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🚀 Coding Interview Questions with Answers Part 1

1️⃣ What is Programming?

Answer: Programming is the process of writing instructions (code) that tell a computer how to perform specific tasks or solve problems.

2️⃣ What is an Algorithm?

Answer: An algorithm is a step-by-step procedure or set of instructions used to solve a problem or perform a task efficiently.

3️⃣ What is Pseudocode?

Answer: Pseudocode is a simple, human-readable way of writing the logic of a program without following the syntax of any specific programming language. It helps plan the solution before coding.

4️⃣ What is a Flowchart?

Answer: A flowchart is a graphical representation of an algorithm or process using symbols and arrows to show the sequence of steps.

5️⃣ What is a Variable?

Answer: A variable is a named memory location used to store data. Its value can change during program execution.

Example:

int age = 25;

6️⃣ What are Data Types?

Answer: Data types define the type of data a variable can store, such as integers, floating-point numbers, characters, booleans, and strings.

Examples:

• Integer: 10

• Float: 3.14

• Character: 'A'

• Boolean: true

• String: "Hello"

7️⃣ What is Type Casting?

Answer: Type casting is the process of converting a value from one data type to another.

Examples:

• Implicit Casting: int to double

• Explicit Casting: (int)3.75 to 3

8️⃣ What are Operators in Programming?

Answer: Operators are symbols used to perform operations on variables and values.

Common Types:

• Arithmetic: +, -, *, /, %

• Comparison: ==, !=, >, <

• Logical: &&, ||, !

• Assignment: =, +=, -=

• Increment/Decrement: ++, --

9️⃣ What are Conditional Statements?

Answer: Conditional statements allow a program to execute different blocks of code based on whether a condition is true or false.

Examples:

• if

• if-else

• else if

• switch

🔟 What are Loops?

Answer: Loops are used to execute a block of code repeatedly until a specified condition is met.

Common Types:

• for loop: Used when the number of iterations is known

• while loop: Used when the condition is checked before each iteration

• do-while loop: Executes at least once before checking the condition

Double Tap ❤️ For Part-2

🚀 Coding Interview Questions with Answers (Part 2)

1️⃣1️⃣ What is the difference between "for", "while", and "do-while" loops?
Answer:
All three loops are used to execute a block of code repeatedly, but they differ in how and when the condition is checked.

• for loop: Best when the number of iterations is known.
• while loop: Best when the number of iterations is unknown and depends on a condition.
• do-while loop: Executes the code at least once because the condition is checked after the loop body.

1️⃣2️⃣ What are Functions?
Answer:
A function is a reusable block of code that performs a specific task. Functions improve code readability, reusability, and maintainability by avoiding code duplication.

Benefits:
• Code reusability
• Better organization
• Easier debugging
• Improved maintainability

1️⃣3️⃣ What is the difference between Parameters and Arguments?
Answer:
• Parameters are variables declared in a function definition.
• Arguments are the actual values passed to the function when it is called.

Example:

function add(a, b) {   // a and b are parameters
    return a + b;
}
add(5, 10);            // 5 and 10 are arguments

1️⃣4️⃣ What is Recursion?
Answer:
Recursion is a programming technique in which a function calls itself to solve a problem by breaking it into smaller subproblems. Every recursive function should have a base case to stop infinite recursion.

Example: Calculating factorial or Fibonacci numbers.

1️⃣5️⃣ What is Scope?
Answer:
Scope defines where a variable can be accessed in a program.

Types of Scope:
• Local Scope
• Global Scope
• Block Scope (in many modern programming languages)
• Function Scope

Variables can only be accessed within their defined scope.

1️⃣6️⃣ What are Global and Local Variables?
Answer:
• Global Variable: Declared outside functions and can be accessed throughout the program.
• Local Variable: Declared inside a function or block and can only be accessed within that function or block.

Generally, local variables are preferred because they reduce unintended side effects.

1️⃣7️⃣ What are Arrays?
Answer:
An array is a data structure that stores multiple elements of the same data type in contiguous memory locations. Each element is accessed using an index.

Example:

int numbers[] = {10, 20, 30, 40};

1️⃣8️⃣ What are Strings?
Answer:
A string is a sequence of characters used to represent text. Depending on the programming language, strings may be immutable (e.g., Java, Python) or mutable using specific classes.

Example:
"Hello, World!"

