This project focuses on analyzing various healthcare-related datasets using Python. It covers data manipulation, visualization, and geospatial analysis of healthcare facilities. The analysis is divided into multiple sections:
- Explore Patient Visit Demo Dataset:
- Analyzes patient visit data to extract key insights.
- Uses statistical summaries and visualizations for patient visits.
- Explore Pharmacy Sales Demo Dataset:
- Analyzes pharmacy sales data, focusing on sales trends and medication patterns.
- Utilizes data wrangling and visualization techniques to highlight significant sales metrics.
- Patient Experience Analytics:
- Analyzes patient feedback and experience data to understand satisfaction levels and areas for improvement.
- Practical Data Manipulation and Wrangling: Focuses on handling medication data.
- Parses a dataset of 1000 generic medication names, extracting and cleaning medication names and classes.
- Outputs a cleaned dataset containing medication names and their respective classes.
- Public Health Facilities GeoSpatial Analysis:
- Geospatial analysis of public health facility locations.
- Uses Geopandas and Folium to plot facility types and their geographical distribution.
- Implements buffer zones around facilities and visualizes them using interactive maps.
- Includes heatmaps and marker clustering to showcase facility density and spatial relationships.
- Pandas: For data manipulation and analysis.
- Seaborn, Matplotlib, Plotly: For data visualization.
- Geopandas, Folium: For geospatial analysis and map plotting.
- Folium Plugins: Utilized for creating heatmaps and marker clusters.
- Cleaned and structured multiple healthcare datasets.
- Conducted detailed geospatial analysis of health facility distribution.
- Created interactive maps for visualization of healthcare infrastructure.