New Honeybadger Insights Features: Auto-refresh Dashboards, YAML Config & Clickable URLs

Project:  Built and deployed an end-to-end Sentiment Analysis web application using modern ML and backend tools. Model: DistilBERT Dataset Source: Kaggle : 🔗 https://lnkd.in/g-4v3TUv TechStack: Python Hugging Face Transformers DistilBERT FastAPI uvicorn HTML, CSS, JavaScript Git & GitHub 🔗 GitHub Repository: https://lnkd.in/g9XymNHv #machinelearning #python #huggingface #sentimentanalysis #fastapi #uvicorn

🚀 Project Launch: Matrix Operations Tool (Web App) I’ve built a Matrix Operations Tool using Python, NumPy, and Streamlit that allows users to perform common matrix operations through an interactive web interface. 🔹 Features: • Matrix addition, subtraction, and multiplication • Transpose and determinant calculation • Input validation with user-friendly error handling • Clean, structured UI (no terminal usage) This project helped me strengthen my understanding of: • Linear algebra fundamentals • NumPy matrix operations • Building Python-based web apps with Streamlit 🔗 GitHub Repository: https://lnkd.in/gv6G7Dud Feedback and suggestions are welcome! #Python #Streamlit #NumPy #LinearAlgebra #WebApp #LearningByBuilding #OpenSource

🚀 𝗧𝗵𝗲 𝘀𝗸𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁 𝘁𝗲𝗮𝗺 𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝘀: 𝗦𝗸𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗦𝘁𝘂𝗱𝗶𝗼 Today we’re launching something we’ve been building for a long time. 👉 https://lnkd.in/eGv-i8P7 𝗦𝗸𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗦𝘁𝘂𝗱𝗶𝗼 𝗶𝘀 𝗮𝗻 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲, 𝗻𝗼-𝗰𝗼𝗱𝗲 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 that allows you to build time series forecasting models visually, while automatically generating 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗣𝘆𝘁𝗵𝗼𝗻 𝗰𝗼𝗱𝗲 𝘂𝘀𝗶𝗻𝗴 skforecast. 𝗜𝘁’𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮 𝗱𝗲𝗺𝗼. 𝗜𝘁’𝘀 𝗮 𝗯𝗿𝗶𝗱𝗴𝗲. Our goal with skforecast has always been clear: Make forecasting more accessible, without sacrificing rigor. With 𝗦𝗸𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗦𝘁𝘂𝗱𝗶𝗼, you can: 🧩 Build forecasting models visually 📊 Explore your data with interactive charts 📈 Generate predictions with confidence intervals 🔁 Validate models using backtesting 🧠 Export clean, production-ready Python code This is a first step for anyone who wants to understand forecasting without the initial barrier of writing new code from scratch. 𝗦𝘁𝗮𝗿𝘁 𝘃𝗶𝘀𝘂𝗮𝗹𝗹𝘆. 𝗚𝗿𝗼𝘄 𝗶𝗻𝘁𝗼 𝗿𝗲𝗮𝗹 𝗣𝘆𝘁𝗵𝗼𝗻. This is just the beginning. We’re looking forward to your feedback and ideas. 👨💻 Joaquin Amat Rodrigo 👨💻 Javier Escobar Ortiz Happy forecasting! 📈 #skforecast #timeseries #machinelearning #forecasting #python 

🚀 Built a GenAI Project: AI Study Buddy 🤖📘 Developed a GenAI-powered web app that "explains concepts" in simple words and generates "MCQ quizzes" for practice. 🛠 Tech: Python (Flask), HTML, CSS, Groq API (LLaMA 3.1) 🔗 GitHub: https://lnkd.in/gCAMZU3g #GenerativeAI #Python #Flask #AIProjects #LearningByDoing

LeetCode Problem 19: "Given the head of a linked list, remove the nth node from the end of the list and return its head." The below implementation is simple and clear- Approach Double traverse the list, once to calculate the length of the list and second time to find the position of the node to be deleted. Maintain two pointers at each iteration, one to keep track present node and second to keep track of previous node. position is calculated by using the formula: pos = (len of list-n)+1 Complexity Time complexity: O(m) where m is length of list Space complexity: O(1) i.e. constant #Python #LeetCode #CompetitiveProgramming #TwoPointers #ProblemSolving #LinkedList #Algorithms #DataStructures

