This repository contains code and resources related to the Decision Tree Classification assignment using Python in Google Colab. The assignment covers loading and understanding the dataset, training a decision tree model, preparing data for training, visualizing the decision tree, evaluating the model, and conducting additional analysis.
The dataset used for this project is a modified version of the Kaggle dataset, available in the weather.csv file. It includes 1281 rows with 4 features and 1 output, representing weather conditions.
- 01_import_libraries.ipynb: Import necessary libraries.
- 02_load_dataset.ipynb: Load the dataset and print its content.
- 03_prepare_data.ipynb: Split the dataset into training and testing sets.
- 04_train_decision_tree.ipynb: Train a decision tree model with Scikit-Learn.
- 05_visualize_decision_tree.ipynb: Visualize the trained decision tree.
- 06_evaluate_model.ipynb: Evaluate the model and calculate accuracy.
- 07_extra_credit.ipynb: Additional analysis for extra credit.
- Open and run each notebook in order.
- Follow the instructions and code comments within each notebook.
This project is licensed under the MIT License.