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 Feature Engineering, I quickly realized there was a gap between theory and what actually happens in practice. This post is about what i learned about feature engineering that no tutorial tells you. 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 Feature Engineering As I reflect on my journey in machine learning, I've come to realize that feature engineering is often the unsung hero of a successful model. It's easy to get caught up in the latest algorithms and techniques, but at the end of the day, good features matter more than a fancy model. I've learned this the hard way, through trial and error, and I'm excited to share my experiences with you. One of the most important lessons I've learned is that domain knowledge beats any automated feature selection algorithm. There's no substitute for understandin...

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'...

Introduction When working with data, I learned that data is rarely perfect. Most real-world data contains errors, missing values, and inconsistencies. Before any analysis or modeling, this data needs to be cleaned. Data cleaning is one of the most important steps in data science. Without it, even the best models can give wrong results. What Is Data Cleaning? Data cleaning is the process of identifying and correcting errors in a dataset to improve its quality. It involves: • Removing incorrect data • Fixing missing values • Correcting inconsistencies • Preparing data for analysis Clean data leads to reliable insights. Why Data Cleaning Is Important Data cleaning is important because: • Raw data often contains mistakes • Dirty data leads to incorrect conclusions • Clean data improves model performance • Analysis becomes more accurate In short, better data means better results. Common Data Quality Issues Some common issues found in datasets include: • Missing values • Duplicate records • ...