Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This project focuses on developing a machine learning-driven system to classify hospital claims and treatment outcomes, offering a second opinion on healthcare costs and decision-making for insurance claims and treatment efficacy.Key Features and Tools:Machine Learning Algorithms: Leveraging Python (pandas, numpy, scikit-learn) for predictive modeling to assess claim validity and treatment outcomes.APIs Integration: Used Google Maps API to retrieve and map the locations of private hospitals in Malaysia.GIS Mapping Dashboard: Created a GIS-enabled dashboard in Microsoft Power BI to visualize private hospital distribution across Malaysia, aiding healthcare planning and analysis.Advanced Analytics Tools: Integrated Microsoft Excel, Python, and Google Collab for data processing and automation workflows.
Web map created by Research and Stats for PowerBi DashboardMap of Current Sales and Use Tax Rates
Section and Beat Outline for Power BI maps
This hosted feature layer has been developed in-house by the VDOT CO TED Highway Safety section for crash analysis purpose based on updates from the Power BI Crash Tool. The Crash Data Dictionary can be found here. The main source of the data is owned and maintained by DMV. In providing this web map, we assume no responsibility for the accuracy and completeness of the data. In the process of recording and compiling the data, some deletions and/or omissions of data may occur and VDOT is not responsible for any such occurrences.
The main source of the data is owned and maintained by DMV. In providing this tool, VDOT assumes no responsibility for the accuracy and completeness of the data. In the process of recording and compiling the data, some deletions and/or omissions of data may occur and VDOT is not responsible for any such occurrences. The most recent data contained in this report is preliminary and subject to change. Please be advised that, under Title 23 United State Code – Section 409, this crash information cannot be used in discovery or as evidence in a Federal or State court proceeding or considered for other purposes in any action for damages against VDOT or the State of Virginia arising from any occurrence at the location identified.
All users shall comply with and be subject to all applicable laws and regulations, whether federal or state, in connection with any of the receipt and use of DMV data including, but not limited to, (1) the Federal Drivers Privacy Protection Act (18 U.S.C. § 2721 et seq.), (2) the Government Data Collection and Dissemination Practices Act (Va. Code § 2.2-3800 et seq.), (3) the Virginia Computer Crimes Act (Va. Code § 18.2-152.1 et seq.), (4) the provisions of Va. Code §§ 46.2-208 and 58.1-3, and (5) any successor rules, regulations, or guidelines adopted by DMV with regard to disclosure or dissemination of any information obtained from DMV records or files.
Section and Beat Outline for Power BI maps
This data layer is a draft of selected species ranges from ECOS. The layer has to be made public to test for integration into a Power BI dashboard. Please do not use for any other purposes. Most current geospatial data including area of influence, current and historical ranges, data are available through the Threatened and Endangered Species System (TESS) here. https://ecos.fws.gov/tess/speciesModule/home.doOther information may be found on the FWS Endangered Species web page here https://www.fws.gov/program/endangered-species
The River Authority Water Quality Viewer is a tabbed web mapping application. The following tabs are defined below:The first tab - Water Quality – is an embedded interactive Power BI dashboard. It displays monitoring efforts for the Texas Commission on Environmental Quality’s (TCEQ) Clean Rivers Program and summarizes how the water quality in the San Antonio River Basin compares to the Texas Surface Water Quality Standards (TSWQS) using the latest TCEQ Integrated Report assessment. Through the Texas Clean Rivers Program, The River Authority and its partners collect surface water, stormwater and sediment chemistry, physical, and biological data within the San Antonio River Basin. The second tab – Primary Contact Recreation Use –summarizes assessment outcomes for the San Antonio River Watershed as identified in the Texas Commission on Environmental Quality (TCEQ) 2018, 2016, 2014, and 2012 Integrated Report (IR). It displays the Primary Contact Recreation Use Designations by color.The third tab – Aquatic Life Use – summarizes assessment outcomes for the San Antonio River Watershed as identified in the Texas Commission on Environmental Quality (TCEQ) 2018, 2016, 2014, and 2012 Integrated Report (IR). This Web Map displays the Aquatic Life Use Designations. It is the dependent of the SARA Water Quality Viewer and uses the Aquatic Life Use Web Map.The fourth tab – General Use – summarizes assessment outcomes for the San Antonio River Watershed as identified in the Texas Commission on Environmental Quality (TCEQ) 2016, 2014, and 2012 Integrated Report (IR). It displays the General Use Designations. It is the dependent of the SARA Water Quality Viewer and uses the General Use Web Map.The fifth tab – Fish Consumption Use – summarizes assessment outcomes for the San Antonio River Watershed as identified in the Texas Commission on Environmental Quality (TCEQ) 2016, 2014, and 2012 Integrated Report (IR). This Web Map displays the Fish Consumption Use Designations. It is the dependent of the Fish Consumption Use Tab and the SARA Water Quality Viewer.Please do not delete or modify The River Authority GISESD
A data dashboard in the form of a document link to Microsoft Power BI Dashboard of the same name, prepared and maintained by the Department of Economic Prosperity and Housing. Data is updated quarterly.NOTE: This product and the information shown is provided "AS IS" and exists for informational purposes only. The City of Vancouver (COV) makes no warranties regarding the accuracy of such data. This product and information is not prepared, nor is suitable, for legal, engineering, or surveying purposes. Any sale, reproduction or distribution of this information, or products derived therefrom, in any format is expressly prohibited. Data are provided by multiple sources and subject to change without notice.
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This project focuses on developing a machine learning-driven system to classify hospital claims and treatment outcomes, offering a second opinion on healthcare costs and decision-making for insurance claims and treatment efficacy.Key Features and Tools:Machine Learning Algorithms: Leveraging Python (pandas, numpy, scikit-learn) for predictive modeling to assess claim validity and treatment outcomes.APIs Integration: Used Google Maps API to retrieve and map the locations of private hospitals in Malaysia.GIS Mapping Dashboard: Created a GIS-enabled dashboard in Microsoft Power BI to visualize private hospital distribution across Malaysia, aiding healthcare planning and analysis.Advanced Analytics Tools: Integrated Microsoft Excel, Python, and Google Collab for data processing and automation workflows.