Life expectancy is a complex measure that’s affected by many different factors. Genetics, lifestyle choices, access to healthcare, and even geographical location can all play a role in how long the average person can expect to live.
Water quality can have a huge impact on mortality rates, as it largely affects whether an area experiences outbreaks of waterborne diseases such as cholera, dysentery and typhoid. Poor water quality can lead to long-term health problems for those living in the contaminated areas too, with individuals consuming large quantities of water providing harmful contaminants over time. Water contaminants have also been linked to cancers such as bladder and stomach cancer, increasing the risk of mortality if left untreated.
Generally speaking, access to safe drinking water is essential for a healthy lifestyle and it’s imperative to regularly monitor and update our infrastructure so everyone can feel safe when accessing their clean drinking water source.
Our health is affected not only by the purity of water, but also by its softness/hardness (pH) level. As scientists have shown in numerous studies, water that is too soft or too hard can also be detrimental to our health. I had the opportunity to work with data on water hardness in the largest cities in England. I was interested in identifying patterns in them and understanding if and how strong the correlation between these parameters really is.
The link below shows you how I explored this data using Python and its libraries: Pandas, Matplotlib, Seaborn, Statsmodels, and Sklearn.