Learning Quicksort: A Fast Sorting Algorithm

📚🌃 Continuing my dive into data structures and algorithms. 🙂 🃏↪️🃏Tonight’s focus: Chapter 23: Insertion Sort Insertion Sort is like organizing cards 🃏 in your hand one at a time, you compare and place each card in its correct spot. ⚙️ How Insertion Sort Works -Start with the second item in the list (the first item is considered sorted). -Mark it as the active item. 📌 -Compare the active item to the items on its left (aka the sorted region). -Keep shifting left until you find a value smaller than the active item, then insert it to the right. -Repeat for each item in the unsorted region, reassigning the active item 📌 each time. ✅ Key Notes -Follows a “look left” approach: each new active item 📌 is compared to those before it, one by one, moving toward the first index, until it finds a value that is less than itself. ⚡ Performance -Time: O(n²) in the average and worst case. Best case (already sorted): O(n). -Space: Uses constant memory, no extra space needed. -Not ideal for large datasets, but useful when memory is limited, the list is mostly sorted. -Ok for small data sets. If you're learning too, or just love a good emoji-powered breakdown, follow along for more chapters in this series! 🚀 #JavaScript #Algorithms #Coding #DevNotes

📚🌃 Continuing my dive into data structures and algorithms. 🙂 🫧 🔁 🫧 Tonight’s focus: Chapter 22: Bubble Sort ❌ Bubble Sort is one of the simplest sorting algorithms, but also one of the most inefficient. It’s often taught not because it’s fast, but to help you recognize and avoid sluggish 🐌 sorting patterns in your own code. ⚙️ How Bubble Sort Works -Start at the beginning of the list. -Compare each pair of adjacent elements. -If the first is greater than the second, swap them. -Move one step forward and repeat the comparison. -Once you reach the end, go back to the beginning and repeat the process. -Continue this cycle until all items are sorted. -When it runs a full pass with no swaps, the list is officially sorted. ✅ Key Notes -Its best-case scenario is a sorted list, ironically, the one time you don’t need a sort. -It’s slowest when the list is in reverse order because every element needs to be moved. ⚡🐢🐌 Performance -Time complexity: Worst and average case is O(n²). -Even small lists can take many steps, sorting 4 numbers might involve 8+ steps. -Requires multiple passes through the list. It will still pass through elements that have already been sorted, so it’s inefficient for large datasets. If you're learning too, or just love a good emoji-powered breakdown, follow along for more chapters in this series! 🚀 #JavaScript #Algorithms #Coding #DevNotes

📚🌃 Continuing my dive into data structures and algorithms. 🙂 🔍 Tonight’s focus: Chapter 24: Selection Sort ⚙️ How Selection Sort Works -Assume the first item is the smallest. -Scan the entire list to find the actual smallest item. 🔎 -Swap it with the first item and add it to the sorted region. -Move to the next unsorted item and repeat: ----Assume it's the smallest. ----Compare it with the rest of the unsorted region. ----Swap the new smallest into place in the sorted region. -Continue until all items are sorted! ✅ Key Notes -Splits the list into a sorted region (usually at the front) and an unsorted region. -Swaps each new smallest item into place, expanding the sorted region one item at a time. -Simple to understand and implement, but not very efficient. ⚡ Performance -Time: O(n²) -Space: The items sort in their place, so memory usage is minimal. -Not ideal for large datasets. -Slightly less practical than Insertion Sort, but still a good learning tool. 💻 Implementation Tips -Outer loop (i): tracks the boundary between sorted and unsorted. -Inner loop (j): finds the smallest item in the unsorted region. -Swap smallest item with the first unsorted item. If you're learning too, or just love a good emoji-powered breakdown, follow along for more chapters in this series! 🚀 #JavaScript #Algorithms #Coding #DevNotes

