Transformed Regression Model to Production-ready ML Project with Python

I built a small tracer from scratch in Python. It automatically creates spans for function calls (like requests.get) using: 𝗠𝗼𝗻𝗸𝗲𝘆 𝗽𝗮𝘁𝗰𝗵𝗶𝗻𝗴 – intercepting existing library functions and wrapping them with tracing logic, without modifying the original code. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝘃𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀 (𝗰𝗼𝗻𝘁𝗲𝘅𝘁𝘃𝗮𝗿𝘀) – these are like thread-local storage but designed for async and concurrent programs. They let each coroutine or thread safely keep its own tracing context, so even with parallel requests, the tracer knows which span belongs to which flow. 𝗪𝗵𝗮𝘁’𝘀 𝗮 𝘀𝗽𝗮𝗻?  A span represents one unit of work — for example, a function execution or an API call.  Spans can be nested (parent-child), forming a tree that shows the entire flow of a request across different components. Each span is logged to a file (trace_log.jsonl), capturing parent-child relationships and timing details. Later, this data can be visualized to see how requests flow through a system — similar to what full-fledged tracing tools like OpenTelemetry do. GitHub: https://lnkd.in/gcR7XCwc

🚀 Test CI/CD Pipeline with GitHub Actions 🧑💻 I have created a simple test CI/CD pipeline using GitHub Actions for a Python project. It's just a basic test to get familiar with the process. This could be helpful for beginners who would like to learn how to set up a CI/CD pipeline. 🚀 🔧 What it does: Automatically runs tests when code is pushed. Deploys if tests pass. Check out the repo here: https://lnkd.in/eB5cyKbZ #GitHubActions #CICD #TestProject #Python #Beginners #Automation

I’ve created and uploaded a Python repository on GitHub — perfect for beginners who want to practice Python, review core concepts, or simply understand the syntax in an easy and organized way. This repository includes well-structured examples and simple scripts that can help anyone starting their Python journey or refreshing their knowledge. 📂 GitHub Repository: https://lnkd.in/ecHVT7F2 Whether you’re learning, revising, or exploring Python, this repo can be a great starting point! Feel free to fork, explore, and contribute. 💻 #Python #Programming #GitHub #Coding #Developers #PythonForBeginners #LearnToCode

Library Management System using Python & Streamlit I am pleased to share my recent project — a Library Management System developed using Python, Streamlit, and Pandas. This web-based application allows users to efficiently manage a library’s collection through the following key functionalities: 📘 View Books: Display the complete list of books available in the library. ➕ Add Books: Add new book entries with title, author, and availability status. 📖 Issue Books: Update the record when a book is issued. 🔁 Return Books: Mark an issued book as returned. 💾 Persistent Storage: Data is stored and managed using a CSV file for easy access and updates. 🧠 Technologies Used: Python – Core programming logic and data handling Streamlit – Web interface and user interaction Pandas – Data manipulation and storage File Handling (CSV) – Persistent local data storage This project enhanced my understanding of data management, Streamlit-based UI development, and Python file operations. 🔗 Live Application: https://lnkd.in/gDMXu4-E 💻 GitHub Repository: https://lnkd.in/gdjNRSaa #Python #Streamlit #DataScience #SoftwareDevelopment #WebApplication #ProjectShowcase #LibraryManagementSystem #GitHub #LearningByBuilding

💻 Python Command-Line Quiz Game Project (Project 17)! I just wrapped up a fun little project: a fully functional, object-oriented quiz game implemented right in the Python command line! This project was a great way to practice Object-Oriented Programming (OOP) principles in Python by breaking the game down into logical, reusable components. 🌟 Key Features OOP Structure: The project is organized into three main classes, making the code clean, readable, and easy to maintain: Question: Simple class to hold the text and the correct answer for each question. QuizBrain: The core logic class that manages the question list, tracks the user's score, checks answers, and moves to the next question. main.py: Drives the game. It takes raw data, converts it into Question objects, and uses the QuizBrain to run the quiz loop. Dynamic Question Flow: The QuizBrain class handles the entire game flow, prompting the user for answers and automatically keeping score. Case-Insensitive Answer Checking: The check_answer method ensures the quiz is user-friendly by accepting "True," "true," "tRuE," etc., as correct. 🛠️ Tech Stack & Files File NamePurposequestion_model.pyDefines the Question class.quiz_brain.pyContains the QuizBrain logic (score tracking, answer checking, next question).game_data.pyStores the raw list of questions and answers.main.pyInitializes the game, builds the question bank, and runs the main quiz loop.🚀 How It Runs The game loop continues as long as there are questions left, giving the user their score after every question, and finally printing a final score summary. Python # Output Example: Q.1. Ada Lovelace is often considered the first computer programmer. (true/false)?: true you got it right the correct answer is True your score is : 1/1 Q.2. JavaScript derives from a later version of Java (true/false)?: true that's wrong the correct answer is False your score is : 1/2 you've completed the game, your final score is 8/10 github link: https://lnkd.in/eKCuaQTv This was a great exercise in applying fundamental Python concepts and good coding structure! #Python #OOP #Programming #CodingProject #CommandlineGame #QuizGame

