## Code for scatterplots

scatter_plot <- function(data, x, y, max_points = 1000, fit = F) {

if (fit) {

lr <- h2o.glm(x = x, y = y, training_frame = data, family = "gaussian")

coeff <- lr@model$coefficients_table$standardized_coefficients

}

df <- data[,c(x, y)]

runif <- h2o.runif(df)

df.subset <- df[runif < max_points/nrow(data),]

df.R <- as.data.frame(df.subset)

if (fit) h2o.rm(lr@model_id)

plot(x = df.R[,x], y = df.R[,y], col = "blue", xlab = x,

ylab = y, ylim = c(0, 550))

if (fit) abline(coef = coeff, col = "black")

}

## Load library and initialize h2o

library(h2o)

h2o.init(nthreads = -1)

## Set file path and import data. Drop constant column (23).

pathToAirlines <- "https://s3.amazonaws.com/h2o-airlines-unpacked/allyears2k.csv"

airlines.hex <- h2o.importFile(path = pathToAirlines, destination_frame = "airlines.hex")

airlines.hex <- airlines.hex[-23]

dim(airlines.hex)

## Get a summary of the data. Build a histogram examining the "Year" column using h2o.hist()

summary(airlines.hex)

h2o.hist(airlines.hex$Year)

## Scatter plot of airlines dataset examining the relationship between the "Distance" and "AirTime" columns

scatter_plot(data = airlines.hex, x = "Distance", y = "AirTime", max_points = 10000)

## Use h2o.group_by to calcualte the flights in a given month

## Use as.factor to change the "Year," "Month," "DayOfWeek," and "Cancelled" columns to factors

airlines.hex$Year <- as.factor(airlines.hex$Year)

airlines.hex$Month <- as.factor(airlines.hex$Month)

airlines.hex$DayOfWeek <- as.factor(airlines.hex$DayOfWeek)

airlines.hex$Cancelled <- as.factor(airlines.hex$Cancelled)

## Calculate and plot travel timef

hour1 <- airlines.hex$CRSArrTime %/% 100

mins1 <- airlines.hex$CRSArrTime %% 100

arrTime <- hour1*60+mins1

hour2 <- airlines.hex$CRSDepTime %/% 100

mins2 <- airlines.hex$CRSDepTime %% 100

depTime <- hour2*60+mins2

## Impute missing travel times by the "Origin" and "Dest" columns and re-plot.

## Create test/train split

## Set predictor and response variables

myY <- "IsDepDelayed"

## Simple GLM and GBM models - Predict Delays

## Get summary of models

## Get variable importances for both models