This project features a fully-implemented AVL tree structure, intricately integrated into a machine learning solution for automating the SpartaHack 9 application review process. The project combines this advanced data structure with the k-Nearest Neighbors algorithm, aiming to optimize the selection process for efficiency, fairness, and bias elimination.
- AVL_implementation.py contains the full AVL tree implementation as well as the application functions(fit,predict).
Facing the challenge of manually reviewing a large number of applications, this project builds on the successes and lessons learned from SpartaHack 8. It introduces a data-driven strategy, enhancing the review process through machine learning and complex data structures.
AVL Tree Implementation Details
Complete AVL Tree Structure: The AVLTree class embodies a comprehensive implementation of AVL trees, facilitating efficient data management. Key methods include: insert, remove, search: Core operations for managing tree nodes. left_rotate, right_rotate, rebalance: Essential for maintaining tree balance. inorder, preorder, postorder, levelorder: Traversal methods for various use cases. visualize: Generates a visual representation of the tree structure. Integration with Machine Learning: The NearestNeighborClassifier leverages the AVL tree for efficient data storage and retrieval, crucial for the k-Nearest Neighbors algorithm.
Machine Learning Application
NearestNeighborClassifier: A one-dimensional classifier using AVL tree lookups, designed for high-resolution data classification. Training and Prediction: Incorporates methods like fit and predict for training the classifier and making predictions based on the most common labels in training data.
-Efficient Data Handling: AVL tree ensures optimal data management with logarithmic operation time.
-Fairness and Precision: The system leverages past data for unbiased decision-making in application reviews.
-Automated and Streamlined Process: Facilitates a faster, more efficient, and bias-free review process.
Python
AVL Trees
k-Nearest Neighbors Algorithm
Clone the repository to your local machine.
Ensure Python is installed on your system.
Navigate to the project directory.
Run solution.py to execute the application.
The system processes application data, classifying each application based on its similarity to past applicants using the AVL tree for data management and the k-Nearest Neighbors algorithm for predictive analysis. The outcome (accept or reject) for each application is predicted using the k-Nearest Neighbors algorithm.
Developed by: Shams Alkhalidy
Inspired by SpartaHack 8's success and challenges.
For more information, please contact alkhali7@msu.edu