Decision Tree (Part 3): Titanic Passenger Survival Prediction(Practical Data Analysis 13)
Learn how to build a decision tree classifier with sklearn to predict Titanic passenger survival. Explore data cleaning, feature selection, and K-fold validation.
Welcome to the "Practical Data Analysis" Series
In the previous two articles, I introduced the decision tree algorithm. Decision tree algorithms are commonly used in data mining because they represent decision-making models in a way that is very intuitive, similar to how decisions are made in the human brain.
Many data mining algorithms have been developed based on decision trees, such as Random Forest. Today, I will guide you through a practical project using decision trees.
Decision tree classification has a wide range of applications in various industries. For example, in the financial sector, decision trees can be used for loan risk assessment; in the medical field, they can generate auxiliary diagnoses; in e-commerce, decision trees can be used for sales forecasting, and so on.
After understanding the principles of decision trees, today we will use the sklearn tool to solve a practical problem: predicting the survival of Titanic passengers.