Read Data

Format Type

 CSV Format

 Code :   read.table("c:/mydata.csv", header=TRUE, sep=",")

 read.csv(file = file.chose())

 After Setting Working Directory

read.table("file name")

 Other Formats

 SPSS, SAS, Stata & more

 use "readxl" package

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Apriori

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# Data Preprocessing # install.packages('arules') library(arules) dataset = read.csv('Market_Basket_Optimisation.csv', header = FALSE) #sparce matrix dataset = read.transactions('Market_Basket_Optimisation.csv', sep = ',', rm.duplicates = TRUE) distribution of transactions with duplicates: 1  5  summary(dataset) transactions as itemMatrix in sparse format with  7501 rows (elements/itemsets/transactions) and  119 columns (items) and a density of 0.03288973  most frequent items: mineral water          eggs     spaghetti  french fries     chocolate       (Other)           1788          1348          1306          1282          1229         22405  element (itemset/transaction) length distribution: sizes    1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16   18   19   20  1754 1358 1044  816  667  493  391  324  259  139  102   67   40   22   17    4    1    2    1 
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Decision Tree Classification

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# Importing the dataset dataset = read.csv('Social_Network_Ads.csv') dataset = dataset[3:5] # Encoding the target feature as factor dataset$Purchased = factor(dataset$Purchased, levels = c(0, 1)) # Splitting the dataset into the Training set and Test set # install.packages('caTools') library(caTools) set.seed(123) split = sample.split(dataset$Purchased, SplitRatio = 0.75) training_set = subset(dataset, split == TRUE) test_set = subset(dataset, split == FALSE) # Feature Scaling training_set[-3] = scale(training_set[-3]) test_set[-3] = scale(test_set[-3]) # Fitting Decision Tree Classification to the Training set # install.packages('rpart') library(rpart) classifier = rpart(formula = Purchased ~ .,                    data = training_set) # Predicting the Test set results y_pred = predict(classifier, newdata = test_set[-3], type = 'class') # Making the Confusion Matrix cm = table(test
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