Hackathon Application Analyzer

Welcome to my Hackathon project, where I implemented AVL Trees within a k-Nearest Neighbors algorithm as part of SpartaHack 9.

Key Features:

• Self-Balancing Trees: Ensures efficient data management and quick access times.
• Classification Accuracy: Enhances the accuracy of predictions by effectively organizing data.
• Efficient Processing: Handles large datasets swiftly, making it ideal for real-time applications.

This project leverages the power of AVL Trees to improve the performance of k-Nearest Neighbors, making data classification faster and more reliable. The features include:

• Node Structure: Each node in the tree holds values and maintains a balance to ensure quick data access and modifications.
• Tree Operations: Supports efficient insertion, deletion, and lookup operations.
• Algorithm Integration: Combines AVL Trees with the k-Nearest Neighbors algorithm to classify data based on similarity.

This project demonstrates my capability to integrate advanced data structures with machine learning algorithms, creating a powerful tool for data analysis and prediction. It’s a testament to my problem-solving skills and technical expertise.