaggregation with Hidden Markov Models\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/farwacheema/DA-electricity-price-forecasting\"\u003ePrice Forecasting\u003c/a\u003e - Forecasting Day-Ahead \u003cem\u003eelectricity\u003c/em\u003e prices in the German bidding zone with deep neural networks.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/gschivley/carbon-index\"\u003eCarbon Index\u003c/a\u003e - Calculation of \u003cem\u003eelectricity\u003c/em\u003e CO₂ intensity at national, state, and NERC regions from 2001-present.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hvantil/ElectricityDemandForecasting\"\u003eDemand Forecasting\u003c/a\u003e - \u003cem\u003eElectricity\u003c/em\u003e demand forecasting for Austin.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/un-modelling/Electricity_Consumption_Surveys\"\u003eElectricity Consumption\u003c/a\u003e - Estimating \u003cem\u003eElectricity\u003c/em\u003e Consumption from Household Surveys\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/amirrezaeian/Individual-household-electric-power-consumption-Data-Set-\"\u003eHousehold power consumption\u003c/a\u003e - Individual household power consumption LSTM.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://inseaddataanalytics.github.io/INSEADAnalytics/groupprojects/group_energy.html\" rel=\"nofollow\"\u003eElectricity French Distribution\u003c/a\u003e - An analysis of electricity data provided by the French Distribution Network (RTE)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Open-Power-System-Data/renewable_power_plants\"\u003eRenewable Power Plants\u003c/a\u003e - Time series of cumulated installed capacity.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/FUSED-Wind/FUSED-Wake\"\u003e Wind Farm Flow\u003c/a\u003e - A repository of wind plant flow models connected to FUSED-Wind.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/YungChunLu/UCI-Power-Plant\"\u003ePower Plant\u003c/a\u003e - The dataset contains 9568 data points collected from a Combined Cycle Power Plant over 6 years (2006-2011).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-utilities-coal\"\u003e\u003c/a\u003e\n\u003cstrong\u003eCoal, Oil \u0026amp; Gas\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/samarthiith/DE_CoalPhaseOut\"\u003eCoal Phase Out\u003c/a\u003e - Generation adequacy issues with Germany’s coal phaseout.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Jean-njoroge/coal-exploratory/tree/master/notebooks\"\u003eCoal Prediction\u003c/a\u003e - Predicting coal production.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sdasadia/Oil-Natural-Gas-Price-Prediction\"\u003eOil \u0026amp; Gas\u003c/a\u003e - Oil \u0026amp; \u003cem\u003eNatural\u003c/em\u003e \u003cem\u003eGas\u003c/em\u003e price prediction using ARIMA \u0026amp; Neural Networks\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/cep-kse/natural_gas_formula\"\u003eGas Formula\u003c/a\u003e - Calculating potential economic effect of price indexation formula.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/victorpena1/Natural-Gas-Demand-Prediction\"\u003eDemand Prediction\u003c/a\u003e - Natural gas demand prediction.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/williamadams1/natural-gas-consumption-forecasting\"\u003eConsumption Forecasting\u003c/a\u003e - Natural gas consumption forecasting.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/bahuisman/NatGasModel\"\u003eGas Trade\u003c/a\u003e - World Model for \u003cem\u003eNatural\u003c/em\u003e \u003cem\u003eGas\u003c/em\u003e Trade.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-utilities-water\"\u003e\u003c/a\u003e\n\u003cstrong\u003eWater \u0026amp; Pollution\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/codeforboston/safe-water\"\u003eSafe Water\u003c/a\u003e - Predict health-based drinking water violations in the United States.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mroberge/hydrofunctions\"\u003eHydrology Data\u003c/a\u003e - A suite of convenience functions for exploring water data in Python.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/sentinel-hub/water-observatory-backend\"\u003eWater Observatory\u003c/a\u003e - Monitoring water levels of lakes and reservoirs using satellite imagery.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/wassname/pipe-segmentation\"\u003eWater Pipelines\u003c/a\u003e - Using machine learning to find water pipelines in aerial images.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/awracms/awra_cms_older\"\u003eWater Modelling\u003c/a\u003e - Australian Water Resource Assessment (AWRA) Community Modelling System.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/datadesk/california-ccscore-analysis\"\u003eDrought Restrictions\u003c/a\u003e - A Los Angeles Times analysis of water usage after the state eased drought restrictions\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/cadrev/lstm-flood-prediction\"\u003eFlood Prediction\u003c/a\u003e - Applying LSTM on river water level data\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/jesseanddd/sewer-overflow\"\u003eSewage Overflow\u003c/a\u003e - Insights into the sanitary sewage overflow (SSO). - This has been removed\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/johnpfay/USWaterAccounting\"\u003eWater Accounting\u003c/a\u003e - Assembles water budget data for the US from existing data source\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/txytju/air-quality-prediction\"\u003eAir Quality Prediction\u003c/a\u003e - Predict air quality(aq) in Beijing and London in the next 48 hours.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-utilities-trans\"\u003e\u003c/a\u003e\n\u003cstrong\u003eTransportation\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/xinychen/transdim\"\u003eTransdim\u003c/a\u003e - Creating accurate and efficient solutions for the spatio-temporal traffic data imputation and prediction tasks.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/AlanConstantine/KDD-Cup-2019-CAMMTR\"\u003eTransport Recommendation\u003c/a\u003e - Context-Aware Multi-Modal Transportation Recommendation\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/CityofToronto/bdit_data-sources\"\u003eTransport Data\u003c/a\u003e - Data and notebooks for Toronto transport.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pawelmorawiecki/traffic_jam_Nairobi\"\u003eTransport Demand\u003c/a\u003e - Predicting demand for public transportation in Nairobi.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Lemma1/DPFE\"\u003eDemand Estimation\u003c/a\u003e - Implementation of dynamic origin-destination demand estimation.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/hackoregon/transportation-congestion-analysis\"\u003eCongestion Analysis\u003c/a\u003e - Transportation systems analysis\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/nishanthgampa/Time-Series-Analysis-on-Transportation-Data\"\u003eTS Analysis\u003c/a\u003e - Time series analysis on transportation data.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/fangshulin/Vulnerability-Analysis-for-Transportation-Networks\"\u003eNetwork Graph Subway\u003c/a\u003e - Vulnerability analysis for transportation networks. - Have been taken down\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/akpen/Stockholm-0.1\"\u003eTransportation Inefficiencies\u003c/a\u003e - Quantifying the inefficiencies of Transportation Networks\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/crowdAI/train-schedule-optimisation-challenge-starter-kit\"\u003eTrain Optimisation\u003c/a\u003e - Train schedule optimisation\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/mratsim/McKinsey-SmartCities-Traffic-Prediction\"\u003eTraffic Prediction\u003c/a\u003e - multi attention recurrent neural networks for time-series (city traffic)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Data4Democracy/crash-model\"\u003ePredict Crashes\u003c/a\u003e - Crash prediction modelling application that leverages multiple data sources\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/llSourcell/AI_Supply_Chain\"\u003eAI Supply chain\u003c/a\u003e - Supply chain optimisation system.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/cavaunpeu/flight-delays\"\u003eTransfer Learning Flight Delay\u003c/a\u003e - Using variation encoders in Keras to predict flight delay.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/pratishthakapoor/RetailReplenishement/tree/master/Code\"\u003eReplenishment\u003c/a\u003e - Retail replenishment code for supply chain management.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-wholesale\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWholesale \u0026amp; Retail\u003c/h2\u003e\u003ca id=\"user-content-wholesale--retail\" class=\"anchor\" aria-label=\"Permalink: Wholesale \u0026amp; Retail\" href=\"#wholesale--retail\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-wholesale-whole\"\u003e\u003c/a\u003e\n\u003cstrong\u003eWholesale\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/kralmachine/WholesaleCustomerAnalysis\"\u003eCustomer Analysis\u003c/a\u003e - Wholesale customer analysis.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/Semionn/JB-wholesale-distribution-analysis\"\u003eDistribution\u003c/a\u003e - JB wholesale distribution analysis.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/prakhardogra921/Clustering-Analysis-on-customers-of-a-wholesale-distributor\"\u003eClustering\u003c/a\u003e - Unsupervised learning techniques are applied on product spending data collected for customers\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/tstreamDOTh/Instacart-Market-Basket-Analysis\"\u003eMarket Basket Analysis\u003c/a\u003e - Instacart public dataset to report which products are often shopped together.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca name=\"user-content-wholesale-retail\"\u003e\u003c/a\u003e\n\u003cstrong\u003eRetail\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/SarahMestiri/online-retail-case\"\u003eRetail Analysis\u003c/a\u003e - Studying Online \u003cem\u003eRetail\u003c/em\u003e Dataset and getting insights from it.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/roshank1605A04/Online-Retail-Transactions-of-UK\"\u003eOnline Insights\u003c/a\u003e - Analyzing the Online Transactions in UK\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/IBM-DSE/CyberShop-Analytics\"\u003eRetail Use-case\u003c/a\u003e - Notebooks \u0026amp; Data for CyberShop \u003cem\u003eRetail\u003c/em\u003e Use Case\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/arvindkarir/retail\"\u003eDwell Time\u003c/a\u003e - Customer dwell time and other analysis.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/finnqiao/cohort_online_retail\"\u003eRetail Cohort\u003c/a\u003e - Cohort analysis.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Machine Learning and Data Science Applications in Industry","anchor":"machine-learning-and-data-science-applications-in-industry","htmlText":"Machine Learning and Data Science Applications in Industry"},{"level":2,"text":"🌟 We Are Growing!","anchor":"-we-are-growing","htmlText":"🌟 We Are Growing!"},{"level":3,"text":"🚀 About Sov.ai","anchor":"-about-sovai","htmlText":"🚀 About Sov.ai"},{"level":3,"text":"🔍 Research and Project Opportunities","anchor":"-research-and-project-opportunities","htmlText":"🔍 Research and Project Opportunities"},{"level":3,"text":"🌐 Why Join Sov.ai?","anchor":"-why-join-sovai","htmlText":"🌐 Why Join Sov.ai?"},{"level":3,"text":"🤝 How to Apply","anchor":"-how-to-apply","htmlText":"🤝 How to Apply"},{"level":3,"text":"Admin","anchor":"admin","htmlText":"Admin"},{"level":2,"text":"Table of Contents","anchor":"table-of-contents","htmlText":"Table of Contents"},{"level":3,"text":"Industry Applications","anchor":"industry-applications","htmlText":"Industry Applications"},{"level":2,"text":"ML/DS Career Section for Industry Machine Learning","anchor":"mlds-career-section-for-industry-machine-learning","htmlText":"ML/DS Career Section for Industry Machine Learning"},{"level":3,"text":"Platforms:","anchor":"platforms","htmlText":"Platforms:"},{"level":3,"text":"Reviews:","anchor":"reviews","htmlText":"Reviews:"},{"level":2,"text":"Accommodation \u0026 Food","anchor":"accommodation--food","htmlText":"Accommodation \u0026amp; Food"},{"level":2,"text":"Accounting","anchor":"accounting","htmlText":"Accounting"},{"level":4,"text":"Machine Learning","anchor":"machine-learning","htmlText":"Machine Learning"},{"level":4,"text":"Analytics","anchor":"analytics","htmlText":"Analytics"},{"level":4,"text":"Textual Analysis","anchor":"textual-analysis","htmlText":"Textual Analysis"},{"level":4,"text":"Data, Parsing and APIs","anchor":"data-parsing-and-apis","htmlText":"Data, Parsing and APIs"},{"level":4,"text":"Research And Articles","anchor":"research-and-articles","htmlText":"Research And Articles"},{"level":4,"text":"Websites","anchor":"websites","htmlText":"Websites"},{"level":4,"text":"Courses","anchor":"courses","htmlText":"Courses"},{"level":2,"text":"Agriculture","anchor":"agriculture","htmlText":"Agriculture"},{"level":2,"text":"Banking \u0026 Insurance","anchor":"banking--insurance","htmlText":"Banking \u0026amp; Insurance"},{"level":4,"text":"Consumer Finance","anchor":"consumer-finance","htmlText":"Consumer Finance"},{"level":4,"text":"Management and Operation","anchor":"management-and-operation","htmlText":"Management and Operation"},{"level":4,"text":"Valuation","anchor":"valuation","htmlText":"Valuation"},{"level":4,"text":"Fraud","anchor":"fraud","htmlText":"Fraud"},{"level":4,"text":"Insurance and Risk","anchor":"insurance-and-risk","htmlText":"Insurance and Risk"},{"level":4,"text":"Physical","anchor":"physical","htmlText":"Physical"},{"level":2,"text":"Biotechnological \u0026 Life Sciences","anchor":"biotechnological--life-sciences","htmlText":"Biotechnological \u0026amp; Life Sciences"},{"level":2,"text":"Construction \u0026 Engineering","anchor":"construction--engineering","htmlText":"Construction \u0026amp; Engineering"},{"level":2,"text":"Economics","anchor":"economics","htmlText":"Economics"},{"level":2,"text":"Education \u0026 Research","anchor":"education--research","htmlText":"Education \u0026amp; Research"},{"level":2,"text":"Emergency \u0026 Police","anchor":"emergency--police","htmlText":"Emergency \u0026amp; Police"},{"level":2,"text":"Finance","anchor":"finance","htmlText":"Finance"},{"level":2,"text":"Healthcare","anchor":"healthcare","htmlText":"Healthcare"},{"level":2,"text":"Justics, Law \u0026 Regulations","anchor":"justics-law--regulations","htmlText":"Justics, Law \u0026amp; Regulations"},{"level":4,"text":"Tools","anchor":"tools","htmlText":"Tools"},{"level":4,"text":"Policy and Regulatory","anchor":"policy-and-regulatory","htmlText":"Policy and Regulatory"},{"level":4,"text":"Judicial Applied","anchor":"judicial-applied","htmlText":"Judicial Applied"},{"level":2,"text":"Manufacturing","anchor":"manufacturing","htmlText":"Manufacturing"},{"level":2,"text":"Media \u0026 Publishing","anchor":"media--publishing","htmlText":"Media \u0026amp; Publishing"},{"level":2,"text":"Miscellaneous","anchor":"miscellaneous","htmlText":"Miscellaneous"},{"level":2,"text":"Physics","anchor":"physics","htmlText":"Physics"},{"level":2,"text":"Government and Public Works","anchor":"government-and-public-works","htmlText":"Government and Public Works"},{"level":4,"text":"Social Policies","anchor":"social-policies","htmlText":"Social Policies"},{"level":4,"text":"Election Analysis","anchor":"election-analysis","htmlText":"Election Analysis"},{"level":2,"text":"Real Estate, Rental \u0026 Leasing","anchor":"real-estate-rental--leasing","htmlText":"Real Estate, Rental \u0026amp; Leasing"},{"level":2,"text":"Utilities","anchor":"utilities","htmlText":"Utilities"},{"level":2,"text":"Wholesale \u0026 Retail","anchor":"wholesale--retail","htmlText":"Wholesale \u0026amp; Retail"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Ffirmai%2Findustry-machine-learning"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-9f8a877aa99f.