Max Vowels in Substring with Sliding Window Technique

Day 55 on LeetCode — Subarray Product Less Than K 📈✅ Today’s problem was a powerful application of the Sliding Window technique with multiplication-based constraints. 🔹 Approach Used in My Solution The goal was to count the number of contiguous subarrays where the product is less than k. Key idea in the solution: • Use two pointers l and r to maintain a dynamic window • Expand the window by multiplying the current element with prod • If prod ≥ k, shrink the window from the left by dividing elements • At each step, add (r - l + 1) to the count — representing all valid subarrays ending at r Also handled an important edge case: • If k ≤ 1, no valid subarray exists This approach ensures every element is processed efficiently without recomputation. ⚡ Complexity: • Time Complexity: O(n) • Space Complexity: O(1) 💡 Key Takeaways: • Strengthened understanding of sliding window with multiplicative conditions • Learned how to count subarrays efficiently using window size • Reinforced handling edge cases early for cleaner logic #LeetCode #DSA #Algorithms #DataStructures #SlidingWindow #TwoPointers #Arrays #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #100DaysOfCode #BuildInPublic #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney

Day 48 on LeetCode Minimum Size Subarray Sum 🎯✅ Today’s problem was a perfect application of the Sliding Window technique for optimizing subarray problems. 🔹 Approach Used in My Solution The goal was to find the smallest subarray length whose sum is greater than or equal to the target. Key idea in the solution: • Use two pointers low and high to maintain a dynamic window • Expand the window by moving high and adding elements to currentSum • Once the sum becomes ≥ target, start shrinking the window from the left (low) • Continuously update the minimum length during this process • If no valid subarray is found, return 0 This approach avoids brute force and ensures we process each element at most twice. ⚡ Complexity: • Time Complexity: O(n) • Space Complexity: O(1) 💡 Key Takeaways: • Mastered the Sliding Window technique for variable window size • Learned how to optimize subarray problems from O(n²) to O(n) • Reinforced handling dynamic window expansion and contraction #LeetCode #DSA #Algorithms #DataStructures #SlidingWindow #Arrays #TwoPointers #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #100DaysOfCode #BuildInPublic #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney

Day 50 on LeetCode — Maximum Average Subarray I 📊✅ Half-century milestone! 🚀 Today’s problem was a classic and clean application of the Sliding Window technique. 🔹 Approach Used in My Solution The goal was to find the maximum average of a subarray of size k. Key idea in the solution: • First, compute the sum of the first window of size k • Then slide the window forward by:   – Adding the next element   – Removing the element leaving the window • Continuously update the maximum sum encountered • Finally, divide the maximum sum by k to get the result This avoids recomputing sums and ensures an efficient solution. ⚡ Complexity: • Time Complexity: O(n) • Space Complexity: O(1) 💡 Key Takeaways: • Mastered the fixed-size sliding window pattern • Learned how to optimize subarray sum problems • Reinforced thinking in terms of window reuse instead of recomputation #LeetCode #DSA #Algorithms #DataStructures #SlidingWindow #Arrays #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #100DaysOfCode #BuildInPublic #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney

Day 57 on LeetCode Longest Repeating Character Replacement 🔤✅ Today’s problem was a powerful application of the Sliding Window technique with frequency tracking. 🔹 Approach Used in My Solution The goal was to find the longest substring where we can replace at most k characters to make all characters the same. Key idea in the solution: • Use a frequency array (size 26) to track character counts in the current window • Maintain a variable maxFreq → the highest frequency character in the window • Expand the window using r • If (window size - maxFreq) > k, shrink the window from the left (l) • Continuously update the maximum valid window size This works because we only care about keeping the window valid with at most k replacements. ⚡ Complexity: • Time Complexity: O(n) • Space Complexity: O(1) 💡 Key Takeaways: • Mastered sliding window with dynamic constraints • Learned how tracking max frequency optimizes decisions • Strengthened understanding of window validation logic #LeetCode #DSA #Algorithms #DataStructures #SlidingWindow #Strings #TwoPointers #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #100DaysOfCode #BuildInPublic #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney

Day 52 on LeetCode — Minimum Recolors to Get K Consecutive Black Blocks 🧩✅ Today’s problem was another clean application of the Sliding Window technique with optimization. 🔹 Approach Used in My Solution The goal was to find the minimum number of recolors (W → B) needed to get k consecutive black blocks. Key idea in the solution: • Treat it as finding a window of size k with minimum 'W' (white blocks) • Count the number of 'W' in the first window of size k • Slide the window across the string:   – Remove the left character (if it was 'W')   – Add the new right character (if it is 'W') • Keep track of the minimum changes required This avoids recomputation and ensures an efficient linear solution. ⚡ Complexity: • Time Complexity: O(n) • Space Complexity: O(1) 💡 Key Takeaways: • Strengthened understanding of fixed-size sliding window problems • Learned how to convert problems into min/max count in a window • Reinforced optimizing from brute force to O(n) solutions #LeetCode #DSA #Algorithms #DataStructures #SlidingWindow #Strings #TwoPointers #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #100DaysOfCode #BuildInPublic #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney

