Dataset for Evaluating Hair Hall Causes Using Machine Learning Techniques

Published: 24 June 2024| Version 1 | DOI: 10.17632/g46n66frrh.1
, Oahidul Islam, Md Assaduzzaman Tapos,


This dataset provides comprehensive information on various factors contributing to hair fall. The dataset contains 717 responses from a survey designed to capture details about individual hair care practices, lifestyle choices, and genetic predispositions. Features: 1. Age 2. Gender 3. Usage of hair products 4. Water quality 5. Stress levels 6. Late-night activities 7. Presence of anemia 8. Family history of hair fall 9. Personal health conditions Potential Uses: Researchers, data scientists, and healthcare professionals can use this dataset to analyze the factors influencing hair fall. It is particularly useful for: -- Identifying patterns and correlations among various factors contributing to hair fall. -- Developing predictive models to forecast the likelihood of hair fall based on individual attributes. -- Designing personalized hair care and treatment plans. -- Conducting exploratory data analysis to uncover new insights about hair health. Future Predictions: From this dataset, future predictions can be made regarding: -- The impact of lifestyle choices on hair fall severity. -- The likelihood of hair fall based on genetic predispositions and family history. -- The effectiveness of different hair care products and practices. -- The relationship between stress levels and hair fall. This dataset serves as a valuable resource for advancing the understanding of hair fall causes and developing targeted solutions to mitigate this common issue.


Steps to reproduce

Data for the study was collected through a Google Forms survey, which was distributed via social media, community forums, and in-person outreach to ensure diversity and representativeness. The survey questionnaire was meticulously designed following an exhaustive literature review and consultations with subject matter experts. To achieve balanced representation, the survey targeted individuals both with and without hair loss, encompassing a broad age range, balanced gender representation, and geographic diversity. Participation in the survey was voluntary and anonymous, with informed consent obtained from all participants.


Daffodil International University


Genetics, Health Sciences, Public Health, Medical Education, Machine Learning, Survey, Medical Research, Hair Loss