## Spot-Checking : A Comprehensive Guide to Testing Machine Learning Algorithms in R

Spot-Checking : A Comprehensive Guide to Testing Machine Learning Algorithms in R

## Telco Churn Modelling using Naive Bayes algorithm in R

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 …

## Reshaping Data with R

Reshaping Data with R   Introduction In predictive modeling, it is often necessary to reshape the data to make it ready for conducting analysis or building models. The process of transforming the data into a clear, simple, and desirable form is an integral component of data science. The most common reshaping process is converting the …

## Machine Learning in R | Classification | Data Science for Beginners | IRIS | LDA | CARET tutorials

Machine learning is a method of teaching computers to learn from data without being explicitly programmed. One of the most commonly used algorithms for classification tasks is the Linear Discriminant Analysis (LDA) algorithm. In this article, we will be discussing how to use LDA for classification in R using the IRIS dataset from …

## Deep Learning in R with Dropout Layer | Data Science for Beginners | Regression | Tensorflow | Keras

Deep learning is a powerful machine learning technique that allows for the creation of complex models to solve difficult problems. In this article, we will be discussing how to use dropout layers in R to improve the performance of a deep learning model for regression tasks. Dropout is a regularization technique that is used …

## Deep Learning in R | Data Science for Beginners | Tensorflow | Keras | House Price Data | Regression

Deep learning is a subset of machine learning that involves training artificial neural networks to perform tasks such as image or speech recognition, natural language processing, and predictive modeling. In this article, we will discuss how to use deep learning in R to perform regression on a housing price dataset using the Tensorflow …

## Machine Learning in R | Data Science for Beginners | Neural Networks | House Dataset | Regression

Machine learning is a method of teaching computers to learn from data, without being explicitly programmed. In R, there are many libraries available for machine learning, such as caret, randomForest, and nnet. One of the most popular datasets for machine learning is the Boston house price dataset, which is available in the …

## Machine Learning in R | Data Science for Beginners | Random Forest | Boston House Data | Regression

Machine learning is a technique that allows computers to learn from data and make predictions or decisions without being explicitly programmed. In this article, we will discuss how to use the Random Forest algorithm for regression tasks in R with the Boston House Data from the UCI Machine Learning Repository. First, we …

## Machine Learning in R | Data Science for Beginners | XGBoost | Regression | Boston Dataset | CARET

Machine learning is a powerful tool that allows us to make predictions and analyze data using a variety of algorithms. In this article, we will focus on using the XGBoost algorithm for regression tasks in R. We will be using the Boston Housing Price dataset from the UCI repository, and the CARET …