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-eb3d353f90ce.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"accessible_code_button":true}}}}</script> <div data-target="react-partial.reactRoot"><style data-styled="true" data-styled-version="5.3.11">.iVEunk{margin-top:16px;margin-bottom:16px;}/*!sc*/ 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class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Machine Learning and Data Science Applications in Industry</h1><a id="user-content-machine-learning-and-data-science-applications-in-industry" class="anchor" aria-label="Permalink: Machine Learning and Data Science Applications in Industry" href="#machine-learning-and-data-science-applications-in-industry"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <hr> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">🌟 We Are Growing!</h2><a id="user-content--we-are-growing" class="anchor" aria-label="Permalink: 🌟 We Are Growing!" href="#-we-are-growing"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We're seeking to collaborate with motivated, independent PhD graduates or doctoral students on approximately seven new projects in 2024. If you’re interested in contributing to cutting-edge investment insights and data analysis, please get in touch! This could be in colaboration with a university or as independent study.</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="https://private-user-images.githubusercontent.com/26666267/373640241-da97663a-b63f-4286-94cc-fcd168905109.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.pLc3Ms64VHlsWNOZ92pkT_E2eSMxLqaomowg3hx4wWY"><img src="https://private-user-images.githubusercontent.com/26666267/373640241-da97663a-b63f-4286-94cc-fcd168905109.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.pLc3Ms64VHlsWNOZ92pkT_E2eSMxLqaomowg3hx4wWY" alt="image" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">🚀 About Sov.ai</h3><a id="user-content--about-sovai" class="anchor" aria-label="Permalink: 🚀 About Sov.ai" href="#-about-sovai"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Sov.ai is at the forefront of integrating advanced machine learning techniques with financial data analysis to revolutionize investment strategies. We are working with <strong>three of the top 10</strong> quantitative hedge funds, and with many mid-sized and boutique firms.</p> <p dir="auto">Our platform leverages diverse data sources and innovative algorithms to deliver actionable insights that drive smarter investment decisions.</p> <p dir="auto">By joining Sov.ai, you'll be part of a dynamic research team dedicated to pushing the boundaries of what's possible in finance through technology. Before expressing your interest, please be aware that the research will be predominantly challenging and experimental in nature.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">🔍 Research and Project Opportunities</h3><a id="user-content--research-and-project-opportunities" class="anchor" aria-label="Permalink: 🔍 Research and Project Opportunities" href="#-research-and-project-opportunities"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We offer a wide range of projects that cater to various interests and expertise within machine learning and finance. Some of the exciting recent projects include:</p> <ul dir="auto"> <li><strong>Predictive Modeling with GitHub Logs:</strong> Develop models to predict market trends and investment opportunities using GitHub activity and developer data.</li> <li><strong>Satallite Data Analysis:</strong> Explore non-traditional data sources such as social media sentiment, satellite imagery, or web traffic to enhance financial forecasting.</li> <li><strong>Data Imputation Techniques:</strong> Investigate new methods for handling missing or incomplete data to improve the robustness and accuracy of our models.</li> </ul> <p dir="auto">Please visit <a href="https://docs.sov.ai" rel="nofollow">docs.sov.ai</a> for more information on public projects that have made it into the subscription product. If you already have a corporate sponsor, we are also happy to work with them.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">🌐 Why Join Sov.ai?</h3><a id="user-content--why-join-sovai" class="anchor" aria-label="Permalink: 🌐 Why Join Sov.ai?" href="#-why-join-sovai"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><strong>Innovative Environment:</strong> Engage with the latest technologies and methodologies in machine learning and finance.</li> <li><strong>Collaborative Team:</strong> Work alongside a team of experts passionate about driving innovation in investment insights.</li> <li><strong>Flexible Projects:</strong> Tailor your research to align with your interests and expertise, with the freedom to explore new ideas.</li> <li><strong>Experienced Researchers:</strong> Experts previously from NYU, Columbia, Oxford-Man Institute, Alan Turing Institute, and Cambridge.</li> <li><strong>Post Research:</strong> Connect with alumni that has moved on to DRW, Citadel Securities, Virtu Financial, Akuna Capital, HRT.</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">🤝 How to Apply</h3><a id="user-content--how-to-apply" class="anchor" aria-label="Permalink: 🤝 How to Apply" href="#-how-to-apply"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you’re excited about leveraging your expertise in machine learning and finance to drive impactful research and projects, we’d love to hear from you! Please reach out to us at <a href="mailto:research@sov.ai">research@sov.ai</a> with your resume and a brief description of your research interests.</p> <p dir="auto">Join us in shaping the future of investment insights and making a meaningful impact in the world of finance!</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Admin</h3><a id="user-content-admin" class="anchor" aria-label="Permalink: Admin" href="#admin"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Have a look at the newly started <a href="https://medium.com/firmai" rel="nofollow">FirmAI Medium</a> publication where we have experts of AI in business, write about their topics of interest.</p> <p dir="auto">Please add your tools and notebooks to this <a href="https://docs.google.com/spreadsheets/d/1pVdV3r4X3k5D1UtKbhMTmjU8mJTZSLAhJzycurgh_o4/edit?usp=sharing" rel="nofollow">Google Sheet</a>. Or simply add it to this subreddit, <a href="https://www.reddit.com/r/datascienceproject/" rel="nofollow">r/datascienceproject</a></p> <p dir="auto">Highlight in <strong>YELLOW</strong> to get your package added, you can also just add it yourself with a <strong>pull request</strong>.</p> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="https://github.com/firmai/industry-machine-learning/raw/master/assets/industry.png"><img src="https://github.com/firmai/industry-machine-learning/raw/master/assets/industry.png" style="max-width: 100%;"></a> </p> <p dir="auto">A curated list of applied machine learning and data science notebooks and libraries accross different industries. The code in this repository is in Python (primarily using jupyter notebooks) unless otherwise stated. The catalogue is inspired by <code>awesome-machine-learning</code>. <a href="https://www.reddit.com/r/datascienceproject/" rel="nofollow">r/datascienceproject</a> is a subreddit where you can share all your data science projects.</p> <p dir="auto"><em><strong>Caution:</strong></em> This is a work in progress, please contribute, especially if you are a subject expert in any of the industries listed below. If you are a <strong>[analytical, computational, statistical, quantitive]</strong> researcher/analyst in field <strong>X</strong> or a field <strong>X</strong> <strong>[machine learning engineer, data scientist, modeler, programmer]</strong> then your contribution will be greatly appreciated.</p> <p dir="auto">If you want to contribute to this list (please do), send me a pull request or contact me <a href="https://twitter.com/dereknow" rel="nofollow">@dereknow</a> or on <a href="https://www.linkedin.com/in/snowderek/" rel="nofollow">linkedin</a> or get in contact on the website <a href="https://www.firmai.org" rel="nofollow">FirmAI</a>. Also, a listed repository should be deprecated if:</p> <ul dir="auto"> <li>Repository's owner explicitly say that "this library is not maintained".</li> <li>Not committed for long time (2~3 years).</li> </ul> <br> <p dir="auto"><strong>Help Needed:</strong> If there is any contributors out there willing to help first populate and then maintain a Python analytics section <strong>in any one of the following sub/industries,</strong> please get in contact with me. Also contact me to add <strong>additional industries</strong>.</p> <br> <markdown-accessiblity-table><table> <thead> <tr> <th></th> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td><a href="#accommodation">Accommodation & Food</a></td> <td><a href="#agriculture">Agriculture</a></td> <td><a href="#bankfin">Banking & Insurance</a></td> </tr> <tr> <td><a href="#biotech">Biotechnological & Life Sciences</a></td> <td><a href="#construction">Construction & Engineering</a></td> <td><a href="#education">Education & Research</a></td> </tr> <tr> <td><a href="#emergency">Emergency & Relief</a></td> <td><a href="#finance">Finance</a></td> <td><a href="#manufacturing">Manufacturing</a></td> </tr> <tr> <td><a href="#public">Government and Public Works</a></td> <td><a href="#healthcare">Healthcare</a></td> <td><a href="#media">Media & Publishing</a></td> </tr> <tr> <td><a href="#legal">Justice, Law and Regulations</a></td> <td><a href="#miscellaneous">Miscellaneous</a></td> <td><a href="#accounting">Accounting</a></td> </tr> <tr> <td><a href="#realestate">Real Estate, Rental & Leasing</a></td> <td><a href="#utilities">Utilities</a></td> <td><a href="#wholesale">Wholesale & Retail</a></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Table of Contents</h2><a id="user-content-table-of-contents" class="anchor" aria-label="Permalink: Table of Contents" href="#table-of-contents"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Industry Applications</h3><a id="user-content-industry-applications" class="anchor" aria-label="Permalink: Industry Applications" href="#industry-applications"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="#accommodation">Accommodation & Food</a> <ul dir="auto"> <li><a href="#accommodation-food">Food</a></li> <li><a href="#accommodation-rest">Restaurant</a></li> <li><a href="#accommodation-acc">Accommodation</a></li> </ul> </li> <li><a href="#accounting">Accounting</a> <ul dir="auto"> <li><a href="#accounting-ml">Machine Learning</a></li> <li><a href="#accounting-analytics">Analytics</a></li> <li><a href="#accounting-text">Textual Analysis</a></li> <li><a href="#accounting-data">Data</a></li> <li><a href="#accounting-ra">Research and Articles</a></li> <li><a href="#accounting-web">Websites</a></li> <li><a href="#accounting-course">Courses</a></li> </ul> </li> <li><a href="#agriculture">Agriculture</a> <ul dir="auto"> <li><a href="#agriculture-econ">Economics</a></li> <li><a href="#agriculture-dev">Development</a></li> </ul> </li> <li><a href="#bankfin">Banking & Insurance</a> <ul dir="auto"> <li><a href="#bankfin-cf">Consumer Financial</a></li> <li><a href="#bankfin-mo">Management and Operations</a></li> <li><a href="#bankfin-value">Valuation</a></li> <li><a href="#bankfin-fraud">Fraud</a></li> <li><a href="#bankfin-ir">Insurance and Risk</a></li> <li><a href="#bankfin-ph">Physical</a></li> <li><a href="#bankfin-data">Data</a></li> </ul> </li> <li><a href="#biotech">Biotechnological & Life Sciences</a> <ul dir="auto"> <li><a href="#biotech-general">General</a></li> <li><a href="#biotech-seq">Sequencing</a></li> <li><a href="#biotech-chem">Chemoinformatics and drug discovery</a></li> <li><a href="#biotech-gene">Genomics</a></li> <li><a href="#biotech-life">Life-sciences</a></li> </ul> </li> <li><a href="#construction">Construction & Engineering</a> <ul dir="auto"> <li><a href="#construction-const">Construction</a></li> <li><a href="#construction-eng">Engineering</a></li> <li><a href="#construction-mat">Material Science</a></li> </ul> </li> <li><a href="#economics">Economics</a> <ul dir="auto"> <li><a href="#economics-general">General</a></li> <li><a href="#economics-ml">Machine Learning</a></li> <li><a href="#economics-computational">Computational</a></li> </ul> </li> <li><a href="#education">Education & Research</a> <ul dir="auto"> <li><a href="#education-student">Student</a></li> <li><a href="#education-school">School</a></li> </ul> </li> <li><a href="#emergency">Emergency & Relief</a> <ul dir="auto"> <li><a href="#emergency-prevent">Preventative and Reactive</a></li> <li><a href="#emergency-crime">Crime</a></li> <li><a href="#emergency-ambulance">Ambulance</a></li> <li><a href="#emergency-disaster">Disaster Management</a></li> </ul> </li> <li><a href="#finance">Finance</a> <ul dir="auto"> <li><a href="#finance-trade">Trading & Investment</a></li> <li><a href="#finance-data">Data</a></li> </ul> </li> <li><a href="#healthcare">Healthcare</a> <ul dir="auto"> <li><a href="#healthcare-general">General</a></li> </ul> </li> <li><a href="#legal">Justice, Law and Regulations</a> <ul dir="auto"> <li><a href="#legal-tools">Tools</a></li> <li><a href="#legal-pr">Policy and Regulatory</a></li> <li><a href="#legal-judicial">Judicial</a></li> </ul> </li> <li><a href="#manufacturing">Manufacturing</a> <ul dir="auto"> <li><a href="#manufacturing-general">General</a></li> <li><a href="#manufacturing-maintenance">Maintenance</a></li> <li><a href="#manufacturing-fail">Failure</a></li> <li><a href="#manufacturing-quality">Quality</a></li> </ul> </li> <li><a href="#media">Media & Publishing</a> <ul dir="auto"> <li><a href="#media-marketing">Marketing</a></li> </ul> </li> <li><a href="#miscellaneous">Miscellaneous</a> <ul dir="auto"> <li><a href="#miscellaneous-art">Art</a></li> <li><a href="#miscellaneous-tour">Tourism</a></li> </ul> </li> <li><a href="#physics">Physics</a> <ul dir="auto"> <li><a href="#physics-general">General</a></li> <li><a href="#physics-ml">Machine Learning</a></li> </ul> </li> <li><a href="#public">Government and Public Works</a> <ul dir="auto"> <li><a href="#public-social">Social Policies</a></li> <li><a href="#public-elect">Election Analysis</a></li> <li><a href="#public-dis">Disaster Management</a></li> <li><a href="#public-poli">Politics</a></li> <li><a href="#public-charity">Charities</a></li> </ul> </li> <li><a href="#realestate">Real Estate, Rental & Leasing</a> <ul dir="auto"> <li><a href="#realestate-real">Real Estate</a></li> <li><a href="#realestate-rental">Rental & Leasing</a></li> </ul> </li> <li><a href="#utilities">Utilities</a> <ul dir="auto"> <li><a href="#utilities-elect">Electricity</a></li> <li><a href="#utilities-coal">Coal, Oil & Gas</a></li> <li><a href="#utilities-water">Water & Pollution</a></li> <li><a href="#utilities-transport">Transportation</a></li> </ul> </li> <li><a href="#wholesale">Wholesale & Retail</a> <ul dir="auto"> <li><a href="#wholesale-whole">Wholesale</a></li> <li><a href="#wholesale-retail">Retail</a></li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">ML/DS Career Section for Industry Machine Learning</h2><a id="user-content-mlds-career-section-for-industry-machine-learning" class="anchor" aria-label="Permalink: ML/DS Career Section for Industry Machine Learning" href="#mlds-career-section-for-industry-machine-learning"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">See <a href="https://github.com/firmai/data-science-career">data-science-career repo</a> for more.