Objective: This project aims to develop a decision tree model to classify countries' continent ("landmass regions") based on flag characteristics. The primary goal is to predict whether a country belongs to either Europe or Oceania based on flag attributes.

Data Source: Flag dataset

Here's an overview of the tasks conducted using Python and various libraries:

1. Exploratory Data Analysis (EDA) and Decision Tree Creation: I'll begin by exploring the dataset and checking the distribution of landmass categories (Europe and Oceania). Then, I'll construct a decision tree model using Scikit-Learn for country classification.

2. Data Visualization: I'll create visualizations to understand the relationships between predictor variables and visualize the decision tree structure.

3. Data Preprocessing: Data will be preprocessed by selecting relevant predictor variables and encoding categorical features.

4. Data Splitting and Decision Tree Fitting: The dataset will be split into training and testing subsets. Subsequently, I'll fit a decision tree classifier and evaluate its accuracy.

5. Hyperparameter Tuning: Hyperparameters of the decision tree, specifically maximum depth and complexity cost parameter (ccp_alpha), will be optimized to enhance model performance.

6. Pruning the Decision Tree: Different values of ccp_alpha will be experimented with to prune the decision tree, improving its generalization ability.

7. Visualization of the Final Decision Tree: I'll provide a visualization of the final decision tree, showcasing the optimal hyperparameters.