#Day52 of my fourth #100DaysOfCode Working on a collection of Python projects, including: 1. Scientific Calculator: A simple scientific calculator built with Python's tkinter library, featuring basic arithmetic operations, trigonometric functions, and more! 2. Scrabble Game: A multiplayer Scrabble game built with Python's tkinter library, featuring word validation, score tracking, and definition lookup. 3. Screen Capture Tool: A screen capture tool built with Python's OpenCV and PIL libraries, allowing for screen recording, screenshot capture, and delayed screenshot capture. 4. Search Bot: A search bot built with Python's requests and BeautifulSoup libraries, featuring search query input, search engine selection, summary generation, and word cloud generation. Check out the code on GitHub let me know what you think! Github Link:- https://lnkd.in/gsTcpcAw #Python #Coding #Projects #Scrabble #Calculator #ScreenCapture #SearchBot #OpenCV #tkinter #BeautifulSoup #requests #PIL #gensim #wordcloud #matplotlib #nltk #PyDictionary

🚀 Matplotlib: Data Visualization (Python) Matplotlib is a widely used library for creating static, interactive, and animated visualizations in Python. It provides a flexible framework for generating plots, charts, histograms, and other visual representations of data. Matplotlib integrates well with NumPy and Pandas, allowing you to easily visualize data stored in arrays and DataFrames. Data visualization is crucial for understanding patterns, trends, and insights in data. Learning Matplotlib enables you to effectively communicate data findings. 💪 Build your knowledge empire, brick by brick! 🚀 Accelerate your tech journey — 10k+ bite-sized concepts, 4k+ deep-dive articles, 12k+ quizzes! 🚀 Start learning: https://lnkd.in/gefySfsc 🌐 Learn more: https://techielearn.in #Python #PythonDev #DataScience #WebDev #professional #career #development

🎧 Python Mini Data Analysis: Ranking Artists by Playcount Built a fun little project to explore how far pure Python can go without Pandas or Power BI. 🎯 Used a simple dataset of songs to: • Calculate total, average, and share of total playcount per artist • Automatically group them into Top, Mid, and Low performance tiers • Identify the top and bottom 3 songs overall 💡 Using only map(), filter(), and reduce() reminded me how powerful Python’s core functions can be for real analysis no fancy tools required, just logic. #Python #DataAnalysis #LearningByDoing #Analytics #FunctionalProgramming #ProjectBasedLearning

-🎬 Excited to share my latest project: a Movie Recommendation System! Built with Python, NumPy, Pandas, scikit-learn, and Streamlit, this app uses machine learning algorithms to: Recommend movies based on your favorite films Display movie posters, plot summaries, and IMDb ratings Provide an interactive, user-friendly interface It fetches movie data via OMDB & TMDB APIs and helps you discover new movies to watch! Check it out on GitHub: [https://lnkd.in/gfqzVVCt] Would love your feedback! #Python #MachineLearning #scikitLearn #Streamlit #MovieRecommendation #DataScience #WebApp

✨ Small projects can spark big learning! It was a 3-day personal challenge: building an automated test suite for a portfolio website using Playwright + Python + SQLite 💻🧪 🎯 The goal was to explore Playwright with Python and track test results in SQLite, instead of relying solely on built-in reports. Designing a simple tracker that logs each test with a timestamp made the project feel structured and data-driven 🗂️⏱️ Along the way, I practiced Python , explored data storage 📊, and learned how small experiments add up. With AI accelerating learning , exploring and building feels more exciting than ever 🤖✨ 🔧Next step: Improve the tests, apply similar setup to a medium-sized project to tackle more complex scenarios and continue to explore 💡 #softwaretesting #playwright #python #automation #qualityassurance #testautomation

Side-by-side comparison of 3D PCA visualization in Python and R. Python focuses on high-quality, presentation-ready visual output, while R offers lightweight, interactive 3D exploration with minimal code. Includes: dataset, Python script, R notebook, and visual examples. Full code available on my GitHub: https://lnkd.in/ghgTA75d #DataScience #PCA #DimensionalityReduction #3DPCA #MachineLearning #Python #Rstats #DataAnalytics #BusinessIntelligence #CustomerSegmentation #DataVisualization #ExploratoryDataAnalysis #Clustering #HighDimensionalData #Analytics #InsightDiscovery #RProgramming #PythonProgramming #GitHubProject

