Machine learning projects often fail not because of bad algorithms but because of messy workflows. In this post, I share five lessons I’ve learned while improving my ML pipelines.

Neural networks are the backbone of deep learning. In this post, I’ll walk through building a multi-layer neural network from scratch, focusing on the math and intuition behind the process.

When working with high-dimensional datasets, visualization and computation become complex. This is where Principal Component Analysis (PCA) comes in—a powerful dimensionality reduction technique. In this post, I’ll break PCA down step by step and show you how I built it from scratch.

Customer churn is one of the most common challenges faced by businesses today. Retaining existing customers is often more cost-effective than acquiring new ones, which makes churn prediction a high-impact area for data science. In this post, I’ll walk you through how I approached this problem using logistic regression and explained the results with SHAP values.