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Platforms:</h3><a id="user-content-platforms" class="anchor" aria-label="Permalink: Platforms:" href="#platforms"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ol dir="auto"> <li><a href="https://triplebyte.com/a/Nosq7GM/d" rel="nofollow">Triplebyte</a> - Take a quiz. Get offers from multiple top tech companies at once (now have a machine learning track).</li> <li><a href="https://www.toptal.com/" rel="nofollow">Toptal</a> - Developers seeking to gain entry into the Toptal community are put through a battery of personality and technical tests.</li> <li><a href="https://hired.com/" rel="nofollow">Hired</a> - Hired matches employers with qualified candidates through a combination of in-house algorithms and online support.</li> <li><a href="https://www.kaggle.com/jobs" rel="nofollow">Kaggle</a> - Scalable Path is a premium talent matching service.</li> </ol> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Reviews:</h3><a id="user-content-reviews" class="anchor" aria-label="Permalink: Reviews:" href="#reviews"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://www.glassdoor.com/index.htm" rel="nofollow">Glassdoor</a> - Best employee narratives.</li> <li><a href="https://www.indeed.com/" rel="nofollow">Indeed</a> - Best coverage.</li> <li><a href="https://www.kununu.com/us" rel="nofollow">Kununu</a> - Best well-rounded infromation.</li> <li><a href="https://www.comparably.com/" rel="nofollow">Comparably</a> - Best comparison functionality.</li> <li><a href="https://www.inhersight.com/" rel="nofollow">InHerSight</a> - Best female-friendly perspective.</li> </ul> <p dir="auto"><a name="user-content-accommodation"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Accommodation & Food</h2><a id="user-content-accommodation--food" class="anchor" aria-label="Permalink: Accommodation & Food" href="#accommodation--food"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-accommodation-food"></a> <strong>Food</strong></p> <ul dir="auto"> <li><a href="https://github.com/bschreck/robo-chef">RobotChef</a> - Refining recipes based on user reviews.</li> <li><a href="https://github.com/Ankushr785/Food-amenities-demand-prediction">Food Amenities</a> - Predicting the demand for food amenities using neural networks</li> <li><a href="https://github.com/catherhuang/FP3-recipe">Recipe Cuisine and Rating</a> - Predict the rating and type of cuisine from a list of ingredients.</li> <li><a href="https://github.com/stratospark/food-101-keras">Food Classification</a> - Classification using Keras.</li> <li><a href="https://github.com/Murgio/Food-Recipe-CNN">Image to Recipe</a> - Translate an image to a recipe using deep learning.</li> <li><a href="https://github.com/jubins/DeepLearning-Food-Image-Recognition-And-Calorie-Estimation">Calorie Estimation</a> - Estimate calories from photos of food.</li> <li><a href="https://github.com/Architectshwet/Amazon-Fine-Food-Reviews">Fine Food Reviews</a> - Sentiment analysis on Amazon Fine Food Reviews.</li> </ul> <p dir="auto"><a name="user-content-accommodation-rest"></a> <strong>Restaurant</strong></p> <ul dir="auto"> <li><a href="https://github.com/nd1/DC_RestaurantViolationForecasting">Restaurant Violation</a> - Food inspection violation forecasting.</li> <li><a href="https://github.com/alifier/Restaurant_success_model">Restaurant Success</a> - Predict whether a restaurant is going to fail.</li> <li><a href="https://github.com/josephofiowa/dc-michelin-challenge/tree/master/submissions">Predict Michelin</a> - Predict the likelihood that restaurant is a Michelin restaurant.</li> <li><a href="https://github.com/gzsuyu/Data-Analysis-NYC-Restaurant-Inspection-Data">Restaurant Inspection</a> - An inspection analysis to see if cleanliness is related to rating.</li> <li><a href="https://github.com/ayeright/sales-forecast-lstm">Sales</a> - Restaurant sales forecasting with LSTM.</li> <li><a href="https://github.com/anki1909/Recruit-Restaurant-Visitor-Forecasting">Visitor Forecasting</a> - Reservation and visitation number prediction.</li> <li><a href="https://github.com/everAspiring/RegressionAnalysis">Restaurant Profit</a> - Restaurant regression analysis.</li> <li><a href="https://github.com/klin90/missinglink">Competition</a> - Restaurant competitiveness analysis.</li> <li><a href="https://github.com/nvodoor/RBA">Business Analysis</a> - Restaurant business analysis project.</li> <li><a href="https://github.com/sanatasy/Restaurant_Risk">Location Recommendation</a> - Restaurant location recommendation tool and analysis.</li> <li><a href="https://github.com/Lolonon/Restaurant-Analytical-Solution">Closure, Rating and Recommendation</a> - Three prediction tasks using Yelp data.</li> <li><a href="https://github.com/Myau5x/anti-recommender">Anti-recommender</a> - Find restaurants you don’t want to attend.</li> <li><a href="https://github.com/bzjin/menus">Menu Analysis</a> - Deeper analysis of restaurants through their menus.</li> <li><a href="https://github.com/rphaneendra/Menu-Similarity">Menu Recommendation</a> - NLP to recommend restaurants with similar menus.</li> <li><a href="https://gist.github.com/analyticsindiamagazine/f9b2ba171a0eef9ad396ce6f1b83bbbc">Food Price</a> - Predict food cost.</li> <li><a href="https://github.com/firmai/interactive-corporate-report">Automated Restaurant Report</a> - Automated machine learning company report.</li> </ul> <p dir="auto"><a name="user-content-accommodation-acc"></a> <strong>Accommodation</strong></p> <ul dir="auto"> <li><a href="https://github.com/rochiecuevas/shared_accommodations">Peer-to-Peer Housing</a> - The effect of peer to peer rentals on housing.</li> <li><a href="https://github.com/SiddheshAcharekar/Liveright">Roommate Recommendation</a> - A system for students seeking roommates.</li> <li><a href="https://github.com/nus-usp/room-allocation">Room Allocation</a> - Room allocation process.</li> <li><a href="https://github.com/marcotav/hotels">Dynamic Pricing</a> - Hotel dynamic pricing calculations.</li> <li><a href="https://github.com/Montclair-State-University-Info368/Assignment-6">Hotel Similarity</a> - Compare brands that directly compete</li> <li><a href="https://github.com/EliadProject/Hotels-Data-Science">Hotel Reviews</a> - Cluster hotel reviews.</li> <li><a href="https://github.com/morenobcn/capstone_hotels_arcpy">Predict Prices</a> - Predict hotel room rates.</li> <li><a href="https://github.com/morenobcn/hotels_vs_airbnb_proof_of_concept">Hotels vs Airbnb</a> - Comparing the two approaches.</li> <li><a href="https://github.com/argha48/smarthotels">Hotel Improvement</a> - Analyse reviews to suggest hotel improvements.</li> <li><a href="https://github.com/Hasan330/Order-Cancellation-Prediction-Model">Orders</a> - Order cancellation prediction for hotels.</li> <li><a href="https://github.com/danielmachinelearning/HotelSpamDetection">Fake Reviews</a> - Identify whether reviews are fake/spam.</li> <li><a href="https://github.com/starfoe/Eye-bnb">Reverse Image Lodging</a> - Find your preferred lodging by uploading an image.</li> </ul> <p dir="auto"><a name="user-content-accounting"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Accounting</h2><a id="user-content-accounting" class="anchor" aria-label="Permalink: Accounting" href="#accounting"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-accounting-ml"></a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Machine Learning</h4><a id="user-content-machine-learning" class="anchor" aria-label="Permalink: Machine Learning" href="#machine-learning"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/agdgovsg/ml-coa-charging">Chart of Account Prediction</a> - Using labeled data to suggest the account name for every transaction.</li> <li><a href="https://github.com/GitiHubi/deepAI/blob/master/GTC_2018_CoLab.ipynb">Accounting Anomalies</a> - Using deep-learning frameworks to identify accounting anomalies.</li> <li><a href="https://github.com/rameshcalamur/fin-stmt-anom">Financial Statement Anomalies</a> - Detecting anomalies before filing, using R.</li> <li><a href="http://www.firmai.org/documents/Aged%20Debtors/" rel="nofollow">Useful Life Prediction (FirmAI)</a> - Predict the useful life of assets using sensor observations and feature engineering.</li> <li><a href="https://github.com/Niels-Peter/XBRL-AI">AI Applied to XBRL</a> - Standardized representation of XBRL into AI and Machine learning.</li> </ul> <p dir="auto"><a name="user-content-accounting-analytics"></a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Analytics</h4><a id="user-content-analytics" class="anchor" aria-label="Permalink: Analytics" href="#analytics"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/mschermann/forensic_accounting">Forensic Accounting</a> - Collection of case studies on forensic accounting using data analysis. On the lookout for more data to practise forensic accounting, <em>please get in <a href="https://github.com/mschermann/">touch</a></em></li> <li><a href="http://www.firmai.org/documents/General%20Ledger/" rel="nofollow">General Ledger (FirmAI)</a> - Data processing over a general ledger as exported through an accounting system.</li> <li><a href="http://www.firmai.org/documents/Bullet-Graph-Article/" rel="nofollow">Bullet Graph (FirmAI)</a> - Bullet graph visualisation helpful for tracking sales, commission and other performance.</li> <li><a href="http://www.firmai.org/documents/Aged%20Debtors/" rel="nofollow">Aged Debtors (FirmAI)</a> - Example analysis to invetigate aged debtors.</li> <li><a href="https://github.com/CharlesHoffmanCPA/charleshoffmanCPA.github.io">Automated FS XBRL</a> - XML Language, however, possibly port analysis into Python.</li> </ul> <p dir="auto"><a name="user-content-accounting-text"></a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Textual Analysis</h4><a id="user-content-textual-analysis" class="anchor" aria-label="Permalink: Textual Analysis" href="#textual-analysis"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/EricHe98/Financial-Statements-Text-Analysis">Financial Sentiment Analysis</a> - Sentiment, distance and proportion analysis for trading signals.</li> <li><a href="https://github.com/TiesdeKok/Python_NLP_Tutorial/blob/master/NLP_Notebook.ipynb">Extensive NLP</a> - Comprehensive NLP techniques for accounting research.</li> </ul> <p dir="auto"><a name="user-content-accounting-data"></a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Data, Parsing and APIs</h4><a id="user-content-data-parsing-and-apis" class="anchor" aria-label="Permalink: Data, Parsing and APIs" href="#data-parsing-and-apis"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/TiesdeKok/UW_Python_Camp/blob/master/Materials/Session_5/EDGAR_walkthrough.ipynb">EDGAR</a> - A walk-through in how to obtain EDGAR data.</li> <li><a href="https://github.com/gaulinmp/pyedgar">PyEDGAR</a> - A library for downloading, caching, and accessing EDGAR filings.</li> <li><a href="http://social-metrics.org/sox/" rel="nofollow">IRS</a> - Acessing and parsing IRS filings.</li> <li><a href="http://raw.rutgers.edu/Corporate%20Financial%20Data.html" rel="nofollow">Financial Corporate</a> - Rutgers corporate financial datasets.</li> <li><a href="http://raw.rutgers.edu/Non-Financial%20Corporate%20Data.html" rel="nofollow">Non-financial Corporate</a> - Rutgers non-financial corporate dataset.</li> <li><a href="https://github.com/danshorstein/python4cpas/blob/master/03_parsing_pdf_files/AR%20Aging%20-%20working.ipynb">PDF Parsing</a> - Extracting useful data from PDF documents.</li> <li><a href="https://github.com/danshorstein/ficpa_article">PDF Tabel to Excel</a> - How to output an excel file from a PDF.</li> </ul> <p dir="auto"><a name="user-content-accounting-ra"></a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Research And Articles</h4><a id="user-content-research-and-articles" class="anchor" aria-label="Permalink: Research And Articles" href="#research-and-articles"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="http://social-metrics.org/accountinganalytics/" rel="nofollow">Understanding Accounting Analytics</a> - An article that tackles the importance of accounting analytics.</li> <li><a href="http://www.vlfeat.org/" rel="nofollow">VLFeat</a> - VLFeat is an open and portable library of computer vision algorithms, which has Matlab toolbox.</li> </ul> <p dir="auto"><a name="user-content-accounting-web"></a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Websites</h4><a id="user-content-websites" class="anchor" aria-label="Permalink: Websites" href="#websites"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="http://raw.rutgers.edu/" rel="nofollow">Rutgers Raw</a> - Good digital accounting research from Rutgers.</li> </ul> <p dir="auto"><a name="user-content-accounting-course"></a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Courses</h4><a id="user-content-courses" class="anchor" aria-label="Permalink: Courses" href="#courses"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://www.youtube.com/playlist?list=PLauepKFT6DK8TaNaq_SqZW4LIDJhCkZe2" rel="nofollow">Computer Augmented Accounting</a> - A video series from Rutgers University looking at the use of computation to improve accounting.</li> <li><a href="https://www.youtube.com/playlist?list=PLauepKFT6DK8_Xun584UQPPsg1grYkWw0" rel="nofollow">Accounting in a Digital Era</a> - Another series by Rutgers investigating the effects the digital age will have on accounting.</li> </ul> <p dir="auto"><a name="user-content-agriculture"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Agriculture</h2><a id="user-content-agriculture" class="anchor" aria-label="Permalink: Agriculture" href="#agriculture"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-agriculture-econ"></a> <strong>Economics</strong></p> <ul dir="auto"> <li><a href="https://github.com/deadskull7/Agricultural-Price-Prediction-and-Visualization-on-Android-App">Prices</a> - Agricultural price prediction.</li> <li><a href="https://github.com/Vipul115/Statistical-Time-Series-Analysis-on-Agricultural-Commodity-Prices">Prices 2</a> - Agricultural price prediction.</li> <li><a href="https://github.com/DFS-UCU/UkrainianAgriculture">Yield</a> - Agricultural analysis looking at crop yields in Ukraine.</li> <li><a href="https://github.com/vicelab/slaer">Recovery</a> - Strategic land use for agriculture and ecosystem recovery</li> <li><a href="https://github.com/gumballhead/mpr">MPR</a> - Mandatory Price Reporting data from the USDA's Agricultural Marketing Service.</li> </ul> <p dir="auto"><a name="user-content-agriculture-dev"></a> <strong>Development</strong></p> <ul dir="auto"> <li><a href="https://github.com/chrieke/InstanceSegmentation_Sentinel2">Segmentation</a> - Agricultural field parcel segmentation using satellite images.