The *Water Potability Classification* project leverages machine learning to classify water as potable or non-potable based on key physicochemical properties, aiming to enhance water safety and quality monitoring.

Using Using voting classifier as the final model to predict water potability.

ML model deployment using docker, kubernetes; API deployment with FastAPI; and MLOps using MLFlow for water potability dataset

Project ini bertujuan untuk membandingkan algoritma SVM sebelum dan sesudah dilakukan forward selection sebagai seleksi fitur untuk memprediksi kualitas air. Dataset yang digunakan berasal dari kaggle, Pada dataset tersebut terdapat 10 atribut yang terdiri dari 9 atribut ciri dan 1 atribut label, 9 atribut bebas diantaranya ph, hardness, solids,…

Prediction of water potability with parameters like ph, hardness, turbidity etc.

This project is part of a Hackathon organized by the Frontier Tech Leaders community, aiming to contribute solutions to the United Nations' Sustainable Development Goals (SDG), particularly focusing on ensuring clean water and sanitation for all.

Spigot is a machine learning project to see whether a sample of water is potable or not, based on specific features