machine learning, data science, and environmental analytics
Water pollution is a critical global issue affecting millions of people. This project focuses on predicting water quality by analyzing multiple factors such as pH, dissolved oxygen, turbidity, and contaminant levels using machine learning models. It helps in identifying unsafe water sources, monitoring pollution trends, and ensuring access to clean drinking water. Techniques like decision trees, neural networks, and regression models can be used to predict water safety levels efficiently.
Programming Languages: Python, R Libraries & Frameworks: Pandas, NumPy, Scikit-learn, TensorFlow/Keras, XGBoost, Matplotlib, Seaborn Databases: PostgreSQL, MySQL (for storing water quality data) APIs & Data Sources: Environmental Protection Agency (EPA), World Health Organization (WHO) datasets, government water quality reports Tools & Platforms: Jupyter Notebook, Google Colab, Tableau/Power BI for visualization Before Commencing the project the following links have to be examined.
https://www.kaggle.com/
https://www.who.int/
https://www.waterqualitydata.us/
https://www.epa.gov/
