Hey everyone 👋 I recently built a small project that I’m really excited about — a CSV AI Agent 📊🤖 Github Repo: https://lnkd.in/djDbQJ5z Live Demo: https://lnkd.in/ddJTzTw2 The idea was simple: What if you could just talk to your data instead of writing code? 🔍 Analyzing Data 📊 Visualizing Insights 🤖 AI-Powered Responses ⚡ Instant Results You can upload any CSV file and ask questions in simple English like: 👉 “What’s the average sales?” 👉 “Show top 10 categories” And it gives you answers + creates charts automatically! 💻 Built with: Python, Streamlit, LangChain, Groq (Llama 3.3), Pandas, Matplotlib & Seaborn 🔐 Note: To try the app from my link, you’ll need your own Groq API key — just plug it into the sidebar and you’re good to go! Still improving this project—would love your feedback and suggestions 😊 #AI #DataScience #Python #Streamlit #LangChain #Groq #MachineLearning #DataAnalytics #BuildInPublic #LearningJourney #TechProjects #AIProjects

Built something interesting this week. I created a simple tool that turns raw datasets into step-by-step analysis without the usual mess. You upload your file, describe what you need, add your own API key, and it handles the rest. Clean code, proper flow, and most importantly complete outputs (not half-baked results). Kept it very intentional: – No internal API usage – No guessing or skipping steps – No unnecessary visuals unless asked Just a controlled system that does exactly what you tell it to do. Also added export options (Python, Jupyter, Colab, Streamlit) so you can actually use the work outside the tool. UI is minimal, fast, and built with a futuristic feel (green + black theme). Still early, but it works and that’s what matters. Curious to hear: What would you improve in something like this? Hisham Sarwar Saad Hamid Mehroze Munawar Muhammad Umar Nazir #AISeekho2026 #VibeKaregaPakistan #DataScience #AI #Python #BuildInPublic #SaaS

Nobody talks about the quiet revolution that already happened in Python data tooling. Pandas was the default for years. Comfortable. Familiar. Everywhere. But in 2024–2025, something shifted. Here's what the modern Python data stack actually looks like now: → DuckDB for analytical queries on local files No server. No setup. Just SQL that runs faster than you expect directly on CSVs and Parquets. → Polars for dataframe operations Written in Rust. Built from scratch for multi-core CPUs. Lazy evaluation by default. On large datasets, it's not 2× faster than Pandas. It's often 10–50×. → Pandas is still useful. But mostly as a last step for compatibility, not for computation. The real insight here isn't the tools. It's the mental model. The old stack was: load → transform → analyze (all in Pandas). The new stack is: query first (DuckDB) → transform fast (Polars) → output clean (Pandas if needed). If you're still running df.groupby() on a 5M-row CSV in Pandas and wondering why your laptop fan is screaming this is for you. I wrote a deep dive on exactly this shift covering benchmarks, real code comparisons, and when to use which tool. Follow for more practical AI & data engineering content. What's your current go-to for data wrangling? Still Pandas, or have you made the switch? 👇 #Pandas #Python #DataScience #AI #DataCleaning

Week 1 of building SmartOps in public. SmartOps is an AI-powered customer follow-up tool The problem I am solving: A food seller has 20 customers. 11 of them silently stopped ordering last month. She has no idea. That is lost revenue she will never recover because nobody told her to call them. This week I built the first piece, a Python script that: - Tracks all customers in one place - Calculates how long each person has been inactive - Generates a daily "call these people today" list automatically Next week: cleaning real messy business data with Pandas. This is Week 1 of 12. Every week I will share what I built, what I struggled with and what I learned. Built with Python + Pandas. Synthetic data used for demonstration. Full writeup on Medium: [https://lnkd.in/ekQBT5Zf] GitHub: [https://lnkd.in/eD4suFWY] Here is what the output looks like #BuildingInPublic #AIML #SmartOps #NigerianTech

I recently had an interview where I was asked how I would build an AI system that can answer questions from 10,000 files. I didn’t have a strong answer. My AI experience was mostly chat history and summarization — not retrieval across a large document set. At the end the interviewer gave me a hint: RAG. So I built it from scratch — a document Q&A API where you upload files and ask questions about them. The workflow: 1. Split documents into chunks 2. Embed each chunk locally using sentence-transformers (free, runs on your machine) 3. Store vectors in PostgreSQL with pgvector 4. Embed the user query 5. Retrieve top 20 candidates via approximate nearest neighbor search 6. Rerank using a cross-encoder model to select the true top 5 7. Generate a grounded answer via Groq API (free tier, Llama 3.1) Built with Python, FastAPI, and containerized with Docker Compose. Used Azure Blob Storage (free tier) for file storage and Groq for inference — the entire stack costs $0 to run. I didn’t get the job. But I turned one weak answer into a project and a much better understanding of retrieval systems. Next time I get that question, I’ll have a real answer. GitHub: https://lnkd.in/e7cDAjdx #RAG #Python #FastAPI #PostgreSQL #LLM #SoftwareEngineering

