Build a Google Maps Alternative with Python

I just shipped my first fully deployed, end-to-end Python project It's an Auto EDA (Exploratory Data Analysis) web app — upload any CSV dataset, and it instantly gives you: → Dataset overview (rows, columns, missing values) → Summary statistics → Outlier detection using the IQR method → Skewness & correlation analysis → Auto-generated, plain-language insights → Interactive, dynamic visualizations for any numeric or categorical column What made this project valuable wasn't just the code it was the full workflow: designing cleaning logic, validating statistical assumptions, version-controlling with Git/GitHub, and deploying it live on Streamlit Community Cloud. Built with: Python, Streamlit, Pandas, Matplotlib, Seaborn 🔗 Try it: [https://lnkd.in/dGF_TJte] 💻 Code: [https://lnkd.in/d59FMS_H] Open to feedback and suggestions for what to add next! #DataAnalytics #Python #Streamlit #MachineLearning #DataScience #Portfolio

Microsoft just dropped Agent Framework 1.0 for .NET and Python, and the timing is wild. It lands seven months after the framework race went full sprint—LangGraph, OpenAI Agents SDK, Claude Agent SDK, Google ADK, Pydantic AI, CrewAI, and Mastra all shipped major updates between February and August 2026. That's not a release cycle; that's a land grab. The interesting part isn't the SDK itself. It's the runtime. Microsoft is betting on production readiness over experimentation. While others focus on flexibility, this framework aims to be the boring, reliable choice—the one you can put in front of a customer without crossing your fingers. What does that mean for builders? The real differentiator is no longer the ability to chain LLM calls. It's observability, error handling, and deployment paths. If you're evaluating frameworks, don't compare feature lists. Compare how each handles a failed tool call at 2 AM. One practical takeaway: pick a framework that matches your team's existing stack. Python teams have a dozen options; .NET teams finally have a first-class choice. That alone might justify the switch. Which framework are you betting on for production, and what made you choose it?

🚀 Started My Machine Learning Journey with My First End-to-End Project! I've recently started learning Machine Learning, and to put my learning into practice, I built a Food Delivery Time Prediction application. Instead of stopping at training a model, I wanted to build a complete application where users can interact with it through a web interface. 🛠 Tech Stack Frontend: React.js + Vite Backend: FastAPI (Python) Machine Learning: Linear Regression (Scikit-learn) Model Serialization: Joblib What I Learned ✅ Building REST APIs with FastAPI ✅ Connecting a React frontend with a Python backend ✅ Training and serving an ML model ✅ Building an end-to-end ML application I know this is just the beginning. The current model uses Linear Regression on a static dataset, while real-world delivery prediction depends on factors like traffic, weather, rider availability, and more. Next Steps Try Random Forest and XGBoost Integrate Google Maps and Weather APIs Dockerize and deploy the application Improve the UI and overall prediction accuracy 🔗 GitHub: https://lnkd.in/dD4PVsfa 🌐 Live Demo: https://lnkd.in/dNMPsQ9C I'd love to hear your feedback or suggestions as I continue learning and building! #MachineLearning #Python #FastAPI #ReactJS #ScikitLearn #FullStackDevelopment #SoftwareEngineering #LearningInPublic #StudentDeveloper

🚀 Hi Power Platform Enthusiasts!! 🚀 Recently I've been working on migrating data 📊 from Azure SQL into Dataverse, and while looking around for the right approach I discovered Microsoft now has an official Dataverse SDK for Python 🐍✨ Turns out it's not just for migrations 🔄 - it's built to support automation ⚙️, AI-driven workflows 🤖, and data analysis 📈 on top of Dataverse. Feels like a big step in making Dataverse more accessible beyond traditional Power Platform development! I'm not a Python developer myself 🙋, so since we're in the AI era 🌐, I had Claude set up the whole thing for me - install, connection, everything ✅. What's neat is the SDK comes with two built-in Claude Skills 🧠 (dataverse-sdk-use and dataverse-sdk-dev) that guide the AI to follow Microsoft's recommended patterns instead of just guessing 🎯. So even without knowing Python, I could still get a working setup done right! 💪 Still early days on my actual migration 🚧, but this was a nice discovery along the way which I wanted to share🙌 Official docs here 📄: https://lnkd.in/ebd73Xcs

