Learning Data Preprocessing with Python and California Housing Dataset

Most beginners ignore this pandas feature in Python! 🐍 If you're learning data science, understanding modern Pandas data types is very important for efficient data analysis. In this short video, you will quickly learn: ✔️ What modern Pandas data types are ✔️ Why they are better than traditional types ✔️ How they help in better data handling Perfect for Python, Data Science, and Machine Learning learners. 💬 Question: Have you used modern data types in Pandas before? Follow TuxAcademy and subscribe to our YouTube channel for more content on AI, Data Science, and Machine Learning. https://lnkd.in/gaipCupJ #Python #DataScience #Pandas #MachineLearning #Programming #TuxAcademy

🎓 New Tutorial: Predicting Hospital Costs using Machine Learning in Python I have shared a step-by-step tutorial demonstrating how Multiple Linear Regression and PCA can be used to predict hospital costs using Python. In this session, we work with a dataset of 248 patients and build a complete machine learning workflow using Scikit-Learn. Key concepts covered in the tutorial: • Data preprocessing and cleaning • Handling categorical variables using One-Hot Encoding • Feature scaling with StandardScaler • Identifying and addressing multicollinearity • Applying Principal Component Analysis (PCA) • Building and comparing multiple regression models • Evaluating models using RMSE One interesting insight from the analysis was the correlation between height and weight, which motivated the use of PCA to improve the model performance. This tutorial is particularly useful for: • Students learning Data Science and Business Analytics • Researchers working with healthcare datasets • Professionals interested in predictive analytics The video walks through a complete end-to-end machine learning pipeline, from raw data to model evaluation. 📺 Watch the full tutorial here: https://lnkd.in/dzjapWyF I hope it helps learners understand how analytics can support data-driven decision making in healthcare. #MachineLearning #DataScience #Python #HealthcareAnalytics #BusinessAnalytics #PredictiveAnalytics #ScikitLearn #HigherEducation

🐍 Learning Python – Understanding Data Types Today, I practiced Python data types and learned how different types of values are stored in variables. 📌 What this program demonstrates: ✅ str → for storing text (name) ✅ int → for storing whole numbers (age) ✅ float → for storing decimal values (price) ✅ bool → for storing True/False values ✅ NoneType → for representing no value 🔍 I also learned how to use the type() function to check the data type of a variable, which is very helpful while debugging and understanding code behavior. This practice strengthened my understanding of Python basics and how data is handled internally. Step by step, I’m building a solid foundation in Python for my future goals in AI & Machine Learning 🚀 #Python #PythonBeginner #DataTypes #LearningPython #CodingJourney #Programming #SoftwareEngineering #AI #MachineLearning

Starting my NumPy journey with a simple observation: Python List vs NumPy Array While learning Python, I mostly worked with lists to store data. They are simple and flexible. But after starting NumPy, I noticed that the same data can also be stored in something called a NumPy array. At first glance, both look very similar. But internally they are built for different purposes. Python List • Flexible and easy to use • Can store different data types • Mostly used for general programming tasks NumPy Array • Stores elements of the same type • Optimized for numerical and mathematical operations • Much faster when working with large datasets So, Output should be: <class 'list'> <class 'numpy.ndarray'> This is one of the main reasons why NumPy is widely used in Data Science, Machine Learning, and AI applications. Right now I’ve started exploring NumPy step by step as part of my Python → Data → ML learning journey. Next, I’ll explore multi-dimensional arrays in NumPy. #Python #NumPy #MachineLearning #DataScience #LearningInPublic

I used to think learning Data Analytics would feel exciting all the time. Like solving smart problems with cool Python library. But some days? -It’s just me, a messy dataset, and 20 minutes of confusion. -Trying to understand what the question even means. -Trying to figure out why my result looks wrong. Trying to find that one small mistake that changed everything. Most of learning isn’t glamorous. It’s slow. It’s quiet. It’s repetitive. But I’m starting to realize… That’s actually where real understanding builds. #DataAnalytics #Python #LearningJourney

Transforming Categorical Data for Machine Learning 🔄📊 Continuing my Machine Learning learning journey, today I explored One-Hot Encoding, an essential technique used to convert categorical data into numerical format so that machine learning algorithms can process it effectively. Today I implemented One-Hot Encoding using Python and explored how each category is converted into separate binary columns (0s and 1s). For example: Gender_Male → 1 or 0 Gender_Female → 1 or 0 I also explored the Dummy Variable Trap and how using drop='first' helps avoid multicollinearity by removing redundant columns while still preserving the necessary information. Tools used in this exercise: • Python • Pandas • NumPy • Scikit-Learn (OneHotEncoder) • Jupyter Notebook 🖇️GitHub Repository: https://lnkd.in/gXa9zEBs #MachineLearning #DataScience #Python #DataPreprocessing #OneHotEncoding #Pandas #ScikitLearn #LearningJourney

NumPy Cheatsheet Every Data Analyst Should Know 🧠 If you're learning Python for Data Analytics or Data Science, NumPy is the foundation of everything — Pandas, Machine Learning, and Deep Learning all depend on it. Here’s a quick NumPy cheatsheet covering the most commonly used operations. Save this post for later and practice these commands. Which Python library should I create the next cheat sheet for? #Python #NumPy #DataAnalytics #DataScience #MachineLearning #PythonProgramming #LearnPython #DataAnalyst #CodingTips #TechLearning

I'm committing to building popular ML algorithms from scratch daily without using anything but Python built-ins and NumPy. No sklearn. No shortcuts. Just pure code and first principles. Day 4: Naive Bayes ✅ Naive Bayes intuition is simple: imagine you receive an email with the words "free", "win", and "prize". What's the probability it's spam? That's exactly what Naive Bayes does. It uses Bayes Theorem to calculate the probability of each class given the input features, and picks the most likely one. The "Naive" part? It assumes all features are independent of each other. That's rarely true in real life, but surprisingly, it still works really well. This is fully open if you want to collaborate, add an algorithm, or drop a suggestion in the comments or issues tab. Feel free to do so. 🤝 👉 GitHub: https://lnkd.in/duTd7jie #MachineLearning #Python #NumPy #DataScience #OpenSource #LearnML #100DaysOfCode #NaiveBayes #Classification

Day 4 of learning Python in public 🚀 Today I focused on understanding Python Lists and how Python works with collections of data. Key things I learned: • Creating lists and storing multiple values in a single structure • Accessing elements using indexing and negative indexing • Using slicing to retrieve specific ranges of elements • Adding items using append(), insert(), and extend() • Removing items using remove() and pop() • Updating list elements using indexing • Checking if an element exists in a list using the in operator • Sorting lists using sort() and sort(reverse=True) • Important list methods like count(), index(), copy(), and clear() • Working with nested lists and understanding matrix[row][column] access • Using enumerate() to get both index and value while looping • Using zip() to combine multiple lists together • Writing concise transformations using list comprehension Big takeaway: Lists are one of the most fundamental data structures in Python. Understanding how they work makes data manipulation much easier and builds a strong foundation for more advanced concepts. Continuing to strengthen the fundamentals step by step. #Python #DataScience #LearningInPublic #Programming #DataScienceJourney #softwareengineering #AI #MachineLearning