Handling missing data is a crucial step in the data preprocessing pipeline for any machine learning project. Imputation, the process of replacing missing data with substituted values, is essential for building robust and reliable models. This article explores various imputation techniques, provides code examples, and …

Data leakage is a critical issue in machine learning that can lead to overly optimistic performance metrics and poor generalization to new data. This article explains what data leakage is, why it is problematic, and how to avoid it during data pre-processing. What is Data Leakage? Data leakage occurs when information …

Definition Simply put, a Bloom filter is a space-efficient probabilistic data structure with which we can determine the probable existence of a certain thing in a certain data set, and we can determine the non-existence of a certain thing in a certain data set with utmost accuracy. Doing all this in a memory space …

I have been wondering on how the math behind a Linear regression works as in most of the ML books that you encounter, the focus will be on giving you a linear equation and just plugging this equation in a Python library to solve for the slope and bias and then use it to predict the new values. It is very rare that they …

I wanted to play around with OpenCV and thought it might be a good idea to try OpenCV with a real life use case. DIY'ing a home camera system that can do motion detection and click images when there is some movement in the frame sounded like a cool idea. So I researched on how I could get this set up done. There were …