#Some libraries
rm(list=ls())
library(car)
library(caret)
library(class)
library(devtools)
library(e1071)
library(ggplot2)
library(klaR)
library(klaR)
library(MASS)
library(nnet)
library(plyr)
library(pROC)
library(psych)
library(scatterplot3d)
library(SDMTools)
library(dplyr)
library(ElemStatLearn)
library(rpart)
library(rpart.plot)
library(randomForest)
library(neuralnet)
##Set working directory
setwd("D:/GREAT LAKES/MACHINE LEARNING")
getwd()
[1] "D:/GREAT LAKES/MACHINE LEARNING"
Cars<-read.csv("Actual_Cars_data.csv", header=T)
sapply(Cars, function(y) sum(length(which(is.na(y)))))
Age Gender Engineer MBA Work.Exp Salary Distance
0 0 0 1 0 0 0
license Transport
0 0
Cars[is.na(Cars$MBA),4]<-0
sapply(Cars, function(y) sum(length(which(is.na(y)))))
Age Gender Engineer MBA Work.Exp Salary Distance
0 0 0 0 0 0 0
license Transport
0 0
View(Cars)
na.omit(Cars)
Age Gender Engineer MBA Work.Exp Salary Distance license
1 28 Male 0 0 4 14.3 3.2 0
2 23 Female 1 0 4 8.3 3.3 0
3 29 Male 1 0 7 13.4 4.1 0
4 28 Female 1 1 5 13.4 4.5 0
5 27 Male 1 0 4 13.4 4.6 0
6 26 Male 1 0 4 12.3 4.8 1
7 28 Male 1 0 5 14.4 5.1 0
8 26 Female 1 0 3 10.5 5.1 0
9 22 Male 1 0 1 7.5 5.1 0
10 27 Male 1 0 4 13.5 5.2 0
11 25 Female 1 0 4 11.5 5.2 0
12 27 Male 1 0 4 13.5 5.3 1
13 24 Male 1 0 2 8.5 5.4 0
14 27 Male 1 0 4 13.4 5.5 1
15 32 Male 1 0 9 15.5 5.5 0
16 25 Male 1 1 4 11.5 5.6 0
17 34 Male 1 0 13 16.5 5.9 0
18 26 Female 1 0 4 12.3 5.9 0
19 23 Male 1 0 2 8.5 6.1 0
20 23 Male 0 0 2 8.6 6.1 0
21 26 Male 1 0 5 11.4 6.1 0
22 24 Male 1 0 6 10.6 6.1 0
23 27 Female 1 0 9 15.5 6.1 0
24 30 Male 0 0 8 14.6 6.1 0
25 24 Male 0 0 2 8.5 6.2 0
26 26 Female 0 0 3 9.5 6.2 0
27 28 Male 1 1 7 13.6 6.3 0
28 25 Male 0 0 1 7.6 6.3 0
29 26 Male 0 0 2 8.5 6.3 1
30 30 Female 1 0 8 14.6 6.3 0
31 26 Female 0 0 3 9.5 6.3 0
32 24 Male 1 1 2 8.6 6.4 0
33 27 Male 1 0 7 16.6 6.4 0
34 27 Female 1 0 5 12.5 6.4 0
35 30 Female 1 0 8 14.6 6.5 0
36 27 Male 1 0 6 12.6 6.5 0
37 25 Male 0 0 2 8.6 6.7 1
38 25 Female 0 0 3 9.6 6.7 0
39 26 Male 0 0 3 9.5 6.8 0
40 22 Male 1 0 3 8.4 6.8 0
41 28 Male 1 0 6 13.6 6.9 1
42 25 Female 1 0 4 11.5 7.0 0
43 20 Male 1 0 2 8.5 7.0 0
44 25 Male 1 0 3 10.5 7.1 0
45 33 Male 0 0 13 36.6 7.1 1
46 23 Female 1 1 4 8.4 7.1 0
47 21 Male 0 0 3 9.5 7.1 0
48 30 Male 1 0 8 14.6 7.1 0
49 28 Female 0 1 5 14.6 7.2 0
50 23 Female 1 1 1 7.5 7.2 0
51 23 Male 1 1 3 11.7 7.2 0
52 30 Female 1 0 8 14.4 7.2 0
