Exploring the Titanic Dataset

In this project, you will explore the Titanic passenger dataset from Kaggle. The ultimate goal is to answer the following question: What kind of passengers were more likely to survive?

This is one of the most used datasets for beginners. It has a clear goal, interesting variables, and enough missing data to practice real exploratory data analysis (EDA) skills.

Project Requirements

• Download train.csv from the Kaggle Titanic competition

• Inspect the data: shape, types, missing values

• Analyze survival rates by gender, class, and age group

• Create bar plots and histograms using Matplotlib or Seaborn

• Use relative frequencies when comparing distributions between survivors and non-survivors

• Write short observations below each chart

Technologies to Use

• Python

• Pandas

• Matplotlib / Seaborn

• Jupyter Notebook

What You Will Learn

You will practice asking questions about data and answering them with charts. You will also learn to think carefully about what a chart actually shows — for example, why absolute frequency can be misleading when two groups have different sizes.

Want to See a Solution?

A full walkthrough of this project is available on Towards Data Science: 🔗 Exploratory Data Analysis & Visualization in Python