In this tutorial we will take a look at deep learning with neuralnet package – a package with which you can create neural networks. Objectives: deep learning with neuralnet package; create a model for mushroom classification Requirements: R Basics
In this tutorial you will learn what the bias-variance tradeoff is and how this problem can be solved with cross validation.
We will create a Random Forest and a Support Vector Machine model for income prediction. We will also learn what a Receiver Operating Characteristic (ROC) is and how to interpret it. Objectives: Model Creation, Random Forest, SVM, Prediction, ROC-Curve Requirements: R Basics, R Data Mining
broom is a helpful package for transforming models into dataframes for further analysis. modelr has some useful functions for working with models. Objectives: learn to handle packages broom and modelr, randomForest model, linear, quadratic and cubic model, and a bit map() from purrr package Requirement: piping, experience of dplyr might help
Exploratory factor analysis is performed to find a lower number of unobserved variables (factors) in a larger number of correlated variables. It is a statistical technique for dimension reduction. We will analyse “forest fires” dataset.
In this tutorial we will learn what wide and tidy data is, why it is used and how you can transform it from one format in the other. Transforming data from one format in the other is a very common task in data analysis.
We will learn to detect outliers for univariate distributions. We will analyse “iris” dataset for this purpose. Learning Objective: Outlier Detection Requirements: ggplot2, plyr, dplyr
dplyr is one of the most popular and most useful packages in the R-universe. It covers all aspects of data manipulation: filtering, selection, sorting and many more.
In this tutorial we will learn how to use dplyr within functions. For this we will use movie data.
If you deal with datasets that contain date and time, these are usually imported as factors, which is not helpful for further processing. You need to apply some special measures to transform it to an R date object or to extract information like days, or hours. In this tuturial you will learn how this works.
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