53 30 Male 0 1 7 14.4 7.3 0
54 23 Male 0 0 0 6.5 7.3 0
55 31 Male 0 0 9 15.6 7.3 0
56 28 Female 1 0 9 21.7 7.3 0
57 26 Male 1 0 4 12.5 7.4 0
58 23 Female 1 0 0 7.7 7.4 0
59 25 Male 1 0 2 8.6 7.4 0
60 24 Male 1 0 4 8.5 7.5 0
61 28 Male 1 0 6 13.7 7.5 1
62 26 Male 0 0 4 12.6 7.5 0
63 23 Male 1 0 1 7.5 7.5 0
64 28 Female 1 0 5 13.6 7.5 0
65 22 Female 1 0 0 6.5 7.6 0
66 29 Male 1 0 5 15.4 7.6 1
67 31 Male 1 0 9 15.6 7.6 0
68 24 Male 0 0 2 8.7 7.6 0
69 29 Male 1 0 6 14.6 7.6 0
70 26 Male 1 1 4 12.4 7.6 0
71 21 Male 0 1 3 10.6 7.7 0
72 24 Male 1 1 1 8.5 7.7 0
73 29 Female 0 0 7 14.6 7.7 0
74 28 Male 1 0 5 13.6 7.9 0
75 27 Male 0 0 3 9.5 7.9 1
76 20 Female 0 1 1 8.5 7.9 0
77 24 Male 1 0 2 8.5 8.0 0
78 25 Male 1 0 3 10.6 8.1 0
79 21 Female 1 0 3 9.6 8.1 0
80 26 Male 1 1 8 21.6 8.1 1
81 22 Female 1 0 2 11.7 8.1 0
82 29 Male 0 0 7 14.6 8.1 0
83 19 Female 1 0 1 7.5 8.1 0
84 22 Female 1 0 2 8.5 8.1 0
85 30 Female 1 0 8 14.6 8.1 0
86 29 Male 1 0 6 14.7 8.1 0
87 27 Male 0 0 6 12.6 8.1 0
88 27 Female 1 0 4 13.8 8.1 0
89 27 Female 0 0 4 13.6 8.2 0
90 27 Female 0 1 4 13.6 8.2 0
91 31 Female 0 0 9 14.6 8.2 0
92 20 Male 0 1 2 8.8 8.3 0
93 25 Male 1 0 4 11.6 8.3 0
94 24 Male 1 1 6 10.6 8.4 1
95 32 Male 1 0 11 14.7 8.4 0
96 25 Male 1 0 3 9.8 8.4 0
97 32 Male 1 0 10 15.7 8.4 0
98 24 Male 0 0 2 8.7 8.4 0
99 31 Male 1 0 10 14.8 8.4 0
100 33 Male 1 0 11 15.6 8.5 0
101 30 Female 1 0 8 14.7 8.5 0
102 24 Male 0 1 2 8.5 8.5 1
103 24 Male 1 0 2 8.5 8.5 0
104 28 Male 1 0 4 14.6 8.6 0
105 23 Male 0 0 2 8.8 8.6 0
106 28 Female 0 0 6 13.6 8.6 0
107 30 Male 1 0 7 15.6 8.6 1
108 28 Male 0 0 6 13.8 8.6 0
109 20 Male 1 0 2 8.8 8.7 1
110 24 Male 1 0 6 12.7 8.7 0
111 27 Male 1 1 5 13.5 8.8 0
112 26 Male 1 0 5 12.8 8.8 0
113 25 Female 1 0 3 10.6 8.8 0
114 25 Male 1 0 7 17.8 8.8 0
115 26 Female 1 0 3 10.8 8.9 0
116 29 Male 1 0 9 22.8 8.9 0
117 24 Female 1 0 6 10.5 8.9 0
118 21 Female 1 0 3 9.8 8.9 0
119 28 Male 1 0 6 13.7 8.9 0
120 27 Male 0 1 8 15.6 9.0 0
121 28 Male 0 1 3 9.5 9.0 0
122 28 Female 0 0 10 19.7 9.0 0
123 26 Female 1 0 3 10.8 9.0 0
124 26 Male 1 0 4 12.7 9.0 1
125 24 Male 1 0 4 10.9 9.0 0
126 28 Male 0 1 10 20.7 9.0 0
127 39 Male 1 1 19 38.9 9.0 1
128 28 Female 1 0 5 14.6 9.0 0
129 26 Female 1 1 3 10.9 9.1 0
130 24 Male 1 1 0 7.9 9.1 0
131 25 Male 0 0 4 11.9 9.1 0
132 31 Female 1 0 6 16.9 9.1 0
133 27 Male 0 0 7 12.5 9.1 0
134 29 Male 1 1 11 25.9 9.1 0
135 23 Male 1 0 2 8.8 9.2 1
