Generating 2^n Subsets via Backtracking and Decision Tree

📌 Problem: Reverse Vowels of a String 💡 Approach: Used the two-pointer technique to reverse only the vowels in the string. Initialize one pointer at the beginning and one at the end. Move both pointers inward until vowels are found, then swap them. Continue this process until both pointers meet. ⚙️ Key Insight: Use a set for fast vowel lookup (O(1)) Two-pointer approach avoids extra space for storing vowels separately ⏱️ Time Complexity: O(n) 📦 Space Complexity: O(n) (due to string → list conversion) 📚 What I learned: Efficient string manipulation using two pointers Optimizing lookups with hash sets #LeetCode #DSA #Algorithms #Coding #ProblemSolving #Python #TwoPointers #InterviewPreparation #CodingJourney

Subsets: Backtracking with Include/Exclude Decision Tree Generate all 2^n subsets via recursive binary choices — include current element or skip. Base case: processed all elements, save current subset. Backtracking pattern: modify state, recurse, undo modification. Backtracking Pattern: Modify shared state, explore branch, restore state before exploring alternate branch. This template applies to permutations, combinations, constraint satisfaction problems. Time: O(2^n) | Space: O(n) recursion depth #Backtracking #Subsets #DecisionTree #StateRestoration #Recursion #Python #AlgorithmDesign #SoftwareEngineering

Day 109 Backtracking patterns are repeating again — and that’s a good sign. #Day109 🧩 78. Subsets How today went: • Used recursion to explore all elements • At each step, decide to include or skip the current element • Append current subset → explore → then pop to backtrack • Move to the next index and repeat What I’m noticing: Subsets is one of the cleanest backtracking patterns: → choose → explore → undo Another revision day, but clarity is improving. Consistency continues. #LeetCode #DSA #Python #Backtracking #Recursion #LearningInPublic #Consistency

Day 43/100 – #100DaysOfCode 🚀 Solved LeetCode #2610 – Convert an Array Into a 2D Array With Conditions (Python). Today I practiced hashmap (frequency counting) to construct a 2D array based on given conditions. Approach: 1) Create a frequency map to count occurrences of each element. 2) Initialize an empty result list. 3) While the frequency map is not empty: 4)  Create a new row. 5)  Iterate through keys and add each number once to the row. 6)  Decrease its frequency and remove it if it becomes zero. 7) Add the row to the result. 8) Return the final 2D array. Time Complexity: O(n) Space Complexity: O(n) Learning how frequency maps help in structuring data efficiently 💪 #LeetCode #Python #DSA #HashMap #Arrays #ProblemSolving #100DaysOfCode

🚀 Day 48 – LeetCode Journey Today’s challenge: Substring with Concatenation of All Words ✔️ Solved using sliding window + hashmap ✔️ Processed string in fixed-length chunks ✔️ Tracked word frequency efficiently 💡 Key Insight: Instead of checking every substring blindly, splitting the string into word-sized pieces and using a hashmap helps validate matches efficiently. Sliding window keeps the solution optimized. This problem was a great exercise in string manipulation, hashing, and window techniques 🧠 Consistency is the real game changer 🚀 #LeetCode #Day48 #SlidingWindow #HashMap #Strings #Python #ProblemSolving #CodingJourney #100DaysOfCode https://lnkd.in/gxf4RBT6

Just Published: My NumPy Blog Series (Part 1) Most beginners learn NumPy… but still struggle to actually use it. So I decided to break it down in the simplest way possible 👇 Part 1: NumPy Basics & Array Creation In this blog, I’ve covered: • How NumPy arrays really work • Creating 1D, 2D, 3D arrays • Important functions like arange, linspace, zeros, ones • Understanding shape, size, dtype (the stuff people usually skip) • Why changing data types can improve performance This is not just theory — I’ve added examples and explanations the way I wish I had when I started. Blog Link : - https://lnkd.in/d4_BfSzg #NumPy #Python #DataScience #MachineLearning #Coding #LearnInPublic

💡 Solved Group Anagrams today! 🧠 Key idea: Instead of comparing strings, I used a frequency array (size 26) to create a unique key for each word. ⚠️ Catch in constraints: All characters are lowercase English letters, which allows us to use a fixed-size array of 26 for efficient hashing. 🚀 Result: Achieved O(n · k) time complexity. Great example of converting a comparison problem into a hashing problem! #LeetCode #DSA #Algorithms #Python #CodingInterview

✅ Day 26 of #DSAPrep > Problem: Merge Sorted Array > Platform: LeetCode > Concept: Two Pointers / Merge Technique Solved this problem by merging two sorted arrays using two pointers and storing the result in sorted order. > Key Idea: Use one pointer for each array Compare current elements Insert smaller value into result Add remaining elements at the end > Time Complexity: O(m + n) > Space Complexity: O(m + n) #DSAPrep #Algorithms #Python #TwoPointers #Arrays #ProblemSolving #CodingJourney

💧 Max Water Problem — Two Pointer Optimization Solved Container With Most Water using the Two Pointer technique in O(n) time. Key Idea: Move the pointer with the smaller height, since it limits the water capacity. 📈 Complexity: Time → O(n) Space → O(1) Small logic shift, big optimization. #DSA #Python #TwoPointers #CodingInterview #Algorithms

🚀 Day 62 of #100DaysOfCode Solved “Remove Nth Node From End of List” 🔗 💡 Today’s focus: Two Pointer Technique (Fast & Slow pointers) Instead of calculating length, I used an efficient one-pass approach to remove the target node. 🧠 Key Learnings: Dummy node helps handle edge cases (like removing head) Fast pointer moves n steps ahead Then move both pointers until fast reaches the end Slow pointer lands just before the node to delete ⚡ Clean, efficient & optimal solution (O(n) time, O(1) space) Consistency is starting to feel powerful now 💪 #DSA #LeetCode #CodingJourney #LinkedInLearning #100DaysOfCode #Day62 #Python #ProblemSolving