{"id":28255101,"url":"https://github.com/arnabushna24/titanic-disaster-analysis","last_synced_at":"2025-10-03T17:01:39.584Z","repository":{"id":292411116,"uuid":"980826095","full_name":"ArnabUshna24/Titanic-Disaster-Analysis","owner":"ArnabUshna24","description":"Titanic - Machine Learning from Disaster","archived":false,"fork":false,"pushed_at":"2025-05-11T05:39:54.000Z","size":291,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-19T20:19:36.476Z","etag":null,"topics":["data-analysis","data-visualization","python","statistical-analysis"],"latest_commit_sha":null,"homepage":"https://www.kaggle.com/c/titanic/data","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/ArnabUshna24.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":"2025-05-09T19:30:02.000Z","updated_at":"2025-05-11T05:39:58.000Z","dependencies_parsed_at":"2025-05-09T20:42:34.509Z","dependency_job_id":null,"html_url":"https://github.com/ArnabUshna24/Titanic-Disaster-Analysis","commit_stats":null,"previous_names":["arnabushna24/titanic-disaster-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ArnabUshna24/Titanic-Disaster-Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ArnabUshna24%2FTitanic-Disaster-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ArnabUshna24%2FTitanic-Disaster-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ArnabUshna24%2FTitanic-Disaster-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ArnabUshna24%2FTitanic-Disaster-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ArnabUshna24","download_url":"https://codeload.github.com/ArnabUshna24/Titanic-Disaster-Analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ArnabUshna24%2FTitanic-Disaster-Analysis/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":260114435,"owners_count":22960907,"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-analysis","data-visualization","python","statistical-analysis"],"created_at":"2025-05-19T20:17:05.862Z","updated_at":"2025-10-03T17:01:39.566Z","avatar_url":"https://github.com/ArnabUshna24.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Titanic Disaster Analysis\n\n\n\n## Overview\nThis project aims to analyze [Titanic - Machine Learning from Disaster](https://www.kaggle.com/c/titanic/data) dataset and find insights using statistical analysis. It serves the following concerns:\n\n* Retriveing the data from the target location.\n* Handling the missing values and outliers (if there is any).\n* Performing data visualization.\n* Performing basis statistical analyses.\n\n\n## Data Retrieval\n`Titanic - Machine Learning from Disaster` dataset is available on Kaggle. It contains three (3) `.csv` files - `gender_submission.csv`, `test.csv`, and `train.csv`. Among them, `train.csv` file was used for this project. It contains twelve (12) columns - `PassengerId`,\t`Survived`,\t`Pclass`,\t`Name`,\t`Sex`,\t`Age`,\t`SibSp`, \t`Parch`, `Ticket`, `Fare`, `Cabin`, and\t`Embarked`. `train.csv` file was loaded into a `pandas` dataframe for further analysis.\n\n\n## Data Cleaning and Manipulation\nTo find missing values in the dataset, `isnull` function was used. There were 177 missing `Age` values, 687 missing `Cabin` values, and 2 `Embarked` values. For the missing values in `Age` column, it was imputed with the median of the column values, whereas `Cabin` and `Embarked` columns were handled using `notnull` function and mode, respectively. After that, outlier identification was performed and outliers were then capped. However, there were no duplicated records.\n\n\n## Data Visualizations\n\u003ctable\u003e\n  \n  \u003ctr\u003e\n    \u003ctd align=\"center\"\u003e\u003cimg src=\"https://github.com/ArnabUshna24/Titanic-Disaster-Analysis/blob/main/data_visualizations/gender_distribution.png\" alt=\"Distribution of Passengers by Gender\" width=\"300\"/\u003e\u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\u003cimg src=\"https://github.com/ArnabUshna24/Titanic-Disaster-Analysis/blob/main/data_visualizations/age_distribution.png\" alt=\"Age Distribution Histogram\" width=\"300\"/\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n  \n  \u003ctr\u003e\n    \u003ctd align=\"center\"\u003e Fig. 1: Distribution of Passengers by Gender \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e Fig. 2: Age Distribution Histogram \u003c/td\u003e\n  \u003c/tr\u003e\n  \n  \u003ctr\u003e\n    \u003ctd align=\"center\"\u003e\u003cimg src=\"https://github.com/ArnabUshna24/Titanic-Disaster-Analysis/blob/main/data_visualizations/gender-wise_survival_rate.png\" alt=\"Survival Rate by Gender\" width=\"300\"/\u003e\u003c/td\u003e\n    \u003ctd align=\"center\"\u003e\u003cimg src=\"https://github.com/ArnabUshna24/Titanic-Disaster-Analysis/blob/main/data_visualizations/class-wise_survival_rate.png\" alt=\"Survival Rate by Class\" width=\"300\"/\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n  \n   \u003ctr\u003e\n    \u003ctd align=\"center\"\u003e Fig. 3: Survival Rate by Gender \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e Fig. 4: Survival Rate by Class \u003c/td\u003e\n  \u003c/tr\u003e\n  \n\u003c/table\u003e\n\n\n\n## Statistical Analysis\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Mean, Median and Mode of 'Fare' and 'Age' Columns \u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"5\"\u003e\n  \u003ctr\u003e\n    \u003cth align=\"left\"\u003e Columns \u003c/th\u003e\n    \u003cth align=\"center\"\u003e Mean \u003c/th\u003e\n    \u003cth align=\"center\"\u003e Median \u003c/th\u003e\n    \u003cth align=\"center\"\u003e Mode \u003c/th\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd align=\"left\"\u003e Fare \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e 32.2042 \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e 14.4542 \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e 8.05 \u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd align=\"left\"\u003e Age \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e 29.3616 \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e 28.0 \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e 28.0 \u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\n\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Gender-wise Survival Rate \u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"5\"\u003e\n  \u003ctr\u003e\n    \u003cth align=\"left\"\u003e Test Component \u003c/th\u003e\n    \u003cth align=\"center\"\u003e Result \u003c/th\u003e\n  \u003c/tr\u003e\n  \n  \u003ctr\u003e\n    \u003ctd align=\"left\"\u003e Null hypothesis \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e Significant difference in survival rates \u003c/td\u003e\n  \u003c/tr\u003e\n\n  \u003ctr\u003e\n    \u003ctd align=\"left\"\u003e Significance level (α) \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e 0.05 \u003c/td\u003e\n  \u003c/tr\u003e\n\n  \u003ctr\u003e\n    \u003ctd align=\"left\"\u003e T-statistic \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e -18.672 \u003c/td\u003e\n  \u003c/tr\u003e\n\n  \u003ctr\u003e\n    \u003ctd align=\"left\"\u003e P-value \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e 2.28 × 10⁻⁶¹ \u003c/td\u003e\n  \u003c/tr\u003e\n\n  \u003ctr\u003e\n    \u003ctd align=\"left\"\u003e Decision \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e Reject the null hypothesis \u003c/td\u003e\n  \u003c/tr\u003e\n\n   \u003ctr\u003e\n    \u003ctd align=\"left\"\u003e Interpretation \u003c/td\u003e\n    \u003ctd align=\"center\"\u003e There is a significant difference in survival rates between males and females on the Titanic \u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\n\n## Build from Source\nInstructions are provided in the `.ipynb` file.\n\n\nIf you have any queries, contact me: arnabnushna24@gmail.com\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farnabushna24%2Ftitanic-disaster-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farnabushna24%2Ftitanic-disaster-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farnabushna24%2Ftitanic-disaster-analysis/lists"}