136 23 Male 1 1 0 6.6 9.2 0
137 36 Male 1 1 16 34.8 9.2 0
138 29 Female 0 0 7 14.6 9.2 0
139 31 Male 1 1 12 28.8 9.3 0
140 28 Male 1 0 5 13.6 9.3 0
141 27 Male 1 0 5 12.5 9.3 0
142 23 Female 0 0 0 6.8 9.3 0
143 27 Male 1 0 3 10.6 9.3 0
144 33 Male 1 1 11 15.6 9.3 0
145 28 Female 0 0 6 13.7 9.4 0
146 25 Male 1 0 1 8.6 9.4 0
147 25 Female 1 0 1 8.6 9.4 0
148 26 Male 0 0 3 9.9 9.4 0
149 26 Female 0 0 4 12.9 9.4 0
150 29 Male 1 0 9 23.8 9.4 0
151 26 Male 1 1 3 10.8 9.4 0
152 22 Female 1 1 2 8.5 9.5 0
153 26 Male 1 0 2 9.6 9.5 0
154 27 Male 1 1 6 12.9 9.5 0
155 24 Male 0 0 2 8.6 9.5 1
156 28 Male 1 0 6 13.9 9.5 0
157 24 Male 1 1 0 7.6 9.5 0
158 26 Male 1 0 4 12.9 9.6 0
159 26 Male 1 0 2 9.5 9.6 0
160 26 Male 1 0 3 10.6 9.6 0
161 28 Male 1 1 5 14.8 9.7 0
162 26 Male 1 0 3 10.5 9.7 1
163 25 Male 1 0 1 8.6 9.7 0
164 28 Male 1 0 6 13.6 9.7 1
165 31 Male 0 1 7 15.9 9.7 0
166 27 Female 1 0 5 12.8 9.7 0
167 28 Male 0 0 5 14.5 9.8 1
168 27 Female 1 1 4 13.8 9.8 0
169 27 Female 1 1 5 13.8 9.8 0
170 31 Female 1 0 10 14.9 9.9 0
171 25 Male 1 1 3 9.7 9.9 0
172 30 Male 1 0 4 16.8 9.9 0
173 26 Female 0 0 3 9.6 9.9 0
174 24 Male 1 0 0 7.6 10.0 0
175 26 Male 0 0 3 9.8 10.0 0
176 39 Male 1 0 21 39.9 10.0 1
177 36 Male 0 0 17 39.0 10.0 1
178 26 Male 1 1 4 12.9 10.0 0
179 30 Male 0 1 8 14.6 10.0 0
180 27 Male 0 1 5 13.9 10.0 0
181 25 Female 1 0 6 11.6 10.1 0
182 32 Female 1 1 9 16.9 10.1 0
183 24 Male 0 1 2 8.7 10.1 0
184 29 Male 1 0 6 14.8 10.1 0
185 27 Male 1 0 4 13.8 10.1 0
186 29 Male 0 0 6 14.6 10.1 0
187 35 Female 1 0 16 28.7 10.2 0
188 25 Male 1 0 2 8.7 10.2 0
189 29 Male 1 1 6 14.6 10.2 1
190 24 Male 1 0 0 7.6 10.2 0
191 28 Male 1 0 3 10.8 10.2 1
192 34 Male 1 1 14 36.9 10.4 1
193 36 Male 1 1 18 28.7 10.4 1
194 27 Male 1 1 4 13.6 10.4 0
195 29 Male 0 0 6 14.7 10.4 0
196 30 Male 1 1 8 14.9 10.4 0
197 26 Female 1 0 6 17.8 10.4 0
198 24 Male 1 1 1 7.9 10.5 0
199 28 Male 1 0 5 14.7 10.5 1
200 28 Female 1 0 6 13.9 10.5 0
201 30 Female 1 0 7 14.6 10.5 0
202 29 Male 0 0 5 15.9 10.5 0
203 30 Male 1 1 8 14.6 10.6 0
204 27 Female 1 0 5 12.9 10.6 0
205 27 Male 0 1 8 20.7 10.7 0
206 28 Male 0 0 6 13.9 10.7 0
207 26 Female 1 0 2 9.8 10.7 0
208 23 Female 0 0 4 11.6 10.7 0
209 30 Male 1 0 6 15.8 10.7 0
210 24 Male 1 1 0 7.8 10.7 0
211 25 Male 1 1 7 13.6 10.7 0
212 25 Male 1 0 3 10.7 10.8 0
213 28 Male 1 1 5 14.8 10.8 1
214 26 Female 1 1 4 12.8 10.8 0
215 30 Male 0 0 8 14.6 10.9 1
