Max Area of Island: LeetCode Challenge with BFS

✅ Day 43 of 100 Days LeetCode Challenge Problem: 🔹 #684 – Redundant Connection 🔗 https://lnkd.in/gWtKJ-FA Learning Journey: 🔹 Today’s problem focused on detecting an extra edge that creates a cycle in an undirected graph. 🔹 I used the Union-Find (Disjoint Set Union) data structure to track connected components. 🔹 For each edge, I checked whether both nodes already belong to the same set. 🔹 If they do, adding that edge forms a cycle, making it the redundant connection. Concepts Used: 🔹 Union-Find (Disjoint Set) 🔹 Path Compression 🔹 Graph Cycle Detection 🔹 Connected Components Key Insight: 🔹 Union-Find provides an efficient way to detect cycles during edge additions. 🔹 Path compression optimizes repeated parent lookups. 🔹 Incrementally building connectivity helps quickly identify redundant edges. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

✅ Day 44 of 100 Days LeetCode Challenge Problem: 🔹 #3713 – Longest Balanced Substring 🔗 https://lnkd.in/gfryNgam Learning Journey: 🔹 Today’s problem focused on finding the longest substring where all characters appear with equal frequency. 🔹 I explored all possible substrings by fixing a starting index and expanding the window step by step. 🔹 A frequency array helped track character counts along with the number of distinct characters and maximum frequency. 🔹 By validating whether all active characters share the same count, I identified balanced substrings efficiently. Concepts Used: 🔹 String Processing 🔹 Frequency Counting 🔹 Nested Traversal 🔹 Sliding Window Concepts Key Insight: 🔹 Balanced substring problems rely on maintaining strict frequency conditions. 🔹 Tracking distinct characters and maximum frequency simplifies validation logic. 🔹 Smart bookkeeping can make brute-force approaches effective for constrained problems. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

Precision and logic are at the heart of every great application. 🔢 I recently developed a Simple Calculator project, focusing on creating a clean interface and robust arithmetic logic. This project was a fantastic way to practice Python functions and error handling to ensure every calculation is accurate and user-friendly. Check out the demo video below to see it in action! 🔗 GitHub Repository: https://lnkd.in/g8rrigDe #SoftwareEngineering #Coding #ProgrammingLogic #CalculatorProject #WebDevelopment #TechSkills

✅ Day 38 of 100 Days LeetCode Challenge Problem: 🔹 #323 – Number of Connected Components in an Undirected Graph 🔗 https://lnkd.in/gRVWsWgZ Learning Journey: 🔹 Today’s problem focused on counting connected components in an undirected graph. 🔹 I represented the graph using an adjacency list built from the edge list. 🔹 Using Depth-First Search (DFS), I explored all nodes reachable from a starting node. 🔹 Each new DFS traversal from an unvisited node indicates a new connected component. Concepts Used: 🔹 Depth-First Search (DFS) 🔹 Graph Representation (Adjacency List) 🔹 Connected Components 🔹 Graph Traversal Key Insight: 🔹 Graph problems often reduce to exploring connectivity between nodes. 🔹 Tracking visited nodes prevents redundant traversals. 🔹 Iterative DFS using a stack provides a clean and efficient solution. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

✅ Day 46 of 100 Days LeetCode Challenge Problem: 🔹 #91 – Decode Ways 🔗 https://lnkd.in/gpZQshBr Learning Journey: 🔹 Today’s problem focused on counting the number of ways to decode a numeric string into letters. 🔹 I used a Dynamic Programming approach with space optimization, tracking only the previous two states. 🔹 At each step, I checked both single-digit and two-digit decoding possibilities. 🔹 Careful handling of edge cases like leading zeros was essential to ensure valid decoding paths. Concepts Used: 🔹 Dynamic Programming 🔹 Space Optimization 🔹 String Parsing 🔹 State Transition Key Insight: 🔹 Many decoding problems depend on evaluating valid transitions from previous states. 🔹 Maintaining only necessary previous results reduces space complexity to O(1). 🔹 Edge cases involving zeros are critical in avoiding invalid combinations. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

