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.

This layer shows health insurance coverage by type and by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent uninsured. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B27010 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Table contains county residents without health insurance. Data are summarized as people of all ages and those 19 to 64 years old. Data are presented at county, city, zip code and census tract level. Data are presented for zip codes (ZCTAs) fully within the county. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table B27001; data accessed on June 30, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (Numeric): Geography IDNAME (String): Name of geographypop (Numeric): Population for whom health insurance coverage was assessedt_uninsured (Numeric): Number of people (all ages) who were without health insurancep_uninsured (Numeric): Percent of people (all ages) who were without health insurancet_19_64 (Numeric): Population ages 19 to 64 years for whom health insurance coverage was assessedt_unins_19_64 (Numeric): Number of people ages 19 to 64 years who were without health insurancep_unins_19_64 (Numeric): Percent of people ages 19 to 64 years who were without health insurance

Table from the American Community Survey (ACS) 5-year series on disabilities and health insurance related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes C21007 Age by Veteran Status by Poverty Status in the Past 12 Months by Disability Status, B27010 Types of Health Insurance Coverage by Age, B22010 Receipt of Food Stamps/SNAP by Disability Status for Households. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.

Historical ownership data of GIS by Cincinnati Specialty Underwriters Insurance CO

Historical ownership data of GIS by CINCINNATI INSURANCE CO

This dataset provides locations and related information for Insurance Navigators and Application Organizations as of 08/29/2014 based on information provided by the Indiana Department of Insurance. Navigators are individuals who help Hoosier insurance consumers complete health coverage applications on the federall-facilitated marketplace or state-based insurance affordabillty program applications. An Application Organization is an organization that has employees and/or volunteers helping Hoosier insurance consumers complete state or federal applications for health coverage. Visit http://www.in.gov/idoi/2823.htm for more information about this resource.

Historical holdings data showing quarterly positions, market values, shares held, and portfolio percentages for GIS held by CINCINNATI INSURANCE CO from Q3 2013 to Q4 2017

Link to the Library of Congress Sanborn Fire Insurance Map dated July 1902 for Sioux Falls, South Dakota.Sanborn Fire Maps were originally prepared for the use of fire insurance companies. The maps include parcel boundaries, building information, business names, street names, house numbers, fire hydrants, utilities, and more.

Historical holdings data showing quarterly positions, market values, shares held, and portfolio percentages for GIS held by Cincinnati Specialty Underwriters Insurance CO from Q3 2013 to Q4 2017

Polygon features representing the area within a five mile driving distance of a specific fire department. The polygons represent the total area covered for a single fire jurisdiction which may include more than one fire station.Chatham GIS SOP: "MAPSERV-66"

This web map is part of the Centers for Disease Control and Prevention (CDC) PLACES. It provides model-based estimates of lack of health insurance prevalence among adults aged 18-64 years at county, place, census tract, and ZCTA levels in the United States. PLACES is an expansion of the original 500 Cities Project and a collaboration between the CDC, the Robert Wood Johnson Foundation, and the CDC Foundation. Data sources used to generate these estimates include the Behavioral Risk Factor Surveillance System (BRFSS), Census 2020 population counts or Census annual county-level population estimates, and the American Community Survey (ACS) estimates. For detailed methodology see www.cdc.gov/places. For questions or feedback send an email to places@cdc.gov.Measure name for lack of health insurance is ACCESS2.

Compilations of digital GIS data representing the same information presented on FIRMs, and in the Flood Insurance Study Report.rn

REQUIRED: A brief narrative summary of the data set.

Feature layer generated from running the Join Features solution

This dataset contains data from the National Flood Hazard Layer, a GIS database of flood risks and regulatory flood determination data. Flood hazard and supporting data are developed using specifications for horizontal control consistent with 1:12,000–scale mapping. If you plan to display maps from the National Flood Hazard Layer with other map data for official purposes, ensure that the other information meets FEMA’s standards for map accuracy. The minimum horizontal positional accuracy for base map hydrographic and transportation features used with the NFHL is the NSSDA radial accuracy of 38 feet. USGS imagery and map services that meet this standard can be found by visiting the Knowledge Sharing Site (KSS) for Base Map Standards (420). Other base map standards can be found at http://pm.riskmapcds.com/kss/MapChanges/default.aspx. You will need a username and password to access this information. The NFHL data are from FEMA’s Flood Insurance Rate Map (FIRM) databases. New data are added continually. The NFHL also contains map changes to FIRM data made by Letters of Map Revision (LOMRs). The NFHL is stored in North American Datum of 1983, Geodetic Reference System 80 coordinate system, though many of the NFHL GIS web services support the Web Mercator Sphere projection commonly used in web mapping applications. This dataset displays FEMA's Flood Insurance Rate Map (FIRM) panels. The spatial entities representing FIRM panels are polygons. The polygon for the FIRM panel corresponds to the panel neatlines. Panel boundaries are generally derived from USGS DOQQ boundaries. As a result, the panels are generally rectangular. FIRM panels must not overlap or have gaps within a study. In situations where a portion of a panel lies outside the jurisdiction being mapped, the user must refer to the S_Pol_Ar table to determine the portion of the panel area where the FIRM Database shows the effective flood hazard data for the mapped jurisdiction. This information is needed for the FIRM Panel Index and the following tables in the FIS report: Listing of NFIP Jurisdictions, Levees, Incorporated Letters of Map Change, and Coastal Barrier Resources System Information.View Dataset on the Gateway

