Boost ML Performance with Python's lru_cache Decorator

Sorting "Messy Photo Folder" with Applied ML 📸 We’ve all been there: a massive, disorganized folder of photos from a wedding or a trip or a backup that's impossible to sort manually. I wanted a simple, local way to group these by person without over-engineering it. So, I built and open-sourced image-clustering-ai. It’s a straightforward Python pipeline that: ✅ Filters out blurry or tiny photos. ✅ Uses InsightFace to "see" the people. ✅ Automatically clusters them into folders by ID. I optimized for the "boring" stuff that matters: making it easy to run, readable, and actually useful for a real-world photo dump. It’s not production ready. But it’s a solid, working baseline for anyone interested in practical Machine Learning. Check out the repo, try it on your own "messy folder," and let me know what you think! 👉 Repo: https://lnkd.in/dKHeRjKv #OpenSource #Python #MachineLearning #SoftwareEngineering #ComputerVision

✨Project No. 1 🚢 Just shipped my end-to-end ML project — Titanic Survival Prediction! Built a complete pipeline from raw data to a live interactive web app using Python, scikit-learn, and Streamlit. 🔧 What's under the hood: • Auto-fetches the Titanic dataset from OpenML • Engineered features: passenger title, family size, fare per person • Trained & compared 4 models — RandomForest, GradientBoosting, Logistic Regression & XGBoost • Best model selected via 5-fold cross-validation • Deployed as a Streamlit app with real-time survival probability prediction 📊 Metrics tracked: Accuracy, Precision, Recall, F1-Score, ROC-AUC This project helped me get hands-on with the full ML workflow — from EDA and feature engineering to model evaluation and deployment. Always more to learn, but proud of this one! 🙌 #MachineLearning #AI #Python #DataScience #Streamlit #MLProject #Sklearn #OutriX

Ever wonder what happens when you give an LLM the ability to write and execute its own Python code? 🤯 I built StatBot Pro, an Autonomous AI Data Analyst using LangChain, Google Gemini 2.5, and Streamlit. You upload a CSV, ask a question, and the agent writes the Pandas logic, calculates the math, and draws the Matplotlib charts for you in real-time. Watch the video to see it dynamically generate a chart and serve it in a custom UI popup! 👇 #Python #LangChain #Streamlit #DataScience #AI #GenerativeAI Streamlit LangChainPythonGoogleGemini GoogleGemini link: https://lnkd.in/gaar5JPi

📘 What I Learned Today: Python Core Fundamentals Started my AI journey with the basics — and honestly, this is where everything begins. 🔹 Key concepts I covered: → Clean syntax & indentation (PEP8) → Variables, data types & type casting → Input/Output, comments & docstrings → Control flow (if/else, loops) → Functions & return values → Lambda functions → List, dict & set comprehensions 🔹 In simple terms: Python is designed to be simple and readable — which makes it perfect for handling data and building AI logic step by step. 🔹 Why it matters in AI: Every AI system starts with data processing, transformations, and logic — and Python makes all of this seamless. 🔹 My takeaway: Strong fundamentals = faster growth in AI. Don’t skip the basics — they compound later. #AI #Python #LearningInPublic #TechJourney #BuildInPublic

Built an AI-powered Data Cleaning Engine from scratch Raw data is messy, inconsistent, and often the biggest bottleneck in any data workflow. So I built a system that automates the process end to end: • Upload raw CSV data • Detect missing values, duplicates, and schema issues • Generate a structured data quality report • Automatically clean and preprocess the dataset • Download the cleaned output instantly Tech Stack: Python, FastAPI, Pandas, Scikit-learn, React The long-term vision is to evolve this into a more intelligent and scalable system that can handle complex, unstructured data and adapt to different domains automatically. Github Repo: https://lnkd.in/dge5-nEk #AI #MachineLearning #DataEngineering #DataScience #Python #Projects

