Skip to content
geeksforgeeks
  • Courses
    • DSA to Development
    • Get IBM Certification
    • Newly Launched!
      • Master Django Framework
      • Become AWS Certified
    • For Working Professionals
      • Interview 101: DSA & System Design
      • Data Science Training Program
      • JAVA Backend Development (Live)
      • DevOps Engineering (LIVE)
      • Data Structures & Algorithms in Python
    • For Students
      • Placement Preparation Course
      • Data Science (Live)
      • Data Structure & Algorithm-Self Paced (C++/JAVA)
      • Master Competitive Programming (Live)
      • Full Stack Development with React & Node JS (Live)
    • Full Stack Development
    • Data Science Program
    • All Courses
  • Tutorials
    • Data Structures & Algorithms
    • ML & Data Science
    • Interview Corner
    • Programming Languages
    • Web Development
    • CS Subjects
    • DevOps And Linux
    • School Learning
  • Practice
    • Build your AI Agent
    • GfG 160
    • Problem of the Day
    • Practice Coding Problems
    • GfG SDE Sheet
  • Contests
    • Accenture Hackathon (Ending Soon!)
    • GfG Weekly [Rated Contest]
    • Job-A-Thon Hiring Challenge
    • All Contests and Events
  • DSA
  • Practice Backtracking
  • Interview Problems on Backtracking
  • MCQs on Backtracking
  • Tutorial on Backtracking
  • Backtracking vs Recursion
  • Backtracking vs Branch & Bound
  • Print Permutations
  • Subset Sum Problem
  • N-Queen Problem
  • Knight's Tour
  • Sudoku Solver
  • Rat in Maze
  • Hamiltonian Cycle
  • Graph Coloring
Open In App
Next Article:
Bridges in a graph
Next article icon

M-Coloring Problem

Last Updated : 06 May, 2025
Comments
Improve
Suggest changes
Like Article
Like
Report
Try it on GfG Practice
redirect icon

Given an edges of graph and a number m, the your task is to find weather is possible to color the given graph with at most m colors such that no two adjacent vertices of the graph are colored with the same color.

Examples

Input: V = 4, edges[][] = [[0, 1], [0, 2], [0,3], [1, 3], [2, 3]], m = 3
Output: true
Explanation: Structure allows enough separation between connected vertices, so using 3 colors is sufficient to ensure no two adjacent vertices share the same color—hence, the answer is true

M-Coloring-Problem-2

Input:  V = 5, edges[][] = [[0, 1], [0, 2], [0, 3], [1, 2], [1, 4], [2, 3], [2, 4], [3, 4]], m = 3
Output: false
Explanation: In this graph, the vertices are highly interconnected, especially vertex 2, which connects to four others. With only 3 colors, it’s impossible to assign colors so that no two adjacent vertices share the same color, hence, the answer is false.

Approach 1: Generate all possible configurations – O((V + E)*m^V) Time and O(E+V) Space

Generate all possible configurations of length V of colors. Since each node can be colored using any of the m available colors, the total number of color configurations possible is mV. After generating a configuration of color, check if the adjacent vertices have the same color or not. If the conditions are met, print the combination.