FEMA Framework Basemap datasets comprise six of the seven FGDC themes of geospatial data that are used by most GIS applications (Note: the seventh framework theme, orthographic imagery, is packaged in a separate NFIP Metadata Profile): cadastral, geodetic control, governmental unit, transportation, general structures, hydrography (water areas & lines. These data include an encoding of the geographic extent of the features and a minimal number of attributes needed to identify and describe the features. (Source: Circular A16, p. 13)

Basemap datasets comprise five of the seven FGDC themes of geospatial data that are used by most GIS applications (Note: the framework themes of orthoimagery and elevation are packaged in separate NFIP Metadata Profiles): cadastral, geodetic control, governmental unit, transportation, and hydrography (water areas and lines). These data include an encoding of the geographic extent of the features and a minimal number of attributes needed to identify and describe the features. (Source: Circular A16)

The newGeoSure Insurance Product (newGIP) provides the potential insurance risk due to natural ground movement. It incorporates the combined effects of the 6 GeoSure hazards on (low-rise) buildings. This data is available as vector data, 25m gridded data or alternatively linked to a postcode database the Derived Postcode Database. A series of GIS (Geographical Information System) maps show the most significant hazard areas. The ground movement, or subsidence, hazards included are landslides, shrink-swell clays, soluble rocks, running sands, compressible ground and collapsible deposits. The newGeoSure Insurance Product uses the individual GeoSure data layers and evaluates them using a series of processes including statistical analyses and expert elicitation techniques to create a derived product that can be used for insurance purposes such as identifying and estimating risk and susceptibility. The Derived Postcode Database (DPD) contains generalised information at a postcode level. The DPD is designed to provide a summary value representing the combined effects of the GeoSure dataset across a postcode sector area. It is available as a GIS point dataset or a text (.txt) file format. The DPD contains a normalised hazard rating for each of the 6 GeoSure themes hazards (i.e. each GeoSure theme has been balanced against each other) and a combined unified hazard rating for each postcode in Great Britain. The combined hazard rating for each postcode is available as a standalone product. The Derived Postcode Database is available in a point data format or text file format. It is available in a range of GIS formats including ArcGIS (.shp), ArcInfo Coverages and MapInfo (.tab). More specialised formats may be available but may incur additional processing costs. The newGeoSure Insurance Product dataset has been created as vector data but is also available as a raster grid. This data is available in a range of GIS formats, including ArcGIS (.shp), ArcInfo coverages and MapInfo (.tab). More specialised formats may be available but may incur additional processing costs. Data for the newGIP is provided for national coverage across Great Britain. The newGeoSure Insurance Product dataset is produced for use at 1:50 000 scale providing 50 m ground resolution. This dataset has been specifically developed for the insurance of low-rise buildings. The GeoSure datasets have been developed to identify the potential hazard for low-rise buildings and those with shallow foundations of less than 2 m deep. The identification of ground instability and other geological hazards can assist regional planners; rapidly identifying areas with potential problems and aid local government offices in making development plans by helping to define land suited to different uses. Other users of these data may include developers, homeowners, solicitors, loss adjusters, the insurance industry, architects and surveyors. Version 7 released June 2015.

This dataset has been superseded The newGeoSure Insurance Product (newGIP) provides the potential insurance risk due to natural ground movement. It incorporates the combined effects of the 6 GeoSure hazards on (low-rise) buildings. This data is available as vector data, 25m gridded data or alternatively linked to a postcode database - the Derived Postcode Database. A series of GIS (Geographical Information System) maps show the most significant hazard areas. The ground movement, or subsidence, hazards included are landslides, shrink-swell clays, soluble rocks, running sands, compressible ground and collapsible deposits. The newGeoSure Insurance Product uses the individual GeoSure data layers and evaluates them using a series of processes including statistical analyses and expert elicitation techniques to create a derived product that can be used for insurance purposes such as identifying and estimating risk and susceptibility. The Derived Postcode Database (DPD) contains generalised information at a postcode level. The DPD is designed to provide a 'summary' value representing the combined effects of the GeoSure dataset across a postcode sector area. It is available as a GIS point dataset or a text (.txt) file format. The DPD contains a normalised hazard rating for each of the 6 GeoSure themes hazards (i.e. each GeoSure theme has been balanced against each other) and a combined unified hazard rating for each postcode in Great Britain. The combined hazard rating for each postcode is available as a standalone product. The Derived Postcode Database is available in a point data format or text file format. It is available in a range of GIS formats including ArcGIS (.shp), ArcInfo Coverages and MapInfo (.tab). More specialised formats may be available but may incur additional processing costs. The newGeoSure Insurance Product dataset has been created as vector data but is also available as a raster grid. This data is available in a range of GIS formats, including ArcGIS (.shp), ArcInfo coverage's and MapInfo (.tab). More specialised formats may be available but may incur additional processing costs. Data for the newGIP is provided for national coverage across Great Britain. The newGeoSure Insurance Product dataset is produced for use at 1:50 000 scale providing 50m ground resolution. This dataset has been specifically developed for the insurance of low-rise buildings. The GeoSure datasets have been developed to identify the potential hazard for low-rise buildings and those with shallow foundations of less than 2 m deep. The identification of ground instability and other geological hazards can assist regional planners; rapidly identifying areas with potential problems and aid local government offices in making development plans by helping to define land suited to different uses. Other users of these data may include developers, homeowners, solicitors, loss adjusters, the insurance industry, architects and surveyors.