{"id":21245087,"url":"https://github.com/adithivs/prodigyy_ds_03","last_synced_at":"2025-10-07T20:16:32.878Z","repository":{"id":244758245,"uuid":"816179095","full_name":"AdithiVS/PRODIGYY_DS_03","owner":"AdithiVS","description":null,"archived":false,"fork":false,"pushed_at":"2024-06-18T07:37:39.000Z","size":2594,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-15T04:42:22.060Z","etag":null,"topics":["data","data-visualization","datapreprocessing","decision-tree-classifier"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-2-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AdithiVS.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":"2024-06-17T07:43:49.000Z","updated_at":"2024-06-18T07:38:48.000Z","dependencies_parsed_at":"2024-06-17T09:04:56.017Z","dependency_job_id":"fc07293f-ccfe-44da-85f1-61d609fbbfbf","html_url":"https://github.com/AdithiVS/PRODIGYY_DS_03","commit_stats":null,"previous_names":["adithivs/prodigyy_ds_03"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/AdithiVS/PRODIGYY_DS_03","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdithiVS%2FPRODIGYY_DS_03","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdithiVS%2FPRODIGYY_DS_03/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdithiVS%2FPRODIGYY_DS_03/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdithiVS%2FPRODIGYY_DS_03/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AdithiVS","download_url":"https://codeload.github.com/AdithiVS/PRODIGYY_DS_03/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdithiVS%2FPRODIGYY_DS_03/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":278838762,"owners_count":26054790,"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","status":"online","status_checked_at":"2025-10-07T02:00:06.786Z","response_time":59,"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":["data","data-visualization","datapreprocessing","decision-tree-classifier"],"created_at":"2024-11-21T01:46:52.160Z","updated_at":"2025-10-07T20:16:32.844Z","avatar_url":"https://github.com/AdithiVS.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# PRODIGYY_DS_03\n## TASK 3\nBuild a decision tree classifier to predict whether a customer will purchase a product or service based on their demographic and behavioral data. Use a dataset such as the Bank Marketing dataset from the UCI Machine Learning Repository \n\n## About the Dataset\n\u003cp\u003eThis data is related to direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe to a term deposit.\u003c/p\u003e\n  \n  \u003ctable\u003e\n    \u003ctr\u003e\n      \u003cth\u003eVariable Name\u003c/th\u003e\n      \u003cth\u003eRole\u003c/th\u003e\n      \u003cth\u003eType\u003c/th\u003e\n      \u003cth\u003eDescription\u003c/th\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003eage\u003c/td\u003e\n      \u003ctd\u003eFeature\u003c/td\u003e\n      \u003ctd\u003eInteger\u003c/td\u003e\n      \u003ctd\u003eClient's age in years\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003ejob\u003c/td\u003e\n      \u003ctd\u003eFeature\u003c/td\u003e\n      \u003ctd\u003eCategorical\u003c/td\u003e\n      \u003ctd\u003eOccupation (type of job)\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003emarital\u003c/td\u003e\n      \u003ctd\u003eFeature\u003c/td\u003e\n      \u003ctd\u003eCategorical\u003c/td\u003e\n      \u003ctd\u003eMarital Status (married, single, divorced, unknown)\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003eeducation\u003c/td\u003e\n      \u003ctd\u003eFeature\u003c/td\u003e\n      \u003ctd\u003eCategorical\u003c/td\u003e\n      \u003ctd\u003eEducation Level (basic.4y, basic.6y, basic.9y, high.school, illiterate, professional.course, university.degree, unknown)\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003edefault\u003c/td\u003e\n      \u003ctd\u003eFeature\u003c/td\u003e\n      \u003ctd\u003eBinary\u003c/td\u003e\n      \u003ctd\u003eHas credit in default?\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003ebalance\u003c/td\u003e\n      \u003ctd\u003eFeature\u003c/td\u003e\n      \u003ctd\u003eInteger\u003c/td\u003e\n      \u003ctd\u003eAverage yearly balance (euros)\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003ehousing\u003c/td\u003e\n      \u003ctd\u003eFeature\u003c/td\u003e\n      \u003ctd\u003eBinary\u003c/td\u003e\n      \u003ctd\u003eHas housing loan?\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003eloan\u003c/td\u003e\n      \u003ctd\u003eFeature\u003c/td\u003e\n      \u003ctd\u003eBinary\u003c/td\u003e\n      \u003ctd\u003eHas personal loan?\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003econtact\u003c/td\u003e\n      \u003ctd\u003eFeature\u003c/td\u003e\n      \u003ctd\u003eCategorical\u003c/td\u003e\n      \u003ctd\u003eContact communication type (cellular, telephone)\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003eday_of_week\u003c/td\u003e\n      \u003ctd\u003eFeature\u003c/td\u003e\n      \u003ctd\u003eDate\u003c/td\u003e\n      \u003ctd\u003eLast contact day of the week\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003emonth\u003c/td\u003e\n      \u003ctd\u003eFeature\u003c/td\u003e\n      \u003ctd\u003eDate\u003c/td\u003e\n      \u003ctd\u003eLast contact month of year (jan, feb, mar, ..., nov, dec)\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003eduration\u003c/td\u003e\n      \u003ctd\u003eFeature\u003c/td\u003e\n      \u003ctd\u003eInteger\u003c/td\u003e\n      \u003ctd\u003eLast contact duration in seconds (important for benchmark purposes only)\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003ecampaign\u003c/td\u003e\n      \u003ctd\u003eFeature\u003c/td\u003e\n      \u003ctd\u003eInteger\u003c/td\u003e\n      \u003ctd\u003eNumber of contacts performed during this campaign (includes last contact)\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003epdays\u003c/td\u003e\n      \u003ctd\u003eFeature\u003c/td\u003e\n      \u003ctd\u003eInteger\u003c/td\u003e\n      \u003ctd\u003eNumber of days since last contact from previous campaign (-1 means not previously contacted)\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003eprevious\u003c/td\u003e\n      \u003ctd\u003eFeature\u003c/td\u003e\n      \u003ctd\u003eInteger\u003c/td\u003e\n      \u003ctd\u003eNumber of contacts performed before this campaign\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003epoutcome\u003c/td\u003e\n      \u003ctd\u003eFeature\u003c/td\u003e\n      \u003ctd\u003eCategorical\u003c/td\u003e\n      \u003ctd\u003eOutcome of the previous marketing campaign (failure, nonexistent, success)\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd\u003ey\u003c/td\u003e\n      \u003ctd\u003eTarget\u003c/td\u003e\n      \u003ctd\u003eBinary\u003c/td\u003e\n      \u003ctd\u003eSubscribed to term deposit (yes/no)\u003c/td\u003e\n    \u003c/tr\u003e\n  \u003c/table\u003e\n  \u003c/body\u003e\n  \u003c/html\u003e\n\n## Results\n- Training Accuracy: 93.7% (approx)\n- Testing Accuracy: 93.3% (approx)\n\n## Contact Information\n- \u003ca href=\"https://www.linkedin.com/in/adithi-v-345604257/\"\u003eAdithi Vellengara(LinkedIn)\u003c/a\u003e\n- Email 📧: adithivs06@gmail.com\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadithivs%2Fprodigyy_ds_03","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fadithivs%2Fprodigyy_ds_03","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadithivs%2Fprodigyy_ds_03/lists"}