If you are still using pypdf for every project, you are leaving speed and accuracy on the table. In 2026, the PDF-to-data pipeline has shifted. One ecosystem has become the absolute standard for speed and AI integration. Here is my quick decision matrix for Python PDF libraries: 📊 The 2026 Cheat Sheet: 🚀 For Raw Speed: PyMuPDF (fitz) → Why: It's built on a C-engine. It’s blazing fast and handles thousands of pages in seconds. 🤖 For RAG/LLM Input: pymupdf4llm → Why: It provides perfect Markdown output and preserves table structures that AI actually understands. 📐 For "Surgical" Tables: pdfplumber → Why: Unmatched accuracy for those nightmare, borderless tables that other libraries miss. ☁️ For Zero Dependencies: pypdf → Why: Pure Python. Best for restricted cloud environments (like certain AWS Lambda layers). For 90% of my production work, I now default to PyMuPDF. It is the foundation of modern high-performance extraction. Agree or disagree? What’s your default library and why? Let’s fight it out in the comments! 🥊👇 #programming #python #dataengineering #ai #productivity #pymupdf

Explore the full project walkthrough here: https://lnkd.in/gTZkH92a Before building any predictive model, you need to understand the story hidden in the data. This project performs a comprehensive Exploratory Data Analysis on the Brazilian Olist e-commerce dataset using Python. From order trends and delivery performance to customer behavior patterns, this notebook demonstrates how to use Pandas, Matplotlib, and Seaborn to uncover actionable insights from raw transactional data. It's a practical template for anyone starting out in data analytics. For more project guides, tutorials, and technical resources, visit www.codeayan.com #codeayan #DataScience #Python #EDA #ExploratoryDataAnalysis #Pandas #DataAnalytics #Ecommerce #DataVisualization #MachineLearning #TechBlog #Matplotlib #Seaborn #JupyterNotebook #DataDriven #BusinessIntelligence #Analytics #Programming #TechCommunity #AI

Moving beyond Jupyter Notebooks 🚀 I’ve just wrapped up a project that takes Machine Learning from a static script to a fully functional, production-ready pipeline: The Student Performance Predictor. 📊 While many ML projects live and die in a .ipynb file, I wanted to build something that mirrors a real-world industry workflow. This project predicts a student's math score by analyzing a mix of demographic data and academic history. What makes this "Production-Ready"? Instead of one long script, I built a modular architecture: 1. Data Ingestion: Automated loading and train-test splitting. 2. Transformation: A robust pipeline using ColumnTransformer to handle scaling and categorical encoding simultaneously. 3. Model Factory: Systematically trained and tuned multiple algorithms, including XGBoost and CatBoost, to find the highest R2 score. 4. Deployment: Wrapped the final model in a Flask API to serve real-time predictions. The Tech Stack: 🐍 Python | 🐼 Pandas & NumPy | 🤖 Scikit-Learn | 🚀 XGBoost & CatBoost | 🌐 Flask Building this helped me dive deep into writing clean, maintainable code and understanding how to package an ML model for the real world. Check out the code on my GitHub! (Link in comments ⬇️) #MachineLearning #DataScience #Python #SoftwareEngineering #MLOps #WebDevelopment #StudentSuccess

If your CI pipeline is slow, start by looking at how your test suite is being split. 𝗽𝘆𝘁𝗲𝘀𝘁-𝘀𝗽𝗹𝗶𝘁 is a pytest plugin that does one thing well: it splits your test suite into equally timed sub-suites, not equally sized ones. Most naive approaches split by test count. 𝗽𝘆𝘁𝗲𝘀𝘁-𝘀𝗽𝗹𝗶𝘁 stores actual execution times in a .test_durations file and uses that data to balance wall-clock time across groups. Run --store-durations once, commit the file, and your CI groups will finish at roughly the same time. New or renamed tests are handled gracefully by falling back to average durations. No need to re-run --store-durations after every change. 🔗 Link to repo: github(.)com/jerry-git/pytest-split --- ♻️ Found this useful? Share it with another builder. ➕ For daily practical AI and Python posts, follow Banias Baabe.

What is Lazy Evaluation PySpark uses lazy evaluation → transformations are not executed immediately Operations like filter(), select() only build a logical plan (DAG) Execution happens only when an action is called (show(), count(), collect()) Spark optimizes the entire plan before execution → better performance Avoids unnecessary computations and improves efficiency 💡 Example: Python df = spark.read.csv("data.csv", header=True) # Transformations (no execution yet) df_filtered = df.filter(df.salary > 5000) df_selected = df_filtered.select("name", "salary") # Action → triggers execution df_selected.show() ⚡ Without lazy evaluation, each step would execute separately → slower performance. With lazy evaluation, Spark optimizes everything and runs it efficiently. Still learning and exploring more PySpark concepts! 🚀 #PySpark #BigData #DataEngineering #PerformanceTuning #Learning