Comparing Outlier Removal Strategies in Python

Learn how to build a predictive model with Python and Scikit-Learn, including data preparation, model selection, and evaluation techniques, with expert tips and real-world examples https://lnkd.in/ge-CSTzq #PredictiveModelWithPython Read the full article https://lnkd.in/ge-CSTzq

Learn how to build a predictive model with Python and Scikit-Learn, including data preparation, model selection, and evaluation techniques, with expert tips and real-world examples https://lnkd.in/ge-CSTzq #PredictiveModelWithPython Read the full article https://lnkd.in/ge-CSTzq

Learn how to build a predictive model with Python and Scikit-Learn, including data preparation, model selection, and evaluation techniques, with expert tips and real-world examples https://lnkd.in/ge-CSTzq #PredictiveModelWithPython Read the full article https://lnkd.in/ge-CSTzq

🅸🅽🅿🆄🆃 & 🅾🆄🆃🅿🆄🆃 🅵🆄🅽🅲🆄🆃🅸🅾🅽🆂 📦 Definition: In Python, Input is how we get data from the user into our program, and Output is how the program displays information back to the user. 🏠 Real-World Example: Think of a Vending Machine. Input: You press the buttons to tell the machine you want a "B3" (snack code). Output: The machine displays "Price: $1.50" on the screen and then drops your chips! 🍟 Without that type interaction, the machine is just a silent box of snacks. Here is how we do it in Python: 👉 input(): This function pauses the program and waits for you to type something. Python always treats input as a string by default, so if we want numbers, we will need to wrap it in an int() or float(). 👉 print(): It takes whatever is inside the parentheses and splashes it onto the screen for the world to see. #python #inputoutput #codingtips #pythonsimplified #datananalytics #learnpython

Ever tried to sort a list and ended up with None? The logic looks correct: nums = [3, 1, 2] sorted_nums = nums.sort() print(sorted_nums) 👉 Output: None Why did it fail? In Python, there is a big difference between a Method that modifies an object and a Function that returns a new one. 1️⃣ .sort() is a Method: It modifies the original list "in-place." It doesn't need to return anything because the work is done directly on the original variable. 2️⃣ sorted() is a Function: It creates a brand-new list and leaves the original one exactly as it was. Use .sort() when you want to save memory and don't need the original order anymore. Use sorted() when you need to keep your original data safe and want a new sorted version. #Python #30DaysOfCode #BCA #LearningInPublic #Day22 #JECRC

Day 5 of #30DaysOfPython ✅ Today I met two of Python's most powerful data structures. One of them already feels like home. The other? Slightly chaotic. Lists and dictionaries. Day 5. Lists made sense quickly — they're just ordered collections. I can store things, loop through them, sort them, slice them. Intuitive. Dictionaries? At first, the key-value pair concept felt abstract. The bug that got me today? I threw both strings and integers into the same list and tried to sort it. Python did not appreciate that. TypeError showed up like an old enemy. Day 5 done. 25 more to go! 👇 Lists vs dictionaries — when do you reach for one over the other? #Python #30DaysOfPython #DataStructures #StudentLife #AIML

Spent Q1 running around 22,000 DocsBot conversations through a Python analysis pipeline to stop guessing which WPForms docs needed work. Output: 278 existing pages that need fixes, 100+ recurring feature requests, 15 topics users keep asking about that don't have a doc yet. The surprise wasn't the numbers. It was the ranking. The three pages I would have prioritized from gut feel weren't even close to the top. Meanwhile, a couple of pages I assumed were fine had dozens of confused conversations attached to them. Nothing fancy on the pipeline side. Python, an LLM, and clustering on the conversation data. Changed what I'm prioritizing for Q2.

Learn how to create predictive models with Python and Scikit-Learn. This comprehensive guide covers data preparation, model building, evaluation, and deployment. https://lnkd.in/ghAvtz8v #PredictiveModelingWithPython Read the full article https://lnkd.in/ghAvtz8v

Copying vs Reference 🐍 I thought this would create a copy: b = a It didn’t. a = [1, 2, 3] b = a b.append(4) print(a) 👉 [1, 2, 3, 4] Both variables point to the same list. No copy was made. In Python, variables are just labels, not containers. When you use =, you aren’t duplicating data, you’re just giving the same memory address a second name. To actually copy: b = a.copy() Looks the same. Behaves completely differently. 💡 And also: .copy() only goes one layer deep and that's why we need deep copy. ➡️ Assignment ≠ Copy Day 17/30 #Python #30DaysOfCode #SoftwareEngineering

Most implementations of the State pattern in Python look very “clean”. Lots of small classes. A base interface. One class per state. But if you’ve ever worked with one in a real project, you know the downside: transitions are scattered, behaviour is hard to see in one place, and adding new states often means touching multiple files. In today’s video, I rebuild the State pattern in a very different way. Instead of relying on inheritance, I make the state machine explicit as data and use decorators to define transitions. The result is a small, reusable engine where the entire flow becomes visible at a glance. If you’re interested in writing Python that’s easier to reason about and extend, this is a pattern worth understanding. 👉 Watch here: https://lnkd.in/eg22yEHR. #python #softwaredesign #designpatterns #statemachine #cleancode