In this project, we implement a variety of data structures and algorithms to solve a wide range of problems. The following data structures and algorithms are covered:

1. Array: Arrays are used to store collections of elements of the same data type. They provide constant-time access to elements by index, making them suitable for tasks that require rapid retrieval or manipulation of data.

2. Linked List: Linked lists are dynamic data structures that store elements in a linear fashion, with each node containing a value and a reference to the next node. They are useful for implementing operations that require frequent insertion and deletion, such as queue and stack implementations.

3. Stack: Stacks are last-in-first-out (LIFO) data structures that allow elements to be added and removed from the top of the stack. They are commonly used in various algorithms and applications, such as expression evaluation, function calls, and backtracking.

4. Queue: Queues are first-in-first-out (FIFO) data structures that allow elements to be added to the rear and removed from the front. They are useful for modeling scenarios where elements need to be processed in the order they were received, such as task scheduling and event handling.

5. Hash Table: Hash tables are associative data structures that store key-value pairs and provide constant-time average-case performance for common operations, such as insertion, deletion, and retrieval. They are widely used in various applications, including caching, symbol tables, and frequency analysis.

6. Binary Search Tree (BST): Binary search trees are tree-based data structures where each node has at most two child nodes. They maintain the property that the value of all nodes in the left subtree are less than the value of the current node, and the value of all nodes in the right subtree are greater than the value of the current node. BSTs are efficient for operations such as search, insertion, and deletion. By utilizing these data structures, the project is able to achieve efficient storage, retrieval, and manipulation of data, enabling the implementation of various algorithms and functionalities.

ALGORITHMS

1. Sorting Algorithms: Algorithms that rearrange elements in a specific order, such as Quicksort, Merge Sort, and Bubble Sort.

2. Searching Algorithms: Algorithms that find the location of a specific element in a data structure, such as Linear Search and Binary Search.

3. Graph Algorithms: Algorithms that operate on graph data structures, such as Breadth-First Search (BFS), Depth-First Search (DFS), and Dijkstra's Algorithm.

4. Divide and Conquer: A problem-solving technique that breaks a problem into smaller subproblems, solves them independently, and then combines the solutions to solve the original problem, such as the Merge Sort and the Strassen's Algorithm.