🏠 House Pricing Prediction Web App Developed a full-stack ML web application that predicts house prices using a regression model trained on the Boston housing dataset. Built a responsive frontend with HTML/CSS, integrated the model via a Flask backend, and deployed the app to Heroku. Implemented CI/CD with GitHub Actions for automated deployment, gaining hands-on experience with Python, scikit-learn, Flask, and end-to-end ML application deployment. Repository: https://lnkd.in/ghnXYT6y #MachineLearning #Python #Flask #WebDevelopment #DataScience #MLDeployment #Heroku #GitHubActions #FullStack #HousePricePrediction #AI #Projects

Day 3 of #200DaysOfCode! 🚀 Today, I leveled up from finding pairs to finding triplets with the classic "3Sum" problem (LeetCode 15). The goal is to find all unique triplets in an array that sum up to zero. The naive approach (three nested loops) is a painfully slow O(N^3). To solve this efficiently, I had to combine sorting with the Two Pointer technique. My O(N^2) Approach: Sort First: I started by sorting the array. This is crucial because it allows us to use pointers effectively and easily handle duplicates. Fix One, Solve Two: I iterated through the array with a fixed pointer i. For each number, the problem reduces to finding two other numbers that sum to -nums[i]. The "Two Pointer" Sweep: I placed a left pointer at i+1 and a right pointer at the end. If the sum is too small, move left forward. If the sum is too big, move right backward. The Tricky Part (Duplicates): The real challenge in 3Sum is avoiding duplicate triplets (e.g., [-1, -1, 2] appearing twice). As you can see in my code, I implemented while loops to skip over identical elements for both the fixed number and the pointers. It’s satisfying to see how sorting the data upfront makes a complex problem much more manageable. 3 days down! Consistency is building. 🧱 Has anyone tried extending this logic to "4Sum"? Does the recursion get messy? 👇 #200DaysOfCode #Python #LeetCode #Algorithms #TwoPointers #3Sum #ProblemSolving #CodingChallenge #DeveloperJourney

After learning Flask, I’m now diving into FastAPI — and the difference is exciting. 🚀 FastAPI gives me built-in data validation, automatic API docs, async performance, and cleaner, more scalable architecture out of the box. Even better, by using Jinja2 templates with FastAPI, I can still build full web applications — not just APIs — combining modern backend performance with traditional server-rendered pages. Best of both worlds. The animated gif shows a blog web application I am building as I follow the excellent Python FastAPI Tutorial  series on YouTube created by Corey Schafer. Source code: https://lnkd.in/eaNkVYHb #FastAPI #Flask #Python #WebDevelopment #APIDevelopment #BackendEngineering #SoftwareEngineering #LearningJourney

🧩 Day 73 of My Data Science Journey — Jinja Templates & Template Inheritance in Flask Today I explored one of the most powerful features of Flask: Jinja templates. 🔸 Jinja Templates I learned how Jinja allows you to write dynamic HTML using: Variables Loops Conditions Expressions This makes it easy to pass data from Flask to the frontend and render dynamic web pages. 🔸 Template Inheritance I also understood how template inheritance helps structure web pages efficiently: Creating a base template (layout) Extending it in other pages Avoiding repetitive code Keeping the UI consistent across all pages This is super useful for building scalable Flask apps. Flask templates are starting to make full-stack development feel much more organized and powerful. Onwards! 🚀🌐 #Flask #Jinja #WebDevelopment #Python #LearningJourney #BuildInPublic

Async isn’t a nice-to-have when requests start piling up. I’ve been looking at FastAPI for backend work because it makes asynchronous endpoints feel approachable without hiding what’s happening. The combination of Python type hints, automatic request validation, and clear routing keeps the codebase readable, while async/await support helps when the app is doing a lot of I/O (database calls, external APIs). What stands out is how quickly you can go from a small REST endpoint to something that’s structured and testable, without fighting the framework. If you’ve built with FastAPI, where did async genuinely improve performance for you, and where did it add complexity? #Python #FastAPI