1️⃣9️⃣ What is Debugging?
Answer:
Debugging is the process of finding, analyzing, and fixing errors (bugs) in a program to ensure it works correctly.

Common Debugging Techniques:
• Using breakpoints
• Printing variable values
• Reading error messages
• Using debugging tools in an IDE
• Writing unit tests

2️⃣0️⃣ What are Syntax, Logical, and Runtime Errors?
Answer:
• Syntax Error: Occurs when the code violates the programming language's grammar rules. The program won't compile or run.
• Logical Error: The program runs successfully but produces incorrect results because of faulty logic.
• Runtime Error: Occurs while the program is executing, such as dividing by zero or accessing invalid memory.

Understanding these error types helps developers identify and fix problems more efficiently.

Double Tap ❤️ For Part-3

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🚀 Coding Interview Questions with Answers (Part 3)

2️⃣1️⃣ What is Object-Oriented Programming (OOP)?

Answer:

Object-Oriented Programming (OOP) is a programming paradigm that organizes software around objects rather than functions. It helps make code modular, reusable, and easier to maintain.

The four main principles of OOP are:

• Encapsulation

• Abstraction

• Inheritance

• Polymorphism

2️⃣2️⃣ What is a Class?

Answer:

A class is a blueprint or template used to create objects. It defines the properties (attributes) and behaviors (methods) that its objects will have.

Example: A "Car" class may have properties like "color" and "speed", and methods like "start()" and "stop()".

2️⃣3️⃣ What is an Object?

Answer:

An object is an instance of a class. It contains actual values for the class's properties and can perform the actions defined by the class's methods.

Example: If "Car" is a class, then a red Toyota is an object of the "Car" class.

2️⃣4️⃣ What is Encapsulation?

Answer:

Encapsulation is the process of combining data and the methods that operate on that data into a single unit (class). It also restricts direct access to data by using access modifiers like "private", "protected", and "public".

Benefits:

• Protects data

• Improves security

• Makes code easier to maintain

2️⃣5️⃣ What is Abstraction?

Answer:

Abstraction is the concept of hiding implementation details and showing only the essential features of an object. It allows users to focus on what an object does instead of how it works.

Example: You can drive a car without knowing how the engine works internally.

2️⃣6️⃣ What is Inheritance?

Answer:

Inheritance allows one class (child/subclass) to acquire the properties and methods of another class (parent/superclass). It promotes code reuse and supports hierarchical relationships.

Example: A "Dog" class can inherit common properties and methods from an "Animal" class.

2️⃣7️⃣ What is Polymorphism?

Answer:

Polymorphism means "many forms." It allows the same method or interface to behave differently depending on the object using it.

Types:

• Compile-time Polymorphism (Method Overloading)

• Runtime Polymorphism (Method Overriding)

2️⃣8️⃣ What is Method Overloading?

Answer:

Method overloading is defining multiple methods with the same name but different parameter lists in the same class. The compiler decides which method to call based on the arguments.

Example: "add(int, int)" and "add(double, double)".

2️⃣9️⃣ What is Method Overriding?

Answer:

Method overriding occurs when a child class provides its own implementation of a method already defined in the parent class. It enables runtime polymorphism.

Example: An "Animal" class has a "sound()" method, while "Dog" overrides it to return "Bark".

3️⃣0️⃣ What is the Difference Between Method Overloading and Method Overriding?

Answer:

Method Overloading

• Occurs within the same class.

• Uses the same method name but different parameters.

• Achieves compile-time polymorphism.

• Inheritance is not required.

Method Overriding

• Occurs between parent and child classes.

• Uses the same method name and same parameters.

• Achieves runtime polymorphism.

• Requires inheritance.

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🚀 Coding Interview Questions with Answers (Part 5)

4️⃣1️⃣ What is a Data Structure?

Answer:
A data structure is a way of organizing and storing data so that it can be accessed, modified, and processed efficiently.

Common data structures include:
• Arrays
• Linked Lists
• Stacks
• Queues
• Trees
• Graphs
• Hash Tables

Choosing the right data structure can significantly improve a program's performance.

4️⃣2️⃣ What are the Types of Data Structures?

Answer:
Data structures are broadly classified into two categories:

1. Linear Data Structures
• Array
• Linked List
• Stack
• Queue
Elements are arranged sequentially.

2. Non-Linear Data Structures
• Tree
• Graph
• Heap
• Trie
Elements are connected hierarchically or through multiple relationships.

