Meeting Rooms II Explained | Heap (Priority Queue) Solution | Java | NeetCode 150

#🚀 In this video, we solve the popular Meeting Rooms II problem from NeetCode 150 using a Min Heap (Priority Queue) approach. You'll learn: ✅ How to identify overlapping meetings ✅ Why a Min Heap is the best data structure for this problem ✅ Step-by-step dry run with examples ✅ Java implementation explained line by line ✅ Time & Space Complexity Analysis Problem Statement Given an array of meeting intervals, find the minimum number of conference rooms required so that all meetings can be scheduled without conflicts. Topics Covered Priority Queue (Min Heap) Intervals Greedy Algorithms Sorting Java DSA Coding Interviews Time Complexity ⏱️ O(n log n) Space Complexity 📦 O(n) 🔥 This is one of the most frequently asked interview questions at: Google, Amazon, Microsoft, Meta, Uber, Airbnb, LinkedIn, and many more. 📌 Subscribe for more: LetsDoSomeCoding 👍 Like | Share | Subscribe #Java #DSA #CodingInterview #NeetCode150 #PriorityQueue #Heap #MeetingRoomsII #Algorithms #DataStructures #GoogleInterview #AmazonInterview #SoftwareEngineer #LetsDoSomeCoding 🏷️ Tags #meeting rooms ii #meeting rooms 2 #neetcode 150 #priority queue #min heap #heap #dsa a #codinginterview interview #googleinterview interview questions #amazoninterview interview questions #softwareengineer engineer interview #datastructuresandalgorithms structures and algorithms #nterview a problems#codinginterview #greedy algorithms #leetcode heap problems