</li> <li><a href="https://github.com/jfzhang95/LSTM-water-table-depth-prediction">Water Table</a> - Predicting water table depth in agricultural areas.</li> <li><a href="https://github.com/surajmall/Agriculture-Assistant/tree/master/models">Assistant</a> - Notebooks from agricultural assistant.</li> <li><a href="https://github.com/tecoevo/agriculture">Eco-evolutionary</a> - Eco-evolutionary dynamics.</li> <li><a href="https://github.com/gauravmunjal13/Agriculture">Diseases</a> - Identification of crop diseases and pests using Deep Learning framework from the images.</li> <li><a href="https://github.com/divyam3897/agriculture">Irrigation and Pest Prediction</a> - Analyse irrigation and predict pest likelihood.</li> </ul> <p dir="auto"><a name="user-content-bankfin"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Banking & Insurance</h2><a id="user-content-banking--insurance" class="anchor" aria-label="Permalink: Banking & Insurance" href="#banking--insurance"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-bankfin-cv"></a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Consumer Finance</h4><a id="user-content-consumer-finance" class="anchor" aria-label="Permalink: Consumer Finance" href="#consumer-finance"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/Paresh3189/Bankruptcy-Prediction-Growth-Modelling">Loan Acceptance</a> - Classification and time-series analysis for loan acceptance.</li> <li><a href="https://github.com/Featuretools/predict-loan-repayment">Predict Loan Repayment</a> - Predict whether a loan will be repaid using automated feature engineering.</li> <li><a href="https://github.com/RealRadOne/Gyani-The-Loan-Eligibility-Predictor">Loan Eligibility Ranking</a> - System to help the banks check if a customer is eligible for a given loan.</li> <li><a href="http://www.firmai.org/documents/Aggregator/#each-time-step-takes-30-seconds" rel="nofollow">Home Credit Default (FirmAI)</a> - Predict home credit default.</li> <li><a href="https://github.com/abuchowdhury/Mortgage_Bank_Loan_Analtsics/blob/master/Mortgage%20Bank%20Loan%20Analytics.ipynb">Mortgage Analytics</a> - Extensive mortgage loan analytics.</li> <li><a href="https://github.com/IBM-Cloud-DevFest-2018/Data-Science-for-Banking/blob/master/02-CreditCardApprovalModel/CreditCardApprovalModel.ipynb">Credit Approval</a> - A system for credit card approval.</li> <li><a href="https://github.com/Brett777/Predict-Risk">Loan Risk</a> - Predictive model to help to reduce charge-offs and losses of loans.</li> <li><a href="http://www.firmai.org/documents/Amortization%20Schedule/" rel="nofollow">Amortisation Schedule (FirmAI)</a> - Simple amortisation schedule in python for personal use.</li> </ul> <p dir="auto"><a name="user-content-bankfin-mo"></a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Management and Operation</h4><a id="user-content-management-and-operation" class="anchor" aria-label="Permalink: Management and Operation" href="#management-and-operation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/am-aditya/Artificial-Intelligence-for-Banking/blob/master/03_ipy_notebooks/clv_prediction.ipynb">Credit Card</a> - Estimate the CLV of credit card customers.</li> <li><a href="https://github.com/am-aditya/Artificial-Intelligence-for-Banking/blob/master/01_code/01_02_clv_survival/Survival_Analysis.py">Survival Analysis</a> - Perform a survival analysis of customers.</li> <li><a href="https://github.com/am-aditya/Artificial-Intelligence-for-Banking/blob/master/01_code/01_02_clv_survival/Customer_NextTransaction_Prediction.py">Next Transaction</a> - Deep learning model to predict the transaction amount and days to next transaction.</li> <li><a href="https://github.com/am-aditya/Artificial-Intelligence-for-Banking/blob/master/01_code/01_02_clv_survival/Customer_NextTransaction_Prediction.py">Credit Card Churn</a> - Predicting credit card customer churn.</li> <li><a href="https://github.com/sekhansen/mpc_minutes_demo/blob/master/information_retrieval.ipynb">Bank of England Minutes</a> - Textual analysis over bank minutes.</li> <li><a href="https://github.com/kaumaron/Data_Science/tree/master/CEO_Compensation">CEO</a> - Analysis of CEO compensation.</li> </ul> <p dir="auto"><a name="user-content-bankfin-value"></a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Valuation</h4><a id="user-content-valuation" class="anchor" aria-label="Permalink: Valuation" href="#valuation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/eswar3/Zillow-prediction-models">Zillow Prediction</a> - Zillow valuation prediction as performed on Kaggle.</li> <li><a href="https://github.com/denadai2/real-estate-neighborhood-prediction">Real Estate</a> - Predicting real estate prices from the urban environment.</li> <li><a href="https://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/exercises/P1-UsedVehiclePricePrediction.ipynb" rel="nofollow">Used Car</a> - Used vehicle price prediction.</li> </ul> <p dir="auto"><a name="user-content-bankfin-fraud"></a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Fraud</h4><a id="user-content-fraud" class="anchor" aria-label="Permalink: Fraud" href="#fraud"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/KSpiliop/Fraud_Detection">XGBoost</a> - Fraud Detection by tuning XGBoost hyper-parameters with Simulated Annealing</li> <li><a href="https://github.com/longtng/frauddetectionproject/blob/master/A%20Consideration%20Point%20of%20%20Fraud%20Detection%20in%20Bank%20Loans%20Project%20Code.ipynb">Fraud Detection Loan in R</a> - Fraud detection in bank loans.</li> <li><a href="https://github.com/Michaels72/AML-Due-Diligence/blob/master/AML_Finance_DD.ipynb">AML Finance Due Diligence</a> - Search news articles to do finance AML DD.</li> <li><a href="https://github.com/am-aditya/Artificial-Intelligence-for-Banking/blob/master/03_ipy_notebooks/fraud_detection.ipynb">Credit Card Fraud</a> - Detecting credit card fraud.</li> </ul> <p dir="auto"><a name="user-content-bankfin-ir"></a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Insurance and Risk</h4><a id="user-content-insurance-and-risk" class="anchor" aria-label="Permalink: Insurance and Risk" href="#insurance-and-risk"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/neokt/car-damage-detective">Car Damage Detective</a> - Assessing car damage with convolution neural networks for a personal auto <em>claims.</em></li> <li><a href="https://github.com/roshank1605A04/Insurance-Claim-Prediction/blob/master/InsuranceClaim.ipynb">Medical Insurance Claims</a> - Predicting medical insurance claims.</li> <li><a href="https://github.com/slegroux/claimdenial/blob/master/Claim%20Denial.ipynb">Claim Denial</a> - Predicting insurance claim denial</li> <li><a href="https://github.com/rshea3/alpha-insurance">Claim Fraud</a> - Predictive models to determine which automobile claims are fraudulent.</li> <li><a href="https://github.com/dchannah/fraudhacker">Claims Anomalies</a> - Anomaly detection system for medical insurance claims data.</li> <li><a href="https://github.com/JSchelldorfer/ActuarialDataScience">Actuarial Sciences (R)</a> - A range of actuarial tools in R.</li> <li><a href="https://github.com/Shomona/Bank-Failure-Prediction/blob/master/Bank.ipynb">Bank Failure</a> - Predicting bank failure.</li> <li><a href="https://github.com/andrey-lukyanov/Risk-Management">Risk Management</a> - Finance risk engagement course resources.</li> <li><a href="https://github.com/hamaadshah/market_risk_gan_keras">VaR GaN</a> - Estimate Value-at-Risk for market risk management using Keras and TensorFlow.</li> <li><a href="https://github.com/SaiBiswas/Bank-Grievance-Compliance-Management/blob/master/The%20Main%20File.ipynb">Compliance</a> - Bank Grievance Compliance Management.</li> <li><a href="https://github.com/apbecker/Systemic_Risk/blob/master/Generalized.ipynb">Stress Testing</a> - ECB stress testing.</li> <li><a href="https://github.com/kaitai/stress-testing-with-jupyter/blob/master/Playing%20with%20financial%20data%20and%20Python%203.ipynb">Stress Testing Techniques</a> - A notebook with various stress testing exercises.</li> <li><a href="https://github.com/arcadynovosyolov/reverse_stress_testing/blob/master/reverse_stress_testing.ipynb">Reverse Stress Test</a> - Given a portfolio and a predefined loss size, determine which factors stress (scenarios) would lead to that loss</li> <li><a href="https://github.com/VankatPetr/BoE_stress_test/blob/master/BoE_stress_test_5Y_cummulative_imparment_charge.ipynb">BoE stress test</a>- Stress test results and plotting.</li> <li><a href="https://github.com/hkacmaz/Bankin_Recovery/blob/master/Banking_Recovery.ipynb">Recovery</a> - Recovery of money owed.</li> <li><a href="https://github.com/mick-zhang/Quality-Control-for-Banking-using-LDA-and-LDA-Mallet">Quality Control</a> - Quality control for banking using LDA</li> </ul> <p dir="auto"><a name="user-content-bankfin-ph"></a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Physical</h4><a id="user-content-physical" class="anchor" aria-label="Permalink: Physical" href="#physical"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/apoorv-goel/Bank-Note-Authentication-Using-DNN-Tensorflow-Classifier-and-RandomForest">Bank Note Fraud Detection</a> - Bank Note Authentication Using DNN Tensorflow Classifier and RandomForest.</li> <li><a href="https://github.com/ShreyaGupta08/InfosysHack">ATM Surveillance</a> - ATM Surveillance in banks use case.</li> </ul> <p dir="auto"><a name="user-content-biotech"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Biotechnological & Life Sciences</h2><a id="user-content-biotechnological--life-sciences" class="anchor" aria-label="Permalink: Biotechnological & Life Sciences" href="#biotechnological--life-sciences"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-biotech-general"></a> <strong>General</strong></p> <ul dir="auto"> <li><a href="https://github.com/burkesquires/python_biologist">Programming</a> - Python Programming for Biologists</li> <li><a href="https://colab.research.google.com/drive/17E4h5aAOioh5DiTo7MZg4hpL6Z_0FyWr" rel="nofollow">Introduction DL</a> - A Primer on Deep Learning in Genomics</li> <li><a href="https://github.com/talmo/leap">Pose</a> - Estimating animal poses using DL.</li> <li><a href="https://github.com/greenelab/SPRINT_gan">Privacy</a> - Privacy preserving NNs for clinical data sharing.</li> <li><a href="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004845" rel="nofollow">Population Genetics</a> - DL for population genetic inference.</li> <li><a href="https://github.com/ricket-sjtu/bioinformatics">Bioinformatics Course</a> - Course materials for Computational <em>Biology</em>and Bioinformatics</li> <li><a href="https://github.com/waldronlab/AppStatBio">Applied Stats</a> - Applied Statistics for High-Throughput <em>Biology</em></li> <li><a href="https://github.com/mingzhangyang/Mybiotools">Scripts</a> - Python scripts for biologists.</li> <li><a href="https://github.com/mitmedialab/Evolutron">Molecular NN</a> - A mini-framework to build and train neural networks for molecular <em>biology</em>.</li> <li><a href="https://github.com/hallba/WritingSimulators">Systems Biology Simulations</a> - Systems <em>biology</em> practical on writing simulators with F# and Z3</li> <li><a href="https://github.com/jrieke/lstm-biology">Cell Movement</a> - LSTM to predict biological cell movement.</li> <li><a href="https://github.com/deepchem/deepchem">Deepchem</a> - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology</li> </ul> <p dir="auto"><a name="user-content-biotech-seq"></a> <strong>Sequencing</strong></p> <ul dir="auto"> <li><a href="https://github.com/ehsanasgari/Deep-Proteomics">DNA, RNA and Protein Sequencing</a> - Anew representation for biological sequences using DL.</li> <li><a href="https://github.com/budach/pysster">CNN Sequencing</a> - A toolbox for learning motifs from DNA/RNA sequence data using convolutional neural networks</li> <li><a href="https://github.com/hussius/deeplearning-biology">NLP Sequencing</a> - Language transfer learning model for genomics</li> </ul> <p dir="auto"><a name="user-content-biotech-chem"></a> <strong>Chemoinformatics and drug discovery</strong></p> <ul dir="auto"> <li><a href="https://github.com/HIPS/neural-fingerprint">Novel Molecules</a> - A convolutional net that can learn features.</li> <li><a href="https://github.com/aspuru-guzik-group/chemical_vae">Automating Chemical Design</a> - Generate new molecules for efficient exploration.</li> <li><a href="https://github.com/gablg1/ORGAN">GAN drug Discovery</a> - A method that combines generative models with reinforcement learning.</li> <li><a href="https://github.com/MarcusOlivecrona/REINVENT">RL</a> - generating compounds predicted to be active against a biological target.</li> <li><a href="https://github.com/deepchem/deepchem">One-shot learning</a> - Python library that aims to make the use of machine-learning in drug discovery straightforward and convenient.</li> </ul> <p dir="auto"><a name="user-content-biotech-gene"></a> <strong>Genomics</strong></p> <ul dir="auto"> <li><a href="https://github.com/ucsd-ccbb/jupyter-genomics">Jupyter Genomics</a> - Collection of computation biology and bioinformatics notebooks.</li> <li><a href="https://github.com/google/deepvariant">Variant calling</a> - Correctly identify variations from the reference genome in an individual's DNA.</li> <li><a href="https://github.com/mila-iqia/gene-graph-conv">Gene Expression Graphs</a> - Using convolutions on an image.</li> <li><a href="https://github.com/greenelab/adage">Autoencoding Expression</a> - Extracting relevant patterns from large sets of gene expression data</li> <li><a href="https://github.com/uci-cbcl/D-GEX">Gene Expression Inference</a> - Predict the expression of specified target genes from a panel of about 1,000 pre-selected “landmark genes”.</li> <li><a href="https://github.com/widdowquinn/Teaching-EMBL-Plant-Path-Genomics">Plant Genomics</a> - Presentation and example material for <em>Plant</em> and Pathogen Genomics</li> </ul> <p dir="auto"><a name="user-content-biotech-life"></a> <strong>Life-sciences</strong></p> <ul dir="auto"> <li><a href="https://github.com/viritaromero/Plant-diseases-classifier">Plants Disease</a> - App that detects diseases in <em>plants</em> using a deep learning model.</li> <li><a href="https://github.com/AayushG159/Plant-Leaf-Identification">Leaf Identification</a> - Identification of <em>plants</em> through <em>plant</em> leaves on the basis of their shape, color and texture.</li> <li><a href="https://github.com/openalea/eartrack">Crop Analysis</a> - An imaging library to detect and track future position of ears on maize <em>plants</em></li> <li><a href="https://github.com/mfsatya/PlantSeedlings-Classification">Seedlings</a> - <em>Plant</em> Seedlings Classification from kaggle competition</li> <li><a href="https://github.com/Planteome/ontology-of-plant-stress">Plant Stress</a> - An ontology containing plant stresses; biotic and abiotic.</li> <li><a href="https://github.com/sacul-git/hierarpy">Animal Hierarchy</a> - Package for calculating <em>animal</em> dominance hierarchies.</li> <li><a href="https://github.com/A7med01/Deep-learning-for-Animal-Identification">Animal Identification</a> - Deep learning for animal identification.