{"id":50864268,"url":"https://github.com/gsaini/machine-learning-case-studies","last_synced_at":"2026-06-14T23:34:33.303Z","repository":{"id":318418618,"uuid":"1071202873","full_name":"gsaini/machine-learning-case-studies","owner":"gsaini","description":"This repository is a personal collection of machine-learning case studies, study materials, notebooks, datasets, and small projects.","archived":false,"fork":false,"pushed_at":"2026-01-04T03:31:22.000Z","size":15714,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-06-14T23:34:30.138Z","etag":null,"topics":["machine-learning","machine-learning-engineering","machine-learning-practice","ml","mlops"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/gsaini.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,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-10-07T02:44:03.000Z","updated_at":"2026-01-04T03:31:25.000Z","dependencies_parsed_at":"2025-10-07T05:41:25.498Z","dependency_job_id":null,"html_url":"https://github.com/gsaini/machine-learning-case-studies","commit_stats":null,"previous_names":["gsaini/machine-learning-case-studies"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/gsaini/machine-learning-case-studies","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gsaini%2Fmachine-learning-case-studies","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gsaini%2Fmachine-learning-case-studies/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gsaini%2Fmachine-learning-case-studies/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gsaini%2Fmachine-learning-case-studies/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/gsaini","download_url":"https://codeload.github.com/gsaini/machine-learning-case-studies/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gsaini%2Fmachine-learning-case-studies/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34342089,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-14T02:00:07.365Z","response_time":62,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":["machine-learning","machine-learning-engineering","machine-learning-practice","ml","mlops"],"created_at":"2026-06-14T23:34:31.498Z","updated_at":"2026-06-14T23:34:33.267Z","avatar_url":"https://github.com/gsaini.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning (ML)\n\nThis repository is a personal collection of machine-learning case studies, study materials, notebooks, datasets, and small projects. Its goal is to be a practical workspace where you (and contributors) can learn, reproduce experiments, and share compact case-study examples that demonstrate common ML concepts and workflows.\n\n## Repository intent\n\n- Host self-contained case studies that demonstrate end-to-end ML workflows (data, notebooks, models, evaluation).\n- Keep reference materials and short tutorials that complement the case studies.\n- Use an organized folder layout so each case study is easy to run locally or convert into a short tutorial.\n\nThis repo is meant for learning, teaching, and lightweight reproducible examples — not for production model deployments.\n\n## Recommended structure\n\nThe repository follows a simple convention so it's easy to find and add content:\n\n- `case-studies/` — one folder per case study. Example: `case-studies/anime-rating-case-study`.\n- `reference-material/` — papers, PDFs, FAQs and other external references.\n- `README.md` — repository-level documentation and frequently asked questions.\n\nEach case-study folder should ideally contain:\n\n- `README.md` — short summary, how to run, data sources, and expected outputs.\n- `notebooks/` or `.ipynb` — Jupyter notebooks that reproduce the core analysis.\n- `data/` or CSV files (or pointers to download scripts) — small datasets or scripts to fetch larger datasets.\n- `requirements.txt` or `environment.yml` (optional) — dependencies for reproducibility.\n\n## Case Studies\n\n| Case Study | Description | ML Technique |\n|------------|-------------|--------------|\n| [Anime Rating Prediction](case-studies/anime-rating-case-study/) | Predict anime ratings based on various features | Linear Regression |\n| [Clustering Countries - Tourism Services](case-studies/clustering-countries-tourism-services/) | Segment countries based on tourism service data | Clustering |\n| [Gene Data Analysis](case-studies/gene-data/) | Analysis of genetic data patterns | Clustering/PCA |\n| [Hierarchical Clustering](case-studies/hierarchial-clustering/) | Demonstrate hierarchical clustering techniques | Hierarchical Clustering |\n| [Product Segmentation](case-studies/product-segmentation/) | Customer/product segmentation analysis | Clustering |\n| [Insurance Premium Prediction](case-studies/insurance-premium-prediction/) | Predict medical insurance charges based on customer attributes (age, BMI, smoking status, etc.) | Linear Regression |\n\n## How to add a new case study\n\n1. Create a new folder under `case-studies/` with a descriptive name (kebab-case). Example:\n\n```bash\ncase-studies/my-new-case-study/\n```\n\n1. Add a short `README.md` that explains the data, steps, and how to run the analysis.