Hits: 15 Telco Churn Modelling using Linear Discriminant Analysis in R In this Learn by Coding example, we will learn how to predict telco churn using linear discriminant analysis in R. This example is useful for beginners who has excel background and wish to learn Python programming as well as R programming. Free …

Hits: 31 DISPLAY A BEAUTIFUL SUMMARY STATISTICS IN R USING SKIMR PACKAGE This article describes how to quickly display summary statistics using the R package skimr. skimr handles different data types and returns a skim_df object which can be included in a tidyverse pipeline or displayed nicely for the human reader. Key features of skimr: Provides a larger set of …

Hits: 46 GGPLOT LOG SCALE TRANSFORMATION This article describes how to create a ggplot with a log scale. This can be done easily using the ggplot2 functions scale_x_continuous() and scale_y_continuous(), which make it possible to set log2 or log10 axis scale. An other possibility is the function scale_x_log10() and scale_y_log10(), which transform, respectively, the x and y axis scales into a log …

Hits: 174 GGPLOT TITLE, SUBTITLE AND CAPTION This article describes how to add and change a main title, a subtitle and a caption to a graph generated using the ggplot2 R package. We’ll show also how to center the title position, as well as, how to change the title font size and color. In this R graphics tutorial, you will learn how to: Add titles …

Hits: 95 TYPES OF CLUSTERING METHODS: OVERVIEW AND QUICK START R CODE Clustering methods are used to identify groups of similar objects in a multivariate data sets collected from fields such as marketing, bio-medical and geo-spatial. They are different types of clustering methods, including: Partitioning methods Hierarchical clustering Fuzzy clustering Density-based clustering Model-based clustering In this …

Hits: 16 A Practical approach to Simple Linear Regression using R Simple Linear Regression is a statistical method that allows us to summarize and study relationships between two continuous (quantitative) variables. One variable denoted x is regarded as an independent variable and other one denoted y is regarded as a dependent variable. It is …

Hits: 349 Linear Regression using sklearn Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting. Different regression models differ based on – the kind …

Hits: 17 Multiple Linear Regression using R Linear Regression: It is the basic and commonly used used type for predictive analysis.It is a statistical approach for modelling relationship between a dependent variable and a given set of independent variables. These are of two types: Simple linear Regression Multiple Linear Regression Let’s Discuss about …

Hits: 210 Multiple Linear Regression using Python Linear Regression: It is the basic and commonly used type for predictive analysis. It is a statistical approach to modelling the relationship between a dependent variable and a given set of independent variables. These are of two types: Simple linear Regression Multiple Linear Regression Let’s Discuss Multiple …

Hits: 15 Univariate Linear Regression in Python Univariate data is the type of data in which the result depends only on one variable. For instance, dataset of points on a line can be considered as a univariate data where abscissa can be considered as input feature and ordinate can be considered as output/result. For …

Hits: 17 Linear Regression (Python Implementation) This article discusses the basics of linear regression and its implementation in Python programming language. Linear regression is a statistical approach for modelling relationship between a dependent variable with a given set of independent variables. Note: In this article, we refer dependent variables as response and independent variables as features for simplicity. In …

Hits: 15 Momentum-based Gradient Optimizer introduction Gradient Descent is an optimization technique used in Machine Learning frameworks to train different models. The training process consists of an objective function (or the error function), which determines the error a Machine Learning model has on a given dataset. While training, the parameters of this algorithm are initialized …

Hits: 12 Optimization techniques for Gradient Descent Gradient Descent is an iterative optimiZation algorithm, used to find the minimum value for a function. The general idea is to initialize the parameters to random values, and then take small steps in the direction of the “slope” at each iteration. Gradient descent is highly used in …

Hits: 47 Mini-Batch Gradient Descent with Python In machine learning, gradient descent is an optimization technique used for computing the model parameters (coefficients and bias) for algorithms like linear regression, logistic regression, neural networks, etc. In this technique, we repeatedly iterate through the training set and update the model parameters in accordance with the …

Hits: 168 Stochastic Gradient Descent (SGD) What is Gradient Descent? Before explaining Stochastic Gradient Descent (SGD), let’s first describe what Gradient Descent is. Gradient Descent is a popular optimization technique in Machine Learning and Deep Learning, and it can be used with most, if not all, of the learning algorithms. A gradient is the …