Tejashwar Reddy Katika’s Post

Stop letting your powerful models gather dust in a Jupyter Notebook! 🛑 The transition from Data Scientist to MLOps Engineer is key to delivering real business value. I just finished deploying a full-stack time-series forecasting solution and wanted to share the architecture. My pipeline proves that Python models can live outside the notebook: FastAPI: The blazing-fast API layer for serving the Prophet model. React: The simple, interactive UI for visualization. Firestore: The persistence layer for saving and auditing every forecast. If you want to see exactly how these three pillars integrate—and why MLOps is the future of practical data science—check out the detailed breakdown on my blog. 👇 Read the full guide: https://lnkd.in/eHJZvfa8 #MLOps #DataScience #FastAPI #ReactJS #Python #MachineLearning #Deployment

🏠 Project Showcase: House Price Prediction 📊 I’m excited to share my end-to-end machine learning project on predicting house prices! ✨ Project Overview: Built a regression model in Google Colab to predict house prices based on features like number of convenience stores, house area, and more. Applied data preprocessing, feature analysis, regression modeling, and model evaluation for accurate predictions. Exported the trained model as a pickle file and integrated it into a Python app (app.py) in VS Code. Ran the app in Anaconda Prompt, demonstrating real-time predictions. Recorded a video of the workflow and output to showcase the project in action. 💡 Tech Stack: Python | Pandas | NumPy | Scikit-learn | Google Colab | Pickle | VS Code | Anaconda | App Development | Regression Modeling GitHub Repository: [ https://lnkd.in/gQP5c7Qf ] 📈 Key Learnings: Understanding the impact of different features on house prices Debugging model predictions and improving accuracy Deploying a machine learning model into a runnable Python app #MachineLearning #DataScience #Python #Regression #HousePricePrediction #AI #MLDeployment #VSCode #Anaconda #Pickle

📚🌃 Continuing my dive into data structures and algorithms. 🙂 🌳 Tonight’s Focus: Chapter 19 – Binary Tree Traversal In linear structures like arrays or linked lists, we move step-by-step: 0️⃣ ➡️ 1️⃣ ➡️ 2️⃣ ➡️ 3️⃣ But trees are hierarchical 🌳, so we use a different approach: Breadth-First 🔺 and Depth-First 🐋 Traversal ✅ FYI -Tree depth helps us understand how far a node is from the root -The goal is to visit every node and represent the full structure ⚙️ Traversal Basics Each node goes through two phases: Discovered Collection – We identify a node (starting from the root) and add it to this list as soon as it's found. Explored Collection – After a node is discovered, we examine its children. Once all its children have been discovered, we move the node to this list. 🔺 Breadth-First Traversal -Uses a queue (First In, First Out) -Visits nodes level by level, left to right, moving nodes from the discovered to explored collection as they are processed -Example order: A → B → C → D → E… 🐋 Depth-First Traversal -Uses a stack (Last In, First Out) -Nodes are discovered by traversing deep down the left-most path, then backtracked to the nearest unexplored node. During processing, nodes are moved from the discovered collection to the explored collection. ⚡ Performance Time: O(n) Space: O(n) Same across best, average, and worst cases 📚 Might just do half a chapter for the more involved chapters next. If you’re learning too (or just love emoji-powered breakdowns), follow along for more chapters in this series! 🚀 #JavaScript #Algorithms #Coding #DevNotes

🔥 Introducing Pipelines on Gridscript.io — your new way to build data workflows, analytics, and AI models entirely in your browser. Until now, creating a full data workflow meant juggling tools — Jupyter, Excel, VSCode, Colab, and countless scripts. GridScript Pipelines changes that. 🧩 A Pipeline is made of stages — each one doing a part of your process: Import Stage → Load data from CSV, JSON, or XLSX in seconds. Code Stage → Run your own Python 🐍 or JavaScript 💻 code. You can chain multiple stages together to: ✅ Clean and transform datasets ✅ Visualize results using table(), chart(), and log() ✅ Train and test custom AI models right in the browser 💪 With Python, you get pandas, numpy, and scikit-learn. ⚡ With JavaScript, you get TensorFlow.js for deep learning. No setup. No dependencies. Just your browser — and unlimited creativity. ✨ Start building your first Pipeline today: https://gridscript.io #DataScience #AI #MachineLearning #Python #JavaScript #TensorFlow #DataAnalytics #DataEngineering #LowCode #NoCode #GridScript #TechInnovation #WebApp #ProductLaunch