C++
#include <bits/stdc++.h> using namespace std;  bool goodcolor(vector<int> adj[], vector<int> col){           for(int i=0;i<col.size();i++){        for(auto it:adj[i]){         if(i != it && col[i] == col[it])return false;        }     }     return true; } bool genratecolor(int i,vector<int> col, int m, vector<int> adj[]){     if(i>=col.size()){         if(goodcolor(adj, col))return true;          return false;     }               for(int j=0;j<m;j++){         col[i] = j;         if(genratecolor(i+1,col,m, adj)) return true;         col[i] = -1;    }    return false; }  bool graphColoring(int v, vector<vector<int>> &edges, int m) {     vector<int> adj[v];      // Build adjacency list from edges     for (auto it : edges) {         adj[it[0]].push_back(it[1]);         adj[it[1]].push_back(it[0]);      }      vector<int> color(v, -1);      return genratecolor(0, color, m, adj); }  int main() {     int V = 4;      vector<vector<int>> edges = {{0, 1}, {0, 2},{0,3}, {1, 3}, {2, 3}};      int m = 3;       // Check if the graph can be colored with m colors      // such that no adjacent nodes share the same color     cout << (graphColoring(V, edges, m) ? "true" : "false") << endl;      return 0; } 
Java
import java.util.*;  class GfG {      static boolean goodcolor(List<Integer> adj[], int[] col) {         for (int i = 0; i < col.length; i++) {             for (int it : adj[i]) {                 if (i != it && col[i] == col[it]) return false;             }         }         return true;     }      static boolean genratecolor(int i, int[] col, int m, List<Integer> adj[]) {         if (i >= col.length) {             if (goodcolor(adj, col)) return true;             return false;         }          for (int j = 0; j < m; j++) {             col[i] = j;             if (genratecolor(i + 1, col, m, adj)) return true;             col[i] = -1;         }         return false;     }      static boolean graphColoring(int v, int[][] edges, int m) {         List<Integer>[] adj = new ArrayList[v];          // Build adjacency list from edges         for (int i = 0; i < v; i++) {             adj[i] = new ArrayList<>();         }          for (int[] it : edges) {             adj[it[0]].add(it[1]);             adj[it[1]].add(it[0]);         }          int[] color = new int[v];         Arrays.fill(color, -1);          return genratecolor(0, color, m, adj);     }      public static void main(String[] args) {         int V = 4;         int[][] edges = {{0, 1}, {0, 2},{0,3}, {1, 3}, {2, 3}};         int m = 3;          // Check if the graph can be colored with m colors         // such that no adjacent nodes share the same color         System.out.println(graphColoring(V, edges, m) ? "true" : "false");     } } 
Python
def goodcolor(adj, col):     # Check if the coloring is valid     for i in range(len(col)):         for it in adj[i]:             if i != it and col[i] == col[it]:                 return False     return True  def genratecolor(i, col, m, adj):     if i >= len(col):         if goodcolor(adj, col):             return True         return False      for j in range(m):         col[i] = j         if genratecolor(i + 1, col, m, adj):             return True         col[i] = -1     return False  def graphColoring(v, edges, m):     adj = [[] for _ in range(v)]      # Build adjacency list from edges     for u, w in edges:         adj[u].append(w)         adj[w].append(u)      color = [-1] * v     return genratecolor(0, color, m, adj)  # Test V = 4 edges = [[0, 1], [0, 2], [0,3], [1, 3], [2, 3]] m = 3  # Check if the graph can be colored with m colors # such that no adjacent nodes share the same color print("true" if graphColoring(V, edges, m) else "false") 
C#
using System; using System.Collections.Generic;  class GfG{          static bool goodcolor(List<int>[] adj, int[] col){                  for (int i = 0; i < col.Length; i++){                          foreach (int it in adj[i]){                                  if (i != it && col[i] == col[it]) return false;             }         }         return true;     }      static bool genratecolor(int i, int[] col, int m, List<int>[] adj){         if (i >= col.Length){                          if (goodcolor(adj, col)) return true;             return false;         }          for (int j = 0; j < m; j++){                          col[i] = j;             if (genratecolor(i + 1, col, m, adj)) return true;             col[i] = -1;         }         return false;     }      static bool graphColoring(int v, int[,] edges, int m){                  List<int>[] adj = new List<int>[v];          // Build adjacency list from edges         for (int i = 0; i < v; i++){                        adj[i] = new List<int>();         }          for (int i = 0; i < edges.GetLength(0); i++)         {             int u = edges[i, 0];             int v2 = edges[i, 1];             adj[u].Add(v2);             adj[v2].Add(u);         }          int[] color = new int[v];         for (int i = 0; i < v; i++) color[i] = -1;          return genratecolor(0, color, m, adj);     }      public static void Main(string[] args)     {         int V = 4;         int[,] edges = {{0, 1}, {0, 2},{0,3}, {1, 3}, {2, 3}};         int m = 3;          // Check if the graph can be colored with m colors         // such that no adjacent nodes share the same color         Console.WriteLine(graphColoring(V, edges, m) ? "true" : "false");     } } 
JavaScript
function goodcolor(adj, col) {     // Check if the coloring is valid     for (let i = 0; i < col.length; i++) {         for (let it of adj[i]) {             if (i !== it && col[i] === col[it]) return false;         }     }     return true; }  function genratecolor(i, col, m, adj) {     if (i >= col.length) {         if (goodcolor(adj, col)) return true;         return false;     }      for (let j = 0; j < m; j++) {         col[i] = j;         if (genratecolor(i + 1, col, m, adj)) return true;         col[i] = -1;     }     return false; }  function graphColoring(v, edges, m) {     let adj = Array.from({ length: v }, () => []);      // Build adjacency list from edges     for (let [u, w] of edges) {         adj[u].push(w);         adj[w].push(u);     }      let color = new Array(v).fill(-1);     return genratecolor(0, color, m, adj); }  // Test let V = 4; let edges = [[0, 1], [0, 2], [0,3], [1, 3], [2, 3]]; let m = 3;  // Check if the graph can be colored with m colors // such that no adjacent nodes share the same color console.log(graphColoring(V, edges, m) ? "true" : "false"); 

