Machine Learning Pitfall: Avoiding Misleading Accuracy in Imbalanced Datasets

🚀 Day 1 of My Artificial Intelligence Learning Journey Today I started strengthening my Python fundamentals, which are essential for learning Artificial Intelligence and Machine Learning. Here are some concepts I learned today: 🔹 Python Variables – used to store and manipulate data 🔹 Variable Naming Rules – proper naming conventions in Python 🔹 Python Data Types – int, float, string, list, tuple, dictionary, set, boolean 🔹 Strings in Python – text data using single or double quotes 🔹 Variable Scope – local vs global variables 🔹 Python Operators – arithmetic, assignment, comparison, logical, membership, and bitwise operators 📌 Key Takeaway: A strong understanding of Python fundamentals is important before diving deeper into AI and Machine Learning. This is Day 1, and I’m excited to continue learning and sharing my journey. #Python #ArtificialIntelligence #MachineLearning #AIJourney #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

🎉 Setting the ball rolling in Python & Machine Learning! Kicking off my journey by building a Student Perfomance Prediction App using the UCI dataset 📚 - built with Python & Streamlit for an interactive experience. One thing I learned? You can't rely on just one model. So I trained and compared multiple models: 🔹 Linear Regression 🔹 SVM 🔹 Random Forest 🔹 Gradient Boosting 🔹 XGBoost 🔹 LightGBM Now the big question — how did I evaluate them? 🤔 Here comes 📊 R² Score and 📉 Mean Squared Error (MSE). And the best performer was… Gradient Boosting 🏆 But wait… should users just accept predictions without knowing why? 👀 They do deserve transparency. That's where the explainability heroes step in : 🦸♂️ SHAP – to understand overall feature impact 🦸♀️ LIME – to explain individual predictions 🔗 GitHub Repository: https://lnkd.in/gQSRS9iG Even though it’s a simple application, it helped me understand model training, evaluation, ensemble learning, and most importantly — making ML explainable. Long way to go. Just getting started 🔥 #MachineLearning #Python #ExplainableAI #SHAP #LIME #DataScience #LearningJourney

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

Let’s test your understanding of Python 👇 a = [1, 2] b = a b.append(3) print(len(a)) What’s the output? 2 3 1 Error 🧠 Take 5 seconds before answering… ✅ Correct Answer: 3 Why? Because this line: b = a Does NOT create a copy. It makes b point to the same list in memory. So when we do: b.append(3) We modify the SAME object. Now the list becomes: [1, 2, 3] And since a and b reference the same object: len(a) = 3 🔥 Key Insight Lists in Python are mutable. Variables store references, not copies (for mutable objects). That’s why small misunderstandings like this cause real bugs in data & AI pipelines. Have you ever been surprised by Python references before? 👇 #Python #AI #DataScience #LearningInPublic #30DayChallenge

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 2: Linear Regression ✅ Linear Regression intuition is simple: imagine you're trying to draw the best possible straight line through a scatter of points on a graph. That line represents the relationship between your input and output. But how do we find the "best" line? That's where Gradient Descent comes in. We start with a random line, measure how wrong it is using the Mean Squared Error, then slowly nudge the line in the direction that reduces the error, repeating this thousands of times until we converge. 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 #LinearRegression #GradientDescent

🚀 Day 2 of My Artificial Intelligence Learning Journey Continuing my Python learning journey for AI and Machine Learning, today I explored some important data structures and concepts in Python. Here’s what I learned today: 🔹 Stacks and Queues – Understanding how data can be organized and processed using LIFO (Stack) and FIFO (Queue). 🔹 Queue Implementation – Practiced using Python’s queue module and collections.deque. 🔹 Lists – Learned how lists store collections of items and explored common methods like append(), insert(), remove(), and pop(). 🔹 Dictionaries – Key-value data structure used to store and access data efficiently. 🔹 Sets – Unordered collection of unique elements and useful methods like add(), remove(), and discard(). 📌 Key Takeaway: Understanding data structures in Python is essential because they help organize and process data efficiently—an important skill for building AI and machine learning models. Excited to continue learning and building a strong foundation in Python for AI. #Python #ArtificialIntelligence #MachineLearning #DataStructures #LearningInPublic #AIJourney

A lot of people think learning Python for data means memorizing every library. That’s understandable. The ecosystem looks overwhelming at first. But good data work isn’t about knowing everything. It’s about knowing which tool to use, and when. Each library exists for a reason — NumPy for math, Pandas for tables, Polars for speed, Scikit-learn for models, Plotly for interaction, TensorFlow/PyTorch for deep learning. Once you stop treating Python libraries as a checklist and start treating them as purpose-built tools, things get simpler. That’s when data projects move faster and cleaner. [python, datascience, libraries, tools, analytics, machinelearning, learning, clarity] #python #datascience #datatools #machinelearning #analytics

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