Machine learning model for classifying water quality
Safe and easily accessible water is important for public health, a basic human right and a component of effective health protection policy. .
Good water resources management and improved water supply can boost a country’s economic growth. In some regions, it has been shown that investments in water supply and sanitation can generate a net economic benefit, as reductions in adverse health effects and health care costs outweigh the costs of implementing the interventions.
Unsafe water and poor sanitation are linked to the transmission of diseases such as cholera, diarrhoea, dysentery, hepatitis A, typhoid and polio. Absent, inadequate or inappropriately managed water and sanitation services expose people to preventable health risks. This is particularly the case in health care facilities where both patients and staff are at additional risk of infection and disease when water, sanitation and hygiene services are lacking.
Thanks to the data obtained in this link I have managed to model a Machine Learning project that can identify whether water is safe or unsafe to drink by analysing a number of parameters such as:
- pH
- Hardness
- Total dissolved solids
- Chloramines
- Sulfate
- Conductivity
- Organic carbon
- Trihalomethanes
- Turbidity
All this work you can see my Github repository, where you can find more interactive visualizations like this one.