Binary Tree Right Side View LeetCode Challenge

✅ Day 6 of 100 Days LeetCode Challenge Problem: 🔹 #105 – Construct Binary Tree from Preorder and Inorder Traversal 🔗 https://lnkd.in/gzJUTGBp Concepts Used: 🔹 Binary Tree 🔹 Recursion 🔹 Hash Map 🔹 Tree Traversals (Preorder & Inorder) Approach Summary: 🔹 Preorder traversal gives the root node first. 🔹 Inorder traversal helps split left and right subtrees. 🔹 Used a hash map to store indices of inorder elements for O(1) lookup. 🔹 Recursively built the tree by consuming preorder elements in sequence. Key Insight: 🔹 Preorder determines the root, while inorder defines subtree boundaries. 🔹 Combining both traversals enables unique reconstruction of the tree. 🔹 Hashing inorder indices significantly improves performance. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

✅ Day 10 of 100 Days LeetCode Challenge Problem: 🔹 #1448 – Count Good Nodes in Binary Tree 🔗 https://lnkd.in/gDfr48AK Concepts Used: 🔹 Binary Tree 🔹 Depth-First Search (DFS) 🔹 Recursion 🔹 Path-based State Tracking Approach Summary: 🔹 Used DFS to traverse the tree from root to leaves. 🔹 Maintained the maximum value (maxi) seen so far along the path. 🔹 A node is considered good if its value is greater than or equal to maxi. 🔹 Updated maxi at each step and accumulated the count recursively. Key Insight: 🔹 “Good nodes” depend on the path from the root, not the entire tree. 🔹 Passing state (maximum so far) through recursion avoids extra storage. 🔹 This leads to a clean and efficient solution with optimal traversal. #LeetCode #100DaysOfLeetCode #Day10 #DSA #Daily #Programming #ProblemSolving #Python #CodingJourney #TechCareers

🌟Day 7| The journey to becoming 1% better every day Today’s learning focused on how objects interact, share memory, and behave across a program. 📌 What I learned today: ⏱️ Reference Variables and how objects are accessed ⏱️ Pass by Reference and its impact on object behavior ⏱️ Collection of Objects for better data organization ⏱️ Static Variables and Methods for shared class-level data Each concept added more clarity on how efficient and scalable code is actually written✨ #DSA #OOPs #Python #SoftwareEngineer #ComputerScience #DeveloperMindset #BTech

✅ Day 13 of 100 Days LeetCode Challenge Problem: 🔹 #90 – Subsets II 🔗 https://lnkd.in/gCyD9UNb Learning Journey: 🔹 Today’s problem was similar to yesterday’s (#78 – Subsets), but this time the array could contain duplicates, so we had to ensure the solution set did not contain duplicate subsets. 🔹 I approached it using backtracking, again making a binary decision at each index: include or exclude the current element. 🔹 To handle duplicates, I sorted the array first and skipped duplicate elements at the same recursion level, preventing repeated subsets. 🔹 Copying the current solution at each step preserves the subset before backtracking continues. Concepts Used: 🔹 Backtracking 🔹 Recursion 🔹 Depth-First Search (DFS) 🔹 Handling Duplicates Key Insight: 🔹 Sorting and carefully skipping duplicates is essential when generating unique subsets. 🔹 Subset problems map naturally to an include/exclude decision tree. 🔹 Backtracking remains a clean way to explore all combinations, even with duplicate elements. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

🗓 Day 63 / 100 – #100DaysOfLeetCode 📌 Problem 1161: Maximum Level Sum of a Binary Tree Today’s problem focused on tree traversal and level-wise aggregation. The task was to find the level in a binary tree that has the maximum sum of node values, with levels numbered starting from 1. 🧠 My Approach: Used Breadth-First Search (BFS) to traverse the tree level by level. For each level: Calculated the sum of all node values at that level. Compared it with the maximum sum seen so far. Kept track of: the current level number the level with the maximum sum Returned the smallest level number in case of a tie, as required. This level-order traversal makes the solution both intuitive and efficient. 💡 Key Learning: This problem reinforced: ✔ how BFS naturally fits level-based tree problems ✔ careful tracking of level indices during traversal ✔ handling ties correctly based on problem constraints Tree problems often become much simpler once the right traversal strategy is chosen. Another solid step forward in mastering binary tree patterns 🌳🚀 #100DaysOfLeetCode #LeetCodeChallenge #Python #ProblemSolving #BinaryTree #BFS #TreeTraversal #Algorithms #DSA #CompetitiveProgramming #SoftwareEngineering #CodingJourney #DeveloperJourney #LearningInPublic #TechStudent #CareerGrowth #Programming #KeepLearning

