{"id":30360512,"url":"https://github.com/alikhalajii/text-classification-life-sciences","last_synced_at":"2026-05-05T19:31:44.053Z","repository":{"id":309426953,"uuid":"1036245731","full_name":"alikhalajii/text-classification-life-sciences","owner":"alikhalajii","description":"Text classification of Life Science apps","archived":false,"fork":false,"pushed_at":"2025-08-12T09:57:58.000Z","size":38681,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-08-19T14:40:00.751Z","etag":null,"topics":["data-analysis","data-science","datasets","feature-importance","jupyter-notebook","pandas","sbert","scikit-learn","word2vec"],"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/alikhalajii.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":"2025-08-11T19:26:46.000Z","updated_at":"2025-08-12T09:58:01.000Z","dependencies_parsed_at":"2025-08-11T21:44:40.835Z","dependency_job_id":null,"html_url":"https://github.com/alikhalajii/text-classification-life-sciences","commit_stats":null,"previous_names":["alikhalajii/text-classification-life-sciences"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/alikhalajii/text-classification-life-sciences","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alikhalajii%2Ftext-classification-life-sciences","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alikhalajii%2Ftext-classification-life-sciences/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alikhalajii%2Ftext-classification-life-sciences/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alikhalajii%2Ftext-classification-life-sciences/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/alikhalajii","download_url":"https://codeload.github.com/alikhalajii/text-classification-life-sciences/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alikhalajii%2Ftext-classification-life-sciences/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32664796,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-05T11:29:49.557Z","status":"ssl_error","status_checked_at":"2026-05-05T11:29:48.587Z","response_time":54,"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":["data-analysis","data-science","datasets","feature-importance","jupyter-notebook","pandas","sbert","scikit-learn","word2vec"],"created_at":"2025-08-19T14:22:58.131Z","updated_at":"2026-05-05T19:31:44.038Z","avatar_url":"https://github.com/alikhalajii.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Text Classification for Life Sciences Applications\n\n\n\n\n## Introduction\n\nIn this case study, we develop a classification pipeline to automatically determine whether SAP Fiori apps are relevant to the life sciences domain.\n\n**Problem Statement**  \nWe are working with a catalog of 14,145 SAP Fiori apps, each described by 25 metadata fields (e.g. titles, descriptions, roles). The dataset is ***entirely unlabeled***, so we must manually annotate a small subset to train a classifier. Operating under ***low-resource conditions*** with only a few hundred labeled examples; we aim to build a model that can generalize effectively across the full catalog.\n\n\n## Objectives\n1. **Manual annotation**: Create a labeled dataset by manually tagging a representative sample of SAP Fiori apps as Relevant or Irrelevant to the life sciences domain.\n\n2. **Model development**: Train interpretable classifiers, such as logistic regression and embedding-based models, to predict relevance based on app metadata.\n\n3. **Evaluation \u0026 insights**: Assess model performance using metrics like accuracy, precision, recall, and F1-score. Analyze feature importances and embedding spaces to gain insights into model behavior and decision boundaries.\n\n## Notebook structure\n\u003cdiv style=\"display: flex; align-items: center; gap: 10px;\"\u003e\n  \u003ca href=\"https://github.com/alikhalajii/text-classification-life-sciences/blob/master/notebooks/01-data-cleaning.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e1. Data cleaning\u003c/a\u003e\n  \n  \u003ca href=\"https://colab.research.google.com/github/alikhalajii/text-classification-life-sciences/blob/master/notebooks/01-data-cleaning.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\n    \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\n  \u003c/a\u003e\n\u003c/div\u003e\n\n\n\u003cdiv style=\"display: flex; align-items: center; gap: 10px;\"\u003e\n  \u003ca href=\"https://github.com/alikhalajii/text-classification-life-sciences/blob/master/notebooks/02-exploratory-data-analysis-data-labeling.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e2. Exploratory Data Analysis \u0026 Data Labeling\u003c/a\u003e\n  \n  \u003ca href=\"https://colab.research.google.com/github/alikhalajii/text-classification-life-sciences/blob/master/notebooks/02-exploratory-data-analysis-data-labeling.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\n    \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\n  \u003c/a\u003e\n\u003c/div\u003e\n\n\n\u003cdiv style=\"display: flex; align-items: center; gap: 10px;\"\u003e\n  \u003ca href=\"https://github.com/alikhalajii/text-classification-life-sciences/blob/master/notebooks/03-feature-engineering.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e3. Feature Engineering\u003c/a\u003e\n  \n  \u003ca href=\"https://colab.research.google.com/github/alikhalajii/text-classification-life-sciences/blob/master/notebooks/03-feature-engineering.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\n    \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\n  \u003c/a\u003e\n\u003c/div\u003e\n\n\n\u003cdiv style=\"display: flex; align-items: center; gap: 10px;\"\u003e\n  \u003ca href=\"https://github.com/alikhalajii/text-classification-life-sciences/blob/master/notebooks/04-traning-baseline-models.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e4. Baseline Models (LR-RF) Trainig \u0026 Evaluation\u003c/a\u003e\n  \n  \u003ca href=\"https://colab.research.google.com/github/alikhalajii/text-classification-life-sciences/blob/master/notebooks/04-traning-baseline-models.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\n    \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\n  \u003c/a\u003e\n\u003c/div\u003e\n\n\n\u003cdiv style=\"display: flex; align-items: center; gap: 10px;\"\u003e\n  \u003ca href=\"https://github.com/alikhalajii/text-classification-life-sciences/blob/master/notebooks/05-training-embedding-models.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e5. Embedding-based Classification (Word2vec, fastText, SBERT)\u003c/a\u003e\n  \n  \u003ca href=\"https://colab.research.google.com/github/alikhalajii/text-classification-life-sciences/blob/master/notebooks/05-training-embedding-models.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\n    \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\n  \u003c/a\u003e\n\u003c/div\u003e\n\n\n\u003cdiv style=\"display: flex; align-items: center; gap: 10px;\"\u003e\n  \u003ca href=\"https://github.com/alikhalajii/text-classification-life-sciences/blob/master/notebooks/06-ensemble-full-dataset-prediction.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e6. Ensemble \u0026 Full-Dataset Prediction\u003c/a\u003e\n  \n  \u003ca href=\"https://colab.research.google.com/github/alikhalajii/text-classification-life-sciences/blob/master/notebooks/06-ensemble-full-dataset-prediction.ipynb\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\n    \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\n  \u003c/a\u003e\n\u003c/div\u003e\n\n\n---\n\n## Environment Setup\n\nFollow these steps to recreate the full analysis from scratch:\n\n\n**Create and activate a clean virtual environment**\n\n```bash\npython3.12 -m venv .venv_gxp\nsource .venv_gxp/bin/activate\npip install -U pip \u0026\u0026 pip install -r requirements.txt\n```\n\n\n**Note:**\nYou may safely delete any previously generated sub-directories and still reproduce every result.\n\n\n## LICENSE\n\nThis project is licensed under the [MIT License](./LICENSE).","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falikhalajii%2Ftext-classification-life-sciences","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falikhalajii%2Ftext-classification-life-sciences","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falikhalajii%2Ftext-classification-life-sciences/lists"}