-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathFlightDelayModels.R
362 lines (328 loc) · 12.4 KB
/
FlightDelayModels.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
library(readr)
library(VIM)
library(finalfit)
library(naniar)
library(ggplot2)
library(funModeling)
library(GGally)
library(ggthemes)
library(dplyr)
library(tidyverse)
library(caret)
library(e1071)
library(class)
# Get all files from the folder
filenames=list.files(path="C:/Users/Tressy/Desktop/Semester 3/Data Mining/Group Project/DM Project Georgia Ontime 2019",pattern="*.csv",full.names = T)
# filenames=list.files(path="https://github.com/tressythomas/DM-Group-Project/upload/main/Datasets", pattern="*.csv",full.names = T)
# filenames=read.csv("https://github.com/tressythomas/DM-Group-Project/upload/main/Datasets/41792460_T_ONTIME_REPORTING.csv")
georgia=do.call(rbind,lapply(filenames,function(x) read.csv(x, stringsAsFactors = FALSE, header=TRUE, sep=',', na.strings=c("","N/A"," ","NA",'NULL'))))
dim(georgia)
#Get the flights to and from ATL. And remove the last column. empty column from import
atl.ix=which(georgia$ORIGIN=='ATL'|georgia$DEST=='ATL')
atl_data=na.omit(georgia[atl.ix,-c(12,14)])
str(atl_data)
dim(atl_data)
#atl_data=check_miss(atl_data)
#atl_data=atl_data[,-c(24,25)]
#Remove unwanted columns. FL_DATE, ORIGIN_AIRPORT_ID,ORIGIN_CITY_NAME,ORIGIN,DEST_AIRPORT_ID,DEST,DEST_CITY_NAME,DEP_TIME,DEP_DELAY_NEW,DEP_DELAY,DEP_DELAY_NEW,ARR_TIME,ARR_DELAY,ARR_DELAY_NEW
#Convert the datatypes appropriately - to factor- OP_CARRIER_AIRLINE_ID
unique(atl_data$OP_CARRIER_AIRLINE_ID) #15 different airlines operating to and from ATL
atl_data$OP_CARRIER_AIRLINE_ID=as.factor(atl_data$OP_CARRIER_AIRLINE_ID)
atl_data$MONTH=as.factor(atl_data$MONTH)
atl_data$DAY_OF_MONTH =as.factor(atl_data$DAY_OF_MONTH)
atl_data$DAY_OF_WEEK=as.factor(atl_data$DAY_OF_MONTH)
#Create the arrival delay indicator
atl_data$arr_delay_ind=cut(atl_data$ARR_DELAY_GROUP, c(-Inf,0,Inf), c(0, 1)) #1 indicates delay
atl_data$dep_delay_ind=cut(atl_data$DEP_DELAY_GROUP, c(-Inf,0,Inf), c(0, 1)) #1 indicates delay
atl_data$delay_ind=as.factor(as.numeric(atl_data$dep_delay_ind==1 | atl_data$arr_delay_ind==1))
freq(atl_data$delay_ind)
# #Flight operation based on month
# temp=atl_data %>%
# group_by(MONTH,delay_ind) %>%
# summarise(cnt=n())
# month_stat = temp%>%
# group_by(MONTH)%>%
# summarise(delay_ind=delay_ind,cnt=cnt,cntx=paste0(round(cnt*100/sum(cnt),2),'%'))
#
#
# ggplot(data=month_stat,aes(x=as.factor(MONTH),y=cnt,fill=delay_ind,label=cntx))+
# geom_bar(stat="identity")+
# ggtitle("Flight operations by month") +
# xlab("Month")+ylab("No:of Flights")+
# geom_text(size = 3, position = position_stack(vjust = 0.5))
# theme_minimal()+
# scale_x_descrete(breaks=c(1,2,3,4,5,6,7,8,9,10,11,12),
# labels=c('Jan','Feb','Mar','Apr','May','June','July','Aug','Sep','Oct','Nov','Dec'))
#
# #Flight operation based on day of month
# ggplot(data=atl_data,aes(x=as.factor(DAY_OF_MONTH),fill=delay_ind))+
