When it comes to data science and machine learning, creating efficient data pipelines is really important. These pipelines help in organizing and processing data to generate valuable insights and models. Lately, people have been exploring the use of data lakes to make the process of building these pipelines faster. …

Knock the door of our brains now. An excellent simple maths (easy) problem that give birth to how quickly we settle into habits, is the following math exercise. Start by adding a few numbers in your mind. …

A Story of Applying Machine Learning Algorithms — First we'll understand both bias and variance individually and then we will jump into trade-off of bias-variance which most essential step while applying any machine learning algorithm. When we choose any algorithm depending upon our initial data analysis or more specifically exploratory data analysis which we did on our data…

Abstract This compact study focused on the death rate and corresponding growth rate per year in India. The deaths that occurred due to corona during the pandemic, if we tune any news channel in every 20 mins they telecast or broadcast something related to deaths due to corona on the…

The contribution of big data and analytics in the success of Netflix. Netflix’s current company valuation is $234 billion. It is currently renowned as the most valued company/media company in the world and transcends even Disney. …

Pre-requisites for this article: What is a Computer Program? It can be defined as a set of coded instructions given to a system to attain certain objectives. Instructions can be given to a computer by creating or writing programs. The automation of the task is also possible with writing programs or instructions. What is Python? Python is…

Why we need a Hypothesis? Is our experiment generating the output we are hoping for? Due to something we are not capable enough to differentiate between our measurement error. Understanding Hypothesis Testing… Hypothesis Testing is a powerful statistical method to solve such experiments. We will consider a coin-tossing event with unknown parameter p. The first step is to…

Gradient descent is a widely used machine learning algorithm. It tells us how we can do better in predictive modeling with an iterative approach. We will see how we can use the gradient descent algorithm to get better predicting results in linear regression. Simple Linear Regression This is an equation of a line…

“The process that of identifying the anomalous and extreme high or low observations in the dataset.” The mistakes like computational error, wrong data entry, sampling error, value error cause to create some outliers in the data. Say for an example, showing age of a person as 250 or 300 years…