✅ Day 31 of 100 Days LeetCode Challenge Problem: 🔹 #130 – Surrounded Regions 🔗 https://lnkd.in/gzziasbj Learning Journey: 🔹 Today’s problem focused on identifying and capturing regions in a 2D board that are fully surrounded. 🔹 I used graph traversal to explore connected regions of 'O' cells. 🔹 For each region, I tracked whether it touches the boundary of the board. 🔹 Only regions completely enclosed by 'X' were flipped, while boundary-connected regions were preserved. Concepts Used: 🔹 Breadth-First Search (BFS) 🔹 Graph Traversal 🔹 Matrix Traversal 🔹 Connected Components Key Insight: 🔹 Boundary-connected regions should never be captured. 🔹 Tracking region connectivity is essential to determine whether a region is surrounded. 🔹 Grid problems often reduce cleanly to graph traversal with careful boundary handling. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

My professor would give me an "A". My client gave me a timeout error. 📉 In university, we learn on the Titanic dataset (891 rows). At NexusPoint, we process client logs (1.2 million rows). I recently found out the hard way that "Academic Python" doesn't scale. I deployed a script using df.apply()—the standard "for-loop in disguise" they teach in class. On a small scale, it’s readable. On a production scale, it froze the dashboard. The Fix: I refactored the code from row-wise iteration to Vectorization (using np.where). The Results: Old Code: 14.00 seconds New Code: 0.08 seconds Speedup: 175x The Lesson: Students optimize for Grades. Founders must optimize for Margins. If you're building for clients, stop writing loops and start thinking in vectors. #DataScience #Python #NexusPoint #ProductionReady #Vectorization #BSAI

✅ Day 51 of 100 Days LeetCode Challenge Problem: 🔹 #973 – K Closest Points to Origin 🔗 https://lnkd.in/gzp_xraa Learning Journey: 🔹 Today’s problem focused on finding the k points closest to the origin in a 2D plane. 🔹 I calculated the squared Euclidean distance for each point to avoid unnecessary square root operations. 🔹 Using a min-heap, I efficiently retrieved the smallest distances. 🔹 A hashmap helped group points with the same distance, ensuring correct retrieval of coordinates. Concepts Used: 🔹 Heap / Priority Queue 🔹 Euclidean Distance 🔹 Hash Map 🔹 Greedy Selection Key Insight: 🔹 Squared distance is sufficient for comparison and avoids extra computation. 🔹 Heaps are ideal for efficiently extracting the smallest (or largest) k elements. 🔹 Choosing the right data structure significantly simplifies implementation. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

✅ Day 42 of 100 Days LeetCode Challenge Problem: 🔹 #204 – Count Primes 🔗 https://lnkd.in/gjsy54cm Learning Journey: 🔹 Today’s problem focused on counting the number of prime numbers less than a given integer. 🔹 I used the Sieve of Eratosthenes, an efficient algorithm for generating primes by iteratively marking multiples as non-prime. 🔹 Starting from 2, each prime eliminates its multiples, reducing unnecessary checks. 🔹 The remaining true values represent prime numbers. Concepts Used: 🔹 Sieve of Eratosthenes 🔹 Number Theory 🔹 Array Marking Technique 🔹 Optimization Key Insight: 🔹 Instead of checking each number individually, eliminating multiples significantly improves efficiency. 🔹 Starting from i² avoids redundant work because smaller multiples were already handled. 🔹 Preprocessing techniques like sieves are powerful for number-based problems. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

hi connections I just tackled LeetCode 70: Climbing Stairs. It’s a classic Dynamic Programming (DP) problem that teaches you how to break a big challenge into smaller, manageable steps. The Problem: You can climb 1 or 2 steps at a time. How many ways can you reach the top? The Logic: To get to step n, you must have come from either step n-1 or n-2. So: totalWays(n) = ways(n-1) + ways(n-2). The Optimization: Instead of a recursive approach that repeats work, or a DP array that eats up memory, I used two variables to track only the previous two steps. ✅ Time: O(n) ✅ Space: O(1) — Maximum efficiency! Sometimes the most complex-looking problems have the simplest mathematical patterns. 💡 #DynamicProgramming #LeetCode #SoftwareEngineering #Python #Algorithms #Optimization