Decision Tree Classification of Diamonds Dataset with 87% Accuracy This project focuses on building a decision tree classifier using the Diamonds dataset, achieving an accuracy of 87% in predicting diamond qualities. The README provides an official and professional overview of the project, dataset, methodology, results, usage instructions, contributing guidelines, and licensing information.
Table of Contents Introduction Dataset Methodology Results Usage Contributing License Introduction The quality assessment of diamonds is crucial in the jewelry industry, influencing their value and marketability. This project aims to develop a decision tree classifier capable of predicting the quality of diamonds based on various attributes such as carat weight, cut, color, clarity, and dimensions.
Dataset The Diamonds dataset contains information about thousands of diamonds, including their attributes and quality ratings. Each diamond is described by features such as carat weight, cut, color, clarity, depth, and table percentage. The dataset is labeled with quality ratings ranging from 'Fair' to 'Ideal,' providing supervised learning data for training the decision tree classifier.
Methodology The classification task is performed using the decision tree algorithm, a popular machine learning technique for both classification and regression tasks. Decision trees recursively partition the feature space into subsets based on the most discriminative features, resulting in a tree-like structure of decision nodes and leaf nodes. The project includes hyperparameter tuning and pruning techniques to optimize the decision tree's performance and prevent overfitting.
Results The decision tree classifier achieves an accuracy of 87% in predicting diamond qualities, demonstrating its effectiveness in accurately categorizing diamonds based on their attributes. Additionally, performance metrics such as precision, recall, and F1-score are computed to provide a comprehensive evaluation of the classifier's performance.
Usage To use the decision tree classifier for predicting diamond qualities:
Clone the repository to your local machine. Install the required dependencies specified in requirements.txt. Preprocess the Diamonds dataset as necessary, ensuring proper encoding of categorical variables and handling of missing values. Train the decision tree classifier using the provided training data. Evaluate the trained model's performance using the testing data. Make predictions on new diamonds to classify their qualities. Refer to the provided documentation and example scripts for detailed instructions on using the decision tree classifier.
Contributing Contributions to this project are welcome! If you have ideas for improvements, feature enhancements, or bug fixes, feel free to open an issue or submit a pull request. Please adhere to the project's code of conduct and contribution guidelines.
License This project is licensed under the MIT License. You are free to use, modify, and distribute the code for academic, commercial, or personal projects.