🧠 Algorithm Practice: Recursion, Sorting, Linked List, HashMap & BST

This project contains fundamental algorithm and data structure implementations, done as part of The Odin Project curriculum.

It focuses on recursive thinking, sorting, linked lists, hash maps, and now includes a fully-featured binary search tree with balance and traversal operations.

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📂 Files

algo-practice/
├── recursion/
│ ├── iterative.js
│ └── recursive.js
├── merge-sort/
│ └── mergeSort.js
├── linked-list/
│ ├── Node.js
│ ├── LinkedList.js
│ ├── test.js
│ └── README.md
├── hashmap/
│ ├── hashMap.js
│ ├── test.js
│ └── README.md
├── binary-search-tree/
│ ├── Node.js
│ ├── Tree.js
│ ├── driver.js
│ ├── prettyPrint.js
│ └── README.md
└── README.md

🌀 recursion/

• fibs(limit) → Iterative Fibonacci generator
• fibsRec(limit) → Recursive Fibonacci generator

🧮 merge-sort/

• mergeSort(arr) → Recursive merge sort implementation
• Includes a custom merge() function to combine sorted halves

🧩 linked-list/

• Node.js → Defines the Node structure
• LinkedList.js → Contains core linked list methods like:
  • append(), prepend(), size(), at(), pop(), find(), contains(), toString()
• test.js → Sample test cases to validate the implementation

🔐 hashmap/

• Custom HashMap class with:
  • Chained collision handling
  • Resizing logic when load factor exceeds 0.75
  • Methods like set, get, has, remove, clear, keys, values, entries
• Notes + test cases included
  ➡️ View detailed HashMap README →

🌳 binary-search-tree/

• Node.js and Tree.js → Fully-featured Balanced BST implementation
• buildTree() builds a balanced tree from sorted unique array
• Supports:
  • insert(), deleteItem(), find()
  • height(), depth(), isBalanced(), rebalance()
  • Traversals: inOrder, preOrder, postOrder, levelOrder with callbacks
• Includes a prettyPrint() visualizer and driver.js demo ➡️ View detailed BST README →

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🚀 Sample Inputs & Outputs

Recursion:

fibs(8); 
// ➜ [0, 1, 1, 2, 3, 5, 8, 13]

fibsRec(10); 
// ➜ [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]

Merge Sort:

mergeSort([3, 2, 1, 13, 8, 5, 0, 1]);
// ➜ [0, 1, 1, 2, 3, 5, 8, 13]

mergeSort([105, 79, 100, 110]);
// ➜ [79, 100, 105, 110]

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🧩 What I Learned

• The difference between iterative vs recursive solutions.
• How to build data structures from scratch — LinkedList, HashMap, BST.
• Handling collisions and resizing logic in hash-based storage.
• Balancing and traversing binary search trees efficiently.
• The importance of clear, testable, modular code.
• How lower-level data structures power high-level abstractions.

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📄 License

This project is for educational purposes under the MIT License.

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