This repository contains two Python scripts implementing a decision tree classifier and a graphical user interface (GUI) for it.
The decision tree script (decisionTree.py) provides a custom class for training and predicting with a decision tree model using Scikit-Learn.

The GUI script (decisionTreeGUI.py) offers a user-friendly interface to interact with the decision tree, including functions to load data, train the model, predict outcomes, and visualize the decision tree.

Implements the DecisionTree class, which includes methods for data preprocessing, entropy calculation, feature splitting, model training, and prediction.

Builds a GUI using Tkinter, integrating the DecisionTree class for data loading, model training, prediction, and tree visualization.

sklearn
numpy
pandas
tkinter
graphviz
matplotlib

Import DecisionTree from decisionTree.py.
Create an instance with a dataset.
Call train method to train the model.
Use predict method for predictions.

Run decisionTreeGUI.py script.
Use the GUI to load a CSV file, train the model, make predictions, and generate a PDF of the decision tree.

Custom Decision Tree Implementation: Allows entropy-based feature splitting and training with Scikit-Learn's DecisionTreeClassifier.
GUI for User Interaction: Facilitates non-programmers to use the decision tree model for predictions and visualization.