✅ Day 28 of 100 Days LeetCode Challenge Problem: 🔹 #133 – Clone Graph 🔗 https://lnkd.in/gsciP4qv Learning Journey: 🔹 Today’s problem focused on creating a deep copy of a connected graph. 🔹 I used Depth-First Search (DFS) to traverse the graph and clone each node. 🔹 A hashmap stores the mapping between original nodes and their cloned counterparts to avoid duplicate copies. 🔹 This approach also handles cycles gracefully by returning already-cloned nodes when revisited. Concepts Used: 🔹 Depth-First Search (DFS) 🔹 Graph Traversal 🔹 Hash Map 🔹 Deep Copy Key Insight: 🔹 Graph cloning requires tracking visited nodes to prevent infinite loops. 🔹 DFS provides a clean recursive way to clone nodes and their neighbors. 🔹 Maintaining a reference map is essential for handling cyclic graphs correctly. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

🚀 𝟲𝟬 𝗗𝗮𝘆𝘀 𝗼𝗳 𝗖𝗼𝗱𝗶𝗻𝗴 | 𝗗𝗦𝗔 𝘅 𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 #Day50 | 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗔𝗻𝗮𝗹𝘆𝘇𝗲𝗿 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 Built a Performance Analyzer Dashboard to compare Memoization vs Tabulation in Dynamic Programming using real execution-time measurements. Focused on: • Top-Down vs Bottom-Up DP • Time and space trade-offs • Performance comparison • Understanding when to choose the right DP strategy 📌 Code and documentation: https://lnkd.in/gxzGJ4nB Feedback and suggestions are welcome. #DSA #DynamicProgramming #Memoization #Tabulation #Python #60DaysOfCoding #LearningInPublic #SoftwareEngineering

Day 55 of #Python108DaysChallenge 🐍 Learned the Floyd–Warshall Algorithm for computing all-pairs shortest paths using dynamic programming. Key insights: ✔ Works on dense weighted graphs ✔ Handles negative weights (no negative cycles) ✔ Complexity O(V³) ✔ Useful for routing tables and path analysis Another powerful addition to the graph algorithms toolkit! #Python #DSA #Graphs #FloydWarshall #DynamicProgramming #LearningInPublic #CodingJourney

🚀 Day 19: Python Range Deep Dive Today is all about the power of the range() function! It’s not just for loops; it’s a memory-efficient sequence generator. Key takeaways: ✅ Custom Steps: Use range(start, stop, step) to skip numbers ✅ Smart Membership: Use in to instantly check if a number fits the sequence logic ✅ Efficient Length: len() tells you the count without expanding the list x = range(3, 10, 2) print(list(x)) # Output: [3, 5, 7, 9] r = range(0, 10, 2) print(6 in r) # Output: True print(7 in r) # Output: False print(len(r)) # Output: 5 Small steps every day lead to big results in 2026! 💻✨ Stay tuned for more!!! #Python #PythonJourney #Coding #LearnToCode #Programming #PythonCode #DataAnalyst #DataAnalytics #RangeFunction

This solution solves LeetCode 3013: Divide an Array Into Subarrays With Minimum Cost II by applying a sliding window technique with balanced data structures to efficiently track the smallest possible starting elements. Since the first subarray always begins at index 0, its cost is fixed, and the problem reduces to selecting the remaining k−1 starting indices within the given distance constraint such that their values sum to the minimum. By continuously maintaining the smallest k−1 elements as the window moves, the approach achieves optimal performance and avoids time-limit issues common with brute-force methods. #LeetCode #HardProblem #Python #SlidingWindow #DataStructures #Algorithms #CompetitiveProgramming #CodingInterview #Optimization