🩺 Overview This dataset is a simulated directory of doctors in Bangladesh, crafted for data analysis, machine learning projects, and educational purposes. It mimics a real-world healthcare directory, containing structured information about doctors, their qualifications, specializations, locations, and affiliations.

📌 Key Features Name of the doctor

Specialization (e.g., cardiology, dermatology, internal medicine)

Degrees/Qualifications (e.g., MBBS, FCPS, MD)

Hospital/Clinic Affiliation

Location (City/Division)

Contact/Phone (dummy format)

Note: All names, contacts, and institutions in this dataset are fictional or anonymized. This dataset is not intended for real-life use or medical reference.

🎯 Use Cases NLP tasks such as named entity recognition (NER) or text classification

Training models for appointment scheduling or doctor recommendation systems

Practicing data cleaning, visualization, and preprocessing

Geolocation-based analysis and healthcare access mapping

Development of dummy apps or dashboards for health tech projects

📂 File Format Format: CSV

Rows: (based on your file — please confirm the number)

Columns: Includes doctor name, specialization, qualification, hospital, chamber location, etc.

🛡️ Disclaimer This dataset is synthetic and solely for research and academic use. It does not represent any real individual or organization.

Context

This dataset contains comprehensive information of healthcare professionals across Bangladesh scraped from Sasthya seba in 12 Aug 2025. It contains information about the doctor's name, education, speciality, experience, chamber, location, concentration, etc.

• The scraped program of this dataset can be found in bd-healthcare-professional-analysis Github repository.
• Tableau visualization of this dataset can also be found in Analysis of Healthcare Professional in BD
  

Content

The dataset consists of two CSV files: * doctors_combined_data.csv: This file contains the raw, uncleaned data in a single combined file, as it was originally scraped. It is useful for anyone who wishes to perform their own data cleaning and preprocessing steps.

📂*doctors_combined_data.csv*: - Doctor Name: Full name of the healthcare professional - Education: Medical degree(s) and educational qualifications - Speciality: Medical specialization area - Experience: Years of professional experience - Chamber: Practice location/clinic name - Location: Geographical location/address - Concentration: Area of medical concentration/focus

• doctors_processed_data.csv: This file contains the cleaned and processed data used for visualization. > 📂 doctors_processed_data.csv :
  • Doctor ID: Generated Doctor's unique id
  • Doctor Name: Full name of the healthcare professional
  • Education: Medical degree(s) and educational qualifications
  • Speciality: Medical specialization area
  • Experience: Years of professional experience
  • Chamber: Practice location/clinic name
  • Location: Geographical location/address
  • Concentration: Area of medical concentration/focus
  • MBBS, FCPS, BCS, MD, MS, MCPS, CCD, PGT, BDS, MPH: One-hot encoded seperate column for these top 10 degree (1 if present, 0 otherwise)
  • Gynae Problems, Cardiac Medicine, General Medicine, Aesthetic Medicine, Adolescent Medicine, Infectious Diseases, Geriatric Medicine, Polycystic Ovary Syndrome (Pcos), Hormone Dirtubances, Health Checkup (Pediatric) : One-hot encoded seperate column for these top 10 concentration (1 if present, 0 otherwise)

Acknowledgement

The website Sasthya Seba was used to scrape this dataset. Please include citations for this dataset if you use it in your own research.

Inspiration

This dataset can be used to analyze healthcare distribution patterns and accessibility gaps across Bangladesh divisions. Additionally, it can be used to develop resource allocation strategies and policy recommendations.

Important Notes

This dataset represents a significant sample but not the complete universe of healthcare professionals in Bangladesh. All data was collected from publicly available sources following ethical web scraping practices.

Prevalence and factors associated with diagnosis by qualified medical doctors among people with the diagnosis of hypertension, Bangladesh 2017–18.