\n1. Add notebooks and small datasets (or download scripts). Keep datasets small or provide links to hosted data.\n1. Optionally add `requirements.txt` so others can recreate the environment.\n\nIf the content should remain synced with a separate Git repository, prefer adding it as a git submodule. Example (already used in this repo):\n\n```\ngit submodule add https://github.com/gsaini/anime-rating-case-study case-studies/anime-rating-case-study\n```\n\n## Contributing\n\n- Open an issue if you want to propose a new case study or need help.\n- Create a fork and a topic branch for your changes. Keep each PR focused (one case study or one doc update).\n- Add or update `README.md` inside the case-study folder to document how to run the notebook(s).\n- Small data files (\u003c 50MB) can be committed directly. For larger datasets, include a download script and instructions.\n\n## Reproducibility \u0026 running examples\n\n- Use virtual environments (venv, conda) and install dependencies listed in the case-study's `requirements.txt` or in a shared `environment.yml`.\n- To run notebooks locally, install `jupyter` or `jupyterlab` and open the notebook.\n\nExample (macOS / zsh):\n\n```bash\npython3 -m venv .venv\nsource .venv/bin/activate\npip install -r case-studies/\u003cyour-case-study\u003e/requirements.txt\njupyter lab\n```\n\n## License \u0026 notes\n\nThis repository contains examples and learning materials. Check each case-study for license notes on data and code. If you reuse large datasets, ensure you comply with their license and attribution requirements.\n\n## Reference Materials\n\n### Decision Tree\n\n- [A guide on how to compare different models](https://people.duke.edu/~rnau/compare.htm)\n\n- [Document that explains what are decision tree, their working, and different impurity measures](https://tanthiamhuat.files.wordpress.com/2015/10/decision-tree-tutorial-by-kardi-teknomo.pdf)\n\n- [Sklearn - Decision Tree documentation [Documentation of decision trees that gives very concise information on the advantages and disadvantages of the Decision tree along with practical use case and mathematical formulation behind the algorithm]](https://scikit-learn.org/stable/modules/tree.html)\n\n- [An article that explains why and how class weights are used and also sheds light on how to find and assign class weights](https://machinelearningmastery.com/cost-sensitive-decision-trees-for-imbalanced-classification/)\n\n### Clustering\n\n- [Sklearn's article that gives a detailed explanation of clustering](https://scikit-learn.org/stable/modules/clustering.html#hierarchical-clustering)\n\n- [An article on Hierarchical Clustering, dendrograms, and how to decide the number of the clusters from it with Python code](https://stackabuse.com/hierarchical-clustering-with-python-and-scikit-learn/)\n\n- [An article on the concepts of different linkage methods and implementation of Hierarchical Clustering with Python code](https://www.analyticsvidhya.com/blog/2019/05/beginners-guide-hierarchical-clustering/)\n\n- [Sklearn's PCA documentation, code, and parameters used in the algorithms](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html)\n\n- [Conceptual understanding of PCA, how it works, and where it is used](https://towardsdatascience.com/a-one-stop-shop-for-principal-component-analysis-5582fb7e0a9c)\n\n- [sklearn's module on clustering techniques, their mathematical formulation, and overview of clustering algorithms](https://scikit-learn.org/stable/modules/clustering.html)\n\n- [sklearn's documentation about selecting the number of clusters with silhouette analysis on K-means clustering](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html)\n\n- [Overview of K-means with code and detailed examples](https://www.analyticsvidhya.com/blog/2019/08/comprehensive-guide-k-means-clustering/)\n\n- [A web-based visualization of the t-SNE algorithm with a sample dataset](https://jefworks.github.io/tsne-online/)\n\n- [A mathematical deep-dive into the working of t-SNE](https://tivadardanka.com/blog/how-tsne-works)\n\n- [An intuitive explanation of the working and parameters of t-SNE with minimal mathematical details](https://medium.com/swlh/t-sne-explained-math-and-intuition-94599ab164cf)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgsaini%2Fmachine-learning-case-studies","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgsaini%2Fmachine-learning-case-studies","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgsaini%2Fmachine-learning-case-studies/lists"}