</li> <li><a href="https://github.com/NomaanAhmed/BigData_AnimalSpeciesAnalysis">Species</a> - Big Data analysis of different species of <em>animals</em></li> <li><a href="https://github.com/timsainb/AVGN">Animal Vocalisations</a> - A generative network for animal vocalizations</li> <li><a href="https://github.com/hardmaru/estool">Evolutionary</a> - Evolution Strategies Tool</li> <li><a href="https://github.com/OGGM/oggm-edu">Glaciers</a> - Educational material about glaciers.</li> </ul> <p dir="auto"><a name="user-content-construction"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Construction & Engineering</h2><a id="user-content-construction--engineering" class="anchor" aria-label="Permalink: Construction & Engineering" href="#construction--engineering"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-construction-con"></a> <strong>Construction</strong></p> <ul dir="auto"> <li><a href="https://github.com/carolineh101/deep-learning-architecture">DL Architecture</a> - Deep learning classifier and image generator for building architecture.</li> <li><a href="https://github.com/damontallen/Construction-materials">Construction Materials</a> - A course on construction materials.</li> <li><a href="https://github.com/dariusmehri/Social-Network-Bad-Actor-Risk-Tool">Bad Actor Risk Model</a> - Risk model to improve construction related building safety</li> <li><a href="https://github.com/dariusmehri/Tracking-Inspectors-with-Euclidean-Distance-Algorithm">Inspectors</a> - Determine the assigned inspections.</li> <li><a href="https://github.com/dariusmehri/Social-Network-Analysis-to-Expose-Corruption">Corrupt Social Interactions</a> - Uncover potential corrupt social interactions between an industry member and the staff at the DOB</li> <li><a href="https://github.com/dariusmehri/Risk-Screening-Tool-to-Predict-Accidents-at-Construction-Sites">Risk Construction</a> - Identify high risk construction.</li> <li><a href="https://github.com/dariusmehri/Algorithm-for-Finding-Buildings-with-Facade-Risk">Facade Risk</a> - A risk model to predict unsafe facades.</li> <li><a href="https://github.com/dariusmehri/Predicting-Staff-Levels-for-Front-line-Workers">Staff Levels</a> - Predicting staff levels for front line workers.</li> <li><a href="https://github.com/dariusmehri/Topic-Modeling-and-Analysis-of-Building-Related-Injuries">Injuries</a> - Building related injuries topic modelling.</li> <li><a href="https://github.com/dariusmehri/Predictive-Analysis-of-Building-Violations">Building Violations</a> - Predictive analysis of building violations.</li> <li><a href="https://github.com/dariusmehri/Inspection-Productivity-Analysis-and-Visualization-with-Tableau">Productivity</a> - Productivity analysis and inspection with Tableau.</li> </ul> <p dir="auto"><a name="user-content-construction-eng"></a> <strong>Engineering:</strong></p> <ul dir="auto"> <li><a href="https://github.com/ritchie46/anaStruct">Structural Analysis</a> - 2D Structural Analysis in Python.</li> <li><a href="https://github.com/buddyd16/Structural-Engineering">Structural Engineering</a> - Structural engineering modules.</li> <li><a href="https://github.com/JorgeDeLosSantos/nusa">Nusa</a> - Structural analysis using the finite element method.</li> <li><a href="https://github.com/BrianChevalier/StructPy">StructPy</a> - Structural Analysis Library for Python based on the direct stiffness method</li> <li><a href="https://github.com/albiboni/AileronSimulation">Aileron</a> - Structural analysis of the aileron of a Boeing 737</li> <li><a href="https://github.com/vibrationtoolbox/vibration_toolbox">Vibration</a> - Educational vibration programs.</li> <li><a href="https://github.com/ebrahimraeyat/Civil">Civil</a> - Collection of civil engineering tools in FreeCAD</li> <li><a href="https://github.com/manuvarkey/GEstimator">GEstimator</a> - Simple civil estimation software</li> <li><a href="https://github.com/Gunnstein/fatpack">Fatpack</a> - Functions and classes for fatigue analysis of data series.</li> <li><a href="https://github.com/yajnab/pySteel">Pysteel</a> - Automated design of different steel structure</li> <li><a href="https://github.com/davidsteinar/structural-uncertainty">Structural Uncertainty</a> - Quantifying structural uncertainty with deep learning.</li> <li><a href="https://github.com/jellespijker/pymech">Pymech</a> - A Python module for mechanical engineers</li> <li><a href="https://github.com/AlvaroMenduina/Jupyter_Notebooks/tree/master/Introduction_Aerospace_Engineering">Aerospace Engineering</a> - Astrodynamics and Statistics</li> <li><a href="https://github.com/psi4/psi4numpy">Interactive Quantum Chemistry</a> - Combining Psi4 and Numpy for education and development.</li> <li><a href="https://github.com/CAChemE/learn">Chemical and Process Engineering</a> - Various resources.</li> <li><a href="https://github.com/iurisegtovich/PyTherm-applied-thermodynamics">PyTherm</a> - Applied Thermodynamics</li> <li><a href="https://github.com/kshitizkhanal7/Aerogami">Aerogami</a> - Aerodynamics using planes.</li> <li><a href="https://github.com/geoscixyz/em-apps">Electro geophysics</a> - Interactive applications for electromagnetics in geophysics</li> <li><a href="https://github.com/mdeff/pygsp_tutorial_graphsip">Graph Signal</a> - Graph signal processing tutorial.</li> <li><a href="https://github.com/DocVaughan/MCHE485---Mechanical-Vibrations">Mechanical Vibrations</a> - Mechanical Vibrations at the Univsersity of Louisiana.</li> <li><a href="https://github.com/OpenChemE/CHBE356">Process Dynamics</a> - Process Dynamics and Control</li> <li><a href="https://github.com/rdbraatz/data-driven-prediction-of-battery-cycle-life-before-capacity-degradation">Battery Life Cycle</a> - Data driven prediction of batter life cycle.</li> <li><a href="https://github.com/DTUWindEnergy/Python4WindEnergy">Wind Energy</a> - Python for wind energy</li> <li><a href="https://github.com/openeemeter/eemeter/blob/master/scripts/tutorial.ipynb">Energy Use</a> - Standard methods for calculating normalized metered energy consumption</li> <li><a href="https://github.com/HitarthiShah/Radiation-Data-Analysis">Nuclear Radiation</a> - How people are affected by radiations emitted by nuclear power plants</li> </ul> <p dir="auto"><a name="user-content-construction-mat"></a> <strong>Material Science</strong></p> <ul dir="auto"> <li><a href="https://github.com/materialsproject/pymatgen/">Python Materials Genomics</a> - Robust material analysis code used in a well-established project.</li> <li><a href="https://github.com/dchannah/materials_mining">Materials Mining</a> - Scripts for simulations and analysis of materials.</li> <li><a href="https://github.com/materialsproject/emmet">Emmet</a> - Build databases of material properties.</li> <li><a href="https://github.com/materialsvirtuallab/megnet">Megnet</a> - Graph networks as a ML framework for Molecules and Crystals</li> <li><a href="https://github.com/hackingmaterials/atomate">Atomate</a> - Pre-built workflows for computational material science.</li> <li><a href="https://github.com/Mehranov/UnderstandingAndPredictingPropertyMaintenanceFines/blob/master/Assignment4_complete.ipynb">Bylaws Compliance</a> - Predicting property fines.</li> <li><a href="https://github.com/sierraporta/asphalt_binder">Asphalt Binder</a> - Construction materials, free energy and chemical composition of asphalt binder.</li> <li><a href="https://github.com/hbutsuak95/Quality-Optimization-of-Steel">Steel</a> - Optimisation of steel.</li> <li><a href="https://github.com/tilde-lab/awesome-materials-informatics">Awesome Materials Informatics</a> - Curated list of known efforts in materials informatics.</li> </ul> <p dir="auto"><a name="user-content-economics"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Economics</h2><a id="user-content-economics" class="anchor" aria-label="Permalink: Economics" href="#economics"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-economics-general"></a> <strong>General</strong></p> <ul dir="auto"> <li><a href="https://github.com/tradingeconomics/tradingeconomics">Trading Economics API</a> - Information for 196 countries.</li> <li><a href="https://github.com/jhconning/Dev-II/tree/master/notebooks">Development Economics</a> - Development microeconomics are written mostly as interactive jupyter notebooks</li> <li><a href="https://github.com/lnsongxf/Applied_Computational_Economics_and_Finance/blob/master/Chapter05.ipynb">Applied Econ & Fin</a> - Applied Computational Economics and Finance</li> <li><a href="https://github.com/jlperla/ECON407_2018">Macroeconomics</a> - Topics in macroeconomics with notebook examples.</li> </ul> <p dir="auto"><a name="user-content-economics-ml"></a> <strong>Machine Learning</strong></p> <ul dir="auto"> <li><a href="https://github.com/microsoft/EconML">EconML</a> - Automated Learning and Intelligence for Causation and <em>Economics.</em></li> <li><a href="https://github.com/saisrivatsan/deep-opt-auctions">Auctions</a> - Optimal auctions using deep learning.</li> </ul> <p dir="auto"><a name="user-content-economics-comp"></a> <strong>Computational</strong></p> <ul dir="auto"> <li><a href="https://github.com/jstac/quantecon_nyu_2016">Quant Econ</a> - Quantitative economics course by NYU</li> <li><a href="https://github.com/zhentaoshi/econ5170">Computational</a> - Computational methods in economics.</li> <li><a href="https://github.com/QuantEcon/columbia_mini_course">Computational 2</a> - Small course in computational economics.</li> <li><a href="https://github.com/jstac/econometrics/tree/master/notebooks">Econometric Theory</a> - Notebooks of A Primer on Econometric theory.</li> </ul> <p dir="auto"><a name="user-content-education"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Education & Research</h2><a id="user-content-education--research" class="anchor" aria-label="Permalink: Education & Research" href="#education--research"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-education-student"></a> <strong>Student</strong></p> <ul dir="auto"> <li><a href="https://github.com/roshank1605A04/Education-Process-Mining">Student Performance</a> - Mining student performance using machine learning.</li> <li><a href="https://github.com/janzaib-masood/Educational-Data-Mining">Student Performance 2</a> - Student exam performance.</li> <li><a href="https://github.com/RohithYogi/Student-Performance-Prediction">Student Performance 3</a> - Student achievement in secondary education.</li> <li><a href="https://github.com/roshank1605A04/Students-Performance-Analytics">Student Performance 4</a> - Students Performance Evaluation using Feature Engineering</li> <li><a href="https://github.com/eloyekunle/student_intervention/blob/master/student_intervention.ipynb">Student Intervention</a> - Building a student intervention system.</li> <li><a href="https://github.com/arrahman17/Learning-Analytics-Project-">Student Enrolment</a> - Student enrolment and performance analysis.</li> <li><a href="https://github.com/janzaib-masood/Educational-Data-Mining">Academic Performance</a> - Explore the demographic and family features that have an impact a student's academic performance.</li> <li><a href="https://github.com/kaumaron/Data_Science/tree/master/Grade_Analysis">Grade Analysis</a> - Student achievement analysis.</li> </ul> <p dir="auto"><a name="user-content-education-school"></a> <strong>School</strong></p> <ul dir="auto"> <li><a href="https://github.com/nprapps/school-choice">School Choice</a> - Data analysis for education's school choice.</li> <li><a href="https://github.com/tullyvelte/SchoolPerformanceDataAnalysis">School Budgets and Priorities</a> - Helping the school board and mayor make strategic decisions regarding future school budgets and priorities</li> <li><a href="https://github.com/bradleyrobinson/School-Performance">School Performance</a> - Data analysis practice using data from data.utah.gov on school performance.</li> <li><a href="https://github.com/vtyeh/pandas-challenge">School Performance 2</a> - Using pandas to analyze school and student performance within a district</li> <li><a href="https://github.com/benattix/philly-schools">School Performance 3</a> - Philadelphia School Performance</li> <li><a href="https://github.com/adrianakopf/NJPublicSchools">School Performance 4</a> - NJ School Performance</li> <li><a href="https://github.com/whugue/school-closure">School Closure</a> - Identify schools at risk for closure by performance and other characteristics.</li> <li><a href="https://github.com/datacamp/course-resources-ml-with-experts-budgets/blob/master/notebooks/1.0-full-model.ipynb">School Budgets</a> - Tools and techniques for school budgeting.</li> <li><a href="https://github.com/nymarya/school-budgets-for-education/tree/master/notebooks">School Budgets</a> - Same as a above, datacamp.</li> <li><a href="https://github.com/JonathanREB/Budget_SchoolsAnalysis/blob/master/PyCitySchools_starter.ipynb">PyCity</a> - School analysis.</li> <li><a href="https://github.com/1davegalloway/SchoolDistrictAnalysis">PyCity 2</a> - School budget vs school results.</li> <li><a href="https://github.com/jinsonfernandez/NLP_School-Budget-Project">Budget NLP</a> - NLP classification for budget resources.</li> <li><a href="https://github.com/DivyaMadhu/School-Budget-Prediction">Budget NLP 2</a> - Further classification exercise.</li> <li><a href="https://github.com/sushant2811/SchoolBudgetData/blob/master/SchoolBudgetData.ipynb">Budget NLP 3</a> - Budget classification.</li> <li><a href="https://github.com/kaumaron/Data_Science/tree/master/Education">Survey Analysis</a> - Education survey analysis.</li> </ul> <p dir="auto"><a name="user-content-emergency"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Emergency & Police</h2><a id="user-content-emergency--police" class="anchor" aria-label="Permalink: Emergency & Police" href="#emergency--police"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-emergency-prevent"></a> <strong>Preventative and Reactive</strong></p> <ul dir="auto"> <li><a href="https://github.com/aeronetlab/emergency-mapping">Emergency Mapping</a> - Detection of destroyed houses in California</li> <li><a href="https://github.com/roshetty/Supporting-Emergency-Room-Decision-Making-with-Relevant-Scientific-Literature">Emergency Room</a> - Supporting em<em>ergency r</em>oom decision making</li> <li><a href="https://github.com/mesgarpour/T-CARER">Emergency Readmission</a> - Adjusted Risk of <em>Emergency</em> Readmission.</li> <li><a href="https://github.com/LeadingIndiaAI/Forest-Fire-Detection-through-UAV-imagery-using-CNNs">Forest Fire</a> - Forest fire detection through UAV imagery using CNNs</li> <li><a href="https://github.com/sky-t/hack-or-emergency-response">Emergency Response</a> - Emergency response analysis.</li> <li><a href="https://github.com/bayesimpact/bayeshack-transportation-ems">Emergency Transportation</a> - Transportation prompt on <em>emergency</em> services</li> <li><a href="https://github.com/jamesypeng/Smarter-Emergency-Dispatch">Emergency Dispatch</a> - Reducing response times with predictive modeling, optimization, and automation</li> <li><a href="https://github.com/analystiu/LICT-Project-Emergency-911-Calls">Emergency Calls</a> - Emergency calls analysis project.</li> <li><a href="https://github.com/tanoybhattacharya/911-Data-Analysis">Calls Data Analysis</a> - 911 data analysis.</li> <li><a href="https://github.com/amunategui/Leak-At-Chemical-Factory-RL">Emergency Response</a> - Chemical factory RL.