Output
true 

Approach 2: Using Backtracking – O(V * m^V) Time and O(V+E) Space

Assign colors one by one to different vertices, starting from vertex 0. Before assigning a color, check for safety by considering already assigned colors to the adjacent vertices i.e check if the adjacent vertices have the same color or not. If there is any color assignment that does not violate the conditions, mark the color assignment as part of the solution. If no assignment of color is possible then backtrack and return false

Illustration:

C++
#include <bits/stdc++.h> using namespace std;  // Function to check if it's safe to color the current vertex // with the given color bool issafe(int vertex, int col, vector<int> adj[], vector<int> &color) {     for (auto it : adj[vertex]) {         // If adjacent vertex has the same color, not safe         if (color[it] != -1 && col == color[it])             return false;     }     return true; }  // Recursive function to try all colorings bool cancolor(int vertex, int m, vector<int> adj[], vector<int> &color) {     // If all vertices are colored successfully     if (vertex == color.size())         return true;      // Try all colors from 0 to m-1     for (int i = 0; i < m; i++) {         if (issafe(vertex, i, adj, color)) {             color[vertex] = i;              if (cancolor(vertex + 1, m, adj, color))                 // If the rest can be colored, return true                 return true;              color[vertex] = -1;          }     }          // No valid coloring found     return false;  }  bool graphColoring(int v, vector<vector<int>> &edges, int m) {     vector<int> adj[v];      // Build adjacency list from edges     for (auto it : edges) {         adj[it[0]].push_back(it[1]);         adj[it[1]].push_back(it[0]);      }      vector<int> color(v, -1);      return cancolor(0, m, adj, color); }  int main() {     int V = 4;      vector<vector<int>> edges = {{0, 1}, {0, 2},{0,3}, {1, 3}, {2, 3}};      int m = 3;       // Check if the graph can be colored with m colors      // such that no adjacent nodes share the same color     cout << (graphColoring(V, edges, m) ? "true" : "false") << endl;      return 0; } 
Java
import java.util.*;  public class Main {      // Function to check if it's safe to color the current      // vertex with the given color     static boolean issafe(int vertex, int col, List<Integer>[] adj,                                                  int[] color) {                                                               for (int it : adj[vertex]) {             // If adjacent vertex has the same color, not safe             if (color[it] != -1 && col == color[it])                 return false;         }         return true;     }      // Recursive function to try all colorings     static boolean cancolor(int vertex, int m, List<Integer>[] adj,                                                            int[] color) {                                                                        // If all vertices are colored successfully         if (vertex == color.length)             return true;          // Try all colors from 0 to m-1         for (int i = 0; i < m; i++) {             if (issafe(vertex, i, adj, color)) {                 color[vertex] = i;                 if (cancolor(vertex + 1, m, adj, color))                     // If the rest can be colored, return true                     return true;                 color[vertex] = -1;             }         }          return false; // No valid coloring found     }      static boolean graphColoring(int v, int[][] edges, int m) {         List<Integer>[] adj = new ArrayList[v];         for (int i = 0; i < v; i++)             adj[i] = new ArrayList<>();          // Build adjacency list from edges         for (int[] it : edges) {             adj[it[0]].add(it[1]);             adj[it[1]].add(it[0]);         }          int[] color = new int[v];         Arrays.fill(color, -1);         return cancolor(0, m, adj, color);     }      public static void main(String[] args) {         int V = 4;         int[][] edges = {{0, 1}, {0, 2},{0,3}, {1, 3}, {2, 3}};         int m = 3;          // Check if the graph can be colored with m colors          // such that no adjacent nodes share the same color         System.out.println(graphColoring(V, edges, m) ? "true" : "false");     } } 
Python
