Building Stock Prediction Portal with Python, Django & React

For web scraping, in python we use two main libraries: " requests " and " BeautifulSoup " . requests : Used to send HTTP requests and retrieve the webpage content. > requests.get(url) BeautifulSoup: Used to parse the HTML content of the webpage and extract specific elements. > soup.find("div") #datascience #python #pandas

Python Tip: Functions & Return Don’t Just Do, Return Most beginners write functions that do something but don’t return a value. That works… until you need that result later. return is how Python lets your functions communicate. - Pass data back cleanly - Avoid global variables - Make your functions reusable and composable - Write predictable, testable code Smarter Python isn’t about what your function does inside. It’s about what it gives back. FOLLOW FOR MORE PYTHON TIPS & INSIGHTS #Python #CleanCode #Functions #ProgrammingTips #SoftwareEngineering

Python Tip - Inheritance (issubclass() & isinstance()) Know the Smart Way to Check Your Hierarchy Inheritance is powerful, but knowing how your classes relate is what makes it truly Pythonic. Instead of manually checking types or class names, trust Python’s built-ins like issubclass() and isinstance(). They handle deep hierarchies, keep your code clean, and save you from brittle, error-prone checks. Pro Tip: Modern inheritance isn’t just about reusing code, it’s about writing robust, scalable, and readable class relationships. FOLLOW FOR MORE PYTHON TIPS & INSIGHTS #Python #OOP #CleanCode #SoftwareEngineering #Backend #ProgrammingTips

Python Tip of the Day 🐍 In Python, {} doesn’t always mean the same thing. • Set → Unique values, fast membership testing • Dictionary → Key-value mapping, fast lookups Understanding the difference isn’t syntax — it’s structure thinking. Day 15 of building Python Basics. #Python #PythonLearning #DataAnalytics #PythonCode

ML is fun when you get to build real things with it 🚀 Currently learning and building Machine Learning projects, and this is one of them. Built a Student Marks Prediction System using a regression model in Python and integrated it with a Flask API and React frontend for real-time predictions. Tech Stack: Python | Scikit-learn | Flask | React #MachineLearning #ReactJS #Python #LearningByBuilding

Built a House Price Prediction Web App using Python, Flask, and Machine Learning. From data preprocessing to a polished UI, this project predicts prices dynamically and makes ML feel interactive.

Python Tip: len() Still using loops to count elements in a list, string, or dictionary? Python’s built-in len() gives you the length in one clean call. - Works for lists, tuples, strings, dictionaries, sets - No manual counting - Faster and less error-prone - Makes your code readable and professional Smarter Python isn’t about doing more manually. It’s about letting Python do the work for you. FOLLOW FOR MORE PYTHON TIPS & INSIGHTS #Python #Functions #CleanCode #ProgrammingTips #SoftwareEngineering

#CommunityHelp #Python #ML #QuantitativeTrading For those who run their data analysis on large json or jsonl file, you should use a high-performance JSON parser such as orjson or simdjson instead of Python’s built-in json module, because most of the time is spent parsing, not reading the file. Open files in binary mode with large buffering to reduce decoding overhead and system calls. Stream records line-by-line instead of loading the entire file into memory unless batching clearly improves throughput. Avoid heavy Python loops after parsing; push transformations into vectorized engines like Polars, NumPy, or PyArrow that execute in native code. When processing is CPU-bound, split the file into chunks and use multiprocessing rather than threads to bypass the GIL. Profile the pipeline before and after changes so that optimization targets actual bottlenecks rather than assumptions.

Finally understanding keyword-only arguments in Python functions/methods! My goal with them is to reach a code that it is more explicit and less error-prone (avoids mixing similar parameters of the same type). Good lesson on the video below by Trey Hunner : https://lnkd.in/dPhGbcYA #python #softwaredevelopment

🔹 Day 10 | Built-in Functions in Python 🛠️🐍 Today, I explored Python’s powerful built-in functions. Some useful ones: ✔ len() ✔ type() ✔ sum() ✔ max() / min() ✔ sorted() ✔ enumerate() ✔ zip() These functions make coding faster and more efficient. What I learned today 📚 ✔ Writing shorter & cleaner code ✔ Reducing manual effort ✔ Improving readability ✔ Why Python is beginner-friendly Small tools → Big productivity boost 🚀 #Python #Efficiency #DataAnalytics #LearningInPublic