scikit-learn is a widely-used Python module for classic machine learning. It is built on top of SciPy.
A curated list of project tutorials for project-based learning.
Powerful machine learning library for Node.js – uses Python's scikit-learn under the hood.
🧠 An open-source machine learning application for analyzing software defect reports extracted from bug tracking systems.
Final year group project Online Employee Management System.
This Netflix Recommendation System is a web application developed using Node.js and Express. It utilizes a recommendation engine written in Python
Open source Cloud Framework for exposing scalable Machine-Learning-as-a-Service implementation
A distributed ledger-based blockchain implementation of the rates proposed and charged, and the commodity count by hospitals for treatment and consultancy of patients.
Deploying a machine learning model to Heroku.
Agrologer is an integrated platform designed to assist farmers in managing water-related issues, offering real-time data analysis and remote monitoring capabilities.
Estate AI is a machine learning application that predicts the approximate rent a user would need to pay for their requirement across major metro cities of India. It is built using NextJS 13, TailwindCSS, and TypeScript for the frontend, Scikit Learn for Model Training and and Flask for the backend.
A platform for reading personalized research articles from different platforms.
Fresh Farm AI - AI-Powered Crop Quality Control System
A simple website that uses the Fantasy Premier League (FPL) API to show player and manager stats, with a machine learning model for predicting player points in the Predicted Points section
🐱 An image classification ML project, with an interactive website, and a deployed model
🌟 Signez - Interactive ASL learning with real-time hand sign recognition! 🖐️ Learn and practice ASL alphabet with percentage match, predicted words, scores, and a progress dashboard. Powered by advanced ML and full-stack integration 🚀✨
Full-stack, ML-powered churn prediction app using FastAPI, React/TypeScript, and scikit-learn. Predicts churn probability with logistic regression and serves results via a REST API and interactive UI.
Web version of Greenient
Created a prediction model to predict a players points per game (PPG) stat for the upcoming 2024-2025 season based on their PPG from the past 3 seasons (2022-2022, 2022-2023, 2023-2024). Used a Linear Regression model using sklearn to combine a player's data from all three seasons to help predict their PPG in the upcoming 2024-2025 season.
Created by David Cournapeau
Released January 05, 2010
Latest release 4 months ago