Below are the solutions to here. exercises on building a neural network. ############### # # # Exercise 1 # # # ############### install.packages(“neuralnet”) ## Error in contrib.url(repos, type): trying to use CRAN without setting a mirror library(neuralnet) library(ggplot2) library(nnet) library(dplyr) library(reshape2) data(“iris”) set.seed(123) ############### # # # Exercise 2 # # # ############### exploratory_iris <- […]

## Building a Neural Network Using the Iris Data Set: Exercises

Neural Networks is one of the most common machine learning algorithms and with good reason. Neural networks are particularly good when applied to problems, such as image recognition and natural language processing, where there is a large amount of input data. Through an input layer, one or more hidden layers, and an output layer, a […]

## Survival Analysis using GGPlot Exercises (Part 1)

Clinical trials can be planned to the very last detail, but that doesn’t prevent people from losing touch with the study, moving abroad, or never experiencing the expected event. That event could be the curing of a disease, platelet counts falling below a certain threshold, or, in undesirable circumstances, death. In all cases where the […]

## Survival Analysis Using GGPlot Solutions (Part 1)

Below are the solutions to these exercises on GGplot survival plot exercises. ############### # # # Exercise 1 # # # ############### library(survival) data(“lung”) lung$status <- as.factor(lung$status) # 1 Censored 2 Dead ############### # # # Exercise 2 # # # ############### print(paste(round((table(lung$status)[1]/dim(lung)[1])*100,0), “%”, sep = “”)) ## [1] “28%” ############### # # # Exercise […]

## Graph Theory: Using iGraph Exercises (Part-2)

Following on from last time, this tutorial will focus on more advanced graph techniques and existing algorithms such as Dijkstra’s algorithm that can be used to draw real meaning from graphs. This is part 2 in the series of iGraph tutorials, for part 1, click here. When completing these tutorials be sure to read up […]

## Graph Theory: Using iGraph Solutions (Part-2)

Below are the solutions to the second set of iGraph exercises. ############### # # # Exercise 1 # # # ############### library(igraph) matches <- data.frame(name = rep(c("Jerry", "Lilly", "Karl", "Jenny"), each = 4), subject = rep(c("Maths", "English", "Biology", "French"), 4), weight = c(81, 78, 24, 58, 76, 60, 62, 83, 35, 59, 50, 56, 72, […]

## Protected: Bonus: Sentiment Analysis using TidyText Solutions

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## Protected: Bonus: Sentiment Analysis using TidyText Exercises

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## Graph Theory: Using iGraph Solutions (Part-1)

Below are the solutions to these exercises on graph theory part-1. ############### # # # Exercise 1 # # # ############### g <- graph.star(n=10, mode = “undirected”) plot(g) ############### # # # Exercise 2 # # # ############### g <- add_edges(g, c(8,5, 6,3, 6,10, 5,2, 4,8, 2,7, 6,5, 7,9)) ############### # # # Exercise 3 […]

## Graph Theory: Using iGraph Exercises (Part-1)

This is part 1 of a series in analyzing and visualizing network data using iGraph. The rest of the series can be found here. Graph Theory, or network analysis as it is often called, is the mathematical portrayal of a series of edges and vertices. To contextually picture a network, think of each node being […]