# Function to check if it's safe to color the current vertex  # with the given color def issafe(vertex, col, adj, color):     for it in adj[vertex]:         # If adjacent vertex has the same color, not safe         if color[it] != -1 and col == color[it]:             return False     return True  # Recursive function to try all colorings def cancolor(vertex, m, adj, color):     # If all vertices are colored successfully     if vertex == len(color):         return True      # Try all colors from 0 to m-1     for i in range(m):         if issafe(vertex, i, adj, color):             color[vertex] = i             if cancolor(vertex + 1, m, adj, color):                 # If the rest can be colored, return true                 return True             color[vertex] = -1  # Backtrack          # No valid coloring found     return False    # Main function to set up the graph and call coloring logic def graphColoring(v, edges, m):     adj = [[] for _ in range(v)]      # Build adjacency list from edges     for u, w in edges:         adj[u].append(w)         adj[w].append(u)      color = [-1] * v     return cancolor(0, m, adj, color)  # Driver code if __name__ == "__main__":     V = 4     edges = [[0, 1], [0, 2], [0,3], [1, 3], [2, 3]]     m = 3      # Check if the graph can be colored with m colors     # such that no adjacent nodes share the same color     print("true" if graphColoring(V, edges, m) else "false") 
C#
using System; using System.Collections.Generic;  class GfG{     // Function to check if it's safe to color the current      // vertex with the given color     static bool issafe(int vertex, int col, List<int>[] adj, int[] color){                  foreach (int it in adj[vertex]){                          // If adjacent vertex has the same color, not safe             if (color[it] != -1 && col == color[it])                 return false;         }         return true;     }      // Recursive function to try all colorings     static bool cancolor(int vertex, int m, List<int>[] adj, int[] color){                  // If all vertices are colored successfully         if (vertex == color.Length)             return true;          // Try all colors from 0 to m-1         for (int i = 0; i < m; i++){                          if (issafe(vertex, i, adj, color)){                                  color[vertex] = i;                 if (cancolor(vertex + 1, m, adj, color))                     // If the rest can be colored, return true                     return true;                 color[vertex] = -1;             }         }          return false; // No valid coloring found     }      static bool graphColoring(int v, int[,] edges, int m){                  List<int>[] adj = new List<int>[v];         for (int i = 0; i < v; i++)             adj[i] = new List<int>();          // Build adjacency list from edges         for (int i = 0; i < edges.GetLength(0); i++){                          int u = edges[i, 0];             int v2 = edges[i, 1];             adj[u].Add(v2);             adj[v2].Add(u);         }          int[] color = new int[v];         for (int i = 0; i < v; i++)             color[i] = -1;          return cancolor(0, m, adj, color);     }      static void Main(string[] args){                  int V = 4;         int[,] edges = {{0, 1}, {0, 2},{0,3}, {1, 3}, {2, 3}};         int m = 3;          // Check if the graph can be colored with m colors          // such that no adjacent nodes share the same color         Console.WriteLine(graphColoring(V, edges, m) ? "true" : "false");     } } 
JavaScript
// Function to check if it's safe to color the current vertex  // with the given color function issafe(vertex, col, adj, color) {     for (let it of adj[vertex]) {         // If adjacent vertex has the same color, not safe         if (color[it] !== -1 && col === color[it])             return false;     }     return true; }  // Recursive function to try all colorings function cancolor(vertex, m, adj, color) {     // If all vertices are colored successfully     if (vertex === color.length)         return true;      // Try all colors from 0 to m-1     for (let i = 0; i < m; i++) {         if (issafe(vertex, i, adj, color)) {             color[vertex] = i;             if (cancolor(vertex + 1, m, adj, color))                 // If the rest can be colored, return true                 return true;             color[vertex] = -1;         }     }          // No valid coloring found     return false; }  // Main function to set up the graph and call coloring logic function graphColoring(v, edges, m) {     let adj = new Array(v).fill(0).map(() => []);      // Build adjacency list from edges     for (let [u, w] of edges) {         adj[u].push(w);         adj[w].push(u);     }      let color = new Array(v).fill(-1);     return cancolor(0, m, adj, color); }  // Driver code const V = 4; const edges = [[0, 1], [0, 2], [0,3], [1, 3], [2, 3]]; const m = 3;  // Check if the graph can be colored with m colors // such that no adjacent nodes share the same color console.log(graphColoring(V, edges, m) ? "true" : "false"); 

Output
true 

Time Complexity: O(V * mV). There is a total of O(mV) combinations of colors. For each attempted coloring of a vertex you call issafe(), can have up to V–1 neighbors, so issafe() is O(V)
Auxiliary Space: O(V + E). The recursive Stack of the graph coloring function will require O(V) space, Adjacency list and color array will required O(V+E).