📚🌃 Continuing my dive into data structures and algorithms. 🙂 🌳 Tonight’s Focus: Chapter 19 – Binary Tree Traversal In linear structures like arrays or linked lists, we move step-by-step: 0️⃣ ➡️ 1️⃣ ➡️ 2️⃣ ➡️ 3️⃣ But trees are hierarchical 🌳, so we use a different approach: Breadth-First 🔺 and Depth-First 🐋 Traversal ✅ FYI -Tree depth helps us understand how far a node is from the root -The goal is to visit every node and represent the full structure ⚙️ Traversal Basics Each node goes through two phases: Discovered Collection – We identify a node (starting from the root) and add it to this list as soon as it's found. Explored Collection – After a node is discovered, we examine its children. Once all its children have been discovered, we move the node to this list. 🔺 Breadth-First Traversal -Uses a queue (First In, First Out) -Visits nodes level by level, left to right, moving nodes from the discovered to explored collection as they are processed -Example order: A → B → C → D → E… 🐋 Depth-First Traversal -Uses a stack (Last In, First Out) -Nodes are discovered by traversing deep down the left-most path, then backtracked to the nearest unexplored node. During processing, nodes are moved from the discovered collection to the explored collection. ⚡ Performance Time: O(n) Space: O(n) Same across best, average, and worst cases 📚 Might just do half a chapter for the more involved chapters next. If you’re learning too (or just love emoji-powered breakdowns), follow along for more chapters in this series! 🚀 #JavaScript #Algorithms #Coding #DevNotes

Introduction to Web Data Collection (Data Scraping) Ever heard of Data Scraping but weren't quite sure what it was? This quick tutorial is for you! A friend recently asked me about it, and I thought, "Why not create a quick demo to share the concept with all of you?" Imagine a website is like a house built with bricks. These bricks are the HTML code blocks. Each brick holds a small piece of information: a title, a price, an image, a description, and so on. Data Scraping is simply learning how to spot those precise bricks to extract the information you're interested in. Example: Automatically retrieving the titles and prices of books. - How It Works : The script reads the "house blueprint" (the HTML code). It locates the correct "bricks" containing the data. It collects this data and displays it in a well-organized table. That's it! Read, Extract, Organize. - This technique offers significant benefits : Save time (automation). Track product or price evolution. Transform raw data into actionable insights. The GitHub link with the complete code and full explanation is in the comments! Mohamed Hamed #DataScraping #WebScraping #DataCollection #Automation #Python #Coding #DataAnalysis #WebDevelopment #Mohamed_Hamed #TechEducation #Programming #DataScience #DigitalTools #TechLearning #OpenSource #GitHub #DataDriven #LearnToCode #WebData #DeveloperCommunity

It's one thing to analyze data in a notebook. It's another to build a tool that anyone can use to explore it.This was my goal with the COVID-19-Streamlit-Dashboard. I took the massive CORD-19 research dataset from Kaggle (thousands of papers) and built an interactive web app to visualize it.This was my first time using Streamlit, and I was amazed at how quickly you can turn a data script into a live application.The app allows you to:Filter papers by publication year.See publication trends over time.Find the top 10 most-published journals.View a word cloud of the most frequent terms in paper titles.This project was a fantastic lesson in data visualization. It showed me the power of building tools that make complex data easy to understand.You can check out the full code and README on GitHub: https://lnkd.in/dPqfcTcE #DataVisualization #DataScience #Python #DataAnalytics

Announcing dshelper-ayushlokre v0.1.0 🚀 Over time, I noticed that in almost every data science project, I repeat the same small setup steps — checking for missing values, scaling the data, splitting the data into train/test sets, and running the same evaluation metrics. None of these is difficult, but they add friction and clutter notebooks with boilerplate. So I built dshelper — a lightweight helper library that focuses on the boring but necessary parts of the workflow, so analysis stays fast, clear, and consistent. It’s not trying to replace pandas, sklearn, or any big framework. It’s simply a productivity layer on top of them. What dshelper does: • Shows missing value statistics with an optional visual summary • Generates correlation insights and clean heatmaps quickly • Allows train/test split + scaling in one simple call • Auto-detects classification vs regression and evaluates accordingly • Works directly with pandas DataFrames and sklearn models you already use No new syntax to learn. No heavy abstractions. Just small helpers that save minutes repeatedly — which adds up. Why I built it: • To reduce repetitive code in notebooks • To make early analysis cleaner and less error-prone • To help myself (and hopefully others) stay focused on insights and modeling • To build something small, open-source, and genuinely useful This is just v0.1.0 — a starting point. I want to grow it based on real needs. Install: pip install dshelper-ayushlokre Quick usage: from dshelper_ayushlokre import missing, preprocessing # Missing value report report = missing.analyze(df, show_plot=True) # Train/test split + scaling X_train, X_test, y_train, y_test = preprocessing.split_and_scale(X, y, test_size=0.2, scaler='standard') Links: -PyPI → https://lnkd.in/dr7a5kMU -GitHub → https://lnkd.in/dG7juciX If you try it, I’d genuinely appreciate: • A ⭐ on GitHub • Feedback/suggestions • Feature requests • Or PR contributions #Python #DataScience #MachineLearning #OpenSource #Pandas #ScikitLearn #PyPI

