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

This Power BI dashboard shows the COVID-19 vaccination rate by key demographics including age groups, race and ethnicity, and sex for Tempe zip codes.Data Source: Maricopa County GIS Open Data weekly count of COVID-19 vaccinations. The data were reformatted from the source data to accommodate dashboard configuration. The Maricopa County Department of Public Health (MCDPH) releases the COVID-19 vaccination data for each zip code and city in Maricopa County at ~12:00 PM weekly on Wednesdays via the Maricopa County GIS Open Data website (https://data-maricopa.opendata.arcgis.com/). More information about the data is available on the Maricopa County COVID-19 Vaccine Data page (https://www.maricopa.gov/5671/Public-Vaccine-Data#dashboard). The dashboard’s values are refreshed at 3:00 PM weekly on Wednesdays. The most recent date included on the dashboard is available by hovering over the last point on the right-hand side of each chart. Please note that the times when the Maricopa County Department of Public Health (MCDPH) releases weekly data for COVID-19 vaccines may vary. If data are not released by the time of the scheduled dashboard refresh, the values may appear on the dashboard with the next data release, which may be one or more days after the last scheduled release.Dates: Updated data shows publishing dates which represents values from the previous calendar week (Sunday through Saturday). For more details on data reporting, please see the Maricopa County COVID-19 data reporting notes at https://www.maricopa.gov/5460/Coronavirus-Disease-2019.

The Facility Registry Service (FRS) provides quality facility data to support EPA's mission of protecting human health and the environment by identifying and geospatially locating facilities, sites, or places subject to environmental regulations of environmental interest. Facility data is improved with geospatial processing of incoming data and data curation tools to provide an integrated, dataset to partners and the public through a variety of methods and products. For more detailed information about these facilities, use the FRS Query tool. US Power Generation Facilities, compiled from most-current (as of June 2014) Energy Information Administration EIA-860 powerplant data, together with EPA FRS data.

The Facility Registry Service (FRS) provides quality facility data to support EPA's mission of protecting human health and the environment by identifying and geospatially locating facilities, sites, or places subject to environmental regulations of environmental interest. Facility data is improved with geospatial processing of incoming data and data curation tools to provide an integrated, dataset to partners and the public through a variety of methods and products. For more detailed information about these facilities, use the FRS Query tool. US Power Generation Facilities, compiled from most-current (as of June 2014) Energy Information Administration EIA-860 powerplant data, together with EPA FRS data.

The Facility Registry Service (FRS) provides quality facility data to support EPA's mission of protecting human health and the environment by identifying and geospatially locating facilities, sites, or places subject to environmental regulations of environmental interest. Facility data is improved with geospatial processing of incoming data and data curation tools to provide an integrated, dataset to partners and the public through a variety of methods and products. For more detailed information about these facilities, use the FRS Query tool. US Power Generation Facilities, compiled from most-current (as of June 2014) Energy Information Administration EIA-860 powerplant data, together with EPA FRS data.

Data is sourced from Stats SA Data is transformed into a BI format and quality assured. Data is consumed by a dashboard created in Power BI. The following reports exist for this dashboard:Percentage Agriculture GDP growth (2015-2019); filter by year and region - constant value growth rate by year and regionPercentage contribution to provincial GDP (2015-2019); filter by year and region - constant value contribution by year and regionPercentage contribution to agriculture employment (2015-2019); filter by year and quarter - % contribution by year and quarterPublication Date20 January 2023LineageData from Statistics South Africa is sourced, transformed into a BI format and then dynamic dashboard reflecting the Outcome Indicators are developed using Power BI: Percentage agricultural growth ratePercentage contribution of agriculture to provincial GDPPercentage contribution of agriculture to total employment in Western CapeData SourceGDP 2015-2020, Stats SAQLFS 2017 - 2021, Stats SA

Data is sourced from various health resources. Data is transformed into a BI format and quality assured. Data is consumed by a dashboard created in Power BI. Four reports exist for this dashboard:1. HIV Prevalence and TB Success RateHIV prevalence amongst women attending antenatal clinics in the Western Cape (2012-2015) by district and yearHIV prevalence amongst women attending antenatal clinics in the province (2012-2015) by province and yearTB Programme Success Rate (2013/14-2018/19) by TB Measure2. Births and Maternal MortalitiesNeonatal in facility (0-28 days) mortality rate (2015/16-2018/19); by years and neonatal death rate in facility and mortality rate by 1,000 live births Facility maternal mortality rate (2002, 2005, 2008, 2011, 2014); by triennia (3 years) deaths by 1,000 live births in WC (incl count of maternal deaths, count of live births, and infant maternal mortality ration)(Child (under 5) and Infant (under 1) mortality rate (2011, 2012, 2013); filter years, Infant/Child age band; Years, District, Births and Deaths by age bandDelivery rate in facility to women under 20 years (2013/14-2018/19); filter by financial year (FY); delivery rate by FY, delivery rate, numerator (births to women <20), denominator (total births)3. Deaths and Life ExpectancyLeading underlying causes of death in the Western Cape (2012-2016) by years and cause of deathYears of life lost (YLL) by cause of death in the WC (2012-2016) by years and YLL cause of deathAverage Life Expectency (LE) at birth (2006, 2011, 2016) by year, province, and gender4. Travel time to facilitiesTravel time taken to health facility by households with expenditure less than R1200-SA (2013-2018); by year, province, and travel time to health facilityTravel time taken to health facility by households with expenditure less than R1200-WC (2013-2018); by year, province, population group, and travel time to health facilityPublication Date2 September 2021LineageData from various sources transformed to a BI format and used to develop dynamic Power BI dashboards reflecting Outcome Indicators: HIV prevalence amongst women attending antenatal clinics in the provinceAll DS-TB (drug-susceptible tuberculosis) client treatment success rateNeonatal in facility (0-28 days) mortality rateFacility maternal mortality rateDelivery rate in facility to women under 20 yearsLife Expectancy (LE)Leading underlying causes of death in the Western CapeTravel time taken to health facility by households with expenditure less than R1200 (SA and WC)Data Source2019 National Antenatal Sentinel HIV Survey, National Department of Health 2021;Annual report 2014/15-2020/21, DOH;District Health Information Systems;Mid-year population estimates, Stats SA; Life Expectancy Stats SA calculations;Mortality and Causes of Death in South Africa 2018, June 2021, Stats SA

Similar to the InFORM to FOD Tech TIp, this describes the process to extract records from the FPA Fire Occurrence Database (FOD) via Power Query Editor, to insert those records in Excel. Then, a provided VBA macro can be used in that Excel file to create a csv that will import easily into FireFamilyPlus (FFP).

The method to create the Wind Resource Area datasets is to:Query Power Plant point locations from the California Energy Commission, California Power Plants data set by operational status and capacity greater than or equal to 2 MW at each facility from the Quarterly Fuel and Energy Report, CEC-1304A. Plants tracked include those of at least 1 MW, which are considered of commercial size. A polygon was generated around the resulting operational, commercial wind facilities using the Aggregate Points geoprocessing tool with an aggregation distance of 15 survey miles. A 5 mile spatial buffer was added to the resulting polygons. The buffer does not represent information regarding environmental analysis. It is used only to depict plant concentration regions.