4️⃣3️⃣ What is an Array?

Answer:
An array is a linear data structure that stores multiple elements of the same data type in contiguous memory locations.

Characteristics:
• Fixed size (in most languages)
• Fast random access using indexes
• Efficient for storing ordered data

Example:
int numbers[] = {10, 20, 30, 40};

4️⃣4️⃣ What is a Linked List?

Answer:
A linked list is a linear data structure where each element (node) contains data and a pointer (reference) to the next node.

Advantages:
• Dynamic size
• Easy insertion and deletion

Disadvantages:
• Slower access than arrays because elements must be traversed sequentially.

4️⃣5️⃣ What are the Types of Linked Lists?

Answer:
The main types are:
• Singly Linked List: Each node points to the next node.
• Doubly Linked List: Each node points to both the previous and next nodes.
• Circular Linked List: The last node points back to the first node.

Each type is useful for different scenarios depending on traversal and memory requirements.

4️⃣6️⃣ What is a Stack?

Answer:
A stack is a linear data structure that follows the LIFO (Last In, First Out) principle.

Common Operations:
• Push (Insert)
• Pop (Remove)
• Peek/Top (View top element)

Applications:
• Function calls
• Undo/Redo operations
• Expression evaluation
• Backtracking

4️⃣7️⃣ What is a Queue?

Answer:
A queue is a linear data structure that follows the FIFO (First In, First Out) principle.

Common Operations:
• Enqueue (Insert)
• Dequeue (Remove)
• Front/Peek

Applications:
• Task scheduling
• Printer queues
• CPU scheduling
• Breadth-First Search (BFS)

4️⃣8️⃣ What is the Difference Between a Stack and a Queue?

Answer:

Stack
• Follows LIFO
• Insertion and deletion happen at the same end (top)
• Examples: Browser history, Undo operation

Queue
• Follows FIFO
• Insertion happens at the rear, deletion from the front
• Examples: Ticket booking systems, Print queues

4️⃣9️⃣ What is a Deque?

Answer:
A deque (Double-Ended Queue) is a data structure where elements can be inserted and removed from both the front and the rear.

Operations:
• Insert Front
• Insert Rear
• Delete Front
• Delete Rear

It combines the features of both stacks and queues.

5️⃣0️⃣ What is a Priority Queue?

Answer:
A priority queue is a special type of queue where each element is assigned a priority. Elements with higher priority are removed before elements with lower priority, regardless of their insertion order.

Applications:
• CPU scheduling
• Dijkstra's shortest path algorithm
• Task scheduling
• Event-driven simulations

Implementation:
Priority queues are commonly implemented using a Heap, providing efficient insertion and deletion operations.

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🚀 Coding Interview Questions with Answers (Part 6)

5️⃣1️⃣ What is a Hash Table?
Answer:
A hash table (also called a hash map or dictionary) is a data structure that stores data as key-value pairs. It uses a hash function to calculate an index where the value is stored, enabling very fast lookup, insertion, and deletion.

Average Time Complexity:
• Search: O(1)
• Insert: O(1)
• Delete: O(1)

Applications:
• Caching
• Database indexing
• Dictionaries
• Symbol tables

5️⃣2️⃣ What is Hashing?
Answer:
Hashing is the process of converting a key into a fixed-size integer (called a hash value) using a hash function. This hash value determines where the data will be stored in a hash table.

Benefits:
• Fast data retrieval
• Efficient searching
• Reduced lookup time

5️⃣3️⃣ What are Collisions in Hashing?
Answer:
A collision occurs when two or more different keys produce the same hash value and are assigned to the same index in a hash table.

Common Collision Resolution Techniques:
• Separate Chaining
• Linear Probing
• Quadratic Probing
• Double Hashing

5️⃣4️⃣ What is a Binary Tree?
Answer:
A binary tree is a hierarchical data structure in which each node has at most two children, known as the left child and the right child.

Applications:
• Expression trees
• File systems
• Decision trees
• Hierarchical data representation

5️⃣5️⃣ What is a Binary Search Tree (BST)?
Answer:
A Binary Search Tree (BST) is a binary tree in which:
• All values in the left subtree are smaller than the root.
• All values in the right subtree are greater than the root.

This property allows efficient searching, insertion, and deletion.

