R Machine Learning Crash Course

Telco Churn Modelling using Linear Discriminant Analysis in R

  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 Machine …

Data Analytics – GGPLOT THEME BACKGROUND COLOR AND GRIDS

GGPLOT THEME BACKGROUND COLOR AND GRIDS   This article shows how to change a ggplot theme background color and grid lines. The default theme of a ggplot2 graph has a grey background color. You can easily and quickly change this to a white background color by using the theme functions, such as theme_bw(), theme_classic(), theme_minimal() or theme_light() (See ggplot2 themes gallery). Another alternative is to modify directly …

Data Analytics – GGPLOT LEGEND TITLE, POSITION AND LABELS

GGPLOT LEGEND TITLE, POSITION AND LABELS   This R graphics tutorial shows how to customize a ggplot legend. you will learn how to: Change the legend title and text labels Modify the legend position. In the default setting of ggplot2, the legend is placed on the right of the plot. We’ll show examples of how to …

Data Analytics – TYPES OF CLUSTERING METHODS: OVERVIEW AND QUICK START R CODE

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 article, we …

Data Science and Machine Learning for Beginners in R – Tensorflow and Keras with Dropout layers using Mushroom Dataset

  TensorFlow and Keras are two popular open-source tools used for machine learning and deep learning. They are often used together to build and train neural networks, which are a type of model that can be used for tasks such as image recognition, natural language processing, and more. One important technique used in training neural …

Data Science and Machine Learning for Beginners in R – Tensorflow and Keras using Mushroom Dataset

Tensorflow is an open-source software library developed by Google for machine learning. It is a powerful tool that can be used to build and train neural networks. Keras is a high-level library that runs on top of Tensorflow and is used to simplify the process of building and training neural networks. Together, Tensorflow and Keras …

Data Science and Machine Learning for Beginners in R – XGBoost with Grid Search using Mushroom Dataset

  XGBoost is a popular and powerful implementation of the Gradient Boosting algorithm. It is an efficient and scalable implementation of gradient boosting framework. XGBoost stands for “Extreme Gradient Boosting” and is known for its high predictive power and performance on large datasets. In this article, we will be discussing how to use XGBoost with …

Data Science and Machine Learning for Beginners in R – Boosting Ensembles with Grid Search using Mushroom Dataset

  Boosting is another ensemble learning method that is used to improve the performance of machine learning models. Like bagging, boosting combines the predictions of multiple models, but it does so in a different way. Instead of generating multiple subsets of the data and training a model on each subset, boosting trains a model on …

Data Science and Machine Learning for Beginners in R – Random Forest with Grid Search using Mushroom Dataset

  Random Forest is a type of ensemble learning algorithm that can be used for both classification and regression tasks. It works by building multiple decision trees and combining their predictions to make a final prediction. One of the advantages of Random Forest is that it can help to reduce overfitting, which is a common …

Data Science and Machine Learning for Beginners in R – Bagging Ensemble Algorithms using Mushroom Dataset

  Ensemble learning is a powerful technique in machine learning that combines the predictions of multiple models to improve the overall performance of a system. One popular ensemble method is called bagging, which stands for Bootstrap Aggregating. Bagging is a technique that generates multiple subsets of the data, and then trains a model on each …