Building Machine Learning Model with Flask and Scikit-Learn

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

Stop using Pandas for everything. I just published a full breakdown of 7 Python libraries that are redefining how developers build in 2026 with install commands + real code examples for each. Here's the quick cheat sheet: ⚡ Polars → 10x faster than Pandas for big data 📄 MarkItDown → Converts PDFs/Word docs into AI-ready Markdown 🤖 Smolagents → Build your first AI agent in 10 lines 🧑✈️ GPT Pilot → An AI that writes entire features, not just autocomplete 📱 Flet → Build web + mobile + desktop apps in pure Python 🛡️ Pyrefly → Catch bugs BEFORE you run your code (Meta-built) 🌐 Morphik-Core → AI that understands images and videos, not just text You don't need to learn all 7 today. Pick the one that solves YOUR problem right now. Working with data? → Polars Building an app? → Flet Curious about agents? → Smolagents The full blog (with code examples for every library) is linked in the comments 👇 Which of these are you already using? Drop it below 🔽 #Python #AI #MachineLearning #Programming #Developer #TechIn2026 #AITools #OpenSource #PythonDeveloper #CodingTips

💻 Just built something I’m genuinely proud of I created a Real-Time AI System Monitor that not only tracks system performance but also predicts future CPU usage and detects anomalies. What started as a “let me try this” idea turned into a full system with: • Real-time monitoring (CPU, memory, disk, network) • AI-based predictions using a Python ML service • Anomaly detection with explainable insights • Interactive dashboard with live charts At one point, I noticed I had multiple things running at the same time like coding, experimenting with AI tools, and a bunch of random tabs running in the background and it became difficult to understand how much load I was actually putting on my system. That moment made this project feel even more relevant. One of my favorite parts was watching the system respond in real time while I was working and it made everything feel much more tangible. Tech stack: React • Node.js • MongoDB • Python (Flask + NumPy) Built, deployed, and tested end-to-end: 🔗 Live demo: https://lnkd.in/gX_nGWAX 🔗 GitHub: https://lnkd.in/gHbzdVs4 If you found this interesting, feel free to check out the repo, feedback and ⭐ are always appreciated :) #FullStackDevelopment #MachineLearning #WebDevelopment #ReactJS #NodeJS #Python #BuildInPublic

🐍📊 Python + Data Science = A match made in heaven. If you're diving into data science (or leveling up your skills), mastering Python is non-negotiable. Here’s why: ✅ Simplicity – Clean syntax means you focus on solving problems, not fighting the language. ✅ Ecosystem – Pandas for wrangling, NumPy for numbers, Matplotlib/Seaborn for visuals, Scikit-learn for ML. ✅ Community – Thousands of free resources, libraries, and real-world projects to learn from. 🚀 3 Python tricks that saved me hours: df.query() instead of multiple slicing conditions in Pandas. seaborn.set_theme() for instantly better-looking plots. pd.to_datetime() with errors='coerce' to clean messy date columns fast. Whether you’re a beginner or a seasoned analyst, Python scales with you. 👇 What’s your go-to Python library for data work? #Python #DataScience #DataAnalytics #MachineLearning #Pandas #Coding

🚀 Last month, I built and published my first Python package — Pristinizer I wanted to solve a simple but real problem in data science: 👉 Cleaning and understanding raw datasets takes way too much time. So I built Pristinizer, a lightweight Python package that helps streamline data cleaning + EDA in just a few lines of code. 🔍 What Pristinizer does: • Cleans messy datasets (duplicates, missing values, column formatting) • Generates structured dataset summaries • Visualizes missing data (heatmap, matrix, bar chart) ⚙️ Tech Stack: Python • pandas • matplotlib • seaborn 📦 Try it out: >> pip install pristinizer >> import pristinizer as ps df = ps.clean(df) ps.summarize(df) ps.missing_heatmap(df) 🧠 What I learned while building this: • Designing a clean and intuitive API • Structuring a real-world Python package • Publishing to PyPI • Writing proper documentation for users 📌 Next, I’m planning to add: • Outlier detection • Automated preprocessing pipelines • Advanced EDA reports Would love to hear your thoughts or feedback! #Python #DataScience #MachineLearning #OpenSource #Pandas #EDA #Projects

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

🚀 Machine Learning With Python From Scratch — Part 2! This time we level up from single to Multiple Variable Linear Regression — and we also cover something that most beginners skip but is super important in real life: saving your model with Pickle. Multiple Variable Linear Regression is the same idea as single variable, but instead of using one input to predict an output, you use several. In this example I predicted an employee's salary based on: --Years of experience --Test score --Interview score But before even touching the model, the data had to be cleaned: Experience was stored as words ("five", "seven"), had to convert them to numbers Some values were missing, handled with median filling That's the part nobody talks about. Real data is messy. Cleaning it is half the job. And once the model is trained, what do you do with it? You save it using Pickle, so you never have to retrain it again. 🔗 Full notebook + dataset + detailed explanation on GitHub: 👉 https://lnkd.in/dC5Pzygv If you're just getting into ML, follow along, I'm building this series from the ground up, one concept at a time. #MachineLearning #Python #DataScience #LinearRegression #Pickle #DataCleaning #GitHub #BeginnerML #100DaysOfCode

Right now, I’m keeping my stack simple and focused: Python This is where everything starts for me. Most of what I build runs on it. Pandas My go-to for working with data. Cleaning, filtering, analyzing — all in one place. NumPy Helps me understand what’s happening under the hood with arrays and calculations. Matplotlib Still learning, but using it to visualize data and actually “see” patterns. Alongside this, I’ve started focusing more on math for ML. Taking it slow, trying to really understand concepts instead of rushing through. Feels like going back to basics, but in a good way. If you’re on a similar path, what are you focusing on right now?

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