216 33 Male 1 1 14 34.9 10.9 0
217 32 Male 1 0 12 15.7 10.9 0
218 29 Female 0 1 7 14.6 10.9 0
219 33 Male 1 0 11 16.7 10.9 1
220 24 Male 1 1 3 9.9 10.9 0
221 21 Female 0 0 3 9.8 11.0 0
222 26 Female 1 0 4 12.6 11.0 0
223 25 Female 1 0 2 8.6 11.0 0
224 26 Male 1 0 2 8.6 11.0 1
225 24 Male 1 0 0 8.0 11.0 1
226 31 Female 0 1 9 14.6 11.1 0
227 26 Male 0 0 5 12.6 11.1 0
228 26 Female 1 0 4 12.9 11.1 0
229 39 Male 1 0 19 47.0 11.2 1
230 29 Male 1 0 5 14.9 11.2 0
231 25 Female 1 1 1 8.6 11.2 0
232 27 Male 1 1 6 12.8 11.3 0
233 28 Female 1 0 5 13.7 11.3 0
234 29 Male 1 0 11 22.7 11.3 1
235 24 Male 1 1 6 11.6 11.3 1
236 24 Male 1 1 0 7.7 11.3 1
237 30 Male 1 0 10 13.8 11.4 0
238 29 Male 1 0 9 13.7 11.4 0
239 30 Female 1 0 8 14.7 11.4 1
240 23 Male 1 0 4 10.6 11.4 0
241 28 Female 0 0 9 23.8 11.4 0
242 27 Male 1 0 6 12.7 11.5 0
243 26 Male 0 0 4 12.6 11.5 1
244 26 Male 1 1 7 20.9 11.6 0
245 26 Male 1 0 4 12.8 11.6 0
246 33 Male 1 0 9 17.0 11.6 1
247 30 Female 0 0 6 15.6 11.6 0
248 26 Female 1 0 8 14.6 11.6 0
249 32 Male 1 0 9 16.9 11.7 1
250 23 Male 1 0 0 6.9 11.7 0
251 26 Male 1 1 6 11.7 11.7 0
252 29 Female 1 0 7 14.8 11.7 0
253 24 Male 1 0 4 12.7 11.7 0
254 27 Female 1 0 3 10.7 11.7 0
255 23 Male 1 0 0 7.7 11.7 0
256 29 Female 0 0 7 13.6 11.7 0
257 27 Female 1 0 5 12.8 11.8 0
258 32 Female 1 0 10 15.6 11.8 0
259 21 Male 1 1 3 9.9 11.8 0
260 26 Female 1 0 6 11.8 11.9 0
261 30 Male 1 0 6 15.7 11.9 1
262 30 Male 1 1 10 28.8 11.9 1
263 38 Male 1 0 18 36.9 11.9 1
264 30 Female 1 0 6 15.6 11.9 0
265 40 Male 1 0 21 54.0 11.9 1
266 28 Male 1 1 6 13.7 11.9 0
267 30 Male 1 0 8 14.8 12.0 0
268 22 Male 1 1 2 12.7 12.0 0
269 28 Male 1 1 7 13.9 12.1 0
270 30 Male 0 0 10 29.9 12.1 0
271 25 Male 1 1 4 11.7 12.1 1
272 25 Female 1 0 5 17.8 12.1 0
273 31 Male 1 0 10 14.9 12.1 0
274 24 Male 1 1 0 7.7 12.1 0
275 34 Male 1 1 16 34.9 12.2 0
276 28 Male 1 0 5 13.9 12.2 1
277 33 Female 1 0 13 36.0 12.2 1
278 25 Female 1 0 5 18.9 12.2 0
279 32 Male 0 0 9 16.9 12.2 1
280 24 Female 0 0 0 6.8 12.2 0
281 26 Female 1 0 2 9.8 12.2 0
282 26 Male 0 0 3 9.8 12.2 1
283 26 Male 1 0 3 10.7 12.2 1
284 18 Male 1 0 0 6.8 12.2 0
285 26 Male 1 1 5 12.7 12.2 0
286 27 Male 1 0 8 20.7 12.2 0
287 33 Male 0 0 11 17.0 12.3 1
288 29 Male 0 0 7 14.7 12.3 0
289 33 Male 1 0 10 16.9 12.3 0
290 24 Male 1 0 1 7.7 12.4 1
291 31 Male 1 0 7 17.0 12.4 1
292 27 Male 1 0 4 13.8 12.4 0
293 26 Male 1 1 5 12.7 12.5 0
