{"id":24580427,"url":"https://github.com/sahashraee/skeleton_for_machine_learning_projects","last_synced_at":"2026-02-27T01:31:15.663Z","repository":{"id":272231261,"uuid":"915689930","full_name":"SahashRaee/Skeleton_for_Machine_Learning_Projects","owner":"SahashRaee","description":"Process for mechanized ML projects","archived":false,"fork":false,"pushed_at":"2025-01-13T04:34:58.000Z","size":11,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-12T18:00:20.375Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Dockerfile","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/SahashRaee.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":"2025-01-12T14:50:46.000Z","updated_at":"2025-01-13T04:35:01.000Z","dependencies_parsed_at":"2025-01-13T05:26:25.053Z","dependency_job_id":"48a37ed7-5eb0-4e71-b3d3-07c79e7a5587","html_url":"https://github.com/SahashRaee/Skeleton_for_Machine_Learning_Projects","commit_stats":null,"previous_names":["sahashraee/skeleton_for_machine_learning_projects"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/SahashRaee/Skeleton_for_Machine_Learning_Projects","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SahashRaee%2FSkeleton_for_Machine_Learning_Projects","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SahashRaee%2FSkeleton_for_Machine_Learning_Projects/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SahashRaee%2FSkeleton_for_Machine_Learning_Projects/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SahashRaee%2FSkeleton_for_Machine_Learning_Projects/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SahashRaee","download_url":"https://codeload.github.com/SahashRaee/Skeleton_for_Machine_Learning_Projects/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SahashRaee%2FSkeleton_for_Machine_Learning_Projects/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29880692,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-26T23:51:21.483Z","status":"ssl_error","status_checked_at":"2026-02-26T23:50:46.793Z","response_time":89,"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":"2025-01-24T01:54:08.437Z","updated_at":"2026-02-27T01:31:15.640Z","avatar_url":"https://github.com/SahashRaee.png","language":"Dockerfile","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning Project Structure\n\nHaving a well-organized general Machine Learning project structure makes it easy to understand and make changes. Moreover, this structure can be the same for multiple projects, which avoids confusion. \n\n## Steps involved in making a project Structure\n\nStep 01: Make sure that you have latest python and pip installed in your system.\n\nStep 02: Create a sample repository on github.com (For example: Machine-Learning-Project)\n\nStep 03: Clone the repo in your local system `git clone \u003curl\u003e`\n\nStep 04: Change directory to new directory 'Machine-Learning-Project' `cd Machine-Learning-Project`\n\nStep 05: Create and activate virtaul environment\n```\nExample 01:\n\n- Create Virtual Environment\n    python -m venv venv_machine_learning_project\n    \n- Activate Created Virtual Environment\n    For unix based system -\u003e source ./venv_machine_learning_project/bin/activate \n    For windows -\u003e ./venv_machine_learning_project/Scripts/activate \n\nExample 02: \n\n- Create Virtual Environment\n    conda create -n venv_machine_learning_project\n\n- Activate Created Virtual Environment\n    conda activate venv_machine_learning_project \n```\nStep 06: Follow the below directory structure for your project\n\n## Project Directory Structure\n\n```\n├── Machine Learning Project Structure \u003c- Project Main Directory\n|   |── api \u003c- Consists of scripts which serialize the API calls and act as a endpoint faciliating for project functions.\n│   ├── data \u003c- data in different format\n|   |   ├── external \u003c- data from third party source\n|   |   ├── interim \u003c- Intermediate data that has been transformed\n|   |   ├── processed \u003c- The final, canonical data sets for modeling\n|   |   ├── raw \u003c- The original, immutable data dump\n|   ├── evaluation \n|   |   ├── evaluate_model_01.py \u003c- Different Matries used to evaluate the model \n|   |   ├── evaluate_model_02.py \u003c- Different Matries used to evaluate the model \n│   ├── examples\n|   |   ├── feature_01.md \u003c- It consists of doc and example showing how we can use the project, different functions etc.\n|   |   ├── feature_02.md \u003c- It consists of doc and example showing how we can use the project, different functions etc.\n│   ├── notebooks \u003c- All the ipython notebooks used for EDA, visualization and verification of concept (POC).\n│   ├── src\n|   |   ├── dataset \n|   |   |   ├── download_dataset.py \u003c- Scripts to download the dataset or ccesing dataset from data folder\n|   |   ├── model \n|   |   |   ├── train_model.py \u003c- Scripts to train the model\n|   |   |   ├── test_model.py \u003c- scripts to test the model\n|   |   |   ├── predict_model.py \u003c- Scripts to predict the model\n|   |   ├── network \n|   |   |   ├── approach_01.py \u003c- Neural network schema\n|   |   ├── weights \n|   |   |   ├── utils.py.py \u003c- folder to save weights\n|   |   ├── visualization \n|   |   |   ├── visaulization_model.py \u003c- Scripts to visualize the model\n|   |   ├── utils.py \u003c- different utils functions \n|   |   ├── project.py \u003c- project pipeline \n│   ├── project_cli \u003c- Scripts which faciliates Command line interface for taining, testing and other features.\n|   |   ├── train_cli.py \n|   |   ├── test_cli.py \n│   ├── task \u003c- Contains batch script which can be used for downloading files from web or batch to auto test, lint project.\n|   |   ├── download.sh\n|   |   ├── lint.sh\n|   |   ├── est_api.sh \n│   ├── training \u003c- Contains all experiments preperation, way on auto running experiments and updating metadata.\n|   |   ├── experiment\n|   |   |   ├── utils.py\n|   |   ├── prepare_experiment.py\n|   |   ├── run_experiment.py\n|   |   ├── update_metadata.py\n|   ├── sqs\n|   |   ├── SQSSender.py \u003c- sending message to Amazon SQS\n|   ├── aws\n|   |   ├── download_files.py  \u003c- uploading and downloading files from Amazon S3 Bucket\n│   ├── config.ini \u003c- Contains configuration information of project\n│   ├── .pre-commit-config.yaml \u003c- identifying simple issues before submission to code review\n│   ├── .gitignore \u003c- tells Git which files to ignore when committing your project to the GitHub repository\n│   ├── .env \u003c- used to hide the confidential data like AWS Screte Key, AWS  Access Key, S3 Bucket Name etc... \n│   ├── Dockerfile \u003c- This helps in dockerizing whole system\n│   ├── requirements.txt \u003c- requirements files contains all the module used while building the project.\n│   ├── application.py \u003c- python module that processes event i.e. function is invoked, Lambda runs the handler method.\n│   ├── README.md \u003c- The top-level README for developers using this project\n```\n\n****Note****: The `data` folder and `.env` file won’t appear in github. It will be in your local folder. This is not pushed to githhub as it will be in the ignore list (`.gitignore` file). If you want to checkin that also, just comment out in `.gitignore` file and add the data folder to github.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsahashraee%2Fskeleton_for_machine_learning_projects","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsahashraee%2Fskeleton_for_machine_learning_projects","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsahashraee%2Fskeleton_for_machine_learning_projects/lists"}