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  1. library(ggplot2)
  2. library(caret)
  3. library(e1071)
  4.  
  5. data <- read.csv("RidingMowers.csv")
  6.  
  7. data1 <- subset(data, data$Ownership == "Owner")
  8. data2 <- subset(data, data$Ownership == "Nonowner")
  9.  
  10. #A
  11. (dim(data1)[1] / dim(data)[1]) * 100
  12. summary(data$Ownership)
  13.  
  14. #B
  15. ggplot(data = data, aes(x = data$Income, y = data$Lot_Size)) +
  16.   geom_point(aes(col = factor(data$Ownership))) +
  17.   theme_bw()
  18.  
  19. logit.reg <- glm(Ownership ~ ., data = data[, 1:3], family = "binomial")
  20. summary(logit.reg)
  21.  
  22. pred <- predict(logit.reg, data2, type = "response")
  23. valid.predict<-data.frame(actual = data2$Ownership, predicted = pred)
  24. pred_rate <- subset(valid.predict, valid.predict$predicted < 0.5)
  25.  
  26. #C
  27. (dim(pred_rate)[1] / dim(data2)[1]) * 100
  28. confusionMatrix(as.factor(ifelse(valid.predict$predicted < 0.5, "Nonowner", "Owner")), data2$Ownership) #sensitivity
  29.  
  30.  
  31. #E, F
  32. new <- data.frame(Income = 60.0, Lot_Size = 20.0)
  33. pred <- predict(logit.reg, new, type = "response") #nonowner
  34. odds <- pred / (1 - pred)
  35.  
  36. #G
  37. income_table <- data.frame(Income = seq(50,200,1), Probability = rep(0,151))
  38.  
  39. for (i in seq(50,200,1)){
  40.   new_row <- data.frame(Income = i, Lot_Size = 16)
  41.   pred <- predict(logit.reg, new_row, type = "response")
  42.   income_table[income_table$Income == i, 2 ] <- pred
  43. }
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