294 28 Male 0 0 5 14.9 12.5 1
295 24 Female 1 1 1 8.8 12.6 1
296 27 Male 1 1 5 13.7 12.6 0
297 30 Male 1 1 8 14.7 12.6 0
298 24 Female 1 1 2 8.7 12.6 0
299 27 Male 1 0 8 20.7 12.6 0
300 38 Male 0 0 19 44.0 12.6 1
301 25 Male 0 0 5 13.7 12.7 1
302 24 Male 1 0 1 8.9 12.7 0
303 21 Male 0 0 3 9.8 12.7 0
304 22 Male 1 0 0 6.8 12.7 0
305 25 Male 0 1 4 11.8 12.8 0
306 34 Male 1 1 11 17.0 12.8 0
307 27 Male 1 0 6 12.8 12.8 0
308 24 Male 1 1 1 8.8 12.8 0
309 26 Male 1 0 4 12.7 12.8 0
310 25 Male 1 0 3 10.8 12.8 0
311 28 Male 1 0 6 13.8 12.9 0
312 30 Female 1 0 6 15.8 12.9 0
313 34 Male 1 1 15 37.0 12.9 1
314 30 Male 1 1 9 14.9 12.9 0
315 26 Male 1 0 5 11.7 13.0 0
316 30 Male 1 0 9 14.8 13.0 0
317 27 Female 1 0 8 24.9 13.0 0
318 31 Male 1 0 12 43.0 13.0 1
319 22 Male 1 1 0 6.9 13.0 0
320 28 Male 1 0 4 14.8 13.0 0
321 18 Male 0 0 0 6.7 13.0 0
322 23 Female 1 1 2 8.8 13.1 0
323 24 Male 1 0 1 7.8 13.1 0
324 25 Female 0 0 2 8.9 13.2 0
325 26 Female 0 0 5 12.8 13.2 0
326 22 Male 1 0 0 6.9 13.2 0
327 24 Male 1 0 4 13.8 13.2 0
328 26 Male 0 0 4 12.7 13.3 0
329 27 Female 1 1 4 13.7 13.3 0
330 27 Male 1 0 6 12.9 13.3 0
331 24 Male 0 1 2 8.9 13.4 0
332 27 Male 1 1 8 21.8 13.4 0
333 26 Female 1 0 8 20.9 13.4 0
334 22 Male 1 0 0 7.0 13.5 0
335 35 Female 1 0 15 37.0 13.5 1
336 26 Female 1 0 4 12.8 13.6 1
337 38 Male 1 0 19 54.0 13.6 1
338 36 Female 1 0 18 44.0 13.6 1
339 27 Male 1 0 1 8.9 13.6 0
340 28 Male 1 0 7 12.7 13.6 0
341 28 Male 1 0 3 10.9 13.6 0
342 28 Male 1 0 4 14.9 13.7 0
343 31 Male 1 0 7 15.9 13.7 0
344 23 Male 0 0 0 6.9 13.7 0
345 24 Male 1 1 2 8.9 13.8 0
346 24 Female 1 0 2 8.9 13.8 0
347 34 Female 1 0 14 34.0 13.8 0
348 26 Male 0 0 7 18.8 13.8 0
349 26 Male 0 1 5 12.8 13.9 0
350 30 Male 1 0 7 14.9 14.0 0
351 32 Male 1 0 11 15.8 14.1 1
352 27 Male 0 0 9 23.9 14.1 0
353 38 Male 1 0 19 48.0 14.1 1
354 38 Male 1 1 20 42.0 14.1 1
355 40 Male 1 0 22 51.0 14.1 1
356 27 Male 1 0 4 13.9 14.2 0
357 26 Male 1 0 4 12.8 14.2 0
358 24 Female 0 0 2 9.0 14.2 0
359 30 Male 1 0 8 14.8 14.3 0
360 28 Male 1 0 4 14.9 14.3 0
361 30 Male 1 0 6 15.8 14.3 0
362 27 Female 1 0 7 23.8 14.4 0
363 36 Male 1 0 16 45.0 14.4 1
364 31 Male 1 0 12 34.0 14.4 1
365 22 Female 1 0 0 6.8 14.5 0
366 25 Female 0 0 3 9.9 14.6 0
367 32 Female 0 0 10 15.9 14.6 0
368 32 Female 1 1 10 15.8 14.6 1
369 26 Male 1 0 5 12.8 15.0 0
370 34 Female 0 0 14 28.8 15.0 0
371 27 Male 1 0 9 20.8 15.1 0
372 24 Female 1 0 2 9.0 15.1 0
