🚀 Learning by Building: My Weather App Journey 🌦️ I recently built a Weather App — and while I haven’t invented anything groundbreaking, this project turned out to be an incredible learning experience! During development, I explored tools that were completely new to me: Streamlit – I learned how to create a fully interactive and user-friendly web interface, making the app accessible and visually appealing. Plotly – I dived into interactive data visualization, creating dynamic charts to represent weather patterns clearly and intuitively. Alongside these, I also leveraged: Requests – To fetch real-time weather data from the OpenWeatherMap API. Pandas – For organizing, cleaning, and processing data efficiently. Datetime – To handle timestamps and display accurate date & time information. Python-dotenv – To securely manage my API key and environment variables. Through this project, I not only reinforced my Python skills but also gained hands-on experience with APIs, data handling, and interactive UI/visualizations — all in a single application. It’s amazing how a small project can teach so many concepts at once. This journey has expanded my toolkit and boosted my confidence to take on more real-world projects! 💡 #Python #Streamlit #Plotly #DataVisualization #APIs #LearningByDoing #ProjectShowcase #TechJourney Github URL: https://lnkd.in/gNaNWbgY

💡 Project Overview: •The app allows users to:View multipletravel destinations with images •Navigate between tabs (Home, Settings, About) •Click Info buttons to view travel image sets dynamically •Select an age range from the sidebar and view matching users in a clean table format 🎯 Learning Outcomes: •Learned how to design interactive dashboards using Streamlit. •Understood state management and conditional rendering in web apps. •Improved understanding of layout control (columns, tabs, sidebars). •Practiced integrating logic and visuals smoothly in a single Python app. 🔗 Summary: This project was a great hands-on experience to understand how Streamlit can quickly transform Python scripts into beautiful, interactive web applications — ideal for data visualization, filtering, and dynamic content display. Linked:https://lnkd.in/g_7-j4tY A big thank you to Chaitanya Madakasira sir for guiding me through this learning journey. #Python #Streamlit #WebDevelopment #MiniProject #DataApp #LearningByDoing #TravelExplorer #MachineLearningJourney #ProjectShowcase

🚀 Excited to share my latest project — DNL Institute Management System! This Flask-based web app is designed to manage every part of an IT institute, from student records and instructor profiles to payments, expenses, and detailed financial reports — all in one modern, responsive dashboard. 🧩 Built With: Flask + SQLAlchemy + WTForms Bootstrap 5 & TailwindCSS SQLite3 for data storage 💡 Key Features: Student & Instructor Management Financial Tracking (Payments & Expenses) Dynamic Dashboard & Reports Clean, user-friendly interface This project reflects my growing skills in Python Flask development and web app architecture. Check out the video demo below 🎥👇 #Flask #Python #WebDevelopment #PortfolioProject #DNLInstitute #FullStackDevelopment

🎉 Project Launch: PDF Chatbot using Flask & LM Studio I’m super excited to share my latest project — a PDF Chatbot built using Flask (Python) and LM Studio! 🚀 This web app allows users to: 📄 Upload any PDF file 💬 Ask questions about its content 🤖 Get instant AI-generated answers — all offline, without cloud APIs! ⸻ 🧠 Tech Stack: • Python (Flask) • PyPDF2 for text extraction • Requests for API communication • HTML, CSS, JS for frontend • LM Studio for local LLM inference ⸻ ⚙️ Features: ✅ Reads & processes PDF documents ✅ Integrates with local AI (LM Studio) ✅ Simple and clean web UI ✅ Runs completely offline — ensuring privacy #Python #Flask #ArtificialIntelligence #MachineLearning #LMStudio #AIProjects #PDFChatbot #WebDevelopment #OpenSource

I recently built and deployed a YouTube Playlist Class Notes Extractor — a Streamlit web app that automatically extracts all “Class Notes” links from a YouTube playlist and generates a clean, downloadable PDF summary 📄 🔧 Tech Stack: 🐍 Python 🎥 yt_dlp (for extracting YouTube data) 📄 ReportLab (for generating PDFs) 🌐 Streamlit (for the web interface) ⚙️ What it does: 1️⃣ Takes a YouTube playlist link 2️⃣ Extracts every video’s title and its “Class Notes” link 3️⃣ Compiles everything into a well-formatted PDF 4️⃣ Lets users download it instantly — no setup required! ☁️ Deployed on Streamlit Cloud — just paste the playlist link and get your notes right away. 💭 To be honest, I didn’t write every line of code myself — I just wanted to bring an idea to life. It all started with a real problem I faced: having to open every video in a playlist just to find and click the notes link manually. So, I decided to automate it, and the result is this working web app! This project reminded me that it’s not always about how much code you write — it’s about the idea and solving real problems efficiently. 👉 Try it out here: https://lnkd.in/emrg-P6G 💬 Would love to hear your thoughts or suggestions for the next version! #Python #Streamlit #Automation #Innovation #YouTubeAPI #LearningByDoing #ProjectShowcase #ProblemSolving Guruji: Rohit Negi Note: May only work for a single playlist on this earth.