If you live in Jupyter Notebook or Google Colab but your workflow still feels slower than it should, you are not alone. Many professionals overlook the notebook’s built-in magic commands. These are not toys; they are native tools that speed up profiling, execution, and inspection so your code runs faster and reads cleaner. We compiled a concise guide to 8 magic commands that remove friction and make your notebook work look professional. Built for Python users in data science, analytics, and machine learning who want faster runs, clearer diagnostics, and fewer clicks. - Time and profile: %time, %%time, and %prun to measure and compare performance precisely. - Run and discover: %run to execute .py or .ipynb files, and %lsmagic to see what is available. - Inspect and manage: %history, %whos for variables in memory, %bash for shell tasks, plus A and B to add cells without touching the mouse. Think you already know the basics? This goes beyond shortcuts and into evidence-based performance tuning. Worried about complexity? Start with %lsmagic and %history; they are safe, notebook-native, and easy to remember. We see many teams ignore these until they compare two snippets with %time and immediately find the faster approach. Our walkthrough focuses on practical, ready-to-use tips so you can apply each command in your next session, not just read about it. 🚀 Read the full article and upskill today: https://lnkd.in/g-pZ9GDa #borntoDev #Python #Jupyter #DataScience #DeveloperTools #Productivity

Excited to share my latest data analysis project. I analyzed Uber ride patterns in New York City using Python’s data-analysis libraries, including Pandas, NumPy, Matplotlib, Seaborn, Plotly, and Folium. What I worked on: Performed data cleaning and preprocessing, Engineered time-based features (hour, weekday, month, etc.) Built visualizations to explore ride trends Created geospatial heatmaps to understand pickup density across NYC Identified peak activity periods, ride volume patterns, and spatial distribution. https://lnkd.in/gDcgWJ6U

🚀 Excited to share my latest Data Science project: Email Spam Detection System! 📧 What I built: - Developed a machine learning model using Python and scikit-learn - Implemented Support Vector Classification (SVC) achieving high accuracy - Created an interactive web application using Flask - Designed a modern, responsive UI with HTML/CSS 🛠 Tech Stack: • Python • scikit-learn • Flask • HTML/CSS • Pandas • NumPy 💡 Key Features: - Real-time email classification (Spam/Ham) - User-friendly web interface - Responsive design for all devices - Production-ready implementation This project helped me deepen my understanding of: ✅ Machine Learning Pipeline Development ✅ Text Classification ✅ Web Application Development ✅ Model Deployment Try it out: [https://lnkd.in/dNp9TgeG] #MachineLearning #DataScience #Python #WebDevelopment #AI #Programming #Flask Open to feedback and collaboration! Feel free to connect and share your thoughts. 🤝 GitHub Repository: [https://lnkd.in/dNp9TgeG]

🚀 Building Real-Time Data Insights with FastAPI/Flask 🚀 In today’s fast-paced world, real-time telemetry data is a goldmine for businesses making decisions on the fly. So, I built a simple yet powerful RESTful API with FastAPI (Python) that lets you: ✔️ Submit telemetry data effortlessly ✔️ Query processed analytics instantly Why FastAPI? Lightning-fast performance Easy validation with Pydantic Seamless async support for real-time pipelines Imagine the possibilities: monitoring infrastructure health, analyzing user behavior as it happens, or automating security threat detection—all powered by your own scalable API. If you want to level up your backend skills or build production-grade telemetry systems, mastering FastAPI/Flask APIs is a game changer. 💡 Pro Tip: Start with small endpoints, then scale by integrating streaming data, async consumption, and database storage. Are you working on similar real-time data projects? What frameworks do you prefer? Let’s discuss in the comments! #FastAPI #Python #Backend #Telemetry #RealTimeData #APIDevelopment #CloudNative #TechLeadership #CareerGrowth

just completed an end-to-end 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 using K-Means clustering to help businesses better understand their customers. 🔹𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅ Analyzed 541K+ transactions from the UCI Online Retail Dataset. (Link: https://lnkd.in/gwYFJCpz) ✅ Engineered 𝐑𝐅𝐌 (Recency, Frequency, Monetary) features ✅ Determined optimal clusters using 𝐄𝐥𝐛𝐨𝐰 𝐌𝐞𝐭𝐡𝐨𝐝 & 𝐒𝐢𝐥𝐡𝐨𝐮𝐞𝐭𝐭𝐞 𝐒𝐜𝐨𝐫𝐞 ✅ Segmented customers into 4 actionable groups: 𝐕𝐈𝐏 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬, 𝐋𝐨𝐲𝐚𝐥 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬, 𝐏𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐋𝐨𝐲𝐚𝐥𝐢𝐬𝐭𝐬, 𝐚𝐧𝐝 𝐀𝐭 𝐑𝐢𝐬𝐤 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬. ✅ Visualized clusters with PCA and built an interactive Streamlit app 🔹𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤: Python | pandas | scikit-learn | matplotlib | seaborn | Streamlit  🔗 GitHub repo: https://lnkd.in/gm87tpMN 🌐 Live app: https://lnkd.in/gktMkeSh #DataScience #MachineLearning #CustomerSegmentation #Python #KMeans #Clustering #Streamlit

Crawlee: A web scraping and browser automation library for Python to build reliable crawlers. Extract data for AI, LLMs, RAG, or GPTs. Download HTML, PDF, JPG, PNG, and other files from websites. GitHub: https://lnkd.in/eER6gzp9 Website: https://crawlee.dev/