Use Social_Network_Ads.csv File.

Author: dshah98

Random Forest Classification.R

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+# Steps ;
+# Step 1 : Pick at random K data points from the Training set.
+# Step 2 : Build the Decision Tree associated to these K data points.
+# Step 3 : Choose the number Ntree of trees you want to build and repeat step 1 and 2.
+# Step 4 : For a new data point, make each one of your ntree tree predict the category to which the dagta point belongs ans 
+# assign the new data point to the category that wins the majority vote.
+
+# ---------------------------------------------------- Importing Data ------------------------------------------ #
+
+dataset = read.csv('Social_Network_Ad.csv')
+
+# Selecting particular columns
+dataset = dataset[3:5]
+
+# ---------------------------------------- Encoding the target feature as factor ------------------------------- #
+
+dataset$Purchased = factor(dataset$Purchased, levels = c(0, 1))
+
+# ---------------------------------- Splitting the dataset into Training and Test Set ------------------------- #
+
+# install.packages('caTools')
+library(caTools)
+set.seed(123)
+split = sample.split(dataset$Purchased, SplitRatio = 0.75)
+training_set = subset(dataset, split == TRUE)
+test_set = subset(dataset, split == FALSE)
+
+# --------------------------------------------------- Feature Scalling ----------------------------------------- #
+
+training_set[-3] = scale(training_set[-3])
+test_set[-3] = scale(test_set[-3])
+
+# ------------------------------------- Fitting Classifier to the Training Set --------------------------------- #
+
+library(randomForest)
+classifier = randomForest(x = training_set[-3], y = training_set$Purchased, ntree = 10)
+summary(classifier)
+
+# -------------------------------------------- Predicting the Test Set result ---------------------------------- #
+
+y_pred = predict(classifier, newdata = test_set[-3])
+y_pred
+
+# ------------------------------------------------- Confusion Matrix ------------------------------------------- #
+
+cm = table(test_set[, 3], y_pred)
+cm
+
+# --------------------------------------- Visualising the Training Set results --------------------------------- #
+
+library(ElemStatLearn)
+
+set = training_set
+train1 = seq(min(set[, 1]) - 1, max(set[, 1]) + 1, by = 0.01)
+train2 = seq(min(set[, 2]) - 1, max(set[, 2]) + 1, by = 0.01)
+
+grid_set = expand.grid(train1, train2)
+
+colnames(grid_set) = c('Age', 'EstimatedSalary')
+
+y_grid = predict(classifier, newdata = grid_set, type = 'class')
+
+# Plotting
+plot(set[, -3],
+     main = 'Random Forest Classification (Training set)',
+     xlab = 'Age', ylab = 'Estimated Salary',
+     xlim = range(train1), ylim = range(train2))
+
+# Regression Line
+contour(train1, train2, matrix(as.numeric(y_grid), length(train1), length(train2)), add = TRUE)
+
+# Giving Colour
+points(grid_set, pch = '.', col = ifelse(y_grid == 1, 'springgreen3', 'tomato'))
+points(set, pch = 21, bg = ifelse(set[, 3] == 1, 'green4', 'red3'))
+
+# ------------------------------------------- Visualising the Test Set results --------------------------------- #
+
+library(ElemStatLearn)
+
+set = test_set
+
+test1 = seq(min(set[, 1]) - 1, max(set[, 1]) + 1, by = 0.01)
+test2 = seq(min(set[, 2]) - 1, max(set[, 2]) + 1, by = 0.01)
+
+grid_set = expand.grid(test1, test2)
+
+colnames(grid_set) = c('Age', 'EstimatedSalary')
+
+y_grid = predict(classifier, newdata = grid_set, type = 'class')
+
+# Plotting
+plot(set[, -3], main = 'Random Forest Classification (Test set)',
+     xlab = 'Age', ylab = 'Estimated Salary',
+     xlim = range(test1), ylim = range(test2))
+
+# Regression Line
+contour(test1, test2, matrix(as.numeric(y_grid), length(test1), length(test2)), add = TRUE)
+
+# Giving Colour
+points(grid_set, pch = '.', col = ifelse(y_grid == 1, 'springgreen3', 'tomato'))
+points(set, pch = 21, bg = ifelse(set[, 3] == 1, 'green4', 'red3'))