{"id":28291847,"url":"https://github.com/mithildabhi/prodigy_ds_02","last_synced_at":"2026-06-24T06:34:19.515Z","repository":{"id":292800382,"uuid":"981900847","full_name":"mithildabhi/PRODIGY_DS_02","owner":"mithildabhi","description":"Performed data cleaning and exploratory data analysis (EDA) on the Titanic dataset to uncover patterns, trends, and relationships between variables using Python libraries like Pandas, Matplotlib, and Seaborn.","archived":false,"fork":false,"pushed_at":"2025-05-12T07:16:57.000Z","size":550,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-22T04:12:14.738Z","etag":null,"topics":["data-science","intenship","prodigy-infotech"],"latest_commit_sha":null,"homepage":"","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/mithildabhi.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,"zenodo":null}},"created_at":"2025-05-12T04:35:38.000Z","updated_at":"2025-05-12T07:17:00.000Z","dependencies_parsed_at":"2025-05-12T08:40:11.528Z","dependency_job_id":"3791fda4-d543-4a9f-a3b4-052b64d60c44","html_url":"https://github.com/mithildabhi/PRODIGY_DS_02","commit_stats":null,"previous_names":["mithildabhi/prodigy_ds_02"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mithildabhi/PRODIGY_DS_02","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mithildabhi%2FPRODIGY_DS_02","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mithildabhi%2FPRODIGY_DS_02/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mithildabhi%2FPRODIGY_DS_02/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mithildabhi%2FPRODIGY_DS_02/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mithildabhi","download_url":"https://codeload.github.com/mithildabhi/PRODIGY_DS_02/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mithildabhi%2FPRODIGY_DS_02/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34720920,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-24T02:00:07.484Z","response_time":106,"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-science","intenship","prodigy-infotech"],"created_at":"2025-05-22T04:12:11.927Z","updated_at":"2026-06-24T06:34:19.500Z","avatar_url":"https://github.com/mithildabhi.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Prodigy InfoTech Internship: Exploratory Data Analysis (EDA)\n\nWelcome to **Task 2** of my internship at **Prodigy InfoTech**!  \nThis task focuses on performing **data cleaning** and **exploratory data analysis (EDA)** using real-world datasets to extract meaningful insights.\n\n  \u003cimg width=\"742\" alt=\"question\" src=\"https://github.com/mithildabhi/PRODIGY_DS_02/blob/main/question.png\" style ='center'/\u003e\n\n---\n\n## 🔍 Task Summary\n\nPerformed data cleaning and EDA on a dataset of my choice.  \nI used the famous **Titanic dataset** from Kaggle to explore relationships between variables and uncover trends and patterns.\n\n**Sample Dataset:** [Titanic Dataset – Kaggle](https://www.kaggle.com/c/titanic/data)\n\n---\n\n## 📊 Skills \u0026 Knowledge Gained\n\n- Hands-on experience in **data preprocessing**, handling **missing values**, and fixing inconsistent data.\n- Used **Pandas**, **Matplotlib**, and **Seaborn** to explore and visualize the dataset.\n- Discovered correlations and trends that could influence model building and data-driven decisions.\n\n---\n\n## 🤝 Let’s Connect\n\nFeel free to explore the repository, provide feedback, or reach out to collaborate or discuss anything related to **data science** or **internship experiences**.\n\n---\n\n## 📬 Contact\n\n- **Email:** mithildabhi898@gmail.com  \n- **LinkedIn:** [Mithil Dabhi](https://www.linkedin.com/in/mithildabhi)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmithildabhi%2Fprodigy_ds_02","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmithildabhi%2Fprodigy_ds_02","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmithildabhi%2Fprodigy_ds_02/lists"}