water-potability

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.

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

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

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.

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,…

Machine learning project to predict drinking water potability using physicochemical properties. Logistic Regression, Random Forest, and SVM models are trained and compared after data preprocessing and evaluation.

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

A modular deep learning pipeline built with PyTorch to predict water potability. Features include automated data preprocessing, exploratory data analysis with high-quality visualizations, and a tuned feedforward neural network for binary classification.

Water Quality Classification for Safe Drinking Prediction (Project 1 - Predictive Analytics)

Machine Learning-based web application for analyzing water quality parameters, predicting drinking water safety, visualizing results, and generating automated PDF reports.