{"id":20276911,"url":"https://github.com/guptaachin/titanic-data-analysis","last_synced_at":"2026-04-13T04:38:04.310Z","repository":{"id":106631955,"uuid":"148014622","full_name":"guptaachin/Titanic-data-analysis","owner":"guptaachin","description":"This is analysis and modelling of the famous Titanic Data Set from Kaggle. ","archived":false,"fork":false,"pushed_at":"2018-09-28T03:44:18.000Z","size":894,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-14T06:46:08.527Z","etag":null,"topics":["dataanalytics","datamining","datascience","machinelearning","numpy","pandas","python","scikit-learn","structuredpyramidanalysisplan","tableau","tableau-desktop"],"latest_commit_sha":null,"homepage":null,"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/guptaachin.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}},"created_at":"2018-09-09T10:25:19.000Z","updated_at":"2023-10-08T20:21:15.000Z","dependencies_parsed_at":"2023-03-23T11:33:20.839Z","dependency_job_id":null,"html_url":"https://github.com/guptaachin/Titanic-data-analysis","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/guptaachin%2FTitanic-data-analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/guptaachin%2FTitanic-data-analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/guptaachin%2FTitanic-data-analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/guptaachin%2FTitanic-data-analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/guptaachin","download_url":"https://codeload.github.com/guptaachin/Titanic-data-analysis/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241768400,"owners_count":20017117,"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","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":["dataanalytics","datamining","datascience","machinelearning","numpy","pandas","python","scikit-learn","structuredpyramidanalysisplan","tableau","tableau-desktop"],"created_at":"2024-11-14T13:16:12.078Z","updated_at":"2025-12-03T05:06:43.106Z","avatar_url":"https://github.com/guptaachin.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Titanic-data-analysis\nThis is analysis and modelling of the famous Titanic Data Set from Kaggle.\n\u003cbr\u003e In this repository I used best practices to analyze and model the classic Titanic data set.\n\u003cbr\u003e Quick Links :\n1. [tableau story board](https://public.tableau.com/profile/gauscian#!/vizhome/tab-wkb/TitanicDataSetAnalysis?publish=yes)\n2. [jupyter notebook](https://github.com/gauscian/Titanic-data-analysis/blob/master/jupyter-nb.ipynb)\n4. [cleaning code](https://github.com/gauscian/Titanic-data-analysis/blob/master/cleaning_helper.py)\n3. [SPAP](https://github.com/gauscian/Titanic-data-analysis/blob/master/%5BSPAP%5D%20Titanic%20Data%20Set.png)\n\n\u003cbr\u003e Please feel free to fork and contribute.\n\n\u003cbr\u003e My take aways from this project:\n1. Reiterating the basic strategy of working through a Data Science Project.\n2. Importance of carrying out exhaustive analysis.\n3. Use intuition and understanding gained during analysis to mold the data. This is crucial since you would want your molded data to still be representative of the real data set.\n4. Use intuition to decide on the best suitable Machine Learning Algorithms and employ them using Scikit Learn Pipelines.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fguptaachin%2Ftitanic-data-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fguptaachin%2Ftitanic-data-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fguptaachin%2Ftitanic-data-analysis/lists"}