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1. Introduction “A good monitor doesn’t just make your code look better—it makes you better at coding.” I learned this the hard way. For years, I used a standard 24” 1080p monitor, thinking it was “good enough” for programming. But after long coding sessions, I’d end up with tired eyes, headaches, and constant scrolling to … Read more

I. Introduction “Not all data is created equal. And in machine learning, this imbalance can cost you—big time.” If you’ve worked with real-world datasets, you already know the struggle. Most classification problems aren’t neatly balanced, where each class has an equal number of samples. In reality, some categories are severely underrepresented. Think about fraud detection—99.9% … Read more

1. Introduction “If you torture the data long enough, it will confess to anything.” – Ronald Coase I’ve always believed that data has a story to tell, but it won’t reveal its secrets unless you ask the right questions. That’s where Exploratory Data Analysis (EDA) comes in. If you’ve ever worked with raw data, you … Read more

I. Introduction “In God we trust. All others must bring data.” – W. Edwards Deming If there’s one thing I’ve learned in my years working with machine learning models, it’s this: your model is only as good as the decisions it makes. And when it comes to decision-making in machine learning, decision trees are one … Read more

I. Introduction “All models are wrong, but some are useful.” — George Box I’ve spent a good chunk of my career working with time series data—financial trends, energy demand, even biomedical signals. If there’s one thing I’ve learned, it’s that time series forecasting is rarely as straightforward as it seems. You might have tried traditional … Read more

1. Introduction: Why SHAP Matters in Classification? “If you can’t explain it simply, you don’t understand it well enough.” – Albert Einstein. This quote hits hard when you’re working with machine learning models, especially in classification tasks. I’ve worked on enough models to know that just having high accuracy isn’t enough. You need to understand … Read more

1. Introduction “All models are wrong, but some are useful.” – George Box I’ve worked with enough outlier detection techniques to know that traditional methods often fall apart when faced with real-world data. Early in my journey, I relied on Z-score, IQR, and even Local Outlier Factor (LOF), but the moment datasets became high-dimensional, noisy, … Read more

1. Introduction “If you get your learning rate wrong, nothing else matters.” – A lesson I learned the hard way. I remember one of my first deep learning projects where I thought I had everything figured out—perfect architecture, great dataset, solid preprocessing. But no matter what I did, the model just wouldn’t converge. It was … Read more

1. Introduction: Why SHAP Matters for Categorical Features “All models are wrong, but some are useful.” – George Box I’ve worked with machine learning models long enough to know one thing: explainability can make or break your model’s impact. If people don’t trust the predictions, it doesn’t matter how good your accuracy is. That’s where … Read more

1. Introduction Why Explainability Matters in Multiclass Models “If machine learning is a black box, then explainability is the flashlight.” I’ve worked with a lot of machine learning models, and if there’s one thing that always comes up—especially in high-stakes applications—it’s the question of “Why did the model make this prediction?” With binary classification, it’s … Read more