Bubble Sort Algorithm Explained

Day 81 Heap patterns are starting to feel intuitive now. #Day81 🧩 973. K Closest Points to Origin Key idea: • Use a min heap • Calculate distance using squared values (no sqrt needed) • Push points into heap based on distance • Pop k closest elements What I liked about this problem: A small optimization (avoiding sqrt) makes it simpler and faster. These small tricks make a big difference. Consistency is turning patterns into instincts. #LeetCode #DSA #Python #Heap #PriorityQueue #LearningInPublic #Consistency

🚀 Today I Solved… A classic problem: Count pairs with sum < target using the two-pointer technique ⚡ 💡 Key idea: Sort the array Use left and right pointers If arr[left] + arr[right] < target 👉 Add right - left in one go (counts multiple pairs instantly!) 🔥 This simple trick reduces complexity from O(n²) ➝ O(n log n) Small optimization, big impact — that’s the power of patterns in DSA! #DSA #Coding #Python #ProblemSolving #InterviewPrep

Day 102 Backtracking patterns are starting to repeat. #Day102 🧩 90. Subsets II How today went: • Similar to the basic Subsets problem • Key difference: handling duplicates • Sorting the array is important • While iterating, skip duplicates to avoid repeating subsets What clicked: Backtracking becomes easier when you: • Recognize the base pattern • Add constraints like duplicate handling Same structure, new rule. That’s how patterns build. #LeetCode #DSA #Python #Backtracking #Recursion #LearningInPublic #Consistency

🔁 Exploring Sorting Algorithms in Python Today I practiced two fundamental sorting techniques: ✅ Bubble Sort ✅ Selection Sort 💡 Key Learnings: * Bubble Sort repeatedly swaps adjacent elements to push larger elements to the end * Selection Sort selects the minimum element and places it in the correct position * Understanding time complexity becomes clearer when you count operations manually #Python #DataStructures #Algorithms #CodingJourney #100DaysOfCode #LearningInPublic

💡 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 89 — Features and Labels Day 89 of #python365ai 📌 Features (X) → input variables Labels (y) → output Example: X = [size, rooms] y = price 📌 Why this matters: Clear distinction is essential for building ML models. 📘 Practice task: Identify features and labels in a dataset. #python365ai #Features #MachineLearning #Python

DSA Tip: Binary Search If you’re checking every element one by one to find a value… there’s a better way. Use Binary Search. It works by repeatedly dividing the list in half until the target is found. No unnecessary comparisons. No full list scanning. Just fast, efficient searching. Insight: Binary Search reduces millions of checks to just a few steps, that’s the power of working smarter with data. It is not about how long a code is, is about how smart it works. FOLLOW FOR MORE DSA TIPS & INSIGHTS #DSA #BinarySearch #Python #CodingTips #LearnToCode

#DAY28 of LeetCode! Today’s challenge: 540. Single Element in a Sorted Array This problem highlighted how observing patterns in a sorted array can simplify the approach significantly. Instead of checking every element, using Binary Search with index patterns helps quickly identify where the structure breaks. What initially looks tricky becomes much clearer once you focus on how the array behaves around the single element. Problems like this reinforce that strong fundamentals and pattern recognition are key to mastering DSA. #LeetCode #DSA #BinarySearch #CodingJourney #30DaysofCode #Day28 #Python

🚀 Day 34 – LeetCode Journey Today’s problem: Isomorphic Strings ✔️ Solved using a concise Python approach with sets ✔️ Compared unique character patterns efficiently ✔️ Achieved clean and optimized code 💡 Key Insight: By comparing the lengths of set(s), set(t), and set(zip(s, t)), we can quickly verify if both strings follow the same mapping pattern — ensuring a one-to-one relationship. Sometimes, the simplest solutions are the most powerful ✨ Continuing to learn, simplify, and grow every day 🔥💪 #LeetCode #Day34 #Python #Strings #Hashing #ProblemSolving #CodingJourney #100DaysOfCode

You can fit the most common Bayesian regression models in Python using a consistent syntax (similar to brms in R) using the bambi package. It utilizes PyMC to do the simulations. It's remarkably easy and straightforward to use - you just adjust the family name to the right model type. Here are a few examples. More instructions are available here: https://lnkd.in/eGSG3-Bk #statistics #datascience #analytics #rstats #python #peopleanalytics #technology #ai