Solved Majority Element problem using Boyer–Moore Voting Algorithm in Java

🌟 Day 84 of My #100DaysOfCode Challenge 🧩 Problem: LeetCode 108 – Convert Sorted Array to Binary Search Tree 💭 Understanding the Problem Given a sorted array, we need to convert it into a height-balanced Binary Search Tree (BST) — meaning the difference in height between the left and right subtrees of every node should not exceed one. 📘 Example: Input: nums = [-10, -3, 0, 5, 9] Output: [0, -3, 9, -10, null, 5] The middle element (0) becomes the root, left half forms the left subtree, and the right half forms the right subtree. 🧠 Key Idea Pick the middle element as the root for balance. Recursively repeat the same for left and right halves. This approach ensures that the BST remains balanced. ⚙️ Complexity Analysis Time Complexity: O(n) — each element is processed once. Space Complexity: O(log n) — recursion stack in a balanced tree. ✨ Takeaway This problem beautifully combines recursion and binary tree logic, showcasing how dividing the array strategically helps maintain balance in a BST. 💬 "Balanced structures are key to efficiency — both in code and in life!" 😄 #Day84 #LeetCode #Java #DSA #BinaryTree #CodingChallenge #100DaysOfCode #ProblemSolving

🧠 LeetCode Day 97 — Path Sum (Problem 112) Today’s challenge was about checking whether a binary tree has a root-to-leaf path such that adding up all the values along the path equals a given sum. 🔍 Problem Description: Given the root of a binary tree and an integer targetSum, return true if the tree has a root-to-leaf path such that the sum of the node values equals targetSum. Otherwise, return false. 💡 Approach: Traverse the tree recursively. Subtract the current node’s value from targetSum. When a leaf node is reached, check if the remaining sum equals zero. Use Depth First Search (DFS) to explore all paths. 🚀 Key Learnings: Strengthened understanding of recursive tree traversal. Practiced handling base cases in recursion effectively. Improved clarity on root-to-leaf path logic in binary trees. 🌳 #Day97 of #100DaysOfCode Each challenge adds a new layer to my problem-solving skills. Binary tree problems like this sharpen the recursive mindset — thinking from root to leaf and back up! 💪 #LeetCode #CodingChallenge #Java #100DaysOfCode #BinaryTree #ProblemSolving #Recursion

🚀 Day 87/100 of #100DaysOfCode 💡 Problem: 3321. Find X-Sum of All K-Long Subarrays II (Hard) 🔗 Platform: LeetCode Today’s challenge was all about optimizing sliding window logic with frequency-based ordering! The task — find the “x-sum” of every k-sized subarray, keeping only the top x most frequent elements (and if tied, prefer larger values). 🧠 Key Learnings: How to maintain frequency counts efficiently in a sliding window. Managing top-x elements dynamically using TreeSet and custom comparators. Overcoming TLE issues by switching from naive sorting (O(n*k)) to a balanced TreeSet approach (O(n log n)). ⚙️ Concepts Used: → Sliding Window → Frequency Map (HashMap) → Ordered Sets (TreeSet) → Custom Comparator Logic 🔥 This one really tested both logic & optimization — but once it clicked, it felt amazing! #LeetCode #100DaysOfCode #Java #CodingChallenge #ProblemSolving #DSA #SlidingWindow #Optimization

💻 Day 53 of #LeetCode100DaysChallenge Solved LeetCode 160: Intersection of Two Linked Lists — a problem that tests linked list traversal, pointer manipulation, and logical thinking. 🧩 Problem: Given the heads of two singly linked lists headA and headB, return the node where the two lists intersect. If they do not intersect, return null. 💡 Approach — Two Pointer Technique: 1️⃣ Initialize two pointers a and b at headA and headB. 2️⃣ Traverse both lists. When a pointer reaches the end of one list, redirect it to the head of the other list. 3️⃣ If the lists intersect, the pointers will meet at the intersection node. 4️⃣ If not, both pointers will eventually become null. ⚙️ Complexity: Time: O(N + M) — each list is traversed at most twice. Space: O(1) — no extra data structure used. ✨ Key Takeaways: ✅ Strengthened understanding of pointer redirection and traversal synchronization. ✅ Learned an elegant approach to detect intersection without measuring lengths. ✅ Reinforced logical thinking in linked list-based problems. #LeetCode #100DaysOfCode #Java #LinkedList #Pointers #TwoPointerTechnique #DSA #CodingJourney #WomenInTech #IntersectionOfLinkedLists

🌟 Day 63 of 100 Days of LeetCode 🌟 Today’s challenge: LeetCode 101 – Symmetric Tree 🔁 🧩 Problem Summary: Given the root of a binary tree, determine whether it’s a mirror of itself (i.e., symmetric around its center). 🌲✨ 💡 Key Takeaways: 🔹 Strengthened understanding of recursion in binary trees. 🔹 Learned how to compare mirror nodes (left ↔ right). 🔹 Realized that symmetry problems are perfect practice for recursive thinking and structural comparison. 🧠 Approach: 1️⃣ The tree is symmetric if its left and right subtrees are mirror images. 2️⃣ Use a helper function to compare: left.val == right.val left.left == right.right and left.right == right.left 3️⃣ Handle null checks carefully to avoid exceptions. 🔍 This problem beautifully shows how mirror logic + recursion can simplify complex tree checks! #LeetCode #100DaysOfCode #Java #DSA #BinaryTree #ProblemSolving #CodingJourney #Recursion #SymmetricTree

