Synthetic Drinking Water Potability Dataset

This synthetic Drinking Water Potability Dataset is designed for educational and research purposes in the fields of data science, environmental studies, and public health analytics. It contains key water quality indicators such as pH levels, hardness, solids, and other chemical markers that can be used to analyze the potability (drinkability) of water. The dataset is ideal for building predictive models, assessing water quality risks, and studying factors affecting the safety of drinking water.

Dataset Features: pH: A measure of water’s acidity or alkalinity, with safe drinking water typically ranging from 6.5 to 8.5. Hardness (mg/L): The concentration of calcium and magnesium ions in water, which affects taste and scaling. Solids (ppm): Total dissolved solids (TDS) in parts per million, representing the mineral content of water. Chloramines (ppm): Concentration of chloramines, disinfectants used in water treatment. Sulfate (mg/L): Sulfate ion concentration, which in high amounts can affect taste and pose health risks. Conductivity (μS/cm): Water's ability to conduct electricity, related to the concentration of dissolved ions. Organic Carbon (mg/L): Amount of organic compounds in water, impacting water treatment and quality. Trihalomethanes (μg/L): By-products of chlorination, with high levels posing health risks. Turbidity (NTU): Cloudiness of water caused by particles; lower levels are ideal for potable water. Potability: Binary classification indicating water safety: Yes: Potable (safe for drinking). No: Non-potable (unsafe for drinking). Usage This dataset is ideal for various water-quality-related applications:

Water Quality Prediction: Develop machine learning models to classify water as potable or non-potable. Risk Analysis: Identify factors contributing to unsafe drinking water and prioritize improvements. Predictive Modeling: Build predictive models using multiple water quality indicators. Environmental Research: Study relationships between water quality metrics and environmental factors. Public Health Analysis: Analyze unsafe water trends and guide interventions to improve water quality. Coverage This synthetic dataset is anonymized, ensuring compliance with data privacy standards. It is designed for research and learning purposes, providing diverse water quality conditions for analysis and model building.

License CC0 (Public Domain)

Who Can Use It Data Science Practitioners: For practicing data preprocessing, classification, and regression tasks. Environmental Scientists and Researchers: To explore relationships between water quality indicators and potability. Public Health Analysts: To understand water safety trends and recommend interventions. Policy Makers and Regulators: For data-driven decision-making in water safety policies.