Deployed House Price Predictor App with Python and Streamlit

A frustrating problem I've been doing workarounds for... running a Python project on Kaggle's free GPU. I used to zip all my project files, upload them to Kaggle, and run my code. Just to discover a simple bug. A line I forgot to change. An error somewhere. And then repeat the whole process over and over until the code finally works. And even when it does... experimenting with different configurations means the cycle never really ends. So I built repo2nb. Just type `pip install repo2nb` in your terminal and you are ready to go! A simple Python tool you run directly from your terminal inside your project. One command and it converts your entire project into a single Jupyter notebook that reconstructs all your files right inside Kaggle with GitHub support so you can sync any changes back without ever leaving the session. Everything is now in one place. I can edit, delete, and create files directly from Kaggle without going through that old cycle again. It helped me personally so I decided to publish it so every student or hobbyist can save time when working on personal or academic projects. 🔗 GitHub →https://lnkd.in/d-ESX2Hf ▶️ Quickstart Video → https://lnkd.in/dk7hPQPZ If you liked it or had fun using it, a star on the repo means a lot ⭐ #Python #MachineLearning #Kaggle #OpenSource #MLTools #DeepLearning

Day 3/30 – NumPy operations with image I developed an Image Editor web app where most of the image processing is powered by NumPy. 🌐 Live App: https://lnkd.in/gBf2rK7U GitHub: https://lnkd.in/gxENJ_3C Key things I learned: 🔹 NumPy for Image Processing – Images are just arrays (pixels) – Applied operations like brightness, contrast, negative using array math – Used slicing for crop, flip, rotate – Created effects like grayscale, sepia, vignette using matrix operations 🔹 Real-time Transformations – Converted images (Base64 ↔ NumPy array) – Applied filters and returned processed images through APIs 🔹 Advanced Processing – Blur (Gaussian filter), edge detection (Sobel) – Sharpening using array differences This project helped me understand how powerful NumPy is for real-world image manipulation. #NumPy #Python #Flask #ImageProcessing #WebDevelopment #LearningByDoing

🚀 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

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

Day 46/60 of #60DaysOfMiniProjects Built a Smart Study & Mood Tracker using Flask! Excited to share my latest project where I combined productivity tracking with a touch of intelligent suggestions Features: • Track daily study sessions with mood & notes • Smart suggestions based on mood and activity • Productivity score calculation • Daily streak tracking • Search, edit, and manage past sessions • Clean and simple user interface Tech Stack: Python | Flask | JSON | HTML/CSS This project helped me understand how small data insights can improve consistency and focus in daily routines. Would love your feedback and suggestions to improve it further! #Python #Flask #WebDevelopment #StudentProjects #Productivity #CodingJourney #OpenToLearn

📊 Day 27: Getting Started with Matplotlib As part of my continuous learning journey, I’m diving into Matplotlib — one of the most powerful data visualization libraries in Python. 🔹 What is Matplotlib? It’s a plotting library used to create static, animated, and interactive visualizations in Python. 🔹 Why it matters: - Turns raw data into insights - Helps communicate findings clearly - Widely used in Data Science & Engineering 🔹 First example: import matplotlib.pyplot as plt x = [1,2,3,4] y = [10,20,25,30] plt.plot(x, y) plt.title("Simple Line Plot") plt.xlabel("X-axis") plt.ylabel("Y-axis") plt.show() 📌 Simple, but powerful. This is the foundation of data visualization in Python. Tomorrow: Customizing plots 🎯 #M4ACELearningChallenge #LearningInPublic

🚀 Project Showcase: Student Performance Analyzer I recently built a Student Performance Analyzer using Python, Pandas, Matplotlib, Seaborn, and Streamlit 📊 🔍 What this project does: Upload student dataset (CSV) Analyze subject-wise performance Visualize trends using graphs Understand impact of study hours on scores 📈 Key Insights: Identified correlation between study hours and performance Compared subject-wise strengths and weaknesses Generated interactive visual reports 🛠️ Tech Stack: 🤩 Python | Pandas | Matplotlib | Seaborn | Streamlit 💡 This project helped me strengthen my skills in: Data Analysis Data Visualization Building interactive web apps 🔗 I’m excited to keep building more real-world data science projects! #DataScience #Python #Streamlit #DataAnalysis #MachineLearning #StudentProject #LearningJourney

🚀 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

hello Everyone ever wondered how risky a lottery system really is? 🤔 I built a simple Lottery Simulation Program using Python to visualize profit & loss over time. 💡 In this project: Used random to simulate real-life lottery draws Applied basic logic for betting outcomes Tracked account balance over 30 days Visualized results using matplotlib 📉 📉 The graph clearly shows how unpredictable outcomes can impact your balance — a small experiment that highlights risk vs reward in a simple way. 🚀 This project helped me strengthen: Python fundamentals Logic building Data visualization skills Would love your feedback and suggestions! 🙌 #Python #PythonProjects #Coding #Programming #Developer #DataVisualization #MachineLearning #Tech #SoftwareDevelopment #100DaysOfCode #LearnToCode #CodingLife #Programmer #AI #Matplotlib #DataScience #TechCommunity #LinkedInLearning #CodingJourney #Developers even a small programs can lead towards the greater heights ❤️

My first trained model sat in a Jupyter notebook for two weeks. I had no idea how to let anyone else use it. That is the gap between knowing ML and doing ML engineering. Knowing how to serve a model is a different skill from knowing how to train one. Here is how to go from a saved model file to a live REST API in under 30 lines of Python. The key insight that took me too long to learn: never load the model inside the endpoint function. Load it once on startup. Every call after that is instant. FastAPI also generates an interactive docs page automatically at /docs. Zero extra work. Point anyone at the URL and they can test your API from the browser. Four things to add before real traffic: input validation beyond types, request logging, structured error handling, and a /health endpoint for your load balancer. Swipe through for the complete code. What was your first production ML deployment? Flask, FastAPI, something else? #Python #FastAPI #MLOps #MachineLearning