{"id":27629378,"url":"https://github.com/farrelfaricaf/exploratorydataanalyst---titanic","last_synced_at":"2025-07-31T17:07:47.156Z","repository":{"id":289275558,"uuid":"970684400","full_name":"farrelfaricaf/ExploratoryDataAnalyst---Titanic","owner":"farrelfaricaf","description":"This project analyzes the Titanic dataset using exploratory data analysis (EDA) and visualization techniques to identify survival patterns. 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It was completed as part of the Faculty of **Data – Digital Skill Fair 38** program by [@dibimbing.id](https://www.linkedin.com/school/dibimbing-id/)\n## 📑Titanic Survival Analysis\nThis project explores the famous Titanic dataset using exploratory data analysis (EDA) and visualization techniques to understand patterns of survival during the 1912 disaster. The dataset provides information about 500 passengers aboard the Titanic, including their survival status, name, gender, and age. This classic dataset offers insights into how social norms, demographic factors, and possibly evacuation protocols influenced survival outcomes during this tragic maritime disaster.\nYou can access the **dataset** used in this project [here](titanic.xlsx).\n\nThe analysis primarily focuses on identifying correlations between passengers' demographic characteristics and their survival rates. Through various visualization methods including bar charts, box plots, histograms, and correlation matrices, we investigate how factors such as gender and age impacted one's chances of survival during the Titanic disaster.\n## ✨Insight\nInsights I gained from this project are:\n1. Gender played a crucial role in survival outcomes, with women having a significantly higher survival rate (93%) compared to men (25%), clearly demonstrating the \"women and children first\" evacuation protocol in action.\n2. Age showed a weak negative correlation (-0.14) with survival, but when grouped into categories, the pattern becomes clearer: children had the highest survival rate (94%), followed by teenagers (62%), adults (53%), and seniors (35%).\n3. The demographic composition of Titanic passengers showed an unusual distribution with a significant peak at approximately age 34, which might warrant further historical investigation.\n4. Of all survivors, 73% were women despite women making up only 42% of the total passenger count in the dataset, highlighting the gender disparity in evacuation priorities.\nThe box plot analysis shows that while age distributions between survivors and non-survivors appear similar, the interaction of age with gender likely played a more important role in determining survival outcomes.\n\n## 🔨Tools \u0026 Libraries Used\n- Python\n- Pandas\n- Matplotlib\n- Seaborn\n- Jupyter Notebook\n\n## 📊Visualized data\n- Survival Count by Gender\n- Survival by Age Group\n- Age Distribution of Passengers\n- Age Distribution by Survival Status\n- Gender Proportion of Survivors\n\n## 👨‍💻 About Me\nHi! I'm Farrel Farica Firjaturazza, a participant in the Faculty of Data – Digital Skill Fair 38 program by @dibimbing.id. This project was created as part of the program to sharpen my data analytics skills through practical, hands-on experience.\n\nFor any feedback or collaboration opportunities, feel free to reach out!\n- 📩 Email: farrelfaricafirjaturazza@gmail.com\n- 🔗 LinkedIn: [linkedin.com/in/farrel-farica-firjaturazza](https://www.linkedin.com/in/farrel-farica-firjaturazza/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffarrelfaricaf%2Fexploratorydataanalyst---titanic","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffarrelfaricaf%2Fexploratorydataanalyst---titanic","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffarrelfaricaf%2Fexploratorydataanalyst---titanic/lists"}