Decision Tree Analysis in R (Titanic Dataset)

This repository contains an analysis of the Titanic using Decision Trees in R. The project demonstrates how to preprocess data, build a decision tree model & visualize it.

A Decision Tree is a supervised machine learning algorithm used for classification and regression tasks. It models decisions based on feature values, splitting data into branches at each node. Read More

Dataset

The dataset used is a cleaned version of the Titanic dataset from Kaggle. It contains the following features:

• Survived: Survival status (0 = No, 1 = Yes)
• Pclass: Ticket class (1st, 2nd, 3rd)
• Sex: Gender (Male, Female)
• Age: Age of passenger
• SibSp: Number of siblings/spouses aboard
• Parch: Number of parents/children aboard
• Fare: Ticket fare

Steps to run

To run this project, ensure you have R and RStudio installed.

1. Clone the respository: https://github.com/DeeptejD/Decision-Trees-in-R
2. Open decision_tree.R in RStudio.
3. Run the script to:

• Load and preprocess the Titanic dataset.
• Train a decision tree model.
• Visualize the decision tree.
• Evaluate model performance.

References

Decision Tree Classification Clearly Explained - Normalized Nerd (YouTube)

Introduction to R: Decision Trees - DataDaft (YouTube)