Built and deployed my first end-to-end Python Price Tracker & Delta Alert Engine! I wanted to build a practical utility that goes beyond basic web scraping and handles real-world state persistence and alert management. Over the last few days, I built a complete 3-tier automated system: 🔹 Ingestion: Scrapes live e-commerce product pages using BeautifulSoup & Requests. 🔹 Persistence Layer: Logs historical runs into a structured dataset using pandas to track price variance over time. 🔹 Alert Engine: Encrypts network handshakes using Python’s smtplib (TLS) to fire instant email notifications strictly when actionable price drops occur—preventing alert fatigue. Solving real-world edge cases like character encoding issues, data persistence, and conditional event triggers was an awesome learning experience. Check out the full project and source code on my GitHub: https://lnkd.in/dTJEWmg2 #Python #WebScraping #DataEngineering #SoftwareDevelopment #Automation #CodingJourney #GitHub

From raw event logs to a live cloud application! I built and deployed an interactive Marketing Funnel & Root Cause Performance Tracker using a cloud-first architecture via GitHub Codespaces. By processing 10,000 user event touchpoints, the application utilizes an embedded SQL database layer to isolate acquisition drop-off vectors and track week-over-week conversion volatility. The Stack: Python (Pandas, Matplotlib), SQLite3 (CTEs & Window Functions), and Streamlit Cloud. Check out the live interactive app below to filter channels and explore the underlying code! Live Application: https://lnkd.in/dU-XiYZJ GitHub Architecture: https://lnkd.in/dXM5VpKK #DataAnalytics #SQL #Python #Streamlit #BusinessIntelligence #MarketingAnalytics #Portfolio

🚨 Microsoft Azure Functions: typescript-eslint vs. Python — How to Choose the Right Tool When choosing between typescript-eslint and Python for Microsoft Azure Functions, don't just pick your favorite language. Look at what your code actually does. Both languages are fast to write, but they work very differently inside Azure. Here is a simple guide to help you choose the right one for your architecture: 🟢 Choose TypeScript if your function: Handles lots of web requests at the same time: TypeScript (Node.js) is built for speed when waiting on other systems. It is perfect for APIs, webhooks, and passing data between services without slowing down. Shares code with your frontend: If your app is written in React, Vue, or Angular, you can use TypeScript everywhere. This lets you share the same data types and rules between your frontend and backend. Needs fast startup times (Cold Starts): When Azure wakes up a sleeping function, TypeScript starts up very quickly. This keeps your application responsive for real users. 🟡 Choose Python if your function: Works with AI and Data Science: Python is the king of AI. If you are using OpenAI, LangChain, or working with tools like Pandas and NumPy to clean data, Python has the best libraries hands down. Processes heavy files and data pipelines: If your function gets triggered by a file upload and needs to clean, convert, or analyze large amounts of data, Python makes this effortless. Runs long, multi-step workflows: Python works great with Azure Durable Functions to handle complex, step-by-step tasks that need to track their progress over time. 🎯 Quick Summary If your function is a fast web API or connects your frontend to a database ➡️ Go with typescript-eslint. If your function handles AI, heavy data processing, or machine learning ➡️ Go with Python. 👇 To the developers: If you use Python on Microsoft Azure Functions, how do you handle slow startup times when using heavy libraries like Pandas or PyTorch? Drop your best tips in the comments! #TypeScript #NodeJS #Python #SoftwareEngineering #WebDevelopment #CloudArchitecture #Microservices #SystemDesign #DevOps #TechCommunity #AzureFunctions #Azure #Serverless #CloudComputing #MicrosoftAzure

