An end-to-end machine learning pipeline refers to the complete process of building, deploying, and maintaining a machine learning model, starting from collecting and preprocessing data to making predictions on new, unseen data. It encompasses all the steps required to turn raw data into a functional, deployable model. You can watch…

Introduction In the world of web development, creating dynamic and informative websites often involves more than just static content. Being able to populate your website with data from various sources can provide a richer user experience and allow you to showcase valuable information. …

Parameter Tuning: The Key to Model Performance — Introduction In the ever-evolving world of machine learning, building models that generalize well is a paramount goal. The journey to achieving this involves finding the optimal model parameters to avoid the pitfalls of overfitting and underfitting. This article delves into the significance of parameter tuning and introduces K-Fold Cross-Validation as a…

In the realm of machine learning, two critical concepts stand out as challenges that need to be managed to create models that generalize well to unseen data: overfitting and underfitting. …

Navigating the Path between Overfitting and Underfitting Introduction In the realm of machine learning and statistics, achieving the perfect balance between model complexity and generalization is a constant endeavor. The bias-variance tradeoff lies at the heart of this pursuit, serving as a guiding principle to strike the right balance between overfitting and underfitting. …

The few most-asked data engineering questions will clear basic understanding of what a data engineer does in day-to-day life. Data Engineering Basics: 1. What is Data Engineering? Data Engineering involves designing, constructing, and maintaining systems that collect store, and process data for analysis and reporting. 2. What’s the difference between a data engineer…

In this blog, I will explain how to handle missing values in a categorical variable. First of all, let’s brush up on some basics. Categorical/Discrete Variable: Any random variable which can take values from a set of finite values is called a Categorical or Discrete random variable. Example: In a…

Evaluating your model is an essential part of any project. Your model may give a satisfactory result when evaluated using metric say Accuracy score but it may give poor results on other metrics. We will see various performance measurement metrics. Let’s start one by one. 1. Accuracy Accuracy is defined…