Logistic Regression

Deploying R Model as API Web Service using Docker and Microsoft Azure

Objective Our goal here is to create a R Model and put-in into production by deploying it as web service API using Docker to containerize (encapsulate) it and Microsoft Azure to host it. R Model To create the model, we going to use mtcars dataset which one’s is present inside R. head(mtcars) ## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21.0 6 160 110 3.

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Classifying using Logistic Regression

1 - Objective The objective of this example is to identify each of a number of benign or malignant classes. 2 - Data Let’s getting the data. BCData <- read.table(url("https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data"), sep = ",") # setting column names names(BCData)<- c('Id', 'ClumpThickness', 'CellSize','CellShape', 'MarginalAdhesion','SECellSize', 'BareNuclei', 'BlandChromatin','NormalNucleoli', 'Mitoses','Class') 3 - EDA - Exploratory Data Analysis It’s important to extract prelimionary knowledge from the dataset. dim(BCData) ## [1] 699 11 str(BCData) ## 'data.frame': 699 obs.

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