Smallest Subtree with Deepest Nodes using DFS

🚀 #DAY75 of #100DayofDSA Challenge LeetCode Problem #1200 – Minimum Absolute Difference 🧩 Problem Summary Given a list of distinct integers, the goal is to find all element pairs whose absolute difference is the minimum possible and return them in sorted order. 🧠 How It Works First, sort the array to bring close values together Scan once to compute the minimum difference between adjacent elements Scan again to collect all pairs that match this minimum difference This approach leverages the fact that in a sorted array, the smallest difference must occur between neighboring elements. 📘 What I Learned ✔ Why sorting simplifies difference-based problems ✔ How breaking the task into two passes improves clarity ✔ Turning mathematical observations into efficient logic ✔ Writing clean and readable solutions even when performance isn’t maxed 📊 Performance ⏱ Runtime: 95 ms 💾 Memory: 21.57 MB ✅ 38 / 38 testcases passed 📌 Small optimizations, big clarity — consistency is the real win. #LeetCode #DSA #Algorithms #ProblemSolving #Python #CodingPractice #DataStructures #TechJourney #100DaysOfDSA #LearningByDoing #Consistency #InterviewPrep #CompetitiveProgramming

🚀 #DAY74 LeetCode Problem #1200 – Minimum Absolute Difference 🧩 Problem Summary Given a list of distinct integers, the goal is to find all element pairs whose absolute difference is the minimum possible and return them in sorted order. 🧠 How It Works First, sort the array to bring close values together Scan once to compute the minimum difference between adjacent elements Scan again to collect all pairs that match this minimum difference This approach leverages the fact that in a sorted array, the smallest difference must occur between neighboring elements. 📘 What I Learned ✔ Why sorting simplifies difference-based problems ✔ How breaking the task into two passes improves clarity ✔ Turning mathematical observations into efficient logic ✔ Writing clean and readable solutions even when performance isn’t maxed 📊 Performance ⏱ Runtime: 95 ms 💾 Memory: 21.57 MB ✅ 38 / 38 testcases passed 📌 Small optimizations, big clarity — consistency is the real win. #LeetCode #DSA #Algorithms #ProblemSolving #Python #CodingPractice #DataStructures #TechJourney #100DaysOfDSA #LearningByDoing #Consistency #InterviewPrep #CompetitiveProgramming

✅ 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

Python with DSA — Day 33 What I worked on: Prime checks: moved from naive divisibility to the √n optimization and the 6k±1 rule to cut unnecessary iterations. Time complexity intuition: compared O(n) vs O(√n) for primality tests; saw how early exits change best/worst cases. Clean loops & edge cases: handled n <= 1, negative inputs, and printed primes from 1–100 with a tight loop. Key snippet (conceptual): Idea: Only test divisors up to √n; if none divide, the number is prime. #Day33 #PythonWithDSA #DataStructures #DSAJourney #ProblemSolving #PythonProgramming #SoftwareEngineer

🚀 LeetCode Daily Challenge – Day 9 Problem #865: Smallest Subtree with All the Deepest Nodes (Medium) Another solid tree + DFS problem that really tests how well you understand recursion and depth tracking. 🧭 How I Approached the Question: The key was realizing that I don’t just need the depth of each subtree — I also need to know which node represents the smallest subtree containing all deepest nodes. So instead of a normal DFS, I designed a recursive function that returns two things: the height of the subtree the candidate node that could be the answer 🧠 Core Insight: For each node: Recursively compute (height, node) from the left and right subtrees Compare their heights: If left height > right height → deepest nodes are on the left If right height > left height → deepest nodes are on the right If equal → current node becomes the smallest subtree containing all deepest nodes This bottom-up approach ensures that: ✔️ Depth is calculated correctly ✔️ The smallest valid subtree is chosen automatically 🛠️ Why This Works: The deepest nodes define the maximum depth. The lowest common ancestor of all deepest nodes is exactly what the problem asks for. ⏱ Time Complexity: O(n) 📦 Space Complexity: O(h) (recursion stack) This problem was a great reminder that: 👉 Sometimes returning more information from recursion makes the solution much cleaner. 📌 Tree problems get easier once you stop thinking top-down and start thinking bottom-up. #LeetCode #DailyCoding #BinaryTree #DFS #Recursion #ProblemSolving #DSA #Python #CodingJourney

Day 43/50 – DSA Problem-Solving Challenge Problem: Game of Two Stacks Concepts: Stacks | Greedy Approach | Two Pointers Difficulty: Easy–Medium Key Learnings: Greedy strategies work effectively when constraints are well-defined. Two-pointer technique helps optimize decisions across multiple data structures. Stack-based problems require strict order handling (LIFO). Efficient solutions can achieve O(n + m) time complexity with O(1) extra space. Real-World Use Cases: Resource allocation with limited capacity Task scheduling under memory or budget constraints Stream processing with bounded limits Decision-making systems with cumulative thresholds GitHub Link: https://lnkd.in/gEBUDdkK #Day43 #DSA #Stacks #TwoPointers #GreedyAlgorithm #Python #CodingInterview #ProblemSolving #LearningInPublic #50DaysChallenge #DataStructures

✅ 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

Excited to share my latest project Movie Recommendation System built using Python and Machine Learning 🎬 The system follows a content based filtering approach where movie metadata is transformed into feature vectors and cosine similarity is applied to identify similar movies. To make the recommendations interactive and user-friendly, I developed a web application using Streamlit and integrated the TMDB API to fetch movie posters in real time. This project helped me strengthen my understanding of data preprocessing, feature engineering, similarity-based models, and building end-to-end machine learning applications. GitHub repo: [https://lnkd.in/gxgeK5at] #Python #MachineLearning #RecommenderSystems #DataScience #Projects

🚀 𝟲𝟬 𝗗𝗮𝘆𝘀 𝗼𝗳 𝗖𝗼𝗱𝗶𝗻𝗴 | 𝗗𝗦𝗔 𝘅 𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 #Day27 | 𝗙𝗶𝗹𝗲 𝗣𝗲𝗿𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗖𝗵𝗲𝗰𝗸𝗲𝗿 Built a File Permission Checker using Tree Traversals (DFS & BFS) to scan hierarchical file systems efficiently. Focused on: • Depth-first and breadth-first traversal • Tree-based file system modeling • Permission filtering logic Understanding how operating systems traverse directories 📌 𝗖𝗼𝗱𝗲 𝗮𝗻𝗱 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: https://lnkd.in/gxzGJ4nB Feedback and suggestions are welcome. #DSA #TreeTraversal #DFS #BFS #Python #60DaysOfCoding #LearningInPublic #SoftwareEngineering

hi connections I just moved from Maximum Subarrays to LeetCode 167: Two Sum II. The challenge? Find two numbers that hit a target sum. The "cheat code"? The input array is already sorted. When you see "Sorted Array," your mind should immediately go to Two Pointers. The Strategy: Left Pointer: Starts at the beginning (smallest values). Right Pointer: Starts at the end (largest values). The Logic: If the sum is too low, nudge the left pointer up. If it’s too high, slide the right pointer down. Why I love this approach: Zero Extra Space: Unlike the original Two Sum, you don’t need a Hash Map. O(1) space. Speed: You only pass through the data once. O(n) time. Simplicity: It’s clean, readable, and highly optimized. Programming isn't just about solving the problem; it's about finding the most elegant way to do it by leveraging the constraints you're given. #Coding #DataStructures #Algorithms #TwoSum #Python #LeetCode #SoftwareEngineering #TwoPointers