{"id":21245089,"url":"https://github.com/adithivs/prodigy_ds_02","last_synced_at":"2026-05-17T12:32:31.827Z","repository":{"id":244559814,"uuid":"815601272","full_name":"AdithiVS/PRODIGY_DS_02","owner":"AdithiVS","description":null,"archived":false,"fork":false,"pushed_at":"2024-06-16T13:38:26.000Z","size":219,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-21T20:14:57.115Z","etag":null,"topics":["data-science","eda","logistic-regression","python"],"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":"2024-06-15T15:38:09.000Z","updated_at":"2024-06-16T13:39:38.000Z","dependencies_parsed_at":"2024-06-15T17:02:04.844Z","dependency_job_id":"1d9c6483-e137-49d4-a9ef-589769f8deba","html_url":"https://github.com/AdithiVS/PRODIGY_DS_02","commit_stats":null,"previous_names":["adithivs/prodigy_ds_02"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdithiVS%2FPRODIGY_DS_02","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdithiVS%2FPRODIGY_DS_02/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdithiVS%2FPRODIGY_DS_02/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdithiVS%2FPRODIGY_DS_02/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AdithiVS","download_url":"https://codeload.github.com/AdithiVS/PRODIGY_DS_02/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243685528,"owners_count":20330980,"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":["data-science","eda","logistic-regression","python"],"created_at":"2024-11-21T01:46:52.873Z","updated_at":"2026-05-17T12:32:31.774Z","avatar_url":"https://github.com/AdithiVS.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# PRODIGY_DS_02\n## Introduction\nThis project's primary goal is to do exploratory data analysis on the Titanic dataset and derive significant insights from the findings.Here, we attempt to do the necessary preprocessing, such as managing missing values, identifying outliers, determining the correlation between variables, displaying the data, and forecasting the test set's survival.\n\n## About the Dataset\n\u003cp\u003eThe Titanic Dataset divided into \u003ca href = \"https://github.com/AdithiVS/PRODIGY_DS_02/blob/main/train.csv\"\u003e`Train Set`\u003c/a\u003e and \u003ca href = \"https://github.com/AdithiVS/PRODIGY_DS_02/blob/main/test.csv\"\u003e`Test Set`\u003c/a\u003e, contains detailed information about the passengers aboard the Titanic. This dataset includes features that describe the passengers demographics, socio-economic status, and other relevant information, as well as the outcome variable indicating whether the passenger survived or perished in the disaster.\u003c/p\u003e\n\n### Features of the dataset\n\n\u003cp\u003e\u003cstrong\u003ePassengerId:\u003c/strong\u003eA unique identifier for each passenger.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvived:\u003c/strong\u003e  Binary variable indicating survival (0 = No, 1 = Yes).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePclass:\u003c/strong\u003eTicket class (1 = 1st, 2 = 2nd, 3 = 3rd).\u003c/p\u003e\n   \n\u003cp\u003e\u003cstrong\u003eName:\u003c/strong\u003e Full name of the passenger.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSex:\u003c/strong\u003eGender of the passenger (male/female).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAge:\u003c/strong\u003e Age of passenger in years. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSibSp:\u003c/strong\u003e  Number of siblings and spouses aboard the Titanic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParch:\u003c/strong\u003eNumber of parents and children aboard the Titanic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTicket:\u003c/strong\u003e Ticket number.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFare:\u003c/strong\u003e Amount of money the passenger paid for the ticket.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCabin:\u003c/strong\u003e Cabin number. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmbarked:\u003c/strong\u003ePort of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton).\u003c/p\u003e\n\n## Conclusion\nImportant insights into the factors influencing survival rates after the catastrophic Titanic accident were obtained through the thorough data cleaning and exploratory data analysis performed on the Titanic dataset. This study improves our understanding of past events and serves as an example of how data science techniques may be applied to draw important conclusions from large, complicated datasets.Explored relationships between variables such as gender, passenger class, age, fare, and survival rates. \nBy using logistic regression analysis we predicted the survival of test set passengers.\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%2Fprodigy_ds_02","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fadithivs%2Fprodigy_ds_02","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadithivs%2Fprodigy_ds_02/lists"}