I'm thrilled to launch my new end-to-end Data Science project: a live, interactive Customer Churn Prediction App! 🚀 Instead of just building a model in a Jupyter Notebook, I wanted to build a complete, usable product. I'm proud to say I handled the entire stack, from data analysis to a deployed front-end. The Application: The app allows a user to input customer details into a futuristic, animated dashboard and get a real-time churn probability, visualized on a "risk-o-meter" gauge. The Tech Stack: * Model: Logistic Regression (built with Statsmodels and Scikit-learn) to ensure model interpretability. * Back-End API: A high-performance API built with FastAPI & Pydantic to serve the model. * Front-End: A responsive, animated dashboard built from scratch with HTML, TailwindCSS, and JavaScript. * Deployment: The API is container-ready (using Docker) and handles all serialization (.pkl), scaling, and complex CORS errors. The Challenge: The most challenging (and rewarding) part was deploying the API. Debugging the connection between the web app and the server was a fantastic learning experience. I solved CORS (Cross-Origin) errors, port conflicts, and 422 Unprocessable Content issues by implementing FastAPI middleware and using Pydantic aliases to map the JSON payload to the model's exact feature names! This project was a deep dive into MLOps, API development, and front-end design. The full, documented code is available on my GitHub (see the link in the first comment!). Check out the 52-second demo in the video below. #DataScience #MachineLearning #MLOps #FastAPI #Python #Statsmodels #Pydantic #PortfolioProject #FullStack #WebDevelopment #DataAnalyst

📚🌃 Continuing my dive into data structures and algorithms. 🙂 🕸️ Tonight’s Focus: Chapter 20 – Graph Traversal (DFS & BFS) Unlike trees, graphs aren’t always hierarchical. They can be messy, interconnected, and unpredictable. So how do we explore them? With two trusty tools: Depth First 🐋 and Breadth First 🧭 Search ✅ FYI Graphs can represent anything from social networks to maps to recommendation systems We explore them to uncover all reachable nodes and understand their connections At the start, we only see one node. The rest are hidden until discovered ⚙️ Traversal Basics Each node goes through two phases Discovered Collection – Nodes are added here as soon as they’re found Explored Collection – Once all discovered neighbors of a node have been checked, the node moves here 🐋 Depth First Search (DFS) Uses a stack (Last In, First Out) Goes deep into one path before backtracking Newly discovered nodes are added to the beginning of the list Example path: A → B → C → E → F → G… 🧭 Breadth First Search (BFS) Uses a queue (First In, First Out) Explores level by level, checking all immediate neighbors before moving outward Newly discovered nodes are added to the end of the list Example path: A → B → C → D → E… ⚙️ Discovery Order DFS: Order depends on how neighbors are listed and pushed BFS: Order follows the queue. First discovered, first explored ⚡ Performance Time: O(n) Space: O(n) Same across best, average, and worst cases. Depends on graph size and structure 📚 These chapters are getting deeper. Might split them up going forward If you’re learning too (or just love emoji-powered breakdowns), follow along for more chapters in this series 🚀 #JavaScript #Algorithms #Coding #DevNotes

If you are a student building your portfolio or a data enthusiast, I want to introduce a free-of-cost, quick solution for model deployment on a web interface. Streamlit is a great UI to quickly deploy your locally trained model, free-of-cost. It sources your model from your GitHub repository, takes user inputs and displays your results in an aesthetic user interface. Streamlit's codebase requirements are in Python, with built-in formatting. This has come handy in times where I want someone to test/use a model without sending them into a complex codebase. I remember my years in college pouring my heart and soul into cleaning and perfecting data, building a model that tests with great accuracy, only to dump it in my GitHub with no way for someone to learn about or interact with. Streamlit helps you create an interface in almost no time and have your model deployed with ease. You even get a shareable link to this new interface which can be added to you portfolio. Some example apps I've built recently: Salmon outmigration timing prediction: https://lnkd.in/gw3SwX6b Computer vision model for Pinniped census: https://lnkd.in/gzgaU7Fh Check it out!