https://github.com/farrelfaricaf/exploratorydataanalyst---titanic
This project analyzes the Titanic dataset using exploratory data analysis (EDA) and visualization techniques to identify survival patterns. The goal is to understand how demographic factors like gender and age influenced survival rates during the 1912 disaster.
https://github.com/farrelfaricaf/exploratorydataanalyst---titanic
data data-analysis data-science data-visualization eda python titanic-dataset
Last synced: 11 months ago
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This project analyzes the Titanic dataset using exploratory data analysis (EDA) and visualization techniques to identify survival patterns. The goal is to understand how demographic factors like gender and age influenced survival rates during the 1912 disaster.
- Host: GitHub
- URL: https://github.com/farrelfaricaf/exploratorydataanalyst---titanic
- Owner: farrelfaricaf
- Created: 2025-04-22T11:36:24.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-22T13:35:32.000Z (about 1 year ago)
- Last Synced: 2025-04-23T15:16:47.323Z (about 1 year ago)
- Topics: data, data-analysis, data-science, data-visualization, eda, python, titanic-dataset
- Language: Jupyter Notebook
- Homepage:
- Size: 7.78 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🚢 EDA Project - Titanic Dataset
## đź“ťProject Portfolio Exploratory Data Analysis
This project is a mini data analysis exercise using the famous Titanic dataset. 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/)
## đź“‘Titanic Survival Analysis
This 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.
You can access the **dataset** used in this project [here](titanic.xlsx).
The 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.
## ✨Insight
Insights I gained from this project are:
1. 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.
2. 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%).
3. The demographic composition of Titanic passengers showed an unusual distribution with a significant peak at approximately age 34, which might warrant further historical investigation.
4. 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.
The 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.
## 🔨Tools & Libraries Used
- Python
- Pandas
- Matplotlib
- Seaborn
- Jupyter Notebook
## đź“ŠVisualized data
- Survival Count by Gender
- Survival by Age Group
- Age Distribution of Passengers
- Age Distribution by Survival Status
- Gender Proportion of Survivors
## 👨‍💻 About Me
Hi! 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.
For any feedback or collaboration opportunities, feel free to reach out!
- đź“© Email: farrelfaricafirjaturazza@gmail.com
- đź”— LinkedIn: [linkedin.com/in/farrel-farica-firjaturazza](https://www.linkedin.com/in/farrel-farica-firjaturazza/)