373 34 Male 1 0 14 45.0 15.1 1
374 28 Female 1 0 6 13.9 15.1 0
375 27 Male 0 0 5 13.8 15.2 0
376 22 Male 0 0 0 6.8 15.2 1
377 25 Female 1 0 2 8.8 15.2 0
378 23 Male 1 0 1 7.9 15.2 0
379 24 Male 0 0 0 6.9 15.3 0
380 21 Male 1 1 3 9.8 15.3 0
381 29 Male 0 0 5 14.8 15.4 0
382 30 Male 1 0 8 15.0 15.5 1
383 40 Female 1 0 20 42.9 15.5 0
384 28 Male 1 0 5 13.8 15.5 0
385 30 Male 1 0 8 14.9 15.5 1
386 28 Female 0 1 9 23.8 15.5 0
387 27 Male 1 1 6 12.9 15.6 0
388 26 Male 1 0 6 18.8 15.6 0
389 26 Female 1 0 3 10.8 15.6 0
390 27 Male 0 0 3 9.8 15.7 1
391 26 Female 0 0 7 18.8 15.7 0
392 22 Male 1 1 0 6.9 15.7 0
393 24 Female 1 0 1 8.8 15.8 0
394 23 Male 1 1 2 8.9 15.8 0
395 25 Male 1 0 2 8.9 15.8 0
396 37 Male 1 1 18 41.0 15.9 1
397 25 Male 0 0 3 9.9 15.9 0
398 23 Male 1 0 0 8.0 15.9 0
399 20 Female 1 0 2 9.0 16.2 0
400 22 Male 0 0 1 7.9 16.3 1
401 39 Male 1 0 21 40.9 16.3 0
402 26 Female 1 0 6 23.0 16.3 0
403 26 Male 1 0 2 10.0 16.4 1
404 28 Male 1 0 5 13.9 16.4 0
405 31 Male 1 0 8 15.9 16.4 0
406 32 Female 1 0 14 30.9 16.5 0
407 34 Male 1 0 12 16.9 16.6 0
408 32 Female 1 1 9 15.9 16.6 0
409 24 Male 1 0 2 8.9 16.8 0
410 25 Female 1 0 1 8.9 16.8 0
411 40 Male 1 1 20 41.9 16.9 1
412 38 Female 1 0 20 43.0 17.0 1
413 29 Female 1 0 6 14.9 17.0 0
414 29 Male 1 1 8 13.9 17.1 0
415 24 Male 1 0 0 7.9 17.1 0
416 25 Male 1 0 3 9.9 17.2 0
417 27 Female 0 0 4 13.9 17.3 0
418 33 Male 1 0 14 33.0 17.3 0
419 26 Male 1 1 2 9.9 17.7 0
420 34 Male 1 1 16 36.0 17.8 1
421 31 Male 0 0 11 33.0 17.8 1
422 23 Female 1 1 2 9.0 17.9 0
423 23 Male 0 0 3 9.9 17.9 0
424 36 Female 1 0 17 38.0 18.0 1
425 39 Male 1 0 21 46.0 18.1 1
426 38 Male 1 0 18 45.0 18.1 1
427 40 Male 1 0 20 48.0 18.2 1
428 30 Male 1 1 11 35.0 18.3 1
429 39 Male 0 0 21 51.0 18.6 1
430 38 Male 1 0 19 51.0 18.8 1
431 42 Male 1 0 22 55.0 19.0 1
432 29 Male 0 1 7 15.0 19.0 1
433 33 Male 1 1 10 17.0 19.1 0
434 26 Male 1 0 4 13.0 19.1 1
435 40 Male 1 0 22 45.0 19.8 1
436 37 Male 0 0 19 42.0 20.7 1
437 43 Male 1 1 24 52.0 20.8 1
438 28 Female 1 1 7 13.0 21.0 1
439 34 Male 1 0 14 38.0 21.3 1
440 40 Male 1 0 20 57.0 21.4 1
441 38 Male 1 0 19 44.0 21.5 1
442 37 Male 1 0 19 45.0 21.5 1
443 37 Male 0 0 19 47.0 22.8 1
444 39 Male 1 1 21 50.0 23.4 1
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219 Car
220 Public Transport
221 2Wheeler
222 2Wheeler
223 2Wheeler
224 Public Transport
225 2Wheeler
226 Public Transport
227 Public Transport
