{"id":16653174,"url":"https://github.com/anuraganalog/try-every-ml-algorithm","last_synced_at":"2025-07-04T12:33:50.475Z","repository":{"id":112157659,"uuid":"233876034","full_name":"AnuragAnalog/Try-every-ML-algorithm","owner":"AnuragAnalog","description":"Trying every Machine learning algorithm on a given dataset and measuring the efficiency.","archived":false,"fork":false,"pushed_at":"2024-05-17T02:32:31.000Z","size":7588,"stargazers_count":8,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-05-20T02:50:08.434Z","etag":null,"topics":["accuracy","algorithms","analysis","classification","data","deep-learning","efficiency","learning","machine","metrics","neural-networks","regression","streamlit"],"latest_commit_sha":null,"homepage":"https://share.streamlit.io/anuraganalog/try-every-ml-algorithm/app.py","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AnuragAnalog.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}},"created_at":"2020-01-14T15:44:39.000Z","updated_at":"2025-02-16T17:05:00.000Z","dependencies_parsed_at":"2024-12-20T14:59:47.120Z","dependency_job_id":"6cf990c4-540a-46ed-9cd6-07976a2e43fc","html_url":"https://github.com/AnuragAnalog/Try-every-ML-algorithm","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/AnuragAnalog/Try-every-ML-algorithm","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AnuragAnalog%2FTry-every-ML-algorithm","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AnuragAnalog%2FTry-every-ML-algorithm/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AnuragAnalog%2FTry-every-ML-algorithm/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AnuragAnalog%2FTry-every-ML-algorithm/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AnuragAnalog","download_url":"https://codeload.github.com/AnuragAnalog/Try-every-ML-algorithm/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AnuragAnalog%2FTry-every-ML-algorithm/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":263542603,"owners_count":23477454,"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":["accuracy","algorithms","analysis","classification","data","deep-learning","efficiency","learning","machine","metrics","neural-networks","regression","streamlit"],"created_at":"2024-10-12T09:43:26.301Z","updated_at":"2025-07-04T12:33:50.456Z","avatar_url":"https://github.com/AnuragAnalog.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Try every ML algorithm\n\nWe as a Data Scientist are very lazy in trying out every different algorithm on a given dataset.\n\nThis web interface provides you a convinent way of switching between algorithms are seeing there results. With this now, you can apply machine learning models without writing a Single Piece of code.\n\nThe web application is written in streamlit.\n\nLink to the web app is [here](https://share.streamlit.io/anuraganalog/try-every-ml-algorithm/app.py)\n\n## How it works\n\n* Open the App\n* Upload the dataset*\n* Inspect your data, if you wish\n* Select the features\n* Select the label\n* Make a Train Test split\n* Select an appropriate algorithm\n* Start tweaking the hyperparameters\n\n\u003e This app expects a preprocessed dataset with all the NaN, Null values handled properly, One Hot encoded, and scaled\n\n## Demo\n![demo](./demo.gif)\n\n## Required Modules\n\n* Pandas\n* Streamlit\n* Scikit-learn\n\n## Getting a copy of this repo\nClone the repository before running any commands\n```python3\n$ git clone https://github.com/AnuragAnalog/Try-every-ML-algorithm.git\n$ cd Try-every-ML-algorithm\n```\n\n## Installation\nRun the below command to install all the dependencies in your local machine to run the py script.\n\n```python3\n$ sudo pip3 install -r requirements.txt\n```\n\n## Running the app\n```python3\n$ streamlit run app.py\n```\n\n## Algorithms\n\n* Regression\n    * Linear Regression\n    * K Nearest Neighbours\n    * Decision Trees\n    * Random Forest\n    * Ada Boost\n    * Gradient Boosting\n\n* Classification\n    * Logistic Regression\n    * K Nearest Neighbours\n    * Decision Trees\n    * Random Forest\n    * Ada Boost\n    * Gradient Boosting\n\n*Want to contribute? then fork, develop, and create a pull-request*\n\n## Future Work\n\n* [x] Add an option to show the code which implements the above selected algorithm with the corresponding hyperparameters.\n* [x] Added code for One Hotencoding.\n* [ ] Add more algorithms.\n* [ ] Add some functionality for preprocessing data too.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanuraganalog%2Ftry-every-ml-algorithm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanuraganalog%2Ftry-every-ml-algorithm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanuraganalog%2Ftry-every-ml-algorithm/lists"}