Python water quality EDA and Potability analysis

Being able to provide enough fresh drinking water is a core requirement. Within the climate change debate, one of the largest challenges is ensuring enough freshwater to survive. Water quality is a big concern that impacts all the specifies. Only about three percent of Earth’s water is freshwater. Of that, only 1.2 percent can be used as drinking water, with the remainder locked up in glaciers, ice caps, and permafrost, or buried deep in the ground. Using a data-driven approach to assess the features that impact the water quality could greatly improve our understanding of what makes water drinkable. At its most basic level, the potability of water relates to water safety. Data techniques can be used to review this target feature. Other questions emerge that are outside of our current review: Can we consume all types of freshwater? What percentage of the world's freshwater can be accessed? Has the water table increased as sea levels have risen? In this article, we will go on a journey with a small water quality dataset. From the data, we will seek to find hidden insights with data analysis techniques using pandas and numpy. For the data visualizations, the matplotlib and seaborn libraries will be used. A range of exploratory data analysis (EDA) techniques will be employed to provide further clarity for the data quality. Each data visualization will aim to highlight different characteristics of the data. They will also provide the user with templates to apply to other challenges. <a href="https://towardsdatascience.com/python-water-quality-eda-and-potability-analysis-ebc1cf553081">Click Here</a>