Titanic Survival Analysis with Python

Explore one of the most famous datasets in data science. The sinking of the Titanic in 1912 remains one of history’s most tragic events—and also one of the most studied datasets in data analysis. In this project, you’ll work with the Titanic dataset, which includes passenger information such as age, gender, ticket class, fare, and survival outcome. Using Pandas for data manipulation and Seaborn for visualization, you’ll analyze patterns that influenced survival rates. You’ll start by loading and cleaning the dataset—handling missing values in columns like age and embarked port, and converting categorical variables into usable forms. With Pandas, you’ll summarize passenger distributions across class, gender, and age groups, and calculate survival percentages within these categories. Next, you’ll apply Seaborn visualizations to highlight findings. Bar plots will compare survival by class or gender, histograms will show age distributions, and heatmaps can reveal correlations among multiple features. You’ll explore questions such as: Were women and children more likely to survive? How did ticket class affect survival chances? What patterns exist between age, fare, and survival? By the end of this project, you will be able to: Clean and prepare a historical dataset with Pandas. Group and aggregate data to calculate survival statistics. Create Seaborn plots that clearly communicate survival patterns. Interpret results to explain the human and social dynamics of the disaster. This project is a cornerstone exercise in data analysis, widely recognized as a starting point for beginners and a benchmark for professionals. By completing it, you’ll practice both technical and analytical skills while gaining insight into how data tells the story of one of the most significant events in history.