Feb 23 · 13 min read · This is Week 19 of my data science journey, the week I stopped telling the algorithm what to find and started letting it discover patterns on its own. If you've been following since Week 18, welcome b
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Dec 11, 2025 · 3 min read · Introduction As datasets grow larger and more complex, models often face a common challenge: too many features. While having more data can be beneficial, high-dimensional feature spaces can lead to slower training, overfitting, noisy patterns, and po...
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Nov 9, 2025 · 5 min read · So yes, the topic is about Feature Engineering which is an essential part of Machine Learning Pipelines . Since the topic is very broad and subjective so in this blog, we’ll see some typical definitions, examples, and a high-level intuition of featur...
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Nov 5, 2025 · 4 min read · Imagine you’re moving to a small apartment, but you have an entire room filled with books, clothes, and gadgets. You can’t take everything (I face this issue most often myself), so you have to decide: Keep only the most important things Leave behin...
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Oct 16, 2025 · 2 min read · 📖 Principal Component Analysis (PCA) is a dimensionality reduction technique used in machine learning to simplify datasets without losing essential information.It transforms high-dimensional data into a smaller set of variables called principal comp...
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Sep 5, 2025 · 8 min read · Principal Component Analysis (PCA) is a powerful machine learning technique used for dimensionality reduction. At its core, PCA leverages fundamental concepts from linear algebra and statistics to transform a complex dataset into a simpler, lower-dim...
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Aug 24, 2025 · 3 min read · In the world of data science and machine learning, we often deal with datasets that have dozens, sometimes even hundreds of features. While it might feel like "the more features, the better," reality often proves otherwise. This challenge is known as...
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Jul 29, 2025 · 3 min read · PCA: PCA (Principal Component Analysis) is used for feature extraction, especially when feature selection isn't helpful — for example, when the features are not strongly correlated with the target or when we can't remove any features without losing i...
Join discussionJul 9, 2025 · 4 min read · Introduction : → PCA stands for Principal Component Analysis. The idea of the PCA to reduce the dimensionality by introducing the Principal Components which aren’t correlated with each other.→ The technique is used to get High variance possible. → H...
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