# geom_bar(stat="count")+
# theme_minimal()+
# ggtitle("Flight operations by day month") +
# xlab("Day")+ylab("No:of Flights")
# #Flight operation based on day of week
# ggplot(data=atl_data,aes(x=as.factor(DAY_OF_WEEK),fill=delay_ind))+geom_bar(stat="count")+
# theme_minimal()+
# ggtitle("Flight operations by Day of Week") +
# xlab("Day")+ylab("No:of Flights")
# #Flight operation based on departure time
# temp=atl_data %>%
# group_by(CRS_DEP_TIME,delay_ind) %>%
# summarise(cnt=n())
# time_stat= temp%>%
# group_by(CRS_DEP_TIME)%>%
# summarise(delay_ind=delay_ind,cnt=cnt,cntx=paste0(round(cnt*100/sum(cnt),2),'%'))
#
# ggplot(data=time_stat,aes(x=CRS_DEP_TIME ,y=cnt))+geom_line(aes(color=delay_ind))+
# theme_minimal()
# #Flight operation based on each airline
# temp=atl_data %>%
# group_by(OP_CARRIER_AIRLINE_ID,delay_ind) %>%
# summarise(cnt=n())
# time_stat= temp%>%
# group_by(OP_CARRIER_AIRLINE_ID)%>%
# summarise(delay_ind=delay_ind,cnt=cnt,cntx=paste0(round(cnt*100/sum(cnt),2),'%'))
# ggplot(data=time_stat,aes(x=as.factor(OP_CARRIER_AIRLINE_ID),y=cnt,fill=delay_ind))+
# geom_bar(stat="identity")+
# ggtitle("Flight operations by Airline Operator") +
# xlab("Month")+ylab("No:of Flights")+
# theme_minimal()
#
# #Relation between departure and arrival time delay
# ggplot(data=atl_data,aes(x=DEP_DELAY_GROUP ,y=CRS_DEP_TIME))+geom_point(aes(color=delay_ind))+
# theme_minimal()
#
# #Arrival delay and distance
# ggplot(data=atl_data,aes(x=DISTANCE ,y=ARR_DELAY_GROUP))+geom_point(aes(color=delay_ind))+
# theme_minimal()
## Most Delyayed Routes
#
temp=atl_data %>%
group_by(ORIGIN,DEST,delay_ind) %>%
summarise(cnt=n())
ROUTE_stat = temp%>%
group_by(ORIGIN,DEST)%>%
summarise(delay_ind=delay_ind,cnt=cnt,cntx=round(cnt*100/sum(cnt),2))
ROUTE_stat_delayed_ATL_orgn=ROUTE_stat[ROUTE_stat$delay_ind=='1'&ROUTE_stat$ORIGIN=='ATL',]
ROUTE_stat_delayed_ATL_dest=ROUTE_stat[ROUTE_stat$delay_ind=='1'&ROUTE_stat$DEST=='ATL',]
arrange(ROUTE_stat_delayed_ATL_orgn,desc(cntx))
arrange(ROUTE_stat_delayed_ATL_dest,desc(cntx,cnt))
#Most operated route
temp=atl_data %>%
group_by(ORIGIN,DEST) %>%
summarise(cnt=n())
ROUTE_stat = temp%>%
group_by(ORIGIN,DEST)%>%
summarise(cnt=cnt)
ROUTE_stat_ATL_orgn=ROUTE_stat[ROUTE_stat$ORIGIN=='ATL',]
ROUTE_stat_ATL_dest=ROUTE_stat[ROUTE_stat$DEST=='ATL',]
arrange(ROUTE_stat_ATL_orgn,desc(cnt))
arrange(ROUTE_stat_ATL_dest,desc(cnt))
#Routes withoud delay
temp=atl_data %>%
group_by(ORIGIN,DEST,delay_ind) %>%
summarise(cnt=n())
ROUTE_stat = temp%>%
group_by(ORIGIN,DEST)%>%
summarise(delay_ind=delay_ind,cnt=cnt,cntx=round(cnt*100/sum(cnt),2))
ROUTE_stat_ontime_ATL_orgn=ROUTE_stat[ROUTE_stat$delay_ind=='0'&ROUTE_stat$ORIGIN=='ATL',]
ROUTE_stat_ontime_ATL_dest=ROUTE_stat[ROUTE_stat$delay_ind=='0'&ROUTE_stat$DEST=='ATL',]
arrange(ROUTE_stat_ontime_ATL_orgn,desc(cntx))
arrange(ROUTE_stat_ontime_ATL_dest,desc(cntx,cnt))
