{"id":28984379,"url":"https://github.com/noahbjohnson/senior-project","last_synced_at":"2026-04-29T10:02:02.658Z","repository":{"id":105917150,"uuid":"178870956","full_name":"noahbjohnson/senior-project","owner":"noahbjohnson","description":"An Analysis of National Correlates of Tourist Industry Activity","archived":false,"fork":false,"pushed_at":"2020-07-28T02:39:51.000Z","size":25087,"stargazers_count":0,"open_issues_count":1,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-06-24T17:11:28.167Z","etag":null,"topics":["data-science","econometrics","jupyter-notebook","luther-college","pandas","tourism"],"latest_commit_sha":null,"homepage":"https://noahbjohnson.github.io/senior-project/","language":"Jupyter Notebook","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/noahbjohnson.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":"2019-04-01T13:36:37.000Z","updated_at":"2019-05-17T12:40:18.000Z","dependencies_parsed_at":null,"dependency_job_id":"6b5a837e-0089-463f-88a4-04300f7bf552","html_url":"https://github.com/noahbjohnson/senior-project","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/noahbjohnson/senior-project","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/noahbjohnson%2Fsenior-project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/noahbjohnson%2Fsenior-project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/noahbjohnson%2Fsenior-project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/noahbjohnson%2Fsenior-project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/noahbjohnson","download_url":"https://codeload.github.com/noahbjohnson/senior-project/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/noahbjohnson%2Fsenior-project/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32420356,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-29T06:29:02.080Z","status":"ssl_error","status_checked_at":"2026-04-29T06:29:00.631Z","response_time":110,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 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-science","econometrics","jupyter-notebook","luther-college","pandas","tourism"],"created_at":"2025-06-24T17:10:18.527Z","updated_at":"2026-04-29T10:02:02.653Z","avatar_url":"https://github.com/noahbjohnson.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"Analysis of National Correlates of Tourist Industry Activity\n==================================================================\n\nCompleted by Noah B Johnson, in fulfillment of the senior project \nfor the Data Science major at Luther College, Decorah, IA\n\nIntroduction\n-------------------\n\n\nTourism is often regarded as a positive force for its perceived \neffects on economic development, modernization, and globalization \nof destination populations. Much of the analysis of this issue relies \non a number of assumptions about the nature of 'development' and its accoutrement. \nSignificant relationships are often taken out of context or extrapolated inappropriately. \nThe purpose of this analysis is to identify other correlations with tourist activity to \nprovide further context to relationships and demonstrate the limitations of underlying assumptions.\n\n\nReport\n-------------\nThe report can be view on the master branch build [here](https://noahbjohnson.github.io/senior-project).\n\n\n\nMilestones\n---------------------\n\nMidterm\n____________\n\n- [x] Tourism data prep\n\n- [x] Tourism exploratory analysis\n \n  - [x] Dimension reduction\n \n  - [x] Descriptive statistics\n   \n  - [x] Colinearity/Correlation matrix\n   \n- [x] Economic data prep\n   \n - [ ] Tourism \u0026 Economic data analysis\n \n  - [ ] Dimension reduction\n \n  - [ ] Colinearity/Correlation matrix\n   \n  - [ ] Clustering\n   \n  - [ ] Modeling (Economic data features as response)\n   \n \nFinal\n_____________\n\n\u003e Repeated for each subject area\n\n- [ ] $Subject data prep\n   \n- [ ] Tourism \u0026 $Subject data analysis\n \n - [ ] Dimension reduction\n \n - [ ] Colinearity/Correlation matrix\n   \n - [ ] Clustering\n   \n - [ ] Modeling ($Subject data features as response)\n   \n- [ ] Tourism and $Subject data report\n \n \n\u003e Final Report\n\n- [ ] Complete dataset creation (all subject areas)\n \n- [ ] Anomaly detection\n \n- [ ] Full cluster analysis (ANN/K-NN/AP/LCA)\n \n- [ ] Subject matter interaction analysis \u0026 dimension reduction\n \n  - [ ] (colinearity/correlation/multivariate/MDS/PCA)\n   \n- [ ] Dependency Modeling\n \n  - [ ] Association rule learning (Apriori/Eclat/FP-Growth)\n   \n \n- [ ] Findings\n \n  - [ ] Model summaries\n   \n  - [ ] Instrumental Explanations\n   \n  - [ ] Visualizations\n   \n  - [ ] Written report with methodology\n   \n \n\nProject Organization\n------------\n\n    ├── LICENSE\n    ├── Makefile           \u003c- Makefile with commands like `make data` or `make train`\n    ├── README.md          \u003c- The top-level README for developers using this project.\n    ├── data\n    │   ├── external       \u003c- Data from third party sources.\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    │\n    ├── docs               \u003c- A default Sphinx project; see sphinx-doc.org for details\n    │\n    ├── models             \u003c- Trained and serialized models, model predictions, or model summaries\n    │\n    ├── notebooks          \u003c- Jupyter notebooks. Naming convention is a number (for ordering),\n    │                         the creator's initials, and a short `-` delimited description, e.g.\n    │                         `1.0-jqp-initial-data-exploration`.\n    │\n    ├── references         \u003c- Data dictionaries, manuals, and all other explanatory materials.\n    │\n    ├── reports            \u003c- Generated analysis as HTML, PDF, LaTeX, etc.\n    │   └── figures        \u003c- Generated graphics and figures to be used in reporting\n    │\n    ├── requirements.txt   \u003c- The requirements file for reproducing the analysis environment, e.g.\n    │                         generated with `pip freeze \u003e requirements.txt`\n    │\n    ├── setup.py           \u003c- makes project pip installable (pip install -e .) so src can be imported\n    ├── src                \u003c- Source code for use in this project.\n    │   ├── __init__.py    \u003c- Makes src a Python module\n    │   │\n    │   ├── data           \u003c- Scripts to download or generate data\n    │   │   └── make_dataset.py\n    │   │\n    │   ├── features       \u003c- Scripts to turn raw data into features for modeling\n    │   │   └── build_features.py\n    │   │\n    │   ├── models         \u003c- Scripts to train models and then use trained models to make\n    │   │   │                 predictions\n    │   │   ├── predict_model.py\n    │   │   └── train_model.py\n    │   │\n    │   └── visualization  \u003c- Scripts to create exploratory and results oriented visualizations\n    │       └── visualize.py\n    │\n    └── tox.ini            \u003c- tox file with settings for running tox; see tox.testrun.org\n\n\n--------\n\n\u003cp\u003e\u003csmall\u003eProject based on the \u003ca target=\"_blank\" href=\"https://drivendata.github.io/cookiecutter-data-science/\"\u003ecookiecutter data science project template\u003c/a\u003e. #cookiecutterdatascience\u003c/small\u003e\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnoahbjohnson%2Fsenior-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnoahbjohnson%2Fsenior-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnoahbjohnson%2Fsenior-project/lists"}