</li> </ul> <p dir="auto"><a name="user-content-emergency-crime"></a> <strong>Crime</strong></p> <ul dir="auto"> <li><a href="https://github.com/datadesk/lapd-crime-classification-analysis">Crime Classification</a> - Times analysis of serious assaults misclassified by LAPD.</li> <li><a href="https://github.com/chicago-justice-project/article-tagging">Article Tagging</a> - Natural Language Processing of Chicago news article</li> <li><a href="https://github.com/chrisPiemonte/crime-analysis">Crime Analysis</a> - Association Rule Mining from Spatial Data for <em>Crime</em> Analysis</li> <li><a href="https://github.com/search?o=desc&q=crime+language%3A%22Jupyter+Notebook%22+NOT+%22taxi%22+NOT+%22baseline%22&s=stars&type=Repositories">Chicago Crimes</a> - Exploring public Chicago <em>crimes</em> data set in Python</li> <li><a href="https://github.com/pedrohserrano/graph-analytics-nederlands">Graph Analytics</a> - The Hague Crimes.</li> <li><a href="https://github.com/vikram-bhati/PAASBAAN-crime-prediction">Crime Prediction</a> - <em>Crime</em> classification, analysis & prediction in Indore city.</li> <li><a href="https://github.com/tina31726/Crime-Prediction">Crime Prediction</a> - Developed predictive models for <em>crime</em> rate.</li> <li><a href="https://github.com/felzek/Crime-Review-Data-Analysis">Crime Review</a> - Crime review data analysis.</li> <li><a href="https://github.com/benjaminsingleton/crime-trends">Crime Trends</a> - The <em>Crime</em> Trends Analysis Tool analyses <em>crime</em> trends and surfaces problematic <em>crime</em> conditions</li> <li><a href="https://github.com/cmenguy/crime-analytics">Crime Analytics</a> - Analysis of <em>crime</em> data in Seattle and San Francisco.</li> </ul> <p dir="auto"><a name="user-content-emergency-ambulance"></a> <strong>Ambulance:</strong></p> <ul dir="auto"> <li><a href="https://github.com/kaiareyes/ambulance">Ambulance Analysis</a> - An investigation of Local Government Area ambulance time variation in Victoria.</li> <li><a href="https://github.com/ankitkariryaa/ambulanceSiteLocation">Site Location</a> - Ambulance site locations.</li> <li><a href="https://github.com/DimaStoyanov/Ambulance-Dispatching">Dispatching</a> - Applying game theory and discrete event simulation to find optimal solution for ambulance dispatching</li> <li><a href="https://github.com/scngo/SD-ambulance-allocation">Ambulance Allocation</a> - Time series analysis of ambulance dispatches in the City of San Diego.</li> <li><a href="https://github.com/nonsignificantp/ambulance-response-time">Response Time</a> - An analysis on the improvements of ambulance response time.</li> <li><a href="https://github.com/aditink/EMSRouting">Optimal Routing</a> - Project to find optimal routing of ambulances in Ithaca.</li> <li><a href="https://github.com/ArpitVora/Maryland_Crash">Crash Analysis</a> - Predicting the probability of accidents on a given segment on a given time.</li> </ul> <p dir="auto"><a name="user-content-emergency-disaster"></a> <strong>Disaster Management</strong></p> <ul dir="auto"> <li><a href="https://github.com/Polichinel/Master_Thesis">Conflict Prediction</a> - Notebooks on conflict prediction.</li> <li><a href="https://github.com/Polichinel/Master_Thesis">Burglary Prediction</a> - Spatio-Temporal Modelling for burglary prediction.</li> <li><a href="https://github.com/ab-bh/Disease-Outbreak-Prediction/blob/master/Disease%20Outbreak%20Prediction.ipynb">Predicting Disease Outbreak</a> - Machine Learning implementation based on multiple classifier algorithm implementations.</li> <li><a href="https://github.com/leportella/federal-road-accidents">Road accident prediction</a> - Prediction on type of victims on federal road accidents in Brazil.</li> <li><a href="https://github.com/rajaswa/Disaster-Management-">Text Mining</a> - Disaster Management using Text mining.</li> <li><a href="https://github.com/paultopia/concrete_NLP_tutorial/blob/master/NLP_notebook.ipynb">Twitter and disasters</a> - Try to correctly predict whether tweets that are about disasters.</li> <li><a href="https://github.com/arijitsaha/FloodRisk">Flood Risk</a> - Impact of catastrophic flood events.</li> <li><a href="https://github.com/Senkichi/The_Catastrophe_Coefficient">Fire Prediction</a> - We used 4 different algorithms to predict the likelihood of future fires.</li> </ul> <p dir="auto"><a name="user-content-finance"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Finance</h2><a id="user-content-finance" class="anchor" aria-label="Permalink: Finance" href="#finance"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-finance-trading"></a> <strong>Trading and Investment</strong></p> <ul dir="auto"> <li>For <strong>more</strong> see <a href="https://github.com/firmai/financial-machine-learning">financial-machine-learning</a></li> <li>For <strong>asset management</strong> see <a href="https://github.com/firmai/machine-learning-asset-management">financial-machine-learning</a></li> <li><a href="https://github.com/DLColumbia/DL_forFinance">Deep Portfolio</a> - Deep learning for finance Predict volume of bonds.</li> <li><a href="https://github.com/borisbanushev/stockpredictionai/blob/master/readme2.md">AI Trading</a> - Modern AI trading techniques.</li> <li><a href="https://github.com/ishank011/gs-quantify-bond-prediction">Corporate Bonds</a> - Predicting the buying and selling volume of the corporate bonds.</li> <li><a href="https://github.com/chenbowen184/Computational_Finance">Simulation</a> - Investigating simulations as part of computational finance.</li> <li><a href="https://github.com/SeanMcOwen/FinanceAndPython.com-ClusteringIndustries">Industry Clustering</a> - Project to cluster industries according to financial attributes.</li> <li><a href="https://github.com/MiyainNYC/Financial-Modeling/tree/master/codes">Financial Modeling</a> - HFT trading and implied volatility modeling.</li> <li><a href="http://inseaddataanalytics.github.io/INSEADAnalytics/ExerciseSet2.html" rel="nofollow">Trend Following</a> - A futures trend following portfolio investment strategy.</li> <li><a href="https://github.com/MAydogdu/TextualAnalysis">Financial Statement Sentiment</a> - Extracting sentiment from financial statements using neural networks.</li> <li><a href="https://github.com/chenbowen184/Data_Science_in_Applied_Corporate_Finance">Applied Corporate Finance</a> - Studies the empirical behaviors in stock market.</li> <li><a href="https://github.com/sarachmax/MarketCrashes_Prediction/blob/master/LPPL_Comparasion.ipynb">Market Crash Prediction</a> - Predicting market crashes using an LPPL model.</li> <li><a href="https://github.com/chenbowen184/Research_Documents_Curation_with_NLP">NLP Finance Papers</a> - Curating quantitative finance papers using machine learning.</li> <li><a href="https://github.com/imhgchoi/Corr_Prediction_ARIMA_LSTM_Hybrid">ARIMA-LTSM Hybrid</a> - Hybrid model to predict future price correlation coefficients of two assets</li> <li><a href="https://github.com/SeanMcOwen/FinanceAndPython.com-Investments">Basic Investments</a> - Basic investment tools in python.</li> <li><a href="https://github.com/SeanMcOwen/FinanceAndPython.com-Derivatives">Basic Derivatives</a> - Basic forward contracts and hedging.</li> <li><a href="https://github.com/SeanMcOwen/FinanceAndPython.com-BasicFinance">Basic Finance</a> - Source code notebooks basic finance applications.</li> <li><a href="https://github.com/jjakimoto/finance_ml">Advanced Pricing ML</a> - Additional implementation of Advances in Financial Machine Learning (Book)</li> <li><a href="https://github.com/aluo417/Financial-Engineering-Projects">Options and Regression</a> - Financial engineering project for option pricing techniques.</li> <li><a href="https://github.com/LongOnly/Quantitative-Notebooks">Quant Notebooks</a> - Educational notebooks on quant finance, algorithmic trading and investment strategy.</li> <li><a href="https://github.com/bukosabino/financial-forecasting-challenge-gresearch">Forecasting Challenge</a> - Financial forecasting challenge by G-Research (Hedge Fund)</li> <li><a href="https://github.com/firmai?after=Y3Vyc29yOnYyOpK5MjAxOS0wNS0wMlQwNToyMzoyMSswMTowMM4KBjIV&tab=stars">XGboost</a> - A trading algorithm using XgBoost</li> <li><a href="https://github.com/rawillis98/alpaca">Research Paper Trading</a> - A strategy implementation based on a paper using Alpaca Markets.</li> <li><a href="https://github.com/arcadynovosyolov/finance">Various</a> - Options, Allocation, Simulation</li> <li><a href="https://github.com/joelowj/Machine-Learning-and-Reinforcement-Learning-in-Finance">ML & RL NYU</a> - Machine Learning and Reinforcement Learning in Finance.</li> </ul> <p dir="auto"><a name="user-content-finance-data"></a> <strong>Data</strong></p> <ul dir="auto"> <li><a href="https://github.com/mbravidor/PyDSout">Datastream</a> - Datastrem from Thomson Reuters accessible through Python.</li> <li><a href="http://twopirllc" rel="nofollow">AlphaVantage</a> - API wrapper to simplify the process of acquiring free financial data.</li> <li><a href="https://github.com/duncangh/FSA">FSA</a>- A project to transfer SEC Edgar Filings’ financial data to custom financial statement analysis models.</li> <li><a href="https://github.com/tradeasystems/tradeasystems_connector">TradeConnector</a> - A layer to connect with market data providers.</li> <li><a href="https://github.com/healthgradient/sec_employee_information_extraction">Employee Count SEC Filings</a> - Extraction to get the exact employee count values for companies from SEC filings.</li> <li><a href="https://github.com/healthgradient/sec-doc-info-extraction/blob/master/classify_sections_containing_relevant_information.ipynb">SEC Parsing</a> - NLP to find and extract specific information from long, unstructured documents</li> <li><a href="https://github.com/LexPredict/openedgar">Open Edgar</a> - OpenEDGAR (openedgar.io)</li> <li><a href="http://www.ratingshistory.info/" rel="nofollow">Rating Industries</a> - Histories from multiple agencies converted to CSV format</li> </ul> <p dir="auto"><strong>Personal Papers</strong></p> <ul dir="auto"> <li><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3371902" rel="nofollow">Financial Machine Learning Regulation</a></li> <li><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3420490" rel="nofollow">Predicting Restaurant Facility Closures</a></li> <li><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3420889" rel="nofollow">Predicting Corporate Bankruptcies</a></li> <li><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3420722" rel="nofollow">Predicting Earnings Surprises</a></li> <li><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3420952" rel="nofollow">Machine Learning in Asset Management</a></li> </ul> <p dir="auto"><a name="user-content-healtcare"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Healthcare</h2><a id="user-content-healthcare" class="anchor" aria-label="Permalink: Healthcare" href="#healthcare"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-healtcare-general"></a> <strong>General</strong></p> <ul dir="auto"> <li><a href="https://github.com/pzivich/zEpid">zEpid</a> - Epidemiology analysis package.</li> <li><a href="https://github.com/pzivich/Python-for-Epidemiologists">Python For Epidemiologists</a> - Tutorial to introduce epidemiology analysis in Python.</li> <li><a href="https://github.com/rjhere/Prescription-compliance-prediction">Prescription Compliance</a> - An analysis of prescription and medical compliance</li> <li><a href="https://github.com/alistairwallace97/olympian-biotech">Respiratory Disease</a> - Tracking respiratory diseases in Olympic athletes</li> <li><a href="https://github.com/callysto/curriculum-notebooks/blob/master/Humanities/BubonicPlague/bubonic-plague-and-SIR-model.ipynb">Bubonic Plague</a> - Bubonic plague and SIR model.</li> </ul> <p dir="auto"><a name="user-content-legal"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Justics, Law & Regulations</h2><a id="user-content-justics-law--regulations" class="anchor" aria-label="Permalink: Justics, Law & Regulations" href="#justics-law--regulations"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-legal-tools"></a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Tools</h4><a id="user-content-tools" class="anchor" aria-label="Permalink: Tools" href="#tools"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/LexPredict/lexpredict-contraxsuite">LexPredict</a> - Software package and library.</li> <li><a href="https://github.com/davidawad/lobe">AI Para-legal</a> - Lobe is the world's first AI paralegal.</li> <li><a href="https://github.com/hockeyjudson/Legal-Entity-Detection/blob/master/Dataset_conv.ipynb">Legal Entity Detection</a> - NER For Legal Documents.</li> <li><a href="https://github.com/Law-AI/summarization">Legal Case Summarisation</a> - Implementation of different summarisation algorithms applied to legal case judgements.</li> <li><a href="https://github.com/GirrajMaheshwari/Web-scrapping-/blob/master/Google_scholar%2BExtract%2Bcase%2Bdocument.ipynb">Legal Documents Google Scholar</a> - Using Google scholar to extract cases programatically.</li> <li><a href="https://github.com/akarazeev/LegalTech">Chat Bot</a> - Chat-bot and email notifications.</li> <li><a href="https://github.com/propublica/congress-api-docs">Congress API</a> - ProPublica congress API access.</li> <li><a href="https://github.com/toningega/Data_Generator">Data Generator GDPR</a> - Dummy data generator for GDPR compliance</li> <li><a href="https://github.com/ICLRandD/Blackstone">Blackstone</a> - spaCy pipeline and model for NLP on unstructured legal text.</li> </ul> <p dir="auto"><a name="user-content-legal-pr"></a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Policy and Regulatory</h4><a id="user-content-policy-and-regulatory" class="anchor" aria-label="Permalink: Policy and Regulatory" href="#policy-and-regulatory"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/erickjtorres/AI_LegalDoc_Hackathon">GDPR scores</a> - Predicting GDPR Scores for Legal Documents.</li> <li><a href="https://github.com/siddhantmaharana/text-analysis-on-FINRA-docs">Driving Factors FINRA</a> - Identify the driving factors that influence the FINRA arbitration decisions.</li> <li><a href="https://github.com/davidsontheath/bias_corrected_estimators/blob/master/bias_corrected_estimators.ipynb">Securities Bias Correction</a> - Bias-Corrected Estimation of Price Impact in Securities Litigation.</li> <li><a href="https://github.com/anshu3769/FirmEmbeddings">Public Firm to Legal Decision</a> - Embed public firms based on their reaction to legal decisions.</li> <li><a href="https://github.com/Kevin-McIsaac/Nightlife">Night Life Regulation</a> - Australian nightlife and its regulation and policing</li> <li><a href="https://github.com/ProximaDas/nlp-govt-regulations">Comments</a> - Public comments on government regulations.</li> <li><a href="https://github.com/philxchen/Clustering-Canadian-regulations">Clustering</a> - Clustering Canadian regulations.</li> <li><a href="https://github.com/ds-modules/EEP-147">Environment</a> - Regulation of Energy and the Environment</li> <li><a href="https://github.com/vsub21/systemic-risk-dashboard">Risk</a> - Systematic risk of various financial regulations.</li> <li><a href="https://github.com/raymond180/FINRA_TRACE">FINRA Compliance</a> - Topic modelling on compliance.