𝗗𝗔𝗬 𝟮/𝟯𝟬 — 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 𝗕𝘂𝗶𝗹𝗱 𝗔𝗣𝗜𝘀 𝗙𝗮𝘀𝘁. 𝗥𝗲𝗮𝗹𝗹𝘆 𝗙𝗮𝘀𝘁. 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 is a modern, high-performance Python web framework for building APIs with Python 3.7+ — based on standard Python 𝘁𝘆𝗽𝗲 𝗵𝗶𝗻𝘁𝘀. 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 𝘃𝘀 𝗙𝗹𝗮𝘀𝗸: ❌ 𝗙𝗹𝗮𝘀𝗸 (𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝘄𝗮𝘆): from flask import Flask, jsonify app = Flask(__name__) @app.route("/items/{item_id}") def read_item(item_id): return jsonify({"item_id": item_id}) More boilerplate code Manual data validation No automatic documentation Slower performance ✅ 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 (𝗺𝗼𝗱𝗲𝗿𝗻 𝘄𝗮𝘆): from fastapi import FastAPI app = FastAPI() @app.get("/items/{item_id}") def read_item(item_id: int): return {"item_id": item_id} Less code, more productivity Automatic data validation Auto-generated docs (Swagger UI) High performance (Starlette + Pydantic) 𝗪𝗵𝘆 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝗹𝗼𝘃𝗲 𝗙𝗮𝘀𝘁𝗔𝗣𝗜: 𝗕𝗹𝗮𝘇𝗶𝗻𝗴 𝗳𝗮𝘀𝘁 — one of the fastest Python frameworks available 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗯𝗮𝘀𝗲𝗱 — built on OpenAPI (Swagger) and JSON Schema 𝗧𝘆𝗽𝗲 𝘀𝗮𝗳𝗲 — uses Python type hints for fewer runtime errors 𝗔𝘂𝘁𝗼 𝗱𝗼𝗰𝘀 — interactive API docs generated automatically 𝗘𝗮𝘀𝘆 𝘁𝗼 𝘂𝘀𝗲 — simple, intuitive, great developer experience 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗿𝗲𝗮𝗱𝘆 — used by companies like Microsoft, Uber, and more 🎯 𝗣𝗿𝗼 𝘁𝗶𝗽: 𝗦𝘁𝗮𝗿𝘁 𝘀𝗺𝗮𝗹𝗹, 𝘁𝗵𝗶𝗻𝗸 𝗯𝗶𝗴. FastAPI is perfect for building: REST APIs, mobile backends, microservices, and AI/ML APIs. Learning today, leading tomorrow. Save this for later. Follow along for Day 3 — PostgreSQL. 👇 #30DaysOfPython #FastAPI #Python #BackendDevelopment #APIDesign #SoftwareEngineering

Revisiting one of my earlier projects: Real Time Rainfall Prediction System using Machine Learning 👏 During my University, I developed an end-to-end Rainfall Prediction System that combines a trained AI model with a full-stack web application. Looking back, it remains one of the projects that shaped my interest in applying machine learning to real-world problems. Project Overview: The system uses a machine learning model trained on historical weather data to predict rainfall, with a complete web interface for user interaction. Tools & Technologies: Model Development: Kaggle (dataset), Google Colab (model training), Visual Studio Code (development environment) Frontend: HTML, CSS, JavaScript, Bootstrap Backend: Python, MongoDB This project gave me hands-on experience across the full development lifecycle - from data collection and preprocessing to model training and building a functional, user-facing application. Sharing a short video demo below. 👇 Feedback and suggestions are always welcome. #MachineLearning #ArtificialIntelligence #Python #WebDevelopment #MongoDB #DataScience #UniversityProject #SoftwareDevelopment

🚀 Just published VoiceDoc AI on GitHub! An AI-powered document assistant that lets users upload documents, ask questions via text or voice, and get intelligent, context-aware answers using Retrieval-Augmented Generation (RAG). Tech Stack: Flutter • Python • Django • OpenAI • ChromaDB • PostgreSQL • Redis • Celery • Docker • WebSockets Built with a focus on clean architecture, scalability, and real-time AI experiences. 🔗 Repository: https://lnkd.in/d464DK5Z #Flutter #Python #Django #AI #OpenAI #RAG #LLM #SoftwareEngineering #OpenSource #GitHub