🚀 Day 15/60 — LeetCode Discipline Problem Solved: Longest Repeating Character Replacement (Revision) Difficulty: Medium Today’s session revisited a classic sliding window problem that focuses on maximizing a repeating character substring after performing at most k replacements. The challenge here is not just scanning the string, but intelligently maintaining a window where the number of characters that need replacement stays within the allowed limit. By tracking character frequencies and dynamically adjusting the window, the solution efficiently finds the longest valid substring. Problems like this beautifully demonstrate how combining frequency counting with sliding window logic can turn a brute-force approach into a clean linear-time solution. 💡 Focus Areas: • Strengthened variable-size sliding window technique • Practiced frequency array optimization • Improved window shrink/expand decision logic • Reinforced substring pattern recognition • Focused on writing efficient and readable code ⚡ Performance Highlight: Achieved ~89% runtime efficiency on submission. Each day of deliberate practice is sharpening my understanding of algorithmic patterns and strengthening problem-solving intuition. #LeetCode #60DaysOfCode #100DaysOfCode #DSA #SlidingWindow #Strings #Algorithms #DataStructures #ProblemSolving #CodingJourney #SoftwareEngineering #Programming #Developers #TechCareers

🚀 Day 16/60 — LeetCode Discipline Problem Solved: Palindrome Number (Revision) Difficulty: Easy Today’s practice revisited a classic number-based problem — determining whether a given integer reads the same forward and backward. Even though the problem appears simple, it reinforces an important concept: recognizing symmetry in data and applying straightforward transformations to verify it efficiently. In this revision, the focus was on converting the integer into a comparable structure and checking whether the reversed representation matches the original value. 💡 Focus Areas: • Reinforced palindrome symmetry concepts • Practiced number-to-string transformations • Strengthened basic logical validation techniques • Focused on writing clear and readable code Consistent practice of even the simplest problems helps keep fundamental logic sharp and reliable. #LeetCode #60DaysOfCode #100DaysOfCode #DSA #Algorithms #ProblemSolving #Programming #CodingJourney #SoftwareEngineering #Developers #TechCareers

🚀 Day 17/60 — LeetCode Discipline Problem Solved: 4Sum (Revision) Difficulty: Medium Today’s session revisited the classic k-Sum family problem — 4Sum. After solving 2Sum and 3Sum patterns previously, this problem naturally extends the idea by combining sorting with nested iteration and the two-pointer technique to efficiently identify unique quadruplets. The key challenge lies in managing duplicates carefully while keeping the solution efficient and structured. 💡 Focus Areas: • Strengthened k-Sum pattern understanding • Practiced sorting + two-pointer combination • Improved duplicate handling in multi-pointer problems • Reinforced nested iteration optimization • Focused on writing structured and readable code Problems like these highlight how mastering one pattern allows you to scale the same idea to more complex variations. #LeetCode #60DaysOfCode #100DaysOfCode #DSA #TwoPointers #Arrays #Algorithms #DataStructures #ProblemSolving #CodingJourney #SoftwareEngineering #Programming #Developers #TechCareers

Day 47 on LeetCode — Partition Labels ✂️✅ Today’s problem was a great example of greedy strategy with smart indexing. 🔹 Approach Used in My Solution The goal was to split the string into maximum number of partitions such that each character appears in only one part. Key idea in the solution: • First, store the last occurrence of each character in an array • Traverse the string while maintaining a current partition range • Continuously update the end of the partition using the last index of characters encountered • When the current index reaches end, it means the partition is complete • Store the partition size and start a new one This greedy approach ensures each partition is as small as possible while still valid. ⚡ Complexity: • Time Complexity: O(n) • Space Complexity: O(1) 💡 Key Takeaways: • Learned how preprocessing (last occurrence tracking) simplifies problems • Practiced greedy partitioning techniques • Strengthened understanding of interval expansion logic #LeetCode #DSA #Algorithms #DataStructures #GreedyAlgorithm #Strings #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #100DaysOfCode #BuildInPublic #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney

🚀 Day 527 of #750DaysOfCode 🚀 Today I solved LeetCode 3130 – Find All Possible Stable Binary Arrays II (Hard). 🔹 Problem Summary We are given three integers: zero, one, and limit. A binary array is considered stable if: • It contains exactly zero number of 0s • It contains exactly one number of 1s • No subarray longer than limit contains identical elements (meaning we cannot have more than limit consecutive 0s or 1s) The task is to calculate the total number of such stable arrays, with the result taken modulo (10^9 + 7). 🔹 Approach This problem builds on the previous version but with larger constraints, requiring an optimized solution. I used: • Dynamic Programming to track combinations of zeros and ones • State tracking based on the last placed digit (0 or 1) • Sliding window / prefix optimization to efficiently enforce the limit constraint 🔹 Key Concepts Practiced • Dynamic Programming • State transition optimization • Prefix sums / sliding window technique • Modular arithmetic • Handling large constraints efficiently Problems like this are a great reminder that optimization techniques are just as important as the core algorithm. Consistency continues… one day at a time. 💻🔥 #leetcode #dsa #dynamicprogramming #codingchallenge #softwareengineering #programming #developers #learninginpublic #techjourney #750daysofcode