{"id":20580287,"url":"https://github.com/gajendrasharma-github/titanic_case_study","last_synced_at":"2025-03-06T11:49:43.311Z","repository":{"id":253399262,"uuid":"843387002","full_name":"gajendrasharma-github/Titanic_Case_Study","owner":"gajendrasharma-github","description":"Classification Based Learning Project on Survivors of Titanic","archived":false,"fork":false,"pushed_at":"2024-08-16T12:14:12.000Z","size":145,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-16T22:31:52.496Z","etag":null,"topics":["classification","machine-learning","titanic-survival-prediction"],"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/gajendrasharma-github.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":"2024-08-16T12:01:57.000Z","updated_at":"2024-08-17T04:52:51.000Z","dependencies_parsed_at":"2024-08-16T13:32:04.416Z","dependency_job_id":"5fa522d0-a93d-437f-9b1d-fc4ecf503061","html_url":"https://github.com/gajendrasharma-github/Titanic_Case_Study","commit_stats":null,"previous_names":["gajendrasharma-github/titanic_case_study"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gajendrasharma-github%2FTitanic_Case_Study","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gajendrasharma-github%2FTitanic_Case_Study/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gajendrasharma-github%2FTitanic_Case_Study/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gajendrasharma-github%2FTitanic_Case_Study/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/gajendrasharma-github","download_url":"https://codeload.github.com/gajendrasharma-github/Titanic_Case_Study/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242206006,"owners_count":20089252,"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":["classification","machine-learning","titanic-survival-prediction"],"created_at":"2024-11-16T06:22:30.115Z","updated_at":"2025-03-06T11:49:43.278Z","avatar_url":"https://github.com/gajendrasharma-github.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Titanic Survival Prediction\n\nThis project explores the famous Titanic dataset, applying various data analysis and machine learning techniques to predict passenger survival. \nThe dataset is provided by Kaggle and contains information about the passengers, such as age, gender, class, and other features.\n\n## Project Overview\n\nThe main objectives of this project are:\n- **Data Analysis and Visualization:** To explore the dataset, understand the relationships between different features, and visualize the data using plots and graphs.\n- **Data Preprocessing:** Handling missing values, feature engineering, and preparing the data for machine learning models.\n- **Modeling:** Implementing different machine learning models, such as Logistic Regression, Decision Trees, and Random Forest, to predict the survival of Titanic passengers.\n- **Evaluation:** Comparing model performance using accuracy, precision, recall, and other metrics to determine the best model for this task.\n\n## Key Features\n- **Jupyter Notebook:** The project is developed in Jupyter Notebook, with clear explanations and visualizations to understand each step of the process.\n- **Scikit-learn Models:** Several machine learning models are built and compared to select the best-performing model.\n- **Data Visualization:** Various plots (e.g., histograms, bar charts, heatmaps) are used to explore the dataset and feature relationships.\n\n## Conclusion\nThis project provides a hands-on experience with data analysis and machine learning, allowing you to practice and improve your skills in these areas. 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