{"id":18277713,"url":"https://github.com/eva-kaushik/automl","last_synced_at":"2026-06-30T12:31:58.657Z","repository":{"id":251463026,"uuid":"837497482","full_name":"Eva-Kaushik/AutoML","owner":"Eva-Kaushik","description":"AutoML","archived":false,"fork":false,"pushed_at":"2024-08-03T06:46:30.000Z","size":13759,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-09-12T03:13:08.860Z","etag":null,"topics":["automl","automl-algorithms","automl-pipeline"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Eva-Kaushik.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-08-03T06:39:58.000Z","updated_at":"2024-09-13T10:55:51.000Z","dependencies_parsed_at":"2024-08-03T07:45:32.144Z","dependency_job_id":"20a91424-109b-4716-a3fc-23d2b7def935","html_url":"https://github.com/Eva-Kaushik/AutoML","commit_stats":null,"previous_names":["eva-kaushik/automl"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Eva-Kaushik/AutoML","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eva-Kaushik%2FAutoML","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eva-Kaushik%2FAutoML/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eva-Kaushik%2FAutoML/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eva-Kaushik%2FAutoML/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Eva-Kaushik","download_url":"https://codeload.github.com/Eva-Kaushik/AutoML/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eva-Kaushik%2FAutoML/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34967614,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-30T02:00:05.919Z","response_time":92,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["automl","automl-algorithms","automl-pipeline"],"created_at":"2024-11-05T12:20:15.716Z","updated_at":"2026-06-30T12:31:58.626Z","avatar_url":"https://github.com/Eva-Kaushik.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n\n\u003ch3 align=\"center\"\u003eAutoML\u003c/h3\u003e\n\n\u003cdiv align=\"center\"\u003e\n\n[![Downloads](https://pepy.tech/badge/automl-alex)](https://pepy.tech/project/automl-alex)\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/automl-alex)\n![PyPI](https://img.shields.io/pypi/v/automl-alex)\n[![CodeFactor](https://www.codefactor.io/repository/github/alex-lekov/automl_alex/badge)](https://www.codefactor.io/repository/github/alex-lekov/automl_alex)\n[![Telegram](https://img.shields.io/badge/chat-on%20Telegram-2ba2d9.svg)](https://t.me/automlalex)\n[![License](https://img.shields.io/badge/license-MIT-blue.svg)](/LICENSE)\n\n\u003c/div\u003e\n\n---\n\n\u003cp align=\"center\"\u003e State-of-the art Automated Machine Learning python library for Tabular Data\u003c/p\u003e\n\n## Works with Tasks:\n\n-   [x] Binary Classification\n\n-   [x] Regression\n\n-   [ ] Multiclass Classification (in progress...)\n\n### Benchmark Results\n\u003cimg width=800 src=\"https://github.com/Alex-Lekov/AutoML-Benchmark/blob/master/img/Total_SUM.png\" alt=\"bench\"\u003e\n\n### Scheme\n\u003cimg width=800 src=\"https://github.com/Alex-Lekov/AutoML_Alex/blob/develop/examples/img/shema.png\" alt=\"scheme\"\u003e\n\n\n# Features\n\n- Automated Data Clean (Auto Clean)\n- Automated **Feature Engineering** (Auto FE)\n- Smart Hyperparameter Optimization (HPO)\n- Feature Generation\n- Feature Selection\n- Models Selection\n- Cross Validation\n- Optimization Timelimit and EarlyStoping\n- Save and Load (Predict new data)\n\n\n# Installation\n\n```python\npip install automl\n```\n\n# 🚀 Examples\n\nClassifier:\n```python\nfrom automl import AutoMLClassifier\n\nmodel = AutoMLClassifier()\nmodel.fit(X_train, y_train, timeout=600)\npredicts = model.predict(X_test)\n```\n\nRegression:\n```python\nfrom automl import AutoMLRegressor\n\nmodel = AutoMLRegressor()\nmodel.fit(X_train, y_train, timeout=600)\npredicts = model.predict(X_test)\n```\n\nDataPrepare:\n```python\nfrom automl import DataPrepare\n\nde = DataPrepare()\nX_train = de.fit_transform(X_train)\nX_test = de.transform(X_test)\n```\n\nSimple Models Wrapper:\n```python\nfrom automl import LightGBMClassifier\n\nmodel = LightGBMClassifier()\nmodel.fit(X_train, y_train)\npredicts = model.predict_proba(X_test)\n\nmodel.opt(X_train, y_train,\n    timeout=600, # optimization time in seconds,\n    )\npredicts = model.predict_proba(X_test)\n```\n# What's inside\n\nIt integrates many popular frameworks:\n- scikit-learn\n- XGBoost\n- LightGBM\n- CatBoost\n- Optuna\n- ...\n\n\n# Works with Features\n\n-   [x] Categorical Features\n\n-   [x] Numerical Features\n\n-   [x] Binary Features\n\n-   [ ] Text\n\n-   [ ] Datetime\n\n-   [ ] Timeseries\n\n-   [ ] Image\n\n\n# Note\n\n- **With a large dataset, a lot of memory is required!**\nLibrary creates many new features. If you have a large dataset with a large number of features (more than 100), you may need a lot of memory.\n\nRun\n```console\n$ optuna-dashboard sqlite:///db.sqlite3\n```\n\n# Road Map\n\n-   [x] Feature Generation\n\n-   [x] Save/Load and Predict on New Samples\n\n-   [x] Advanced Logging\n\n-   [x] Add opt Pruners\n\n-   [ ] Docs Site\n\n-   [ ] DL Encoders\n\n-   [ ] Add More libs (NNs)\n\n-   [ ] Multiclass Classification\n\n-   [ ] Build pipelines\n\nContact: kaushikeva0026@gmail.com\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feva-kaushik%2Fautoml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feva-kaushik%2Fautoml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feva-kaushik%2Fautoml/lists"}