Top K Frequent Elements - Bucket Sort O(n) Explained Visually | LeetCode 347 | AI/ML Interview

Most people solve Top K Frequent Elements with a heap — but the Bucket Sort approach is O(n) and blows interviewers away. Here's the visual explanation. What you'll learn: ✅ How to build a frequency map in O(n) ✅ Why Bucket Sort beats heap sort here (O(n) vs O(n log k)) ✅ Visual walkthrough: [1,1,1,2,2,3], k=2 → [1, 2] ✅ Bucket index = frequency concept explained clearly ✅ Python implementation: count → bucket → collect 🎯 This problem tests your ability to think beyond the obvious solution — exactly what top AI/ML interview rounds test. 🧠 Practice this problem yourself: 👉 https://codedive.in/top-k-frequent-elements 🚀 codedive.in — 207 curated problems for AI/ML engineers. Count. Bucket. Collect. ━━━━━━━━━━━━━━━━━━━━━━━━━━ ⏱ Timestamps: 0:00 - Problem Statement 0:30 - Frequency Map Step 1:00 - Bucket Sort Intuition 1:45 - Visual Walkthrough 2:30 - Python Code 3:00 - Why O(n) beats Heap ━━━━━━━━━━━━━━━━━━━━━━━━━━ #TopKFrequentElements #LeetCode347 #BucketSort #HashMap #DSAInterview #AIMLEngineer #LeetCode #PythonDSA #CodingInterview