#
# tlat=c(40.7904,40.6895,61.1759,40.2770
# ,37.8044
# ,37.7749
# ,44.0805
# ,38.8339
# ,40.7128
# ,34.0522
# )
# tlon=c(-73.1001,-74.1745,-149.9901,-74.8181
# ,-122.2712
# ,-122.4194
# ,-103.231
# ,-104.8214
# ,-74.006
# ,-118.2437
# )
#
#
# flat=c(33.6407,33.6407,33.6407,33.6407,33.6407,33.6407,33.6407,33.6407,33.6407,33.6407)
# flon=c(-84.4277,-84.4277,-84.4277,-84.4277,-84.4277,-84.4277,-84.4277,-84.4277,-84.4277,-84.4277)
tlat=c(33.6407,33.6407,33.6407,33.6407,33.6407,33.6407,33.6407,33.6407,33.6407,33.6407)
tlon=c(-84.4277,-84.4277,-84.4277,-84.4277,-84.4277,-84.4277,-84.4277,-84.4277,-84.4277,-84.4277)
flat=c(46.8721
,39.5296
,33.8303
,46.8772
,61.1759
,34.1808
,40.7298
,39.1911
,17.7466
,44.0805
)
flon=c(-113.994
,-119.8138
,-116.5453
,-96.7898
,-149.9901
,-118.309
,-73.2104
,-106.8175
,-64.7032
,-103.231
)
delay_map=as.data.frame(cbind(flat,flon,tlat,tlon))
# Plot route
library(maps)
# No margin
par(mar=c(0,0,0,0))
# US map
usMap = borders("world", colour='grey', fill="ivory")
usMap =map('state', proj = 'bonne', param = 45)
usMap=plot_usmap()
allUSA <- ggplot() + usMap +
geom_curve(data=delay_map,
aes(x=flon, y=flat, xend=tlon, yend=tlat),
col="cyan4",
arrow = arrow(length = unit(0.25,"cm")),
size=1.0,
ncp = 500,
curvature=0.4) +
geom_point(data=delay_map,
aes(x=flon, y=flat),
colour="orange",
size=2.5) +
geom_point(data=delay_map,
aes(x=tlon, y=tlat),
size=5.5,
colour="mediumorchid4") +
geom_text_repel(data=delay_map,position = 'identity', aes(x = flon, y = flat,
label = c('Missoula(1)','Reno(2)','Palp Springs(3)','Fargo(4)', 'Anchorage(5)','Burbank(6)','Islip(7)','Aspen(8)','Christianstek(9)','Rapid City(10)')),
#label = c('Islip(1)','Newark(2)','Archonage(3)','Trenton(4)', 'Oackland(5)','San Francisco(6)','Rapid City(7)','Colorado Springs(8)','New York(9)','Los Angeles(10)')),
col = "black", size =3) +
geom_text(data=delay_map,position = 'identity', aes(x = tlon[1], y = tlat[1], label = c('Atlanta')), col = "black", size =5)+
theme(axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
axis.ticks=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.title=element_text(hjust=0.5, size=14)) +
coord_cartesian(ylim=c(16.5, 62.5), xlim=c(-152, -62)) +
ggtitle("Top Ten Origin-locations of Flight delay towards Atlanta")
allUSA
#To do Balancing data, split, modeling-svm, logit , evaluation
options(scipen=999)
# Final dataset
fnl_data=atl_data[,-c(1,9,11,13,14)]
rm(georgia,atl_data)
set.seed(9)
# sub_ix=sample(nrow(fnl_data),20000,replace = F)
# sub_data=fnl_data[sub_ix,]
# freq(sub_data$delay_ind)
#Test train split
train_ix=createDataPartition(fnl_data$delay_ind,p=0.7, list = F)
train_data=fnl_data[train_ix,]
test_data=fnl_data[-train_ix,]
train_data_ds=downSample(x=train_data[,-c(10)],
y=train_data[,c(10)])
### kNN Model
#Convert the factor variables to numeric
set.seed(9)
fnl_data$MONTH=as.numeric(fnl_data$MONTH)
fnl_data$DAY_OF_MONTH=as.numeric(fnl_data$DAY_OF_MONTH)
fnl_data$DAY_OF_WEEK=as.numeric(fnl_data$DAY_OF_WEEK)
fnl_data$OP_CARRIER_AIRLINE_ID=as.numeric(fnl_data$OP_CARRIER_AIRLINE_ID)
