{"id":15673010,"url":"https://github.com/autoviml/autoviml","last_synced_at":"2025-10-03T16:32:03.980Z","repository":{"id":46730542,"uuid":"375496991","full_name":"AutoViML/AutoViML","owner":"AutoViML","description":null,"archived":false,"fork":false,"pushed_at":"2025-03-01T13:39:25.000Z","size":6440,"stargazers_count":16,"open_issues_count":0,"forks_count":7,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-04-24T01:12:03.875Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AutoViML.png","metadata":{"files":{"readme":"README.md","changelog":null,"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,"zenodo":null}},"created_at":"2021-06-09T21:46:39.000Z","updated_at":"2025-03-16T16:25:52.000Z","dependencies_parsed_at":"2023-01-18T19:15:50.597Z","dependency_job_id":"46ce6518-a7b6-46b7-a6ca-4371ce598387","html_url":"https://github.com/AutoViML/AutoViML","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AutoViML%2FAutoViML","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AutoViML%2FAutoViML/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AutoViML%2FAutoViML/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AutoViML%2FAutoViML/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AutoViML","download_url":"https://codeload.github.com/AutoViML/AutoViML/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250540929,"owners_count":21447427,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":[],"created_at":"2024-10-03T15:35:14.409Z","updated_at":"2025-10-03T16:31:58.939Z","avatar_url":"https://github.com/AutoViML.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"[![Repos Badge](https://badges.pufler.dev/repos/AutoViML)](https://badges.pufler.dev)\n[![Updated Badge](https://badges.pufler.dev/updated/AutoViML/featurewiz)](https://badges.pufler.dev)\nJoin our elite team of contributors!\u003cbr\u003e\n[![Contributors Display](https://badges.pufler.dev/contributors/AutoViML/AutoViz?size=40\u0026padding=5\u0026bots=true)](https://badges.pufler.dev)\n[![Contributors Display](https://badges.pufler.dev/contributors/AutoViML/deep_autoviml?size=40\u0026padding=5\u0026bots=true)](https://badges.pufler.dev)\n[![Contributors Display](https://badges.pufler.dev/contributors/AutoViML/Auto_TS?size=40\u0026padding=5\u0026bots=true)](https://badges.pufler.dev)\n[![Contributors Display](https://badges.pufler.dev/contributors/AutoViML/featurewiz?size=40\u0026padding=5\u0026bots=true)](https://badges.pufler.dev)\n![image3000](4000_stars.png)\n\u003ch1 align=\"center\"\u003e👋 Welcome to the Auto Vimal Fan Club Page!\u003cbr\u003e We just hit 4000 stars for our amazing Auto Vimal libraries on Github!!\u003c/h1\u003e\n\u003ch3 align=\"center\"\u003eAuto Vimal creates innovative Open Source libraries to make data scientists' and machine learning engineers' lives easier and more productive! \u003c/h3\u003e\n\u003ch3 align=\"center\"\u003e\n  \u003cimg src=\"https://komarev.com/ghpvc/?username=AutoViML\u0026label=Profile%20views\u0026style=for-the-badge\" alt=\"kanchitank\"/\u003e\n\u003c/h3\u003e\n\n### Our innovative libraries so far:\n- 🤝 [AutoViz](https://github.com/AutoViML/AutoViz) Automatically Visualizes any dataset, any size with a single line of code. Now with Bokeh and Holoviews it can make your charts and dashboards interactive!\n- 🤝 [Auto_ViML](https://github.com/AutoViML/Auto_ViML) Automatically builds multiple ML models with a single line of code. Uses scikit-learn, XGBoost and CatBoost.\n- 🤝 [Auto_TS](https://github.com/AutoViML/Auto_TS) Automatically builds ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with [DASK](https://dask.org/) to handle millions of rows.\n- 🤝 [Deep_AutoViML](https://github.com/AutoViML/deep_autoviml) Builds tensorflow keras models and pipelines for any data set, any size with text, image and tabular data, with a single line of code.\n- 🤝 [Featurewiz](https://github.com/AutoViML/featurewiz) Uses advanced feature engineering strategies and select the best features from your data set fast with a single line of code. Now updated with DASK to handle millions of rows.\n- 🤝 [Featurewiz-Polars](https://github.com/AutoViML/featurewiz_polars) Blazing fast feature engineering and selection using mRMR algorithm and Polars. Also includes categorical and date-time feature handling as well as nans and nulls automatically. This is the simplest and best feature selection tool to use.