🛳️ Titanic Exploratory Data Analysis (EDA)

Status: Work in progress — actively updated as I build my data analysis portfolio. Target completion: 31 January 2026.

This repository contains a complete Exploratory Data Analysis (EDA) of the classic Titanic dataset — one of the most iconic datasets in data science.

Although Titanic is often treated as a “hello world” project, the dataset is not trivial. It includes:

• mixed data types
• categorical variables
• missing values
• interaction effects
• data splits (train/test)

This makes Titanic an excellent dataset for sharpening practical skills across:

• data cleaning & wrangling
• feature-level investigation
• distributions & summary statistics
• categorical vs numerical comparisons
• exploratory visualization and storytelling

For more advanced, interactive visualizations, see my portfolio projects at mydataaijournal.com.

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🚀 Run This Project With Docker Compose

This project includes a full Docker environment so anyone can run the notebooks without installing Python or dependencies.

1️⃣ Start Docker Desktop

Make sure Docker Desktop is running on your machine (Windows / macOS / Linux).

2️⃣ Build and start the container (first time)

docker compose up --build

This will:

• build the Docker image from the included Dockerfile
• install dependencies from requirements.txt
• start Jupyter Notebook inside the container
• map port 8888 → your computer

Once it starts, look for a URL like:

http://127.0.0.1:8888/?token=xxxxxxxxxxxx

Open it in your browser to access the notebook.

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3️⃣ Run on subsequent sessions (no rebuild needed)

docker compose up

Because the repository is mounted via:

volumes:
  - .:/app

Any changes to .ipynb or .py files on your local machine automatically appear inside the container — no rebuild required.

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4️⃣ Stop the container

Press:

CTRL + C

Or run:

docker compose down

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🙏 Thank You

Thank you for viewing this project! More EDA, A/B testing, KPI dashboards, and data engineering work will be added as I build toward a complete full-stack data analyst / data intelligence engineer portfolio.

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