Next Article
Bridges in a graph
author
kartik
Improve
Article Tags :
  • Backtracking
  • DSA
  • Graph
  • Graph Coloring
  • Samsung
Practice Tags :
  • Samsung
  • Backtracking
  • Graph

Similar Reads

  • Depth First Search or DFS for a Graph
    In Depth First Search (or DFS) for a graph, we traverse all adjacent vertices one by one. When we traverse an adjacent vertex, we completely finish the traversal of all vertices reachable through that adjacent vertex. This is similar to a tree, where we first completely traverse the left subtree and
    13 min read
  • DFS in different language

    • C Program for Depth First Search or DFS for a Graph
      Depth First Traversal (or DFS) for a graph is similar to Depth First Traversal of a tree. The only catch here is, that, unlike trees, graphs may contain cycles (a node may be visited twice). To avoid processing a node more than once, use a boolean visited array. A graph can have more than one DFS tr
      4 min read

    • Depth First Search or DFS for a Graph - Python
      Depth First Traversal (or DFS) for a graph is similar to Depth First Traversal of a tree. The only catch here is, that, unlike trees, graphs may contain cycles (a node may be visited twice). To avoid processing a node more than once, use a Boolean visited array. A graph can have more than one DFS tr
      4 min read

    • Java Program for Depth First Search or DFS for a Graph
      Depth First Traversal (or DFS) for a graph is similar to Depth First Traversal of a tree. Prerequisite: Graph knowledge is important to understand the concept of DFS. What is DFS?DFS or Depth First Traversal is the traversing algorithm. DFS can be used to approach the elements of a Graph. To avoid p
      3 min read

  • Iterative Depth First Traversal of Graph
    Given a directed Graph, the task is to perform Depth First Search of the given graph. Note: Start DFS from node 0, and traverse the nodes in the same order as adjacency list. Note : There can be multiple DFS traversals of a graph according to the order in which we pick adjacent vertices. Here we pic
    10 min read
  • Applications, Advantages and Disadvantages of Depth First Search (DFS)
    Depth First Search is a widely used algorithm for traversing a graph. Here we have discussed some applications, advantages, and disadvantages of the algorithm. Applications of Depth First Search:1. Detecting cycle in a graph: A graph has a cycle if and only if we see a back edge during DFS. So we ca
    4 min read
  • Difference between BFS and DFS
    Breadth-First Search (BFS) and Depth-First Search (DFS) are two fundamental algorithms used for traversing or searching graphs and trees. This article covers the basic difference between Breadth-First Search and Depth-First Search. ParametersBFSDFSStands forBFS stands for Breadth First Search.DFS st
    2 min read
  • Depth First Search or DFS for disconnected Graph
    Given a Disconnected Graph, the task is to implement DFS or Depth First Search Algorithm for this Disconnected Graph. Example: Input: Output: 0 1 2 3 Algorithm for DFS on Disconnected Graph:In the post for Depth First Search for Graph, only the vertices reachable from a given source vertex can be vi
    7 min read
  • Printing pre and post visited times in DFS of a graph
    Depth First Search (DFS) marks all the vertices of a graph as visited. So for making DFS useful, some additional information can also be stored. For instance, the order in which the vertices are visited while running DFS. Pre-visit and Post-visit numbers are the extra information that can be stored
    8 min read
  • Tree, Back, Edge and Cross Edges in DFS of Graph
    Given a directed graph, the task is to identify tree, forward, back and cross edges present in the graph. Note: There can be multiple answers. Example: Input: Graph Output:Tree Edges: 1->2, 2->4, 4->6, 1->3, 3->5, 5->7, 5->8 Forward Edges: 1->8 Back Edges: 6->2 Cross Edges
    9 min read
  • Transitive Closure of a Graph using DFS
    Given a directed graph, find out if a vertex v is reachable from another vertex u for all vertex pairs (u, v) in the given graph. Here reachable means that there is a path from vertex u to v. The reach-ability matrix is called transitive closure of a graph. For example, consider below graph: Transit
    8 min read
  • Variations of DFS implementations