\n- 🤝 [lazytransform](https://github.com/AutoViML/lazytransform) Automatically transform all categorical, date-time, NLP variables to numeric in a single line of code, for any data, set any size. \n- 🤝 [pandas_dq](https://github.com/AutoViML/pandas_dq) Automatically find and fix data quality issues in your dataset with a single line of code, for pandas.\n \n## BREAKING News! featurewiz is now blazing fast thanks to Polars!\nA new library named featurewiz-polars has been released to open source. You can check it out \u003ca href=\"https://github.com/AutoViML/featurewiz_polars\" \u003ehere\u003c/a\u003e. \nThis library was born out of the need for efficient feature engineering when working with large datasets using the Polars library. It includes all the feature selection and categorical encoding methods of featurewiz but is computationally inexpensive and memory-efficient for large datasets. You must check it out.\n\n## BREAKING News! AUTO-VIML libraries have been upgraded to be compatible with Python 3.12 and pandas 2.0  ###\nI have finally taken the plunge towards Python 3.12 and pandas 2.0. Yes, it was difficult, but I have now upgraded the following libraries to their latest versions:\n- featurewiz\n- autoviml\n- autoviz\n- lazytransform\n- pandas-dq\n  \nMy humble request to everyone who may have some errors after upgrading my libraries above is to make sure you have these below versions:\n\n- numpy\u003c2\n- category_encoders \u003c=3.6.3\n- xgboost\u003c=1.7.6\n- scikit-learn\u003c=1.5.2\n\nThese are my \"recommended\" versions of those libraries. So please check your machine to see if these libraries are in \"correct\" versions. \u003cbr\u003e\nWish you all the best and thanks for the support always!\u003cbr\u003e\n\n### Feb-2024: Added \"Auto Encoders\" for automatic feature extraction to featurewiz library for #feature-extraction\nOn Feb 8, 2024, we released a major update to our popular \"featurewiz\" library that will transform your input into a latent space with a dimension of latent_dim. This lower dimension (similar to PCA) will enable you to extract the best patterns in your data for the toughest imbalanced class and multi-class problems. Try it and let us know! \u003ca href=\"[https://ibb.co/X5dDqFv](https://github.com/AutoViML/featurewiz)\"\u003e\u003cimg src=\"https://i.ibb.co/sJsKphR/VAE-model-flowchart.png\" alt=\"autoencoders-screenshot\" border=\"0\"\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca target='_blank' href='https://github.com/AutoViML/featurewiz/blob/main/updates.md'\u003ehow to use autoencoders in featurewiz\u003c/a\u003e\u003cbr /\u003e\n\n### April-2023: Released a major new python library \"pandas_dq\" #data_quality #dataengineering\nOn April 2, 2023, we released a major new Python library called \"pandas_dq\" that will automatically find and fix data quality issuesin your train and test dataframes in a single line of code, for any data, set any size. \n\u003ca href=\"[https://ibb.co/X5dDqFv](https://github.com/AutoViML/pandas_dq)\"\u003e\u003cimg src=\"https://i.ibb.co/vdrhSLK/fix-dq-screenshot.png\" alt=\"fix-dq-screenshot\" border=\"0\"\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca target='_blank' href='https://whatsmyscreenresolution.com/'\u003ehow many pixels wide is my screen\u003c/a\u003e\u003cbr /\u003e\n\n### April-2022: Released a major new python library \"lazytransform\" #featureengineering #featureselection\nOn April 3, 2022, we released a major new Python library called \"lazytransform\" that will automatically transform all categorical, date-time, NLP variables to numeric in a single line of code, for any data, set any size. \n\u003ca href=\"https://github.com/AutoViML/lazytransform\"\u003e\u003cimg src=\"https://i.ibb.co/xYm0jwW/lazy-code2.png\" alt=\"lazy-code2\" border=\"0\"\u003e\u003c/a\u003e                                                                                                                                           \n### Jan-2022: Major upgrade to featurewiz: you can now perform feature selection thru fit and transform #MLOps #featureselection\nAs of version 0.0.90, featurewiz has a scikit-learn compatible feature selection transformer called FeatureWiz. You can use it to perform fit and predict as follows. You will get a Scikit-Learn Transformer object that you can add it to other data pipelines in MLops to select the top variables from your dataset. \u003cbr\u003e\n\u003ca href=\"https://github.com/AutoViML/featurewiz\"\u003e\u003cimg align=\"center\" src=\"https://i.ibb.co/VTd0kcv/featurewiz-class2.jpg\" alt=\"featurewiz-class2\" border=\"0\" /\u003e\u003c/a\u003e\n\n### Dec-23-2021 Update: AutoViz now does Wordclouds! #autoviz #wordcloud\nAutoViz can now create Wordclouds automatically for your NLP variables in data. It detects NLP variables automatically and creates wordclouds for them.