Average Time Complexity:
• Search: O(log n)
• Insert: O(log n)
• Delete: O(log n)

5️⃣6️⃣ What is an AVL Tree?
Answer:
An AVL Tree is a self-balancing Binary Search Tree where the height difference (balance factor) between the left and right subtrees of any node is at most 1.
Whenever the tree becomes unbalanced, rotations are performed to restore balance.

Benefit: Maintains O(log n) search, insertion, and deletion time.

5️⃣7️⃣ What is a Heap?
Answer:
A heap is a complete binary tree that satisfies the heap property.

Types:
• Min Heap: The parent node is smaller than or equal to its children.
• Max Heap: The parent node is greater than or equal to its children.

Applications:
• Priority Queues
• Heap Sort
• Scheduling algorithms

5️⃣8️⃣ What is the Difference Between a Min Heap and a Max Heap?
Answer:
Min Heap
• Smallest element is at the root.
• Parent ≤ Children.
• Used when the minimum value is frequently required.

Max Heap
• Largest element is at the root.
• Parent ≥ Children.
• Used when the maximum value is frequently required.

5️⃣9️⃣ What is a Graph?
Answer:
A graph is a non-linear data structure consisting of vertices (nodes) and edges that connect those vertices.
Graphs can be:
• Directed or Undirected
• Weighted or Unweighted
• Cyclic or Acyclic

Applications:
• Social networks
• GPS navigation
• Computer networks
• Recommendation systems

6️⃣0️⃣ What are the Types of Graphs?
Answer:
Graphs are classified into several types based on their structure:
• Directed Graph: Edges have a direction.
• Undirected Graph: Edges have no direction.
• Weighted Graph: Edges have weights or costs.
• Unweighted Graph: All edges have equal weight.
• Cyclic Graph: Contains one or more cycles.
• Acyclic Graph: Does not contain any cycles.
• Connected Graph: Every node is reachable from every other node.
• Disconnected Graph: Some nodes cannot be reached from others.

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🚀 Coding Interview Questions with Answers (Part 7)

6️⃣1️⃣ What is Graph Traversal?
Answer:
Graph traversal is the process of visiting every vertex (node) in a graph in a systematic way.
The two most common graph traversal algorithms are:
• Breadth-First Search (BFS)
• Depth-First Search (DFS)

Applications:
• Finding paths in a graph
• Network routing
• Social network analysis
• Web crawling

6️⃣2️⃣ What is the Difference Between BFS and DFS?
Answer:
Breadth-First Search (BFS)
• Visits nodes level by level.
• Uses a Queue data structure.
• Finds the shortest path in an unweighted graph.
• Requires more memory for large graphs.

Depth-First Search (DFS)
• Explores one path completely before backtracking.
• Uses a Stack (or recursion).
• Does not always find the shortest path.
• Typically uses less memory than BFS.

6️⃣3️⃣ What is a Trie?
Answer:
A Trie (Prefix Tree) is a tree-like data structure used to store and search strings efficiently.
Applications:
• Autocomplete
• Spell checking
• Dictionary lookup
• Search engines
Time Complexity:
• Search: O(L)
• Insert: O(L)
Where L is the length of the word.

6️⃣4️⃣ What is a Segment Tree?
Answer:
A Segment Tree is a binary tree used to perform efficient range queries and updates on an array.
Applications:
• Range Sum Query
• Minimum/Maximum Query
• Competitive Programming
Time Complexity:
• Build: O(n)
• Query: O(log n)
• Update: O(log n)

6️⃣5️⃣ What is a Fenwick Tree (Binary Indexed Tree)?
Answer:
A Fenwick Tree is a data structure used to efficiently calculate prefix sums and update elements in an array.
Advantages:
• Less memory than Segment Tree
• Easier implementation
• Fast updates and queries
Time Complexity:
• Update: O(log n)
• Query: O(log n)

6️⃣6️⃣ What is a Disjoint Set (Union-Find)?
Answer:
A Disjoint Set, also known as Union-Find, is a data structure used to maintain a collection of non-overlapping sets.
It supports two operations:
• Find: Determines which set an element belongs to.
• Union: Merges two sets into one.
Applications:
• Kruskal's Minimum Spanning Tree Algorithm
• Cycle Detection
• Network Connectivity

6️⃣7️⃣ What is an Adjacency Matrix?
Answer:
An Adjacency Matrix is a 2D array used to represent a graph.
• Rows and columns represent vertices.
• A value of 1 (or the edge weight) indicates a connection.
• A value of 0 indicates no connection.
Advantages: Fast edge lookup (O(1))
Disadvantages: Uses O(V²) memory, making it inefficient for sparse graphs.

