R

Predicting large text data with spark via the R package sparklyr

Unlike the classical programming languages that are very slow and even sometimes fail to load very large data sets since they use only a single core, Apache Spark is known as the fastest distributed system that can handle with ease large datasets...

Ordinal data models

This tutorial aims to explore the most popular models used to predict an ordered response variable...

Predicting binary response variable with h2o framework

H2O is an open-source distributed scalable framework used to train machine learning and deep learning models as well as data analysis. It can handle large data sets, with ease of use, by creating...

Bayesian hyperparameters optimization

Machine learning models are called by this name because of their ability to learn the best parameter values that are as closest as possible to the right values of the optimum objective function (or loss function). However, since all models...

Local Snsitivity Hashing Model

This model is an approximate version of knn model which is difficult to be implemented with large data set...

Predicting images using Convolutional neural network

In this article we will make use of the convolutional neural network, the most widely deep learning method used for image classification...

Predicting large and imbalanced data set using the R package tidymodels

The super easy way, at least for me, to deploy machine learning models is by making use of the R package **tidymodels**, which is a collection of many packages that makes...

Count data Models

When we deal with data that has a response variable of integer type, using a linear regression may violate the normality assumption and hence all the classical statistic tests would fail to evaluate the model...

Xgboost model

In this paper we learn how to implement xgboost model to predict the titanic data...

logistic regression

In this paper we will fit a logistic regression model to the **heart disease** data...