Streamlit Scientific Calculator with Python and Math Library

Two claps → my entire workflow is ready 👏👏 I built a small automation using Python and Claude that listens for two consecutive snaps/claps and instantly sets up my working environment. Once triggered, it automatically: • Opens Claude • Launches Chrome with my main tabs (Outlook, tracking Claude usage, and Lovable for web development) The idea was simple: reduce the friction of getting started and make my workflow faster and smoother. Instead of manually opening everything every time, it’s now done in seconds with a single trigger. Projects like this are helping me explore how AI and automation can be integrated into everyday tasks to improve efficiency and productivity. Looking forward to building more systems like this. 🚀 #AI #Python #Automation #Productivity #DeepLearning

Exploring Data Visualization with Bokeh Data becomes powerful when it tells a story—and that’s exactly what visualization helps us achieve. Recently, I explored Bokeh, a Python library designed for creating interactive and visually appealing data visualizations for the web. With Bokeh, you can: • Build interactive plots with zoom, pan, and hover tools • Create dynamic dashboards for real-time insights • Design clean and expressive visualizations with ease What makes Bokeh stand out is its ability to turn static data into interactive experiences, making analysis more engaging and insightful. As I continue learning, I’m excited to dive deeper into building dashboards and integrating Bokeh with real-world datasets. #DataVisualization #Python #Bokeh #LearningJourney #DataScience #Analytics #TIET #ThaparUniversity #ThaparOutcomeBasedLearning #ThaparCoursera #Coursera #UCS654_Predictive_Analytics

5 things people don't realize you can do in Google Sheets now: - Scrape any website (no API, no Python) - Enrich leads with live SERP data - Generate on-brand images with DALL-E, row by row - Summarize a 40-page PDF into one cell - Classify thousands of support tickets in a minute All with a formula. No Zapier. No scripts. The spreadsheet grew up. Most people haven't noticed yet. #GoogleSheets #AI #Productivity #WorkSmarter #SpreadsheetTips #SheetMagic #FutureOfWork #GoogleWorkspace

🚀 Excited to share my latest project: Traitora, the Personality Predictor! 🧠✨ Ever wondered whether you're truly an introvert or an extrovert? This machine learning web app explores that by analyzing your everyday habits through fun, relatable inputs like: -> Time spent alone -> Stage fear -> Social event attendance -> Social media activity and more! Tech Stack: ✅ Python (Pandas, NumPy) for data handling & logic ✅ Scikit-learn for building the classification model ✅ Streamlit for a user-friendly interface ✅ Jupyter Notebook for data exploration and preprocessing The app processes your inputs, scales them using a pre-trained scaler, and predicts whether you lean more toward being an introvert or an extrovert instantly! 🔗 GitHub: https://lnkd.in/deJYiVBT 🔗 Website Link: https://lnkd.in/dxK3ktJd I’d love your feedback! 🙏 What features would you add or improve? Any suggestions to make the model or UI better? #MachineLearning #Python #Streamlit #DataScience #ScikitLearn #ArtificialIntelligence #Programming #coding #development

I built a House Price Prediction App that estimates property prices based on key features such as lot area, construction year, overall condition, basement size, and location-related attributes. 🔧 Tech Stack: Python, Pandas, NumPy Scikit-learn (model development) Streamlit (interactive web application) 💡 Key Learnings: Data preprocessing: handling missing values and encoding categorical variables Maintaining feature consistency between training and prediction Building an end-to-end ML workflow (data → model → UI) Debugging practical issues like feature mismatches and NaN values 🖥️ The app provides a simple interface where users can input property details and get an instant price prediction. This project helped me move beyond theory and understand how to turn an ML model into a working application. 🔗 GitHub: https://lnkd.in/gGtxMZRa #MachineLearning #DataScience #Python #AI #Streamlit #LearningByDoing

