{"id":21245082,"url":"https://github.com/adithivs/prodigy_ds_01","last_synced_at":"2026-05-17T13:35:25.998Z","repository":{"id":244094117,"uuid":"814252970","full_name":"AdithiVS/PRODIGY_DS_01","owner":"AdithiVS","description":null,"archived":false,"fork":false,"pushed_at":"2024-06-16T11:49:14.000Z","size":528,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-21T20:14:56.377Z","etag":null,"topics":["data-science","data-visualization","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":"2024-06-12T16:25:30.000Z","updated_at":"2024-06-16T11:49:16.000Z","dependencies_parsed_at":"2024-06-16T12:42:09.976Z","dependency_job_id":"ae0384c4-7675-4927-aae8-490d9a8f3c4d","html_url":"https://github.com/AdithiVS/PRODIGY_DS_01","commit_stats":null,"previous_names":["adithivs/prodigy_ds_01"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdithiVS%2FPRODIGY_DS_01","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdithiVS%2FPRODIGY_DS_01/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdithiVS%2FPRODIGY_DS_01/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdithiVS%2FPRODIGY_DS_01/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AdithiVS","download_url":"https://codeload.github.com/AdithiVS/PRODIGY_DS_01/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","data-visualization","python"],"created_at":"2024-11-21T01:46:51.237Z","updated_at":"2026-05-17T13:35:20.932Z","avatar_url":"https://github.com/AdithiVS.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# PRODIGY_DS_01\n# Data Science Internship Task 1\n\n## Introduction\nInformation representation is a critical skill in the fields of data analysis and visualisation. Understanding how to make charts that may be customized can help you express ideas succinctly and precisely. Through the visualization of age distribution and gender breakdown, this exercise aims to improve data presentation abilities and facilitate a deeper comprehension of the data under consideration. \n\n## Dataset\nThe dataset used is \u003ca href=\"https://github.com/AdithiVS/PRODIGY_DS_01/blob/main/worldpopulationdata.csv\"\u003eworld_population_dataset\u003c/a\u003e.\nThe dataset covers population records from 2001 to 2022, offering insights into global demographic trends, age distribution, gender demographics, and other population-related factors. It serves as a valuable resource for understanding global population dynamics.\n\n\u003cp\u003eSP.POP.TOTL ==\u003e       Population, total\u003c/p\u003e\n\u003cp\u003eSP.POP.TOTL.FE.IN ==\u003e Population, female\u003c/p\u003e\n\u003cp\u003eSP.POP.TOTL.MA.IN ==\u003e Population, male\u003c/p\u003e\n\u003cp\u003eSP.POP.TOTL.FE.ZS ==\u003e Population, female (% of total population)\u003c/p\u003e\n\u003cp\u003eSP.POP.TOTL.MA.ZS ==\u003e Population, male (% of total population)\u003c/p\u003e\n\n## Conclusion\nImportant insights into the distribution of the chosen variable were obtained from histogram and scatter plot of respective categorical variables of dataset. This inquiry lays the groundwork for future data science modeling assignments and investigations, providing a strong platform for analysis and decision-making.\n\n## Contact Information 📩\nFor any inquiries or feedback regarding this project, please contact:\n\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_01","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fadithivs%2Fprodigy_ds_01","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadithivs%2Fprodigy_ds_01/lists"}