🚀 Day 70 of #100DaysOfCode Today’s problem: Find First and Last Position of Element in Sorted Array 🔍 At first, I thought of linear search… but the O(log n) hint changed everything. Had to dive into binary search and tweak it to find both first and last positions. 💡 What helped me: Run binary search twice → left bias & right bias Don’t stop at the first match, keep searching It’s all about controlling the search space smartly. Binary search is deeper than it looks. #DSA #LeetCode #CodingJourney #Python #BinarySearch

NaNs ruining your analysis? Here’s the quick Pandas trio: use isna() to detect missing values, dropna() to remove incomplete rows, and fillna() to replace gaps with defaults. This tiny example shows all three so you can clean data in seconds.#pandas #python #datascience #dataengineering

🚀 Day 11 of #500DaysOfCoding Today’s problem: Maximum Average Subarray I I used the sliding window technique to solve this problem and reduced the time complexity to O(n). 💡 Key Takeaways: - Precompute the sum of the first window - Slide the window by adding the next element and removing the previous one - Track the maximum sum → compute the maximum average. On to Day 12 🔥 #CodingJourney #DataStructures #Algorithms #Python #ProblemSolving

LeetCode | Count Good Numbers 🔢 🔹 Concept: Combinatorics + Modular Exponentiation 🔹 Idea: Even positions → 5 choices, Odd → 4 choices 🔹 Time Complexity: O(log n) Use math + fast power to handle large inputs efficiently 💡 #LeetCode #DSA #Math #ModularArithmetic #Python #CodingJourney

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

Most analyses without correct inference, are measuring the wrong thing. I worked on a causal inference project using DiD and PSM to find the actual effect of a loyalty program on churn. Not correlation, Not gut feeling. Causation! Two methods. Both agreed: ~8pp churn reduction. Code on GitHub. Full walkthrough on YouTube 👇 #CausalInference #DataScience #Python #Statistics

NumPy Practice – Day 2 🚀 Continued my NumPy learning and practiced: 🔹 Reshaping & flattening arrays 🔹 Stacking arrays (horizontal & vertical) 🔹 Random number generation 🔹 Finding unique & duplicate elements 🔹 Sorting & moving averages Key learning: NumPy enables efficient array operations and reduces the need for loops. 📒 Sharing my Google Colab notebook: https://lnkd.in/gs3aZcfY #Python #NumPy #DataScience #LearningInPublic

Week 1 – Learning Progress in Generative AI 🚀 This week I focused on: Python fundamentals for data handling Working with libraries like pandas, numpy and matplotlib Setting up the development environment in VS Code Key takeaway: Understanding the environment setup and libraries is just as important as writing code. Small setup issues can slow you down, but solving them builds confidence. Looking forward to diving deeper into real-world data problems next. #GenerativeAI #Python #LearningJourney #CareerTransition

🔁 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

🐍 Day 103 — Decision Trees (Implementation) Day 103 of #python365ai 🧑💻 Example: from sklearn.tree import DecisionTreeClassifier model = DecisionTreeClassifier() model.fit(X, y) 📌 Why this matters: Decision Trees handle both classification and regression tasks. 📘 Practice task: Train a simple decision tree model. #python365ai #DecisionTree #MachineLearning #Python