fnl_data$ORIGIN=as.numeric(as.factor(fnl_data$ORIGIN))
fnl_data$DEST=as.numeric(as.factor(fnl_data$DEST))
train_ix=createDataPartition(fnl_data$delay_ind,p=0.7, list = F)
train_data=fnl_data[train_ix,]
test_data=fnl_data[-train_ix,]
train_data_ds=downSample(x=train_data[,-c(10)],
y=train_data[,c(10)])
dim(train_data)
Cls=train_data[,10]
trn=train_data[,1:9]
tst=test_data[,1:9]
scale=preProcess(trn, method = c("center", "scale"))
scaled.train=predict(scale,trn)
scaled.tst=predict(scale,tst)
knn.pred.test_y=knn(train=scaled.train,test=scaled.tst,cl=Cls,k=5)
#Test
knn_CM=confusionMatrix(knn.pred.test_y,test_data[,10])
library(xgboost)
# trn=as.matrix(train_data_ds[,1:9])
# Cls=as.matrix(train_data_ds[,10])
trn=as.matrix(train_data[,1:9])
Cls=train_data[,10]
Cls=as.matrix(Cls)
tst=as.matrix(test_data[,1:9])
# create parameter list
# hyper_grid <- expand.grid(
# eta = c(.01, .05, .1, .3),
# max_depth = c(1, 3),
# min_child_weight = c(1, 3),
# subsample = c(.65, .8),
# colsample_bytree = c(.8,),
# optimal_trees = 0, # a place to dump results
# )
### gbm Model
xgb.fit <- xgboost(
data = trn,
label = Cls,
nrounds = 1000,
objective ="binary:logistic",
#nfold = 5,
early_stopping_rounds = 20,
verbose = 0 # evaluation metric out,
)
# plot error vs number trees
# ggplot(xgb.fit$evaluation_log) +
# geom_line(aes(iter, train_rmse_mean), color = "red") +
# geom_line(aes(iter, test_rmse_mean), color = "blue")
prob.pred_test_y = predict(xgb.fit, tst)
xgb.pred_test_y <- ifelse(prob.pred_test_y > 0.5, 1, 0)
xgb_CM=confusionMatrix(factor(xgb.pred_test_y),test_data[,10])
xgb_CM
# create importance matrix
importance_matrix <- xgb.importance(model = xgb.fit)
# variable importance plot
xgb.plot.importance(importance_matrix, top_n = 9, measure = "Gain")
# Neural Network
fnl_data$delay_ind=as.numeric(fnl_data$delay_ind)
maxs <- apply(fnl_data, 2, max)
mins <- apply(fnl_data, 2, min)
scaled <- as.data.frame(scale(fnl_data, center = mins, scale = maxs - mins))
train_data=scaled[train_ix,]
test_data=scaled[-train_ix,]
library(neuralnet)
n <- names(train_data)
f <- as.formula(paste("delay_ind ~", paste(n[!n %in% "delay_ind"], collapse = " + ")))
nn <- neuralnet(f,data=train_data,hidden=c(8,4),linear.output=F)
dim(train_data_ds)
logit=glm(Class~.,train_data_ds,family="binomial")
logit.model=train(delay_ind~.,
data=train_data,
#preProcess = c("center","scale"),
method = "glm",family="binomial"
)
summary(logit.model)
#Test
# # logit_all=glm(delay_ind~.,train_data,family="binomial")
# # summary(logit_all)
# ######## Train Accuracy
# train_pred=predict(logit,train_data_ds[,1:9],type = "response")
# # Recode factors
# train_pred <- ifelse(train_pred > 0.5, 1, 0)
# mean(train_data_ds$Class==train_pred)
#
test_pred=predict(logit,test_data[,1:9],type = "response")
test_pred =ifelse(test_pred > 0.5, 1, 0)
mean(test_data$delay_ind==test_pred)
test_true=as.factor(test_data$delay_ind)
Logit_CM=confusionMatrix(factor(test_pred), test_true)