Study Titanic passengers to find what factors improved survival odds.
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.
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
Matplotlib / Seaborn
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.
A full walkthrough of this project is available on Towards Data Science: 🔗 Exploratory Data Analysis & Visualization in Python