This week, I built a Python program using NumPy to handle a classic matrix problem: ✅ Create a 2D NumPy array of size 5x4 ✅ Generate its transpose ✅ Calculate column-wise mean and row-wise standard deviation ✅ Compute the dot product of the original matrix with its transpose While it might look simple, this small project taught me how NumPy handles mathematical operations efficiently — and how much power a few lines of code can have when optimized correctly. Here’s what I enjoyed most: Understanding how matrix transposition works at the data structure level Seeing mean and standard deviation come to life across axes Watching the dot product reveal matrix relationships in a clean, vectorized way If you’re also learning data manipulation or Python fundamentals, I’d love to connect and discuss ideas! 💬 #Python #NumPy #DataScience #Programming #AI #LearningJourney #TechStudent

🌟 Day 18 of My AI Fullstack Journey 🌟 Today, I explored one of the most powerful OOP concepts in Python — Polymorphism 🐍 🔑 What I Learned: ✅ What is Polymorphism — the ability of functions/methods to behave differently based on objects ✅ Method Overriding — redefining parent class methods in the child class ✅ Method Overloading — using default or variable arguments to handle multiple parameter cases ✅ Operator Overloading — redefining operators like +, -, etc., for custom objects 🛠️ Mini Project: 👉 Built a Shape Area Calculator using polymorphism Created a base class Shape with a common calculate_area() method Inherited it into Circle, Rectangle, and Triangle classes Each child class implemented its own formula for area This project helped me clearly understand how Python allows flexibility and reusability in object-oriented programming. #engineeringinkannada #Python #AI #FullStack #LearningJourney

Day 4 of My Python Journey — Playing with Conditional Statements! Today, I spent some time getting my hands dirty with Python’s if, elif, and else statements. It might sound simple, but understanding how to make decisions in code is the key to writing programs that actually do something meaningful. Here’s what I did today: Tried out different conditions and logical flows in Python ✅ Built a small project to see how conditional statements work in action ✅ Organized my project folder properly with all scripts and screenshots ✅ Updated my README to make it clear and easy to follow ✅ It’s exciting to see how small pieces of logic come together to create programs that can “think” a little. This is the foundation I’ll need for more complex AI and Machine Learning projects later. 📂 Project folder: Day4_Conditional_Statements/ 📌 Key files: https://lnkd.in/ebtxDiRE, README.md, screenshots 🔗 Check it out on GitHub: https://lnkd.in/eFiYbJh2 Every day I learn something new, and I’m seeing how these small steps add up. Day 4 is done — onto Day 5! #Python #CodingJourney #LearningByDoing #GitHub #DeveloperLife #AI #MachineLearning #Programming

Day 8 of 90-Day Python & AI Journey: Mastered the Art of the Loop! 💪 Just wrapped up a crucial day focusing on Loops (For & While)—the true engine of automation in Python. If you want to process data, automate tasks, or train an AI model, you need to master iteration! Highlights from Day 8: ✅ Deep dive into for loops to iterate over sequences (range() and lists). ✅ Learned essential flow control with break (stop) and continue (skip). ✅ Built a practical Even & Odd Number Finder mini-project using conditional logic inside a loop. ✅ Solidified my understanding of the while loop for condition-based repetition. Every single line of code, concept explanation, and organized folder structure is designed to be clean, industry-standard, and portfolio-ready—because learning is about more than just coding; it's about presentation! Looking forward to Day 9: Python Functions, where I'll transition to modular, reusable code. Onward to mastery! 💡 📂 View the Code & Portfolio This milestone, complete with its organized README.md and working code, is ready for review. 🔗 GitHub Repository: [https://lnkd.in/eJBDAWvX #Python #AI #MachineLearning #DataScience #CodingJourney #GitHub #Portfolio #JobanjitSingh #Automation