</li> </ul> <p dir="auto"><a name="user-content-legal-judicial"></a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Judicial Applied</h4><a id="user-content-judicial-applied" class="anchor" aria-label="Permalink: Judicial Applied" href="#judicial-applied"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/davidmasse/US-supreme-court-prediction">Supreme Court Prediction</a> - Predicting the ideological direction of Supreme Court decisions: ensemble vs. unified case-based model.</li> <li><a href="https://github.com/AccelAI/AI-Law-Minicourse/tree/master/Supreme_Court_Topic_Modeling">Supreme Court Topic Modeling</a> - Multiple steps necessary to implement topic modeling on supreme court decisions.</li> <li><a href="https://github.com/GirrajMaheshwari/Legal-Analytics-project---Court-misclassification">Judge Opinion</a> - Using text mining and machine learning to analyze judges’ opinions for a particular concern.</li> <li><a href="https://github.com/whs2k/GPO-AI">ML Law Matching</a> - A machine learning law match maker.</li> <li><a href="https://github.com/brightmart/sentiment_analysis_fine_grain">Bert Multi-label Classification</a> - Fine Grained Sentiment Analysis from AI.</li> <li><a href="https://www.youtube.com/channel/UC5UHm2J9pbEZmWl97z_0hZw" rel="nofollow">Some Computational AI Course</a> - Video series Law MIT.</li> <li><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3371902" rel="nofollow">Financial Machine Learning Regulation (Paper)</a></li> </ul> <p dir="auto"><a name="user-content-manufacturing"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Manufacturing</h2><a id="user-content-manufacturing" class="anchor" aria-label="Permalink: Manufacturing" href="#manufacturing"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-manufacturing-general"></a> <strong>General</strong></p> <ul dir="auto"> <li><a href="https://github.com/Danila89/kaggle_mercedes">Green Manufacturing</a> - Mercedes-Benz Greener <em>Manufacturing</em> competition on Kaggle.</li> <li><a href="https://github.com/Meena-Mani/SECOM_class_imbalance">Semiconductor Manufacturing</a> - Semicondutor <em>manufacturing</em> process line data analysis.</li> <li><a href="https://github.com/usnistgov/modelmeth">Smart Manufacturing</a> - Shared work of a modelling Methodology.</li> <li><a href="https://github.com/han-yan-ds/Kaggle-Bosch">Bosch Manufacturing</a> - Bosch manufacturing project, Kaggle.</li> </ul> <p dir="auto"><a name="user-content-manufacturing-maintenance"></a> <strong>Maintenance</strong></p> <ul dir="auto"> <li><a href="https://github.com/Azure/lstms_for_predictive_maintenance">Predictive Maintenance</a> 1 - Predict remaining useful life of aircraft engines</li> <li><a href="https://github.com/Samimust/predictive-maintenance">Predictive Maintenance 2</a> - Time-To-Failure (TTF) or Remaining Useful Life (RUL)</li> <li><a href="https://github.com/m-hoff/maintsim">Manufacturing Maintenance</a> - Simulation of maintenance in <em>manufacturing</em> systems.</li> </ul> <p dir="auto"><a name="user-content-manufacturing-fail"></a> <strong>Failure</strong></p> <ul dir="auto"> <li><a href="https://github.com/IBM/iot-predictive-analytics">Predictive Analytics</a> - Method for Predicting failures in Equipment using Sensor data.</li> <li><a href="https://github.com/roshank1605A04/SECOM-Detecting-Defected-Items">Detecting Defects</a> - Anomaly detection for defective semiconductors</li> <li><a href="https://github.com/jorgehas/smart-defect-inspection">Defect Detection</a> - Smart defect detection for pill manufacturing.</li> <li><a href="https://github.com/aayushmudgal/Reducing-Manufacturing-Failures">Manufacturing Failures</a> - Reducing manufacturing failures.</li> <li><a href="https://github.com/mohan-mj/Manufacturing-Line-I4.0">Manufacturing Anomalies</a> - Intelligent anomaly detection for <em>manufacturing</em> line.</li> </ul> <p dir="auto"><a name="user-content-manufacturing-quality"></a> <strong>Quality</strong></p> <ul dir="auto"> <li><a href="https://github.com/buzz11/productionFailures">Quality Control</a> - Bosh failure of quality control.</li> <li><a href="https://github.com/limberc/tianchi-IMQF">Manufacturing Quality</a> - Intelligent <em>Manufacturing</em> Quality Forecast</li> <li><a href="https://github.com/trentwoodbury/ManufacturingAuctionRegression">Auto Manufacturing</a> - Regression Case Study Project on <em>Manufacturing</em> Auction Sale Data.</li> </ul> <p dir="auto"><a name="user-content-media"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Media & Publishing</h2><a id="user-content-media--publishing" class="anchor" aria-label="Permalink: Media & Publishing" href="#media--publishing"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-media-marketing"></a> <strong>Marketing</strong></p> <ul dir="auto"> <li><a href="https://github.com/andrei-rizoiu/hip-popularity">Video Popularity</a> - HIP model for predicting the popularity of videos.</li> <li><a href="https://github.com/hathix/youtube-transcriber">YouTube transcriber</a> - Automatically transcribe YouTube videos.</li> <li><a href="https://github.com/byukan/Marketing-Data-Science">Marketing Analytics</a> - Marketing analytics case studies.</li> <li><a href="https://github.com/ikatsov/algorithmic-examples">Algorithmic Marketing</a> - Models from Introduction to Algorithmic Marketing book</li> <li><a href="https://github.com/HowardNTUST/Marketing-Data-Science-Application">Marketing Scripts</a> - Marketing data science applications.</li> <li><a href="https://github.com/mikhailklassen/Mining-the-Social-Web-3rd-Edition/tree/master/notebooks">Social Mining</a> - Mining the social web.</li> </ul> <p dir="auto"><a name="user-content-miscellaneous"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Miscellaneous</h2><a id="user-content-miscellaneous" class="anchor" aria-label="Permalink: Miscellaneous" href="#miscellaneous"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-miscellaneous-art"></a> <strong>Art</strong></p> <ul dir="auto"> <li><a href="https://github.com/ivan-bilan/Painting_Forensics">Painting Forensics</a> - Analysing paintings to find out their year of creation.</li> </ul> <p dir="auto"><a name="user-content-miscellaneous-tour"></a> <strong>Tourism</strong></p> <ul dir="auto"> <li><a href="https://github.com/xiaofei6677/TourismFlickrMiner">Flickr</a> - Metadata mining tool for tourism research.</li> <li><a href="https://github.com/khanhnamle1994/fashion-recommendation">Fashion</a> <strong>-</strong> A clothing retrieval and visual recommendation model for fashion images</li> </ul> <p dir="auto"><a name="user-content-physics"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Physics</h2><a id="user-content-physics" class="anchor" aria-label="Permalink: Physics" href="#physics"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-physics-general"></a> <strong>General</strong></p> <ul dir="auto"> <li><a href="https://github.com/fvisconti/gammas_machine_learning">Gamma-hadron Reconstruction</a> - Tools used in Gamma-ray ground based astronomy.</li> <li><a href="https://github.com/callysto/curriculum-notebooks/tree/master/Physics">Curriculum</a> - Newtonian notebooks.</li> <li><a href="https://github.com/higgsfield/interaction_network_pytorch">Interaction Networks</a> - Interaction Networks for Learning about Objects, Relations and <em>Physics.</em></li> <li><a href="https://github.com/hep-lbdl/adversarial-jets">Particle Physics</a> - Training, generation, and analysis code for learning Particle <em>Physics</em></li> <li><a href="https://github.com/ernestyalumni/CompPhys">Computational Physics</a> - A computational physics repository.</li> <li><a href="https://github.com/robmarkcole/Useful-python-for-medical-physics">Medical Physics</a> - Useful python for medical physics.</li> <li><a href="https://github.com/pymedphys/pymedphys">Medical Physics 2</a> - A common, core Python package for Medical <em>Physics</em></li> <li><a href="https://github.com/FPAL-Stanford-University/FloATPy">Flow Physics</a> - Flow <em>Physics</em> and Aeroacoustics Toolbox with Python</li> </ul> <p dir="auto"><a name="user-content-physics-ml"></a> <strong>Machine Learning</strong></p> <ul dir="auto"> <li><a href="https://github.com/dkirkby/MachineLearningStatistics">Physics ML and Stats</a> - Machine learning and statistics for physicists</li> <li><a href="https://github.com/arogozhnikov/hep_ml">High Energy</a> - Machine Learning for High Energy <em>Physics</em>.</li> <li><a href="https://github.com/hep-lbdl/CaloGAN">High Energy GAN</a> - Generative Adversarial Networks for High Energy <em>Physics.</em></li> <li><a href="https://github.com/GiggleLiu/marburg">Neural Networks</a> - P<em>hysics</em> meets neural networks</li> </ul> <p dir="auto"><a name="user-content-public"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Government and Public Works</h2><a id="user-content-government-and-public-works" class="anchor" aria-label="Permalink: Government and Public Works" href="#government-and-public-works"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-public-social"></a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Social Policies</h4><a id="user-content-social-policies" class="anchor" aria-label="Permalink: Social Policies" href="#social-policies"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/dssg/triage">Triage</a> - General Purpose Risk Modeling and Prediction Toolkit for Policy and Social Good Problems.</li> <li><a href="https://github.com/worldbank/ML-classification-algorithms-poverty/tree/master/notebooks">World Bank Poverty I</a> - A comparative assessment of machine learning classification algorithms applied to poverty prediction.</li> <li><a href="https://github.com/avsolatorio/world-bank-pover-t-tests-solution">World Bank Poverty II</a> - Repository for the World Bank Pover-t Test Competition Solution Overseas Company Land Ownership .</li> <li><a href="https://github.com/Global-Witness/overseas-companies-land-ownership/blob/master/overseas_companies_land_ownership_analysis.ipynb">Overseas Company Land Ownership</a> - Identifying foreign ownership in the UK.</li> <li><a href="https://github.com/MAydogdu/ConsumerFinancialProtectionBureau/blob/master/CFPB_Complaints_2017September.ipynb">CFPB</a> - Consumer Finances Protection Bureau complaints analysis.</li> <li><a href="https://github.com/tslindner/Effects-of-Cannabis-Legalization">Cannabis Legalisation Effect</a> - Effects of cannabis legalization on crime.</li> <li><a href="https://github.com/dmodjeska/barnet_transactions/blob/master/Barnet_Transactions_Analysis.ipynb">Public Credit Card</a> - Identification of potential fraud for council credit cards. <a href="https://open.barnet.gov.uk/dataset/corporate-credit-card-transaction-2016-17" rel="nofollow">Data</a></li> <li><a href="https://github.com/shayanray/GlassBox/tree/master/mlPredictor">Recidivism Prediction</a> - Transparency and audibility to recidivism risk assessment</li> <li><a href="https://github.com/Featuretools/predict-household-poverty">Household Poverty</a> - Predict poverty in households in Costa Rica.</li> <li><a href="https://github.com/ancilcrayton/nlp_public_policy">NLP Public Policy</a> - An example of an NLP use-case in public policy.</li> <li><a href="https://github.com/roshank1605A04/World-Food-Production">World Food Production</a> - Comparing Top food and feed Producers around the globe.</li> <li><a href="https://github.com/DataScienceForGood/TaxationInequality">Tax Inequality</a> - Data project around taxation and inequality in Basel Stadt.</li> <li><a href="https://github.com/austinbrian/sheriffs">Sheriff Compliance</a> - Compliance to ICE requests.</li> <li><a href="https://github.com/MengchuanFu/Suspecious-Apps-Detection">Apps Detection</a> - Suspicious app detection for kids.</li> <li><a href="https://github.com/farkhondehm/Social-Assistance">Social Assistance</a> - Trending information on social assistance</li> <li><a href="https://github.com/abjer/sds/tree/master/material">Computational Social Science</a> - Social data science summer school course.</li> <li><a href="https://github.com/bhaveshgoyal/safeLiquor">Liquor and Crime</a> - Effect of liquor licenses issued on the crime rate.</li> <li><a href="https://github.com/austinpetsalive/distemper-outbreak">Animal Placement Kennels</a> - Optimising animal placement in shelters.</li> <li><a href="https://github.com/ryanschaub/The-U.S.-Mexican-Border-Wall-and-Staffing-A-Statistical-Approach-">Staffing Wall</a> - Independent exploration project on U.S. Mexican Border wall</li> <li><a href="https://github.com/zischwartz/workerfatalities">Worker Fatalities</a> - Worker Fatalities and Catastrophes Map from OSHA data</li> </ul> <p dir="auto"><a name="user-content-public-charity"></a> <strong>Charities</strong></p> <ul dir="auto"> <li><a href="https://github.com/johnfwhitesell/CensusPull/blob/master/Census_ACS5_Pull.ipynb">Census Data API</a> - Pull variables from the 5-year American Community Survey.</li> <li><a href="https://github.com/datakind/datadive-gates92y-proj3-form990">Philantropic Giving</a> - Work done by numerous DataKind volunteers on harnessing Form 990 data</li> <li><a href="https://github.com/Chris-Manna/charity_recommender">Charity Recommender</a> - NYC <em>Charity</em> Collaborative Recommender System on an Implicit DataSet.</li> <li><a href="https://github.com/gouravaich/finding-donors-for-charity">Donor Identification</a> - A machine learning project in which we need to find donors for <em>charity.</em></li> <li><a href="https://github.com/staceb/charities_in_the_united_states">US Charities</a> - Charity exploration and machine learning.</li> <li><a href="https://github.com/LauraChen/02-Metis-Web-Scraping">Charity Effectiveness</a> - Scraping online data about <em>charities</em> to understand effectiveness</li> </ul> <p dir="auto"><a name="user-content-public-election"></a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Election Analysis</h4><a id="user-content-election-analysis" class="anchor" aria-label="Permalink: Election Analysis" href="#election-analysis"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/1jinwoo/DeepWave/blob/master/DR_Random_Forest.ipynb">Election Analysis</a> - Election Analysis and Prediction Models</li> <li><a href="https://github.com/Akesari12/LS123_Data_Prediction_Law_Spring-2019/blob/master/labs/OLS%20for%20Causal%20Inference/OLS_Causal_Inference_solution.ipynb">American Election Causal</a> - Using ANES data with causal inference models.</li> <li><a href="https://github.com/sfbrigade/datasci-campaign-finance/blob/master/notebooks/ML%20Campaign%20Finance%20and%20Election%20Results%20Example.ipynb">Campaign Finance and Election Results</a> - Investigating the relation between campaign finance and subsequent election results.</li> <li><a href="https://github.com/nealmcb/pr_voting_methods">Voting System</a> - Proportional representation voting methods.</li> <li><a href="https://github.com/austinbrian/portfolio/blob/master/tax_votes/president_counties.ipynb">President Vote</a> - Vote by income level analysis..</li> </ul> <p dir="auto"><a name="user-content-public-politics"></a> <strong>Politics</strong></p> <ul dir="auto"> <li><a href="https://github.com/kaumaron/Data_Science/tree/master/Congressional_Partisanship">Congressional politics</a> - House and senate congressional partisanship.</li> <li><a href="https://github.com/okfn-brasil/perfil-politico">Politico</a> - A platform for profiling public figures in Brazilian <em>politics.