Python Data Structures and Problem Solving

Handling imbalanced datasets is one of the most important steps in building reliable ML models. The Random Sampling techniques like Under Sampling and Over Sampling to improve model fairness and performance. Balanced data → Better learning → Better predictions. GitHub Repository: [https://lnkd.in/gXa9zEBs] #MachineLearning #DataScience #Python #RandomSampling #ImbalancedDataset #MLLearning

Most people learn Python by staring at output. I tried something different. 👇 This is what actually happens when Python executes a basic function — step by step, visually. No theory. No slides. Just execution in real time. Still early in my journey — but this is how I'm learning. If you're learning Python too, drop a 👋 below. 🔧 Tool: pythontutor / staying.fun 🐍 Concept: Functions — how they're called, executed & returned #Python #LearningInPublic #DataAnalytics #BBA #100DaysOfCode #PythonBeginners

Small reminder from today’s Python learning I finally understood that in a function like: def add_vehicle(vehicles, new_vehicle_params): “vehicles” is just a parameter A temporary name for whatever list you pass into the function. So when you write: add_vehicle(existing_vehicles, new_vehicle) Python reads it as: vehicles = existing_vehicles Such a small thing, but understanding it made functions feel way less confusing. Sometimes growth in tech is just understanding one small concept at a time ✨ #Python #DataAnalytics #LearningInPublic #WomenInTech #TechJourney #alx #BeginnerProgrammer

Day 5 Consistency is key! 🚀 I’ve been dedicating time to strengthening my Python fundamentals, specifically diving deep into how to work with data sequences. From understanding immutability to mastering indexing and slicing techniques, I’m building a solid foundation to handle data manipulation more effectively. It’s rewarding to see how these concepts translate into cleaner, more efficient. Today I’ve been practicing advanced sequence manipulation in Python. Key takeaways from my study session: Immutability: Understanding why certain data types (like strings) cannot be changed in place. Slicing Syntax: Mastering [start:stop] and how to omit indices for cleaner, faster code. Negative Indexing: Leveraging indexing from the end to make my code more dynamic. There is always something new to learn when it comes to optimizing data extraction! 💡 #PythonProgramming #SoftwareDevelopment #LearningToCode #DataManipulation #CodingTips #Python #CodingJourney #ContinuousLearning #DataHandling #SelfDevelopment #TechSkills

🐍 Day 116 — Cross Validation Day 116 of #python365ai 🔁 Cross-validation splits data multiple times. Example: from sklearn.model_selection import cross_val_score 📌 Why this matters: Provides more reliable performance estimates. 📘 Practice task: Run cross-validation on a model. #python365ai #CrossValidation #MachineLearning #Python

Started learning Python for Data Analysis 🐍 Not going to lie — it feels confusing at times. But I’m focusing on: • Small steps • Practicing daily • Understanding concepts Progress may be slow, but it’s happening. #Python #DataAnalytics #LearningJourney #Consistency

Day 1 of Data Structures in Python 🚀 Today I learned the basics of: • Lists • Tuples • Sets • Dictionaries Practiced few basic operations like insert, delete, and search. Understanding how data is stored and accessed is the first step toward better problem-solving. Looking forward to applying these concepts in real problems 🔍 #Python #DSA #LearningJourney #DataStructures

🚀 Python Learning Journey – Day 3 Today I explored one of the most important concepts in Python – Strings & String Slicing 🔥 📌 What I Learned: ✔️ A String is a sequence of characters enclosed in quotes. ✔️ We can define strings in 3 ways: a = 'Siddu' # Single quoted string b = "Siddu" # Double quoted string c = '''Siddu''' # Triple quoted string ✔️ String Slicing helps us extract a part of a string. ✔️ Indexing starts from 0 to (length - 1) ✔️ Negative indexing starts from -1 (last character) 📌 Example: name = "Python" print(name[0:3]) # Pyt print(name[-1]) # n 💡 Understanding strings is very important because they are used everywhere in programming! #Python #LearningPython #CodingJourney #Day3 #Programming #TechSkills #FutureDeveloper 🚀

✓ Advance Python Course with Machine and Deep Learning. ✓ Exercise ( Task 12 ). ✓ Statement:- 1) ----- Write a python program that defines a local variable (local_var) inside a function and then prints it's value. #LearningInPublic #CodingNewBie #PythonCourse #Programming #FutureGoals #Coding

One of the biggest mistakes beginners make in Python… is ignoring data types. You might write correct code, But if you don’t understand the type of data you’re working with, Your results can be completely wrong. In Python, everything has a type, from numbers to text to collections of data. Understanding this is what separates someone who copies code from someone who actually understands it. I’ll be breaking down Python data types in a simple way in my next article. 💬 Which one confuses you the most: Booleans, strings, tuples, lists, or dictionaries? #Python #Programming #DataScience #AI #Beginners #LearnToCode #Tech