{"id":20310060,"url":"https://github.com/abeusher/autogluon-test-iris","last_synced_at":"2026-05-11T17:33:31.244Z","repository":{"id":244059138,"uuid":"814152768","full_name":"abeusher/autogluon-test-iris","owner":"abeusher","description":null,"archived":false,"fork":false,"pushed_at":"2024-06-13T11:18:20.000Z","size":395,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-04T08:12:07.771Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/abeusher.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2024-06-12T12:47:23.000Z","updated_at":"2024-06-13T11:18:23.000Z","dependencies_parsed_at":"2024-11-14T17:42:10.451Z","dependency_job_id":null,"html_url":"https://github.com/abeusher/autogluon-test-iris","commit_stats":null,"previous_names":["abeusher/autogluon-test-iris"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/abeusher/autogluon-test-iris","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abeusher%2Fautogluon-test-iris","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abeusher%2Fautogluon-test-iris/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abeusher%2Fautogluon-test-iris/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abeusher%2Fautogluon-test-iris/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/abeusher","download_url":"https://codeload.github.com/abeusher/autogluon-test-iris/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abeusher%2Fautogluon-test-iris/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32906068,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-11T17:09:15.040Z","status":"ssl_error","status_checked_at":"2026-05-11T17:08:45.420Z","response_time":120,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":[],"created_at":"2024-11-14T17:29:50.638Z","updated_at":"2026-05-11T17:33:31.227Z","avatar_url":"https://github.com/abeusher.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# autogluon-test-iris\n\nThis repository includes two sample projects:\n\n - /iris_project/ - and example of how to use the autogluon framework in python to create predictive models\n - /baseball_project/ - a slightly more advanced example of how to use the autogluon framework to create predictive models\n\n___\n\n#### Iris data set \u0026 code\n\nI recommend starting with the iris_project.  Make 5-minutes to read about [the iris data set on wikipedia.](https://en.wikipedia.org/wiki/Iris_flower_data_set) After that, briefly look over notes about the [iris data set on the UC Irvine website.](https://archive.ics.uci.edu/dataset/53/iris)\n\nIn the case of the iris data set, our objective is to create a predictive model so that given different quantitative characteristics about different iris flowers, we can predict which iris species a given observation is about.  The inputs (x variables) also so-called independent variables are the measurements of (sepal length, sepal width, petal length, petal width).  The output (y variable) also called \"depedent\" variable is the class/category name of the flower, in this specific case it should be one of these values [Iris Setosa, Iris Versicolour, or Iris Virginica].\n\nThe data for this sub-project is contained in /data/iris_train.csv and /data/iris_test.csv\n\nThe code uses the python-based [autogluon framework](https://auto.gluon.ai/stable/index.html) which simplifies making machine learning models like Random Forests.\n\n#### Description of code in /iris_project/\n\n**01_build_autogluon_model.py** - this file loads training data (line 6) used to build a model, test data (line 7) used to evaluate the new model, and creates a new predictive model (line 12).  Autogluon automatically tries a bunch of different model types including two types of random forests, a neural network, and gradient boosted trees.  More details about what the different predictive model types are is included in the markdown file [model_descriptions.md](model_descriptions.md).\n\n**02_evaluate_model.py** - this file shows how to print out the performance of different model types and also creates a \"model leaderboard\" that ranks the quality of the different models produced.\n\n**03_use_model_for_prediction.py** - this file shows how to use a trained predictive model to make a prediction for a single input value (e.g., if we have the quantitative attributes of a flower, predict which species/category of flower that it is).\n\n**04_load_specific_model_and_predict.py** - this file is similar to (03_use_model_for_prediction.py) but on line 52 it configures the predictor object to use a specific named model (in this case 'RandomForestGini' which is defined on line 6).\n\n\n#### baseball data set \u0026 code\n\nInstead of data on flowers, this project includes attributes of different baseball players.  There are multiple columns that are \"dependent\" values (Y variables) that relate to players having a specific position.  Rich L can provide additional details on the data.\n\nThe data for this sub-project is contained in /data/raw_data.csv and /data/raw_data_test.csv\n\n#### Description of code in /baseball_project/\n\nThe code is organized similar to the iris project.\n\n**00_unzip_data.py** - this file unzips the compressed data found in /data/raw_data.csv.gz\n\n**01_build_autogluon_model.py** - this file loads training data (line 6) used to build a model, and drops some columns that we do not want to be used for analysis (lines 9-10 and lines 13-15).  Autogluon automatically tries a bunch of different model types including two types of random forests, a neural network, and gradient boosted trees.  More details about what the different predictive model types are is included in the markdown file [model_descriptions.md](model_descriptions.md).\n\n**02_evaluate_model.py** - this file shows how to print out the performance of different model types and also creates a \"model leaderboard\" that ranks the quality of the different models produced.\n\n**03_use_model_for_prediction.py** - this file shows how to use a trained predictive model to make a prediction for a single input value (e.g., if we have the quantitative attributes of a flower, predict which species/category of flower that it is).\n\n**04_load_specific_model_and_predict.py** - this file is similar to (03_use_model_for_prediction.py) but on line 52 it configures the predictor object to use a specific named model (in this case 'RandomForestGini' which is defined on line 6).\n\n___\n\n## installing autogluon pre-requisited on macos\n\nGetting autogluon setup on a Mac is kind of a pain.  I recommend running it on Linux or Windows if possible.  But if you want to try out the code on a mac, here are some installation instructions that may help.\n\n### Uninstall \"LLVM OpenMP library\" (aka \"libomp\") if it was previous installed\nbrew uninstall -f libomp\n\n### install specific version of libomp\nwget https://raw.githubusercontent.com/Homebrew/homebrew-core/fb8323f2b170bd4ae97e1bac9bf3e2983af3fdb0/Formula/libomp.rb\n\n### install libomp\nbrew install libomp.rb\n\n### remove the installer script\nrm libomp.rb\n\nSee also: https://auto.gluon.ai/stable/install.html\n\n### installing autogluon\n\npython3 -m pip install -r requirements.txt\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabeusher%2Fautogluon-test-iris","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabeusher%2Fautogluon-test-iris","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabeusher%2Fautogluon-test-iris/lists"}