</em></li> <li><a href="https://github.com/ParticipaPY/politic-bots">Bots</a> - Tools and algorithms to analyze Paraguayan Tweets in times of election</li> <li><a href="https://github.com/PrincetonUniversity/gerrymandertests">Gerrymander tests</a> - Lots of metrics for quantifying gerrymandering.</li> <li><a href="https://github.com/JulianMar11/SentimentPoliticalCompass">Sentiment</a> - Analyse newspapers with respect to their <em>political</em> conviction using entity sentiments of party representatives.</li> <li><a href="https://github.com/muntisa/Deep-Politics">DL Politics</a> - Prediction of Spanish <em>Political</em> Affinity with Deep Neural Nets: Socialist vs People's Party</li> <li><a href="https://github.com/edmundooo/more-money-more-problems">PAC Money</a> - Effects of PAC money on US <em>politics</em>.</li> <li><a href="https://github.com/abhiagar90/power_networks">Power Networks</a> - Constructing a watchdog for Indian corporate and <em>political</em> networks</li> <li><a href="https://github.com/philippschmalen/Project_tsds">Elite</a> - Political elite in the US.</li> <li><a href="https://github.com/kkirchhoff01/DebateAnalysis">Debate Analysis</a> - Program to analyze <em>political</em> debates.</li> <li><a href="https://github.com/davidjwiner/political_affiliation_prediction">Political Affiliation</a> - Political affiliation prediction using twitter metadata.</li> <li><a href="https://github.com/philiplbean/facebook_political_ads">Political Ads</a> - Investigation into Facebook <em>Political</em> Ads and Targeting</li> <li><a href="https://github.com/pgromano/Political-Identity-Analysis">Political Identity</a> - Multi-axial <em>political</em> model.</li> <li><a href="https://github.com/kmunger/YT_descriptive">YT Politics</a> - Mapping <em>Politics</em> on YouTube</li> <li><a href="https://github.com/albertwebson/Political-Vector-Projector">Political Ideology</a> - Unsupervised learning of <em>political</em> ideology by word vector projections</li> </ul> <p dir="auto"><a name="user-content-realestate"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Real Estate, Rental & Leasing</h2><a id="user-content-real-estate-rental--leasing" class="anchor" aria-label="Permalink: Real Estate, Rental & Leasing" href="#real-estate-rental--leasing"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-realestate-real"></a> <strong>Real Estate</strong></p> <ul dir="auto"> <li><a href="https://github.com/GretelDePaepe/FindingDonuts">Finding Donuts</a> - Finding real estate opportunities by predicting transforming neighbourhoods.</li> <li><a href="https://github.com/denadai2/real-estate-neighborhood-prediction">Neighbourhood</a> - Predicting real estate prices from the urban environment.</li> <li><a href="https://github.com/Sardhendu/PropertyClassification">Real Estate Classification</a> - Classifying the type of property given Real Estate, satellite and Street view Images</li> <li><a href="https://github.com/hyattsaleh15/RealStateRecommender">Recommender</a> - This tools aims to recommend a user the top 5 real estate properties that matches their search.</li> <li><a href="https://github.com/Shreyas3108/house-price-prediction">House Price</a> - Predicting <em>house</em> prices using Linear Regression and GBR</li> <li><a href="https://github.com/girishkuniyal/Predict-housing-prices-in-Portland">House Price Portland</a> - Predict housing prices in Portland.</li> <li><a href="https://github.com/eswar3/Zillow-prediction-models">Zillow Prediction</a> - Zillow valuation prediction as performed on Kaggle.</li> <li><a href="https://github.com/denadai2/real-estate-neighborhood-prediction">Real Estate</a> - Predicting real estate prices from the urban environment.</li> </ul> <p dir="auto"><a name="user-content-realestate-rent"></a> <strong>Rental & Leasing</strong></p> <ul dir="auto"> <li><a href="https://github.com/ual/rental-listings">Analysing Rentals</a> - Analyzing and visualizing rental listings data.</li> <li><a href="https://github.com/mratsim/Apartment-Interest-Prediction">Interest Prediction</a> - Predict people interest in renting specific NYC apartments.</li> <li><a href="https://github.com/5x12/pwc_europe_data_analytics_hackathon">Housing Uni vs Non-Uni</a> - The effect on university lodging after the GFC.</li> <li><a href="https://github.com/Featuretools/predict-household-poverty">Predict Household Poverty</a> - Predict the poverty of households in Costa Rica using automated feature engineering.</li> <li><a href="http://inseaddataanalytics.github.io/INSEADAnalytics/groupprojects/AirbnbReport2016Jan.html" rel="nofollow">Airbnb public analytics competition</a>: - Now strategic management.</li> </ul> <p dir="auto"><a name="user-content-utilities"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Utilities</h2><a id="user-content-utilities" class="anchor" aria-label="Permalink: Utilities" href="#utilities"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-utilities-elec"></a> <strong>Electricity</strong></p> <ul dir="auto"> <li><a href="https://github.com/luqmanhakim/research-on-sp-wholesale/blob/master/research-on-sp-wholesale-plan.ipynb">Electricity Price</a> - Electricity price comparison Singapore.</li> <li><a href="https://github.com/richardddli/state_electricity_rates">Electricity-Coal Correlation</a> - Determining the correlation between state electricity rates and coal generation over the past decade.</li> <li><a href="https://github.com/datadesk/california-electricity-capacity-analysis">Electricity Capacity</a> - A Los Angeles Times analysis of California's costly power glut.</li> <li><a href="https://github.com/PyPSA/WHOBS">Electricity Systems</a> - Optimal Wind+Hydrogen+Other+Battery+Solar (WHOBS) <em>electricity</em> systems for European countries.</li> <li><a href="https://github.com/pipette/Electricity-load-disaggregation">Load Disaggregation</a> - Smart meter load disaggregation with Hidden Markov Models</li> <li><a href="https://github.com/farwacheema/DA-electricity-price-forecasting">Price Forecasting</a> - Forecasting Day-Ahead <em>electricity</em> prices in the German bidding zone with deep neural networks.</li> <li><a href="https://github.com/gschivley/carbon-index">Carbon Index</a> - Calculation of <em>electricity</em> CO₂ intensity at national, state, and NERC regions from 2001-present.</li> <li><a href="https://github.com/hvantil/ElectricityDemandForecasting">Demand Forecasting</a> - <em>Electricity</em> demand forecasting for Austin.</li> <li><a href="https://github.com/un-modelling/Electricity_Consumption_Surveys">Electricity Consumption</a> - Estimating <em>Electricity</em> Consumption from Household Surveys</li> <li><a href="https://github.com/amirrezaeian/Individual-household-electric-power-consumption-Data-Set-">Household power consumption</a> - Individual household power consumption LSTM.</li> <li><a href="http://inseaddataanalytics.github.io/INSEADAnalytics/groupprojects/group_energy.html" rel="nofollow">Electricity French Distribution</a> - An analysis of electricity data provided by the French Distribution Network (RTE)</li> <li><a href="https://github.com/Open-Power-System-Data/renewable_power_plants">Renewable Power Plants</a> - Time series of cumulated installed capacity.</li> <li><a href="https://github.com/FUSED-Wind/FUSED-Wake"> Wind Farm Flow</a> - A repository of wind plant flow models connected to FUSED-Wind.</li> <li><a href="https://github.com/YungChunLu/UCI-Power-Plant">Power Plant</a> - The dataset contains 9568 data points collected from a Combined Cycle Power Plant over 6 years (2006-2011).</li> </ul> <p dir="auto"><a name="user-content-utilities-coal"></a> <strong>Coal, Oil & Gas</strong></p> <ul dir="auto"> <li><a href="https://github.com/samarthiith/DE_CoalPhaseOut">Coal Phase Out</a> - Generation adequacy issues with Germany’s coal phaseout.</li> <li><a href="https://github.com/Jean-njoroge/coal-exploratory/tree/master/notebooks">Coal Prediction</a> - Predicting coal production.</li> <li><a href="https://github.com/sdasadia/Oil-Natural-Gas-Price-Prediction">Oil & Gas</a> - Oil & <em>Natural</em> <em>Gas</em> price prediction using ARIMA & Neural Networks</li> <li><a href="https://github.com/cep-kse/natural_gas_formula">Gas Formula</a> - Calculating potential economic effect of price indexation formula.</li> <li><a href="https://github.com/victorpena1/Natural-Gas-Demand-Prediction">Demand Prediction</a> - Natural gas demand prediction.</li> <li><a href="https://github.com/williamadams1/natural-gas-consumption-forecasting">Consumption Forecasting</a> - Natural gas consumption forecasting.</li> <li><a href="https://github.com/bahuisman/NatGasModel">Gas Trade</a> - World Model for <em>Natural</em> <em>Gas</em> Trade.</li> </ul> <p dir="auto"><a name="user-content-utilities-water"></a> <strong>Water & Pollution</strong></p> <ul dir="auto"> <li><a href="https://github.com/codeforboston/safe-water">Safe Water</a> - Predict health-based drinking water violations in the United States.</li> <li><a href="https://github.com/mroberge/hydrofunctions">Hydrology Data</a> - A suite of convenience functions for exploring water data in Python.</li> <li><a href="https://github.com/sentinel-hub/water-observatory-backend">Water Observatory</a> - Monitoring water levels of lakes and reservoirs using satellite imagery.</li> <li><a href="https://github.com/wassname/pipe-segmentation">Water Pipelines</a> - Using machine learning to find water pipelines in aerial images.</li> <li><a href="https://github.com/awracms/awra_cms_older">Water Modelling</a> - Australian Water Resource Assessment (AWRA) Community Modelling System.</li> <li><a href="https://github.com/datadesk/california-ccscore-analysis">Drought Restrictions</a> - A Los Angeles Times analysis of water usage after the state eased drought restrictions</li> <li><a href="https://github.com/cadrev/lstm-flood-prediction">Flood Prediction</a> - Applying LSTM on river water level data</li> <li><a href="https://github.com/jesseanddd/sewer-overflow">Sewage Overflow</a> - Insights into the sanitary sewage overflow (SSO). - This has been removed</li> <li><a href="https://github.com/johnpfay/USWaterAccounting">Water Accounting</a> - Assembles water budget data for the US from existing data source</li> <li><a href="https://github.com/txytju/air-quality-prediction">Air Quality Prediction</a> - Predict air quality(aq) in Beijing and London in the next 48 hours.</li> </ul> <p dir="auto"><a name="user-content-utilities-trans"></a> <strong>Transportation</strong></p> <ul dir="auto"> <li><a href="https://github.com/xinychen/transdim">Transdim</a> - Creating accurate and efficient solutions for the spatio-temporal traffic data imputation and prediction tasks.</li> <li><a href="https://github.com/AlanConstantine/KDD-Cup-2019-CAMMTR">Transport Recommendation</a> - Context-Aware Multi-Modal Transportation Recommendation</li> <li><a href="https://github.com/CityofToronto/bdit_data-sources">Transport Data</a> - Data and notebooks for Toronto transport.</li> <li><a href="https://github.com/pawelmorawiecki/traffic_jam_Nairobi">Transport Demand</a> - Predicting demand for public transportation in Nairobi.</li> <li><a href="https://github.com/Lemma1/DPFE">Demand Estimation</a> - Implementation of dynamic origin-destination demand estimation.</li> <li><a href="https://github.com/hackoregon/transportation-congestion-analysis">Congestion Analysis</a> - Transportation systems analysis</li> <li><a href="https://github.com/nishanthgampa/Time-Series-Analysis-on-Transportation-Data">TS Analysis</a> - Time series analysis on transportation data.</li> <li><a href="https://github.com/fangshulin/Vulnerability-Analysis-for-Transportation-Networks">Network Graph Subway</a> - Vulnerability analysis for transportation networks. - Have been taken down</li> <li><a href="https://github.com/akpen/Stockholm-0.1">Transportation Inefficiencies</a> - Quantifying the inefficiencies of Transportation Networks</li> <li><a href="https://github.com/crowdAI/train-schedule-optimisation-challenge-starter-kit">Train Optimisation</a> - Train schedule optimisation</li> <li><a href="https://github.com/mratsim/McKinsey-SmartCities-Traffic-Prediction">Traffic Prediction</a> - multi attention recurrent neural networks for time-series (city traffic)</li> <li><a href="https://github.com/Data4Democracy/crash-model">Predict Crashes</a> - Crash prediction modelling application that leverages multiple data sources</li> <li><a href="https://github.com/llSourcell/AI_Supply_Chain">AI Supply chain</a> - Supply chain optimisation system.</li> <li><a href="https://github.com/cavaunpeu/flight-delays">Transfer Learning Flight Delay</a> - Using variation encoders in Keras to predict flight delay.</li> <li><a href="https://github.com/pratishthakapoor/RetailReplenishement/tree/master/Code">Replenishment</a> - Retail replenishment code for supply chain management.</li> </ul> <p dir="auto"><a name="user-content-wholesale"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Wholesale & Retail</h2><a id="user-content-wholesale--retail" class="anchor" aria-label="Permalink: Wholesale & Retail" href="#wholesale--retail"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a name="user-content-wholesale-whole"></a> <strong>Wholesale</strong></p> <ul dir="auto"> <li><a href="https://github.com/kralmachine/WholesaleCustomerAnalysis">Customer Analysis</a> - Wholesale customer analysis.</li> <li><a href="https://github.com/Semionn/JB-wholesale-distribution-analysis">Distribution</a> - JB wholesale distribution analysis.</li> <li><a href="https://github.com/prakhardogra921/Clustering-Analysis-on-customers-of-a-wholesale-distributor">Clustering</a> - Unsupervised learning techniques are applied on product spending data collected for customers</li> <li><a href="https://github.com/tstreamDOTh/Instacart-Market-Basket-Analysis">Market Basket Analysis</a> - Instacart public dataset to report which products are often shopped together.</li> </ul> <p dir="auto"><a name="user-content-wholesale-retail"></a> <strong>Retail</strong></p> <ul dir="auto"> <li><a href="https://github.com/SarahMestiri/online-retail-case">Retail Analysis</a> - Studying Online <em>Retail</em> Dataset and getting insights from it.</li> <li><a href="https://github.com/roshank1605A04/Online-Retail-Transactions-of-UK">Online Insights</a> - Analyzing the Online Transactions in UK</li> <li><a href="https://github.com/IBM-DSE/CyberShop-Analytics">Retail Use-case</a> - Notebooks & Data for CyberShop <em>Retail</em> Use Case</li> <li><a href="https://github.com/arvindkarir/retail">Dwell Time</a> - Customer dwell time and other analysis.</li> <li><a href="https://github.com/finnqiao/cohort_online_retail">Retail Cohort</a> - Cohort analysis.</li> </ul> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="HOhp+u1ucB5i7K33UCX4q0LN2ex3zPvBN/GRngEME+zxy02w6XWskVMyy+Gu9OAHIfgTynqBhaMrhbu6HVfk8g==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" 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1-1.543-2.575Zm1.763.707a.25.25 0 0 0-.44 0L1.698 13.132a.25.25 0 0 0 .22.368h12.164a.25.25 0 0 0 .22-.368Zm.53 3.996v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 11a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path> </svg> <button type="button" class="flash-close js-ajax-error-dismiss" aria-label="Dismiss error"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> You can’t perform that action at this time. </div> <template id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>