3 weeks ago, I didn't know how recommendation systems worked. Today, I built one — and deployed it live. 🎬 👉 https://lnkd.in/gSkd-KC9 The journey wasn't easy: ❌ Python 3.14 broke everything ❌ GitHub rejected 175MB files ❌ Packages wouldn't install ❌ API keys blocked by network But I fixed every single error. One by one. 💪 Here's what CineMatch does: 🎯 Type any movie → Get 5 AI-powered recommendations 🎯 Real posters + IMDb ratings 🎯 4,800+ movies in the database 🎯 Results in under 1 second 🛠️ Built with: Python | Scikit-learn | Pandas | Streamlit | OMDb API 📂 Full code: https://lnkd.in/gpvcfZRj If you're learning Data Science — build projects. Not just tutorials. Real projects with real errors. That's how you actually learn. ✅ What movie should I search first? Comment below! 👇🍿 #DataScience #MachineLearning #Python #AI #Streamlit #OpenToWork #100DaysOfCode #MLProject #Python #Coding

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

💻 Excited to share my latest project! I’ve built a Streamlit-based Scientific Calculator using Python that performs both basic and advanced mathematical operations. 🧮 Features: ●Addition, Subtraction, Multiplication, Division ●Square Root Calculation ●Power (Exponent) Function ●Logarithmic Operations Clean and interactive web UI using Streamlit 🌐 Tech Stack: Python | Streamlit | Math Library 🚀 I also deployed this project on Hugging Face Spaces, making it accessible as a live web application. This project helped me strengthen my understanding of: ✔ Python functions ✔ UI development with Streamlit ✔ Deployment of web apps ✔ Problem-solving logic 🔗 GitHub Repo: https://lnkd.in/d4n946w7 🌐 Live Demo: https://lnkd.in/dMti6kJX ✨ Always learning, building, and improving one project at a time! #Python #Streamlit #MachineLearning #WebDevelopment #Coding #StudentDeveloper #AI #Projects

Continuing our journey into Python, Machine Learning, and Flask! 🚀 As we mentioned recently, we have been receiving a lot of client requests around these technologies. Before diving into the more complex topics, we started with a solid foundation by building a simple CRUD REST API using Flask and SQLite. Now, it is time to take the next major step. We are excited to share a brand new two-part series that bridges the gap between data science and software engineering. If you have ever wondered how to take a model out of a notebook and connect it to a real web application, this is for you. 📘 Part 1: Building a Simple Machine Learning Model with Scikit-Learn in Google Colab We walk you through generating a synthetic dataset, training a Logistic Regression model, evaluating its performance, and saving it for deployment. 🔗 https://lnkd.in/gk9aJStb 📙 Part 2: Serving a Pre-Trained Colab Model as a REST API with Flask We take the model saved in Part 1 and wrap it in a lightweight Flask web server, creating a JSON API that any frontend or mobile app can interact with. 🔗 https://lnkd.in/gft57MYa Check out both guides on our blog and let us know what you build! #MachineLearning #Python #Flask #DataScience #WebDevelopment #ScikitLearn #RESTAPI #QadrLabs

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

Data View v1 is live. No hype — just a clean build. Built with Streamlit, Python, Pandas, NumPy, Seaborn, and Matplotlib, this app cuts through the noise and gets straight to the point: understanding your data without wasting time. What it handles right now: • Upload your dataset • Quick data overview • Basic cleaning • Statistical insights • Correlation analysis • Visuals — bar, histogram, pie It’s not flashy. It’s functional. And it works. But this is just the opening move. Now your move 👇 • What’s one feature you’d add next? • What would make you actually use this daily? • What’s missing? Be direct. I’m listening. I’ll be shipping a sharper version every Monday — better features, tighter experience, smarter analysis. No excuses, just iterations. Because good products aren’t guessed — they’re built, tested, and refined. live demo --> https://lnkd.in/gXda-aZs #BuildInPublic #DataScience #Streamlit #Python #KeepBuilding

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

Headline: Stop wasting time cleaning data manually. Body: Spent hours cleaning a dataset of user feedback today. It was messy—typos, missing values, different formats. I realized I was approaching it like a textbook exercise, not a engineer. Thinking: If I do this again, I’m wasting time. Solution: I created a Python pipeline that automatically handles missing data, maps common typos, and standardizes formats using Pandas. Real Result: Cut data cleaning time from hours to few minutes. #Programming #Productivity #MLOps #qurateHq #thriveabia

🚀 Built My Student Life Tracker Using Python & Streamlit Excited to share my latest project — Student Life Tracker, designed to help students manage productivity, study hours, and expenses in one place. ✨ Features: • 📚 Study Tracker with Weekly Analytics • 💰 Expense Tracker • ⚡ Productivity Tracker • 🎯 Monthly Goals • 🔥 Study Streak Counter • 📅 GitHub-style Heatmap Calendar • 📊 Clean Interactive Dashboard 🛠 Tech Stack: Python | Streamlit | Pandas | Plotly This project helped me improve my skills in: • Data Analysis • Dashboard Development • Python • UI Design Live App: https://lnkd.in/d3-UUYwC Would love your feedback! 😊 #Python #DataAnalytics #Streamlit #StudentProject #PortfolioProject #LearningInPublic #Dashboard #DataScience