🚀 Built a Spam Detection App using Machine Learning  I developed a machine learning model that can classify messages as Spam or Not Spam with ~96% accuracy. 🔍 What I implemented: • Text preprocessing and cleaning   • TF-IDF feature extraction   • Naive Bayes classification   • Interactive web app using Streamlit  💡 You can test it by entering any message and instantly getting predictions. 🛠️ Tech Stack: Python | Pandas | Scikit-learn | Streamlit  🎥 Demo attached below   📂 GitHub: https://lnkd.in/ghuwihsk This project helped me understand the complete ML pipeline — from data preprocessing to deployment. #MachineLearning #Python #DataScience #AI #Projects

Machine Learning from scratch: Lesson 9 Stop treating Pandas like a black box! 🕵️♂️📊 When you write df.groupby() or df.iloc[], do you know what’s actually happening in your computer's memory? In Machine Learning Series, we go beyond the syntax. We look at Pandas as a "Data Detective" and a conveyor belt that prepares your raw data for the AI engine. In this deep dive, you’ll discover: 🔹 Boolean Masking: How data is actually "filtered" (it’s not magic, it’s a True/False mask). 🔹 Split-Apply-Combine: The 3-step internal strategy of GroupBy. 🔹 The Memory Secret: Why DataFrames are actually collections of Vectors (Series). 🔹 loc vs iloc: The definitive logic to never confuse them again. If you want to move from "copy-pasting code" to "understanding the system," this article is for you. 🔗 Read the full lesson 👇 #MachineLearning #DataScience #Pandas #Python #AI #DataCleaning #Analytics #LearningJourney

Today marks day 05 of my AI ML learning progress 😊😊 I have explored key concepts of OOP in python and got to know about a freamework, Streamlit, of python. Concepts of OOP that I have covered: 1️⃣ Class, instances, object, method 2️⃣ Inheritance, Polymorphism 3️⃣ Basic of utilizing Streamlit For Machine Learning, two pillars out of four, Inheritance and Polymorphism are greatly used for visualizing dataset. Along with, Streamlit works almost like frontend like HTML,CSS & JS. It greatly helps to utilize our project to represent for UI. Yet I didn't manage to invest my time on problem solving today. Besides, learning Python, I have learned some basic about Kernel which is one of the best known algorithms in Machine Learning. Learning Kernel has introduced me with Feature Vectors, Support Vector machines, Multi-dimensional data analysis. Furthermore, I have read some articles on API and its advantages in dev community which is greatly helping me to broaden my overview of AI approach in today's world. Everyday I have got to dive deeper into the core basic of AI ML which is helping me to outshine the boundary of AI and ML. #machinelearning #ml #ai #datascience #python #documentation #article #writing #problemsolving

🚀 Day 11 of My Generative & Agentic AI Journey! Today’s learning was all about taking user input and exploring different ways of handling conditions in Python. Here’s what I learned: ⌨️ User Input in Python: • By default, input() takes data as a string • To use numbers, we need to convert the input Example: age = int(input("Enter your age: ")) price = float(input("Enter price: ")) 🔀 Conditional Logic: • Nested if-else → Conditions inside conditions for more control • Ternary Operator → Short form of if-else Example: result = "Adult" if age >= 18 else "Minor" • match-case → Used for pattern matching (like switch case) Example: match day: case 1: print("Monday") case 2: print("Tuesday") 👉 Key takeaway: Taking user input and applying conditions makes programs interactive and smarter. Another step forward towards building real-world applications 🚀 #Day11 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic

🚀 The real power of Python in AI isn’t just models… it’s speed. Most people write loops. Smart people use vectorization. While working on data tasks, I realized: ❌ Traditional loops slow everything down ❌ Manual processing wastes hours But with tools like NumPy, Pandas & AI frameworks: ✅ Boolean indexing replaces loops ✅ Broadcasting handles large data instantly ✅ Vectorized logic runs across entire datasets And the result? 📊 2 hours of work → less than 20 seconds This is where Python + AI truly shines — not just building models, but accelerating everything around them. Still learning, but exploring this ecosystem has completely changed how I approach data. If you're working with data, start thinking beyond loops. 💬 Comment “Python” if you want practical examples of these tricks. #Python #AI #DataScience #NumPy #Pandas #MachineLearning #Automation #LearningJourney