In this exploration of the data landscape, we embark on a journey through the staggering growth of global data generation. The sheer magnitude of information created in 2024, reaching a projected 140 zettabytes, underscores the exponential nature of this phenomenon.
Category: Data Science for All
Articles, posts, and documents tailored for all stages of the Data Science journey. These resources are crafted in an easy-to-understand format, ensuring accessibility for everyone, from beginners to seasoned professionals.
Today in this document I will explain the concept of recall using a confusion matrix and outlines situations where recall should be prioritized, such as in healthcare diagnostics, fraud detection, and imbalanced datasets. I will also discuss instances where recall
Today I will focus on the concept of Precision in data science, particularly in the context of classification models. In the document that I am sharing, outlines when to use precision (when false positives are costly or high confidence in
Accuracy a widely used parameter in Machine Learning model, can be interpreted wrongly sometime. High Accuracy doesn’t alway indicate a strong model. In this article I will talk about when and when not to use Accuracy. Also I will share
As we come together to celebrate Independence Day, it’s a perfect time to reflect on the incredible journey our nation has undertaken since August 15, 1947. On this day, 78 years ago, India embraced freedom, thanks to the relentless efforts
Manhattan distance, a metric used to calculate distances in grid-like structures, is an important metric in distance calculation. In this document I will be sharing details around the same. I will also share real-world applications of Manhattan distance, including optimizing
New to data science? Cosine Similarity might sound complex, but this guide breaks it down in simple terms. Learn how this metric is used to measure similarity between text documents and its real-world applications. Things we will cover in this
This document that I share today, explores the concept of Euclidean distance and its application in measuring user similarity, particularly in the context of recommendation systems. I am sharing clear explanation of Euclidean distance, its calculation, and interpretation of results.
There are 100s of tools available to support Data Scientists to do their day to day job. But due the usefulness, features and ease to use, few are widely popular compared to rest. This document provides a brief overview of
Data Science and AI are the basis of Credit Risk. Knowing how various models are created for credit risk (All Major Scores details are publicly available), you can leverage the same to identify best practices to maintain high credit score.