flaml

an open-sourced highly automated machine learning Python framework for data-driven geochemistry discovery

A simplified setup for users to explore the AutoGen library. Contains example scripts derived from AutoGen notebooks for a hassle-free experience.

A collection of practical machine learning projects and notebooks covering key topics like regression, classification, clustering and feature engineering.More projects and tutorials will be added regularly to help you learn and apply ML techniques.

The Autonomous & Interactive Drag-and-Drop Machine Learning Pipeline Engine. Built with Next.js, FastAPI, Polars, and FLAML.

A two-part project focused on feature engineering on csv data

A lightweight, cost-effective AutoML platform built on AWS serverless architecture. Upload CSV files, automatically detect problem types, and train machine learning models with just a few clicks.

A cutting-edge real-time AI-powered data analysis and machine learning platform that delivers instant insights through live progress updates, background processing, and intelligent automation. Built with modern web technologies and enterprise-grade infrastructure.

Program for analysing and interpreting FLAML optimisation logs.

Binary Classification problem. Contains Classifiers from various AutoML libraries such as AutoGluon, FLAML, Lazypredict, & TPOT

Real-estate buyer-stage classifier on a domain-agnostic, single-node tabular AutoML pipeline (Dagster + FLAML + MLflow + FastAPI), with drift monitoring and an online store.

Using machine learning to predict Goodreads ratings utilizing the Kedro framework. Includes EDA, rich feature engineering and data integration, model building with pipelines, and model comparison

A lightweight, serverless-optimized AutoML library for Python. Build, evaluate, and export high-performance machine learning models with a few lines of code.

Build your own Recommendation Systems !!!

AutoML Libraries for training multiple ML models in one go with less code.

This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of gravelly soils. This model is developed using LightGBM and SHAP.

Best practices for solving Machine Learning Classification problems with advanced tools

Here, I will put Machine learning tasks which are done using Automated Machine Learning.

Using FLAML to build a ML model for predicting University Admission chances

Upload your CSV and get instant insights — automated EDA with visualizations, missing value analysis, feature types, correlations, and more. Built for data scientists and non-tech users alike.