🐍 Day 22 — Functions in Python Day 22 of #python365ai 🧩 Functions group reusable code. Example: def greet(name): print("Hello", name) 📌 Why this matters: Functions improve readability and reduce repetition. 📘 Practice task: Write a function that adds two numbers. #python365ai #PythonFunctions #CleanCode #LearnPython

I just launched three new open-source Python packages! Eric Chat: Run LLMs locally, securely and offline on macOS through a GUI. Quantized models up to 120 billion parameters are supported. Eric Transformer: Pre-train, fine-tune and perform inference with LLMs. Eric Search: A vector database with built-in text ranking that scales to millions of documents while remaining fast. Integrates easily with Eric Transformer to enable RAG. https://lnkd.in/evxHzXjk

🚀 Day 5/50 – LeetCode Challenge Revisiting a core stack-based problem to strengthen logic and edge-case handling. 📌 Problem: Valid Parentheses (Easy) 🧠 Approach: Use a stack to track opening brackets and validate correct closing order ✨ Key Learning: Simple data structures can solve complex-looking problems cleanly Consistency over perfection. #LeetCode #Python #DSA #Consistency #LearningInPublic

Python Powers Naruto Match Analysis Case Study 📌 Python meets Naruto in a creative data science case study, using statistical methods like chi-square tests and SVD to uncover hidden patterns in ninja combat styles and villages. Bold analysis reveals significant relationships, visualized through heatmaps and biplots, making complex data accessible and engaging. A GitHub project offers developers a fun, educational way to explore data science techniques through a beloved anime universe. 🔗 Read more: https://lnkd.in/dTNsRVtm #Python #Contingencytable #Chisquaretest #Naruto

Used Python (pandas & matplotlib) to analyse the QVI Transaction Data & Purchase Behaviour dataset. Handled nulls, duplicates, outliers, merged datasets, created metrics, and generated insights all before touching any dashboard tool. Python truly shines in data preparation and exploration. #Data_Analysis#First_python_project

The project didn’t work perfectly on day one — and that’s the point. I faced: • Corrupt checkpoints • Schema mismatch errors • Duplicate records in incremental loads • Delta table conflicts Instead of quitting, I learned: ✔ why checkpoints exist ✔ how idempotent pipelines save data ✔ how Delta handles ACID in data lakes Real learning happens when things break. #dataengineering #python #minidatalake #acid

Day 3 of my 30 Days Data Analytics Challenge Today I learned how Python repeats tasks automatically and how we can reuse logic using loops and functions. These concepts are used everywhere in data analytics — from cleaning data to running calculations on large datasets. What I learned today: 🔹 Loops: They help run the same block of code again and again. for loop → iterate over a sequence while loop → run until a condition is false 🔹 Functions: Functions are reusable blocks of code that perform a specific task. They make programs: Cleaner Reusable Easier to debug #DataAnalytics #Python #30DaysChallenge #LearningJourney #DataScience #Upskilling

Built a production-ready Stock Market Prediction System in Python — fetches real-time market data (Alpaca), indicator-based feature engineering (SMA, RSI, Volatility), a Random Forest model for next-day return prediction, and publication-quality visualizations & reports. Data can be predicted for any stock available on the open market Possible visualizations and SWIMLANE diagram available in attached repo. Open-source and ready to extend: https://lnkd.in/dDHjmd-H

𝗗𝗮𝘆 𝟭3: 𝗧𝘂𝗽𝗹𝗲𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 🐍 🔹 Tuples are ordered and immutable collections of items, separated by commas and enclosed in round brackets. 🔹 Items can be of multiple data types 🔹 Once created, tuples cannot be changed, added to, or deleted 𝗖𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗧𝘂𝗽𝗹𝗲𝘀:  ✔ Single-item tuple (comma is mandatory) ✔ Using parentheses: (1, 2, 3) ✔ Without parentheses: tup = 1, 2, 3 𝗖𝗼𝗺𝗺𝗼𝗻 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: ✔ Indexing and slicing (same as lists) ✔ Unpacking – assign tuple values to multiple variables ✔ Concatenation – combine tuples using + 📝𝗡𝗼𝘁𝗲:  🔸 Tuples cannot be modified directly.  🔸 To make changes, convert the tuple to a list, modify it, and convert it back to a tuple. Building strong Python fundamentals, one concept at a time 🚀 #Python #Tuples #LearningPython #LearningInPublic #AspiringDataScientist #Consistency