Amit Yadav

What the Blog Covers: You’re about to learn how Principal Component Analysis (PCA) can revolutionize your approach to stock price prediction. At first glance, the stock market might seem like a whirlwind of numbers—historical prices, volumes, moving averages, and technical indicators. But hidden within this chaos are patterns, and one of the most powerful tools […]

“Simplicity is the ultimate sophistication.” – Leonardo da Vinci. That’s precisely what PCA does to your data—it simplifies complex datasets while retaining the most valuable information. So, what exactly is PCA? At its core, PCA is a powerful tool used to reduce the dimensionality of your data. If you’ve ever felt overwhelmed by dealing with

What is PCA? Let’s start with a simple analogy. Imagine you have a messy room filled with dozens of items: books, clothes, gadgets, etc. If you had to tell a friend what your room looks like in just one sentence, you wouldn’t describe each item. Instead, you’d summarize: “It’s a tech-savvy room with books and

Understanding Dimensionality Reduction and Factor Analysis “Data is a precious thing and will last longer than the systems themselves.” – Tim Berners-Lee. In the modern world, data is everywhere. The challenge isn’t getting data—it’s making sense of it. You see, when you’re working with real-world datasets, often, they can have hundreds or even thousands of

What is PCA? You’ve probably heard the saying, “Less is more.” Well, when it comes to data, that’s exactly what Principal Component Analysis (PCA) achieves. PCA is like a highly efficient summary for your data—it takes a large, complex dataset and distills it into a simpler version, all while preserving as much of the original

“You can’t hit a target you can’t see.” This quote perfectly captures the challenge we face with large datasets. When you’re dealing with a mountain of variables, identifying the underlying patterns feels a bit like trying to find a needle in a haystack. That’s where dimensionality reduction techniques like Principal Component Analysis (PCA) and Factor

What is Time Series Forecasting? Time series forecasting is all about predicting future data points based on historical trends. Think of it like trying to predict the weather—you look at the patterns from the past to make an educated guess about tomorrow. In industries like finance, retail, and energy, this is critical. For example, a

Imbalanced data is one of those subtle but critical issues in machine learning that can easily be overlooked—until your model starts favoring one class over another. This might surprise you, but it’s one of the most common challenges faced across industries, from detecting fraudulent transactions to diagnosing rare diseases. Imagine you’re working on a fraud

When you think about it, we’re surrounded by classification problems every day, often without realizing it. From your inbox’s spam filter deciding which emails to keep out to image recognition software identifying objects in a photo, classification tasks are quietly powering some of the most crucial technologies in our lives. It’s everywhere — in medical

You’ve probably heard the phrase, “two heads are better than one.” Well, that’s essentially what ensemble methods are all about—but instead of heads, we’re talking about models. When you’re dealing with a complex problem, sometimes using just one model doesn’t cut it. This might surprise you, but even the most sophisticated machine learning models can