    • Implementation of DFS using adjacency matrix
      Depth First Search (DFS) has been discussed in this article which uses adjacency list for the graph representation. In this article, adjacency matrix will be used to represent the graph.Adjacency matrix representation: In adjacency matrix representation of a graph, the matrix mat[][] of size n*n (wh
      8 min read

    • Graph implementation using STL for competitive programming | Set 1 (DFS of Unweighted and Undirected)
      We have introduced Graph basics in Graph and its representations. In this post, a different STL-based representation is used that can be helpful to quickly implement graphs using vectors. The implementation is for the adjacency list representation of the graph. Following is an example undirected and
      7 min read

    • Graph implementation using STL for competitive programming | Set 2 (Weighted graph)
      In Set 1, unweighted graph is discussed. In this post, weighted graph representation using STL is discussed. The implementation is for adjacency list representation of weighted graph. We use two STL containers to represent graph: vector : A sequence container. Here we use it to store adjacency lists
      7 min read

geeksforgeeks-footer-logo
Corporate & Communications Address:
A-143, 7th Floor, Sovereign Corporate Tower, Sector- 136, Noida, Uttar Pradesh (201305)
Registered Address:
K 061, Tower K, Gulshan Vivante Apartment, Sector 137, Noida, Gautam Buddh Nagar, Uttar Pradesh, 201305
GFG App on Play Store GFG App on App Store
Advertise with us
  • Company
  • About Us
  • Legal
  • Privacy Policy
  • In Media
  • Contact Us
  • Advertise with us
  • GFG Corporate Solution
  • Placement Training Program
  • Languages
  • Python
  • Java
  • C++
  • PHP
  • GoLang
  • SQL
  • R Language
  • Android Tutorial
  • Tutorials Archive
  • DSA
  • Data Structures
  • Algorithms
  • DSA for Beginners
  • Basic DSA Problems
  • DSA Roadmap
  • Top 100 DSA Interview Problems
  • DSA Roadmap by Sandeep Jain
  • All Cheat Sheets
  • Data Science & ML
  • Data Science With Python
  • Data Science For Beginner
  • Machine Learning
  • ML Maths
  • Data Visualisation
  • Pandas
  • NumPy
  • NLP
  • Deep Learning
  • Web Technologies
  • HTML
  • CSS
  • JavaScript
  • TypeScript
  • ReactJS
  • NextJS
  • Bootstrap
  • Web Design
  • Python Tutorial
  • Python Programming Examples
  • Python Projects
  • Python Tkinter
  • Python Web Scraping
  • OpenCV Tutorial
  • Python Interview Question
  • Django
  • Computer Science
  • Operating Systems
  • Computer Network
  • Database Management System
  • Software Engineering
  • Digital Logic Design
  • Engineering Maths
  • Software Development
  • Software Testing
  • DevOps
  • Git
  • Linux
  • AWS
  • Docker
  • Kubernetes
  • Azure
  • GCP
  • DevOps Roadmap
  • System Design
  • High Level Design
  • Low Level Design
  • UML Diagrams
  • Interview Guide
  • Design Patterns
  • OOAD
  • System Design Bootcamp
  • Interview Questions
  • Inteview Preparation
  • Competitive Programming
  • Top DS or Algo for CP
  • Company-Wise Recruitment Process
  • Company-Wise Preparation
  • Aptitude Preparation
  • Puzzles
  • School Subjects
  • Mathematics
  • Physics
  • Chemistry
  • Biology
  • Social Science
  • English Grammar
  • Commerce
  • World GK
  • GeeksforGeeks Videos
  • DSA
  • Python
  • Java
  • C++
  • Web Development
  • Data Science
  • CS Subjects
@GeeksforGeeks, Sanchhaya Education Private Limited, All rights reserved
We use cookies to ensure you have the best browsing experience on our website. By using our site, you acknowledge that you have read and understood our Cookie Policy & Privacy Policy
Lightbox
Improvement
Suggest Changes
Help us improve. Share your suggestions to enhance the article. Contribute your expertise and make a difference in the GeeksforGeeks portal.
geeksforgeeks-suggest-icon
Create Improvement
Enhance the article with your expertise. Contribute to the GeeksforGeeks community and help create better learning resources for all.
geeksforgeeks-improvement-icon
Suggest Changes
min 4 words, max Words Limit:1000

Thank You!

Your suggestions are valuable to us.

What kind of Experience do you want to share?

Interview Experiences
Admission Experiences
Career Journeys
Work Experiences
Campus Experiences
Competitive Exam Experiences