Learn Data Science With Mukeshram

When I started working with Imbalanced Data, I quickly realized there was a gap between theory and what actually happens in practice. This post is about how i handle imbalanced datasets in classification problems. I'll walk you through what I learned, what tripped me up, and the lessons that stuck with me. No fluff — just honest notes from someone who went through it. Introduction to Imbalanced Datasets I still remember the first time I encountered an imbalanced dataset in a classification problem. I was working on a fraud detection model, and my initial results showed a whopping 99 percent accuracy. Sounds great, right? But as I dug deeper, I realized that my model was predicting every single instance as non-fraud. The model was essentially useless, as it was unable to detect any fraudulent cases. This experience taught me a valuable lesson: accuracy is not always the best metric, especially when dealing with imbalanced datasets. The Problem with Imbalanced Datasets Imbalance...

When I started working with ML, I quickly realized there was a gap between theory and what actually happens in practice. This post is about mistakes i made while training my first ml model. I'll walk you through what I learned, what tripped me up, and the lessons that stuck with me. No fluff — just honest notes from someone who went through it. Introduction to Machine Learning Mistakes I still remember the excitement of training my first machine learning model. I had spent weeks collecting and preprocessing the data, and finally, it was time to see the results. But, as it often does, reality had other plans. My model's performance was suspiciously high, and it wasn't until later that I realized the mistakes I had made. In this post, I'll share the lessons I learned from those mistakes, in the hopes that you can avoid them in your own machine learning journey. My Experience with Machine Learning As a beginner, I made a few critical errors that affected my model'...