Neural network in R to analyze diabetes data
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This is my first attempt at writing a neural network and I am experiencing immense difficulty in reducing the error value which is relatively high and thus reduces the accuracy of the model.
library(neuralnet)
normalize <- function(x) {
return ((x - min(x)) / (max(x) - min(x)))
}
df <- read.csv('C:/Users/CaitlinG/data.diabetes.csv', header=T)
df <- as.data.frame(lapply(df, normalize))
training <- df[1:614,]
testing <- df2[615:768,]
my.neural.net <- neuralnet(Outcome ~ Pregnancies + Glucose + BloodPressure + SkinThickness + Insulin + BMI + DiabetesPedigreeFunction + Age, data=training, hidden=c(3,1), linear.output= F, threshold = 0.01)
df <- apply(df, 1, function(row) all(row != 0))
my.neural.net$result.matrix
plot(my.neural.net)
r neural-network
edited 2 days ago
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asked 2 days ago
CaitlinG
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CaitlinG is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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add a comment |
up vote
0
down vote
favorite
This is my first attempt at writing a neural network and I am experiencing immense difficulty in reducing the error value which is relatively high and thus reduces the accuracy of the model.
library(neuralnet)
normalize <- function(x) {
return ((x - min(x)) / (max(x) - min(x)))
}
df <- read.csv('C:/Users/CaitlinG/data.diabetes.csv', header=T)
df <- as.data.frame(lapply(df, normalize))
training <- df[1:614,]
testing <- df2[615:768,]
my.neural.net <- neuralnet(Outcome ~ Pregnancies + Glucose + BloodPressure + SkinThickness + Insulin + BMI + DiabetesPedigreeFunction + Age, data=training, hidden=c(3,1), linear.output= F, threshold = 0.01)
df <- apply(df, 1, function(row) all(row != 0))
my.neural.net$result.matrix
plot(my.neural.net)
r neural-network
edited 2 days ago
200_success
127k15148412
asked 2 days ago
CaitlinG
1011
New contributor
CaitlinG is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Welcome to Code Review. Your question is rather sketchy. Please tell us more about what your CSV file looks like and what you intend to calculate.
– 200_success
2 days ago
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
This is my first attempt at writing a neural network and I am experiencing immense difficulty in reducing the error value which is relatively high and thus reduces the accuracy of the model.
library(neuralnet)
normalize <- function(x) {
return ((x - min(x)) / (max(x) - min(x)))
}
df <- read.csv('C:/Users/CaitlinG/data.diabetes.csv', header=T)
df <- as.data.frame(lapply(df, normalize))
training <- df[1:614,]
testing <- df2[615:768,]
my.neural.net <- neuralnet(Outcome ~ Pregnancies + Glucose + BloodPressure + SkinThickness + Insulin + BMI + DiabetesPedigreeFunction + Age, data=training, hidden=c(3,1), linear.output= F, threshold = 0.01)
df <- apply(df, 1, function(row) all(row != 0))
my.neural.net$result.matrix
plot(my.neural.net)
r neural-network
edited 2 days ago
200_success
127k15148412
asked 2 days ago
CaitlinG
1011
New contributor
CaitlinG is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
This is my first attempt at writing a neural network and I am experiencing immense difficulty in reducing the error value which is relatively high and thus reduces the accuracy of the model.
library(neuralnet)
normalize <- function(x) {
return ((x - min(x)) / (max(x) - min(x)))
}
df <- read.csv('C:/Users/CaitlinG/data.diabetes.csv', header=T)
df <- as.data.frame(lapply(df, normalize))
training <- df[1:614,]
testing <- df2[615:768,]
my.neural.net <- neuralnet(Outcome ~ Pregnancies + Glucose + BloodPressure + SkinThickness + Insulin + BMI + DiabetesPedigreeFunction + Age, data=training, hidden=c(3,1), linear.output= F, threshold = 0.01)
df <- apply(df, 1, function(row) all(row != 0))
my.neural.net$result.matrix
plot(my.neural.net)
r neural-network
r neural-network
edited 2 days ago
200_success
127k15148412
asked 2 days ago
CaitlinG
1011
New contributor
CaitlinG is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 2 days ago
200_success
127k15148412
asked 2 days ago
CaitlinG
1011
New contributor
CaitlinG is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 2 days ago
200_success
127k15148412
edited 2 days ago
200_success
127k15148412
edited 2 days ago
200_success
127k15148412
127k15148412
asked 2 days ago
CaitlinG
1011
New contributor
CaitlinG is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
CaitlinG
1011
asked 2 days ago
CaitlinG
1011
1011
New contributor
CaitlinG is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
CaitlinG is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
CaitlinG is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Welcome to Code Review. Your question is rather sketchy. Please tell us more about what your CSV file looks like and what you intend to calculate.
– 200_success
2 days ago
add a comment |
Welcome to Code Review. Your question is rather sketchy. Please tell us more about what your CSV file looks like and what you intend to calculate.
– 200_success
2 days ago
Welcome to Code Review. Your question is rather sketchy. Please tell us more about what your CSV file looks like and what you intend to calculate.
– 200_success
2 days ago
Welcome to Code Review. Your question is rather sketchy. Please tell us more about what your CSV file looks like and what you intend to calculate.
– 200_success
2 days ago
add a comment |
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Welcome to Code Review. Your question is rather sketchy. Please tell us more about what your CSV file looks like and what you intend to calculate.
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