What is a Raspberry Pi? Raspberry Pi is a credit card-sized mini-computer that allows you to do computing on the go. It has all the features of a desktop/laptop i.e Wifi, Bluetooth, USB ports, HDMI ports, Stereo Audio jack, Camera interface, ethernet port etc. …

Installing CUDA and TensorFlow-GPU can be a very challenging task, in this article I will show to install it in a few simple steps. Before getting started with this installation we need to make sure that your graphics card is CUDA enabled. If it is not then using GOOGLE COLLAB…

KNN is a supervised learning algorithm used for both regression and classification problems. Mostly used for Classification though. KNN tries to predict the correct class of test data by calculating the distance between the test data and all the training points. …

Pipelines are a container of steps, they are used to package workflow and fit a model into a single object. Pipelines are stacked on top of one another, taking input from one block sending output to the next block, the next block takes the input and gives out an output…

LightGBM is a gradient boosting framework that uses tree-based learning algorithms. It is designed to be distributed and efficient as compared to other boosting algorithms. A model that can be used for comparison is XGBoost which is also a boosting method and it performs exceptionally well when compared to other…

We all know the famous Linear Regression algorithm, it is probably the oldest known algorithm in the world used in statistics and other fields. …

Linear Regression is one of the oldest methods in the field of probability/statistics. It works by fitting the best fit line between dependent and independent variables. Let’s get familiar with some common terms.

In this article, I will demonstrate how to detect and remove outliers from your dataset. What is an outlier? In statistics, an outlier is a data point that differs significantly from other observations. Outlier Detection can only be applied to continuous features and it doesn't make much sense using it…

One of the first steps in data cleaning is to check for missing values. This is because missing data in the training dataset can reduce the power of a model or can lead to a biased model. After all, we failed to analyze the relationship with other variables correctly. …