💡 Cracking the Maximum Product Subarray Problem (Without Overcomplicating It) Today I worked on a classic DSA problem: Maximum Product Subarray — and found a simple yet powerful approach worth sharing. Most solutions focus on tracking max/min dynamically. But there’s a cleaner trick: 👉 Traverse the array from both directions (prefix & suffix) Why this works: Negative numbers can flip the product sign A single left-to-right pass might miss the optimal answer Scanning from both ends ensures we capture every possibility 🔁 Key Idea: Maintain two running products: Prefix (left → right) Suffix (right → left) Reset when product becomes 0 Track the maximum throughout ⚡ Complexity: Time: O(n) Space: O(1) Python code : https://lnkd.in/gZtCw9_i What I liked about this approach is its simplicity and elegance — no extra arrays, no complex state tracking. Sometimes, the best solutions aren’t the most complicated ones — just the most thoughtful. Have you tried solving this problem using a different approach? Would love to hear your thoughts 👇 #DataStructures #Algorithms #CodingInterview #Python #LeetCode #ProblemSolving Rajan Arora

🚀 Writing loops in Python for array operations? You’re doing it the hard way ❌ Let’s fix that with NumPy Broadcasting 👇 --- 📌 What is Broadcasting? It allows NumPy to perform operations on arrays of different shapes WITHOUT writing loops 🤯 --- 👀 Example: import numpy as np arr = np.array([1, 2, 3]) result = arr + 10 print(result) ✅ Output: [11 12 13] 👉 NumPy automatically "broadcasts" 10 to match array shape! --- 🔥 Now the real power: arr1 = np.array([[1,2,3], [4,5,6]]) arr2 = np.array([10,20,30]) result = arr1 + arr2 print(result) ✅ Output: [[11 22 33] [14 25 36]] 💡 Smaller array is stretched across rows automatically! --- 📌 Broadcasting Rules (Simple Version): ✔ Shapes must be compatible ✔ Matching from right to left ✔ Dimensions should be equal OR 1 --- 🔥 Why it matters? ✅ No loops → Faster code ✅ Cleaner syntax ✅ Used heavily in ML & Data Science --- ⚠️ Common Mistake: np.array([1,2,3]) + np.array([1,2]) ❌ Error: Shapes not compatible Follow for daily coding clarity 🚀 #Python #NumPy #DataScience #MachineLearning #Coding #Programming #LearnToCode #CodingBlockHisar #Hisar

Skforecast Studio is an amazing tool that simplifies time series analysis and forecasting. This new interactive application helps you easily create forecasting workflows, while automatically generating reproducible Python code with the skforecast library. Here are some of the main features and functionality: 📈 Configure forecasting models through an intuitive interface. 🐍 Every step generates reproducible skforecast code ready for production. 🔍 Visualize time series, seasonality, and detect patterns before modeling. 📊 Evaluate model performance with built-in backtesting and metrics. 🚀 No installation needed, skforecast Studio Runs directly in your browser! Skforecast Studio can be significantly helpful to data scientists and researchers working with time series datasets. Domain experts can also benefit from the tool, by creating forecasting models without writing any code! Check the link below for more information and make sure to follow for regular data science content. 𝗦𝗸𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁 𝗦𝘁𝘂𝗱𝗶𝗼: https://lnkd.in/ga7b9vmQ 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: https://lnkd.in/dyByK4F #python #datascience #forecasting #AI

Every journey begins with a single step — and here’s mine. I’ve built a Code Debugger App using Streamlit as part of my learning path in Data Science and Machine Learning. While it’s a simple project, it helped me understand how to turn logic into an interactive tool. 🔍 What I learned from this project: Building interactive apps with Python Structuring problem-solving logic Handling and analyzing code inputs Creating user-friendly interfaces 🌐 Live App: https://lnkd.in/gkKkyJtc 💡 My goal is to move toward more advanced projects like: Data analysis & visualization Machine learning model integration AI-powered tools This is just the beginning — more exciting projects coming soon! I’d really appreciate your feedback and suggestions 🙌 #DataScience #MachineLearning #Python #Streamlit #LearningJourney #CSE #AI #Projects

💡 From idea → execution 🚀 Ever wondered how chatbots fetch real-time data? I built one! Introducing my Python Weather Chatbot 🌤️ It takes user input and instantly responds with live weather updates. 🔧 What I used: Python + Weather API 🎯 What I learned: Real-world problem solving & API integration Small project. Big learning. 💯 Let’s connect and grow together 🤝 #PythonDeveloper #Projects #Chatbot #LearningByDoing #TechJourney