20/30 learnings: 🚀 Embarking on the Python Automation Journey Starting with Python for Data Automation? You're not alone in finding the roadmap unclear. Many skip over the foundational aspects, but building a strong Python foundation is crucial for effective automation. 🔑 Why Python Fundamentals Matter Before diving into automation, ensure you're comfortable with: Basic syntax and data structures Functions and control flow Modules and libraries This foundational knowledge empowers you to: Write clean, efficient scripts Debug effectively Build scalable automation solutions 📘 A Resource to Guide You: github link: https://lnkd.in/gGBHc_J6 #30dayslearnings #pythonbasics

Approve when build is green 💡 Have you ever asked your peer for a code review and heard back: “I’ll approve it once the build is green.” It drives me crazy 😅 My thinking: you already looked at the code — if it looks good, why not approve? If you approve it and the build fails, I can’t merge it anyway, right? At least that’s my mindset Today I tested it out in GitHub Actions Setup: 1️⃣ A GitHub repository with a protected branch main and 1 required reviewer 2️⃣ .github/workflows/build.yml — workflow triggers on push in all branches except main 3️⃣ Inside there’s a Build job running Python linter Developer creates a branch feature/test with changes that violate the linter and opens a PR Approver approves this PR while the build is still running ⚠️ Final Build is red 🚨 — but the developer can still merge it! That was a bummer. After a couple of hours reading the documentation, I found the solution. As usual, GitHub is a great tool and offers a solution for almost any problem — it’s just not always on the surface ➡️ Go to: Repo Settings → Rules → Rulesets → New branch ruleset → Require status checks to pass → Add checks… 🧩 Fortunately There’s a Terraform module that provisions a fully fledged, security-aware GitHub repository: 👉 https://lnkd.in/eGNibq-A I realized I want to approve PRs without waiting for all checks to complete, so I just released version 1.1.0 of this Terraform module with a new feature: ✨ required_status_checks — lets you specify which checks must pass before a PR can be merged!

🛡️ Learning to Protect APIs — One Rate Limiter at a Time As part of my journey into system design, I recently built a small but important backend feature: a Rate Limiter using FastAPI. Why does this matter? In real-world systems, it's critical to protect your APIs from being overloaded — whether by accident or misuse. Rate limiting is one of the simplest and most effective ways to do that. 🎯 Goal: Limit each user to 5 API requests per minute using an in-memory dictionary and Python's time module. 💡 How I built it: Used FastAPI middleware to intercept every incoming request. Tracked requests per IP using a Python dictionary. Filtered out old timestamps to calculate request frequency in the last 60 seconds. Returned a 429 Too Many Requests response if the user crossed the limit. 📌 Key Concepts Covered: Rate limiting logic using timestamps Middleware for request handling Stateless, in-memory tracking Testing with Postman & local API simulation 💭 Takeaway: System design isn't only about complex architectures — it's also about simple protections that make systems reliable. This mini project helped me understand how to build smarter, safer APIs. Github Repo: https://lnkd.in/gry_zvjx #SystemDesign #FastAPI #BackendDevelopment #RateLimiting #Python #APIProtection #LearningByDoing #BuildInPublic #FullStackDeveloper #DeveloperJourney

Over the past few weeks, I’ve been building a small Python programme to eventually perform fundamental analysis on companies listed on the stock exchange using data from Yahoo Finance. The idea is to automatically extract and categorise balance sheet items — then generate a structured balance sheet in Python for further analysis. So far, I’ve built: 1️⃣ A module that fetches company data via yfinance, assigns items to balance sheet categories, and lets the user adjust anything unassigned. 2️⃣ A second module that defines balance sheet classes (Current Assets, Liabilities, Equity, etc.) and generates a formatted balance sheet on screen. The next step is to link the two modules so that live data feeds directly into the balance sheet class and prints a complete statement. It’s still in progress, but it’s been a great exercise in combining financial structure with code logic. You can follow the project here on GitHub: https://lnkd.in/eCbgUhyx #Python #Finance #DataAnalysis #FundamentalAnalysis #FinancialModeling #GitHub #LearningInPublic