🌳 Day 60 of #100DaysOfCode 🌳 🔹 Problem: Balanced Binary Tree – LeetCode ✨ Approach: Used a post-order DFS traversal to calculate subtree heights while checking balance at every node. If the height difference of any subtree exceeds 1, return -1 immediately for an early exit — efficient and elegant! ⚡ 📊 Complexity Analysis: Time: O(n) — each node visited once Space: O(h) — recursion stack space, where h is the tree height ✅ Runtime: 0 ms (Beats 100%) ✅ Memory: 44.29 MB 🔑 Key Insight: A balanced tree isn’t just about equal heights — it’s about smart recursion that detects imbalance early, saving both time and memory. 🌿 #LeetCode #100DaysOfCode #Java #DSA #BinaryTree #Recursion #ProblemSolving #AlgorithmDesign #CodeJourney #ProgrammingChallenge

🚀 Day 145 of #150DaysOfCode on LeetCode 🔹 Problem: 3318. Find X-Sum of All K-Long Subarrays I 🔹 Topic: Sliding Window | HashMap | Sorting Today’s challenge was about computing the X-Sum for every subarray of length k in an array — where the X-Sum is defined as the sum of the top x most frequent elements (considering frequency first and then value). To solve this: I used a sliding window to iterate over all subarrays of size k. For each window, I maintained a frequency map of elements. Then sorted them by frequency (descending) and by value to select the top x contributing elements. Finally, calculated the cumulative X-Sum for each subarray. It was a great exercise in combining maps, sorting logic, and windowing concepts efficiently! 🧩 Key Learnings: How to balance frequency counting with sorting priorities. Importance of optimizing nested loops for small vs. large constraints. Clear understanding of how sorting custom objects impacts runtime. #LeetCode #CodingChallenge #Java #DSA #SlidingWindow #HashMap #ProblemSolving #CodingJourney #100DaysOfCode #WomenInTech #LearnByDoing

💻 Day 62 of #LeetCode100DaysChallenge Solved LeetCode 142: Linked List Cycle II — a problem that deepens understanding of linked list traversal and cycle detection using pointers. 🧩 Problem: Given the head of a linked list, return the node where the cycle begins. If there’s no cycle, return null. A cycle exists if a node can be revisited by continuously following the next pointer. The goal is to identify the exact node where the cycle starts — without modifying the list. 💡 Approach — Floyd’s Cycle Detection (Tortoise and Hare): 1️⃣ Use two pointers — slow and fast. 2️⃣ Move slow by 1 step and fast by 2 steps until they meet (if a cycle exists). 3️⃣ Once they meet, reset one pointer to the head. 4️⃣ Move both one step at a time; the node where they meet again is the start of the cycle. 5️⃣ If no meeting point (i.e., fast or fast.next becomes null), return null. ⚙️ Complexity: Time: O(N) — each pointer traverses the list at most twice. Space: O(1) — no extra memory used. ✨ Key Takeaways: ✅ Strengthened Floyd’s algorithm understanding for detecting and locating cycles. ✅ Learned how mathematical reasoning helps pinpoint the start node of a loop. ✅ Reinforced efficient use of pointer manipulation in linked list problems. #LeetCode #100DaysOfCode #Java #LinkedList #CycleDetection #FloydsAlgorithm #DSA #CodingJourney #WomenInTech #ProblemSolving

🧮 Day 30 of My #100DaysOfLeetCode Challenge ✅ Problem: Find Target Indices After Sorting Array 🧩 Difficulty: Easy 📂 Category: Array / Counting ⚡ Runtime: 0 ms (Beats 100%) 💾 Memory: 44.54 MB 🔹 Approach: Instead of sorting, I used counting logic to find the number of elements smaller than the target (lessThan) and the count of target elements (count). The resulting indices are then [lessThan, lessThan + 1, ..., lessThan + count - 1]. This avoids unnecessary sorting and keeps the solution O(n). 🧠 Time Complexity: O(n) 💾 Space Complexity: O(1) ✨ Learnings: How counting-based reasoning can replace sorting for index-based problems. Focused on optimization and problem pattern recognition. 📈 Progress: Day 30 ✅ | 70 days remaining 🚀 💭 “Optimization is not about doing more — it’s about doing smarter.” #100DaysOfCode #LeetCode #Java #DSA #ProblemSolving #CodingChallenge #Consistency

🔢 Day 47 of #LeetCode100DaysChallenge Solved LeetCode 79: Word Search — a classic backtracking problem that beautifully blends recursion and grid traversal. 🧩 Problem: Given a 2D grid of characters and a word, determine if the word can be formed using sequentially adjacent cells (up, down, left, right), where each cell can be used only once. 💡 Approach Used — Backtracking (DFS): 1️⃣ Start from each cell that matches the first character. 2️⃣ Explore in all four directions recursively. 3️⃣ Temporarily mark visited cells to avoid reuse. 4️⃣ If the entire word is matched, return true; otherwise, backtrack. ⚙️ Complexity: Time: O(N × 4ᴸ) — where N is the total number of cells, and L is the word length. Space: O(L) — recursion depth. ✨ Key Takeaways: ✅ Strengthened understanding of recursion and backtracking. ✅ Learned to manage visited states effectively in grid problems. ✅ Great exercise in applying DFS to real-world matrix traversal cases. #LeetCode #100DaysOfCode #Java #Backtracking #Recursion #ProblemSolving #DSA #WomenInTech #CodingJourney