Telco Churn Modelling using Bagging algorithm in R In this Learn by Coding example, we will learn how to predict telco churn using bagging algorithms in R. This example is useful for beginners who has excel background and wish to learn Python programming as well as R programming. Free Machine Learning & Data …
Telco Churn Modelling using Naive Bayes algorithm in R In this Learn by Coding example, we will learn how to predict telco churn using naive bayes in R. This example is useful for beginners who has excel background and wish to learn Python programming as well as R programming. Free Machine Learning & Data …
Telco Churn Modelling using KNN algorithm in R In this Learn by Coding example, we will learn how to predict telco churn using KNN in R. This example is useful for beginners who has excel background and wish to learn Python programming as well as R programming. Free Machine Learning & Data Science …
Telco Churn Modelling using support vector machine in R In this Learn by Coding example, we will learn how to predict telco churn using support vector machine in R. This example is useful for beginners who has excel background and wish to learn Python programming as well as R programming. Free Machine …
Telco Churn Modelling using Quadratic Discriminant Analysis in R In this Learn by Coding example, we will learn how to predict telco churn using quadratic 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 Machine Learning …
Linear, Lasso, and Ridge Regression with R Introduction Machine learning is used by many organizations to identify and solve business problems. The two types of supervised machine learning algorithms are classification and regression. This guide will focus on regression models that predict a continuous outcome. You’ll learn how to implement linear and regularized regression models using R. …
Splitting and Combining Data with R Introduction In real-world data science projects, it is often necessary to divide data into two or more subsets or to combine multiple sets into one. This is an integral part of the data wrangling process for predictive modeling. In this guide, you will learn techniques for splitting and …
Working with Data Types in R Introduction As a powerful statistical programming language, R has a wide variety of data types and data structures. To be proficient in R, it is important to understand these data types and learn how to work with them. In this guide, you will learn concepts and techniques for …
Coping with Missing, Invalid and Duplicate Data in R Introduction A vital component of data science is cleaning the data and getting it ready for predictive modeling. The most common problem related to data cleaning is coping with missing data, invalid records and duplicate values. In this guide, you will learn about techniques for …
Visualization of Text Data Using Word Cloud in R Introduction Visualization plays an important role in exploratory data analysis and feature engineering. However, visualizing text data can be tricky because it is unstructured. Word Cloud provides an excellent option to visualize the text data in the form of tags, or words, where the importance …
Machine Learning with Text Data Using R Introduction The domain of analytics that addresses how computers understand text is called Natural Language Processing (NLP). NLP has multiple applications like sentiment analysis, chatbots, AI agents, social media analytics, as well as text classification. In this guide, you will learn how to build a supervised machine …
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 and subtitles by …