\n\u003cimg align=\"center\" src=\"https://i.postimg.cc/DyT466xP/wordclouds.png\"\u003e\n\n### Dec 21, 2021: AutoViz now runs on Docker containers as part of MLOps pipelines. Check out Orchest.io\nWe are excited to announce that AutoViz and Deep_AutoViML are now available as containerized applications on Docker. This means that you can build data pipelines using a fantastic tool like [orchest.io](orchest.io) to build MLOps pipelines visually. Here are two sample pipelines we have created:\n\n\u003cb\u003eAutoViz pipeline\u003c/b\u003e: https://lnkd.in/g5uC-z66\n\u003cb\u003eDeep_AutoViML pipeline\u003c/b\u003e: https://lnkd.in/gdnWTqCG\n\nYou can find more examples and a wonderful video on [orchest's web site](https://github.com/orchest/orchest-examples)\n![banner](https://github.com/rsesha/autoviz_pipeline/blob/main/autoviz_orchest.png)\n\n### Dec-17-2021 AutoViz now uses HoloViews to display dashboards with Bokeh and save them as Dynamic HTML for web serving #HTML #Bokeh #Holoviews\nNow you can use AutoViz to create Interactive Bokeh charts and dashboards (see below) either in Jupyter Notebooks or in the browser. Use chart_format as follows:\n- `chart_format='bokeh'`: interactive Bokeh dashboards are plotted in Jupyter Notebooks.\n- `chart_format='server'`, dashboards will pop up for each kind of chart on your web browser.\n- `chart_format='html'`, interactive Bokeh charts will be silently saved as Dynamic HTML files under `AutoViz_Plots` directory\n\u003cimg align=\"center\" src=\"https://i.postimg.cc/MTCZ6GzQ/Auto-Viz-HTML-dashboards.png\" /\u003e\n\n\u003ch3 align=\"left\"\u003eLanguages and Tools:\u003c/h3\u003e\n\u003cp align=\"left\"\u003e \u003ca href=\"https://www.docker.com/\" target=\"_blank\"\u003e \u003cimg src=\"https://raw.githubusercontent.com/devicons/devicon/master/icons/docker/docker-original-wordmark.svg\" alt=\"docker\" width=\"40\" height=\"40\"/\u003e \u003c/a\u003e \u003ca href=\"https://git-scm.com/\" target=\"_blank\"\u003e \u003cimg src=\"https://www.vectorlogo.zone/logos/git-scm/git-scm-icon.svg\" alt=\"git\" width=\"40\" height=\"40\"/\u003e \u003c/a\u003e \u003ca href=\"https://www.python.org\" target=\"_blank\"\u003e \u003cimg src=\"https://raw.githubusercontent.com/devicons/devicon/master/icons/python/python-original.svg\" alt=\"python\" width=\"40\" height=\"40\"/\u003e \u003c/a\u003e \u003ca href=\"https://scikit-learn.org/\" target=\"_blank\"\u003e \u003cimg src=\"https://upload.wikimedia.org/wikipedia/commons/0/05/Scikit_learn_logo_small.svg\" alt=\"scikit_learn\" width=\"40\" height=\"40\"/\u003e \u003c/a\u003e \u003c/p\u003e\n\n\u003cp\u003e\u0026nbsp;\u003cimg align=\"center\" src=\"https://github-readme-stats.vercel.app/api?username=AutoViML\u0026show_icons=true\u0026locale=en\" alt=\"AutoViML\" /\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003cimg align=\"center\" src=\"https://github-readme-streak-stats.herokuapp.com/?user=AutoViML\u0026\" alt=\"AutoViML\" /\u003e\u003c/p\u003e\n\n\u003ch2 align=\"left\"\u003eOur Kaggle Badges:\u003c/h2\u003e\n\n![notebook](https://road-to-kaggle-grandmaster.vercel.app/api/badges/rsesha/notebook/light) ![discussion](https://road-to-kaggle-grandmaster.vercel.app/api/badges/rsesha/discussion/light)\n\n\u003ch3 align=\"left\"\u003eConnect with us on Linkedin:\u003c/h3\u003e\n\u003cp align=\"left\"\u003e\n\u003ca href=\"https://www.linkedin.com/in/ram-seshadri-nyc-nj/\" target=\"blank\"\u003e\u003cimg align=\"center\" src=\"https://cdn.jsdelivr.net/npm/simple-icons@3.0.1/icons/linkedin.svg\" alt=\"ram seshadri\" height=\"30\" width=\"40\" /\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fautoviml%2Fautoviml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fautoviml%2Fautoviml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fautoviml%2Fautoviml/lists"}