6️⃣8️⃣ What is an Adjacency List?
Answer:
An Adjacency List represents a graph by storing a list of neighboring vertices for each vertex.
Advantages: Requires O(V + E) memory. Efficient for sparse graphs.
Disadvantages: Edge lookup is slower than an adjacency matrix.

6️⃣9️⃣ What is a Circular Linked List?
Answer:
A Circular Linked List is a linked list in which the last node points back to the first node instead of pointing to "NULL".
Applications:
• CPU Scheduling
• Multiplayer Games
• Circular Buffers
• Music Playlists
Benefit: Traversal can continue indefinitely without restarting.

7️⃣0️⃣ What is a Doubly Linked List?
Answer:
A Doubly Linked List is a linked list where each node contains:
• Data
• Pointer to the next node
• Pointer to the previous node
Advantages: Supports forward and backward traversal. Easier insertion and deletion compared to a singly linked list.
Disadvantages: Requires extra memory for the previous pointer. Slightly more complex to implement.

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🚀 Coding Interview Questions with Answers (Part 8)

7️⃣1️⃣ What is a Sparse Matrix?

Answer:

A sparse matrix is a matrix in which most of the elements are 0. Instead of storing every element, only the non-zero elements are stored to save memory.

Applications:

• Machine Learning

• Graph representations

• Scientific computing

• Image processing

Advantages:

• Saves memory

• Improves computational efficiency

7️⃣2️⃣ What is a Dynamic Array?

Answer:

A dynamic array is an array that can automatically resize itself when more elements are added.

Unlike a fixed-size array, it allocates additional memory when its capacity is reached.

Examples:

• ArrayList in Java

• vector in C++

• list (dynamic array implementation) in Python

Advantages:

• Flexible size

• Fast random access

• Easy insertion at the end

7️⃣3️⃣ What is Load Factor?

Answer:

Load factor is the ratio of the number of stored elements to the total number of buckets in a hash table.

Formula:

Load Factor = Number of Elements / Number of Buckets

A high load factor increases the likelihood of collisions, while a low load factor improves performance but uses more memory.

7️⃣4️⃣ What is Collision Resolution?

Answer:

Collision resolution refers to the techniques used to handle situations where multiple keys are mapped to the same location in a hash table.

Common Methods:

• Separate Chaining

• Linear Probing

• Quadratic Probing

• Double Hashing

The goal is to maintain efficient search, insertion, and deletion operations.

7️⃣5️⃣ What is the Difference Between Linear Probing and Chaining?

Answer:

Linear Probing

• Stores collided elements in the next available slot.

• Uses open addressing.

• Requires less memory.

• Performance decreases as the table becomes full.

Separate Chaining

• Stores collided elements in a linked list at the same bucket.

• Easier to handle many collisions.

• Requires additional memory for linked lists.

7️⃣6️⃣ What is Tree Traversal?

Answer:

Tree traversal is the process of visiting every node in a tree exactly once in a specific order.

Common Types:

• Preorder

• Inorder

• Postorder

• Level-order

Tree traversal is used for searching, printing, and processing tree data.

7️⃣7️⃣ What is the Difference Between Preorder, Inorder, and Postorder Traversal?

Answer:

• Preorder: Root → Left → Right

• Inorder: Left → Root → Right

• Postorder: Left → Right → Root

Applications:

• Preorder: Copying a tree

• Inorder: Produces sorted output in a Binary Search Tree

• Postorder: Deleting or freeing a tree

7️⃣8️⃣ What is Level-Order Traversal?

Answer:

Level-order traversal visits the nodes of a tree level by level, starting from the root.

It uses a Queue and is also known as Breadth-First Traversal (BFS) for trees.

Applications:

• Printing trees level by level

• Finding the shortest path in unweighted trees

• Binary tree serialization

7️⃣9️⃣ What is the Recursion Stack?

Answer:

The recursion stack is the memory area used by the system to keep track of active recursive function calls.

Each recursive call adds a new stack frame containing: 

• Function parameters 

• Local variables 

• Return address 

If recursion is too deep, it can lead to a Stack Overflow error.

8️⃣0️⃣ What is the Time Complexity of Common Data Structures?