🤖 My First Ever Machine Learning Web Project is Live! 🚀 I’m thrilled to share my Face Recognition-Based Web Application, now live at: 🔗 https://lnkd.in/gJ-RNd29 This project marks a huge milestone for me — my first complete Machine Learning + Web Integration project. It can recognize faces from images, uploaded videos, or even a live webcam feed directly from your browser! 🧠 How it works: I’ve trained the system with several known faces (stored with their names). When you upload an image/video or use the live webcam, the app detects and identifies faces that match the pretrained data. You can even add new faces through the “Manage Faces” feature to make the system learn and recognize new users in real time. 💻 Tech Stack: Frontend: HTML5, CSS3, JavaScript (Responsive for Desktop & Mobile) Backend: Flask (Python) Libraries: OpenCV, dlib, face_recognition, Pillow, NumPy Deployment: PythonAnywhere This project taught me a lot about integrating Machine Learning and Computer Vision into a full-stack web app — from face detection and training to real-time recognition through a simple and clean interface. 🔗 GitHub Repository: https://lnkd.in/grUPvJTR #MachineLearning #ComputerVision #Python #Flask #FaceRecognition #AI #DeepLearning #OpenCV #WebDevelopment #FullStack #LearningJourney #ResponsiveDesign

🌟 Day 4 — 21 Days Problem Solving Challenge 🌟 This post isn’t extraordinary — it’s just a small part of my learning story. Today, I faced lots of challenges, got stuck many times, and spent hours trying to solve problems. But in the end, I solved them all by myself — no AI, no Google, just pure logic and patience. The challenge was to solve everything without using any string or Math functions — only pure mathematics. Here’s what I worked on today 👇 1️⃣ Split Number into Digits Input: N = 12345 → Output: [1, 2, 3, 4, 5] 2️⃣ Remove the Decimal Point (Mathematically) Input: N = 12.34 → Output: 1234 3️⃣ Separate Whole and Fractional Parts Input: N = 5.75 → Output: Whole = 5, Fraction = 0.75 4️⃣ Count Whole and Fractional Digits Separately Input: N = 12.345 → Output: Whole Count = 2, Fraction Count = 3 5️⃣ Generate a Decimal Number from Whole and Fractional Digits Input: Whole = [1, 2], Fraction = [3, 4] → Output: 12.34 6️⃣ Check if a Number is a Palindrome Input: N = 121 → Output: Palindrome 7️⃣ Check if a Number is an Armstrong Number Input: N = 153 → Output: Armstrong Number ✨ Example: 1³ + 5³ + 3³ = 153 This series is really helping me sharpen my logical thinking and improve how I approach problems. If you’re an absolute beginner and want to take your problem-solving skills to the next level, I highly recommend checking out Anurag Singh’s free YouTube series. Every day is a new challenge, a new lesson, and a small victory. #WebDevelopment #FrontendDevelopment #WebDeveloper #JavaScript #HTML #CSS #ReactJS #React #TailwindCSS #Tailwind #Coding #Programming #100DaysOfCode #LearningJourney #ProjectTips #CodeChallenge #LearningToCode #BuildInPublic #CodingTips #CareerGrowth #Innovation #Technology #WebDesign #DevProjects #LogicBuilding #JSLogic

Just built and deployed my Loan Approval Prediction System! This project takes a complete Machine Learning pipeline — from data preprocessing, model training, and evaluation to an interactive Streamlit web app — and turns it into a real-world prediction system. Tech Stack & Highlights: 🧠 Trained models: Logistic Regression, Decision Tree, and Random Forest ⚙️ Used Scikit-learn Pipelines for data preprocessing and transformations 📈 Focused on key metrics like F1-Score and AUC for performance ⚖️ Balanced class weights to handle data imbalance 🌐 Built an interactive web app with Streamlit for live predictions 📂 What it does: Predicts whether a loan application will be approved or rejected based on applicant financial and demographic information. 💡 Skills demonstrated: Machine Learning · Python · Streamlit · Pandas · Scikit-learn · Model Deployment Every project is a step closer to becoming better — open to feedback & collaboration! GitHub Repository: https://lnkd.in/gaVd_qai #MachineLearning #DataScience #Python #Project #AI #ML #Portfolio

🚀 Excited to share my latest Data Science project: Email Spam Detection System! 📧 What I built: - Developed a machine learning model using Python and scikit-learn - Implemented Support Vector Classification (SVC) achieving high accuracy - Created an interactive web application using Flask - Designed a modern, responsive UI with HTML/CSS 🛠 Tech Stack: • Python • scikit-learn • Flask • HTML/CSS • Pandas • NumPy 💡 Key Features: - Real-time email classification (Spam/Ham) - User-friendly web interface - Responsive design for all devices - Production-ready implementation This project helped me deepen my understanding of: ✅ Machine Learning Pipeline Development ✅ Text Classification ✅ Web Application Development ✅ Model Deployment Try it out: [https://lnkd.in/dNp9TgeG] #MachineLearning #DataScience #Python #WebDevelopment #AI #Programming #Flask Open to feedback and collaboration! Feel free to connect and share your thoughts. 🤝 GitHub Repository: [https://lnkd.in/dNp9TgeG]

🌸 Proud to share my very first Machine Learning Web App — Iris Flower Classification! I developed this project using Python and Streamlit in PyCharm, integrating both model training and web deployment. The app predicts the species of an Iris flower (Setosa, Versicolor, or Virginica) based on four input parameters — sepal length, sepal width, petal length, and petal width. 💡 Project Workflow & Highlights: 🔹 Trained a Random Forest Classifier on the Iris dataset using Scikit-learn 🔹 Split data into train/test sets for accurate evaluation 🔹 Saved the trained model using Pickle for later use 🔹 Deployed the model with Streamlit to create an interactive web app 🔹 Designed a clean UI that provides real-time predictions This being my first end-to-end Machine Learning app, it was a great hands-on experience that strengthened my understanding of the complete ML pipeline — from data preprocessing to model deployment. 💻 GitHub Repository: https://lnkd.in/gSZAVx5K 📸 (Screenshot of the web interface below) #MachineLearning #Python #Streamlit #ScikitLearn #PyCharm #DataScience #AI #RandomForest #ModelDeployment #GitHubProjects #FirstProject #LearningJourney #MLProjects