Clustering migrant information to see which surburbs migrants are coming to. Also find out about education, do they produce the same clusters?
Professional and migrants.
Migrants and land use.
Population aged 70-80 near to hospital and nursing homes.
Population in school and secondary school.
Example 2
I'll do logistic regression with normalized features, or svm with feature selection, and figure out which features contribute to the result.
Some other ideas besides the examples:
Associations:
Migration and education.
Health and hospitals.
Try to find associations or similarities in clusters between the number of presentations to hospitals (there are like 3 features) and some other feature or set of features like profession, age, land use, etc. This way we could suggest that these presentations might be prevented by adding more education, or care centres, etc.
We can try to find how many disadvantaged people (single parents, poor people, people with disabilities, etc) are in an area and try to see which services lack there like care centres, hospitals, or maybe long traveling time to a hospital.
Example 1
Clustering migrant information to see which surburbs migrants are coming to. Also find out about education, do they produce the same clusters?
Professional and migrants.
Migrants and land use.
Population aged 70-80 near to hospital and nursing homes.
Population in school and secondary school.
Example 2
I'll do logistic regression with normalized features, or svm with feature selection, and figure out which features contribute to the result.
Some other ideas besides the examples:
Associations:
Try to find associations or similarities in clusters between the number of presentations to hospitals (there are like 3 features) and some other feature or set of features like profession, age, land use, etc. This way we could suggest that these presentations might be prevented by adding more education, or care centres, etc.
We can try to find how many disadvantaged people (single parents, poor people, people with disabilities, etc) are in an area and try to see which services lack there like care centres, hospitals, or maybe long traveling time to a hospital.