Answer:

Data Structure | Search | Insert | Delete

Array | O(n) | O(n) | O(n)

Linked List | O(n) | O(1) | O(1)

Stack | O(n) | O(1) | O(1)

Queue | O(n) | O(1) | O(1)

Hash Table | O(1) | O(1) | O(1)

Binary Search Tree | O(log n) | O(log n) | O(log n)

Heap | O(n) | O(log n) | O(log n) 

*Average case. Worst-case performance may be higher depending on the implementation.

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🚀 Coding Interview Questions with Answers (Part 9)

9️⃣1️⃣ What is an Algorithm?

Answer:

An algorithm is a finite, step-by-step set of instructions designed to solve a specific problem or perform a task efficiently.

Characteristics of a Good Algorithm:

• Well-defined inputs and outputs

• Unambiguous steps

• Finite number of steps

• Efficient in terms of time and memory

• Produces the correct result

Example: Sorting a list of numbers or finding the shortest path in a graph.

9️⃣2️⃣ What is Time Complexity?

Answer:

Time complexity measures the amount of time an algorithm takes to execute as the input size ("n") increases.

It helps compare the efficiency of different algorithms without depending on hardware or programming language.

Common Time Complexities:

• O(1): Constant time

• O(log n): Logarithmic time

• O(n): Linear time

• O(n log n): Linearithmic time

• O(n²): Quadratic time

• O(2ⁿ): Exponential time

• O(n!): Factorial time

9️⃣3️⃣ What is Space Complexity?

Answer:

Space complexity measures the amount of memory an algorithm requires during execution relative to the input size.

It includes:

• Input storage

• Auxiliary (temporary) memory

• Recursive call stack

Efficient algorithms aim to optimize both time and space complexity.

9️⃣4️⃣ What is Big O Notation?

Answer:

Big O notation describes the upper bound (worst-case) time or space complexity of an algorithm.

It shows how the algorithm's performance grows as the input size increases.

Examples:

• Accessing an array element → O(1)

• Linear Search → O(n)

• Binary Search → O(log n)

• Merge Sort → O(n log n)

9️⃣5️⃣ What is Big Theta (Θ) Notation?

Answer:

Big Theta (Θ) notation describes the exact or tight bound of an algorithm's complexity.

It indicates that the algorithm performs within both the upper and lower bounds for large input sizes.

Example:

Merge Sort has a time complexity of Θ(n log n) because it consistently performs at that rate in the best, average, and worst cases.

9️⃣6️⃣ What is Big Omega (Ω) Notation?

Answer:

Big Omega (Ω) notation describes the lower bound (best-case) time complexity of an algorithm.

It represents the minimum amount of time an algorithm will take under the best possible conditions.

Example:

Linear Search has a best-case complexity of Ω(1) when the target element is found at the first position.

9️⃣7️⃣ What is Binary Search?

Answer:

Binary Search is a searching algorithm that finds an element in a sorted array by repeatedly dividing the search range in half.

Steps:

1. Find the middle element.

2. Compare it with the target.

3. Search the left or right half accordingly.

4. Repeat until the element is found or the search space becomes empty.

Time Complexity: O(log n)

Requirement: The array must be sorted.

9️⃣8️⃣ What is Linear Search?

Answer:

Linear Search checks each element one by one until the target element is found or the end of the collection is reached.

Advantages:

• Works on both sorted and unsorted data.

• Easy to implement.

Time Complexity: O(n)

9️⃣9️⃣ What is the Difference Between Linear Search and Binary Search?

Answer:

Linear Search

• Works on sorted and unsorted data.

• Examines elements sequentially.

• Time Complexity: O(n)

Binary Search

• Requires sorted data.

• Divides the search space into halves.

• Time Complexity: O(log n)

Binary Search is much faster than Linear Search for large sorted datasets.

1️⃣0️⃣0️⃣ What is Merge Sort?

Answer:

Merge Sort is a Divide and Conquer sorting algorithm that recursively divides an array into smaller halves, sorts them, and then merges the sorted halves.

Steps:

1. Divide the array into two halves.

2. Recursively sort each half.

3. Merge the sorted halves into one sorted array.

Time Complexity:

• Best Case: O(n log n)

• Average Case: O(n log n)

• Worst Case: O(n log n)

Advantages:

• Stable sorting algorithm

• Efficient for large datasets

• Guarantees consistent performance

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