This RMarkdown file contains the report of the data analysis done for the project on building and deploying a stroke prediction model in R. It contains analysis such as data exploration, summary statistics and building the prediction models. The final report was completed on Sat Jul 6 05:11:33 2024.
Data Description:
According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths.
This data set is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relevant information about the patient.
install.packages(“caret”) install.packages(“caret”, dependencies=c(“Depends”, “Suggests”)) library(caret) # attach the iris dataset to the environment data(iris) # rename the dataset dataset <- iris # define the filename filename <- “iris.csv” # load the CSV file from the local directory dataset <- read.csv(filename, header=FALSE) # set the column names in the dataset colnames(dataset) <- c(“Sepal.Length”,“Sepal.Width”,“Petal.Length”,“Petal.Width”,“Species”) # create a list of 80% of the rows in the original dataset we can use for training validation_index <- createDataPartition(dataset$Species, p=0.80, list=FALSE) # select 20% of the data for validation validation <- dataset[-validation_index,] # use the remaining 80% of data to training and testing the models dataset <- dataset[validation_index,]
dim(dataset) [1] 120 5 # list types for each attribute sapply(dataset, class) Sepal.Length Sepal.Width Petal.Length Petal.Width Species “numeric” “numeric” “numeric” “numeric” “factor” Sepal.Length Sepal.Width Petal.Length Petal.Width Species “numeric” “numeric” “numeric” “numeric” “factor” Sepal.Length Sepal.Width Petal.Length Petal.Width Species “numeric” “numeric” “numeric” “numeric” “factor” Sepal.Length Sepal.Width Petal.Length Petal.Width Species “numeric” “numeric” “numeric” “numeric” “factor” # take a peek at the first 5 rows of the data head(dataset) Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 5 5.0 3.6 1.4 0.2 setosa 6 5.4 3.9 1.7 0.4 setosa 7 4.6 3.4 1.4 0.3 setosa # list the levels for the class levels(dataset\(Species) # list the levels for the class levels(dataset\)Species) [1] “setosa” “versicolor” “virginica” # summarize the class distribution percentage <- prop.table(table(dataset\(Species)) * 100 cbind(freq=table(dataset\)Species), percentage=percentage)