228 2Wheeler
229 Car
230 Public Transport
231 2Wheeler
232 Public Transport
233 Public Transport
234 2Wheeler
235 Public Transport
236 Public Transport
237 Public Transport
238 Public Transport
239 2Wheeler
240 2Wheeler
241 Public Transport
242 Public Transport
243 Public Transport
244 Public Transport
245 Public Transport
246 Car
247 Public Transport
248 Public Transport
249 Car
250 2Wheeler
251 Public Transport
252 Public Transport
253 2Wheeler
254 Public Transport
255 2Wheeler
256 Public Transport
257 2Wheeler
258 Car
259 Public Transport
260 Public Transport
261 Public Transport
262 2Wheeler
263 Car
264 Public Transport
265 Car
266 Public Transport
267 Public Transport
268 Public Transport
269 Public Transport
270 Public Transport
271 Public Transport
272 Public Transport
273 Public Transport
274 Public Transport
275 Public Transport
276 2Wheeler
277 Car
278 2Wheeler
279 Car
280 Public Transport
281 2Wheeler
282 Public Transport
283 Public Transport
284 Public Transport
285 Public Transport
286 2Wheeler
287 Car
288 Public Transport
289 Car
290 Public Transport
291 Car
292 Public Transport
293 Public Transport
294 2Wheeler
295 2Wheeler
296 Public Transport
297 Public Transport
298 2Wheeler
299 Public Transport
300 Car
301 2Wheeler
302 Public Transport
303 Public Transport
304 Public Transport
305 Public Transport
306 Car
307 Public Transport
308 Public Transport
309 Public Transport
310 Public Transport
311 Public Transport
312 Public Transport
313 2Wheeler
314 Public Transport
315 Public Transport
316 Public Transport
317 Public Transport
318 Car
319 2Wheeler
320 Public Transport
321 2Wheeler
322 2Wheeler
323 Public Transport
324 2Wheeler
325 2Wheeler
326 2Wheeler
327 Public Transport
328 Public Transport
329 Public Transport
330 Public Transport
331 Public Transport
332 Public Transport
333 2Wheeler
334 2Wheeler
335 Car
336 2Wheeler
337 Car
338 Car
339 Public Transport
340 Public Transport
341 Public Transport
342 Public Transport
343 Public Transport
344 2Wheeler
345 Public Transport
346 2Wheeler
347 Car
348 Public Transport
349 Public Transport
350 Public Transport
351 Car
352 Public Transport
353 Car
354 Car
355 Car
356 Public Transport
357 Public Transport
358 2Wheeler
359 Public Transport
360 Public Transport
361 Public Transport
362 2Wheeler
363 Car
364 Car
365 Public Transport
366 Public Transport
367 Car
368 Car
369 Public Transport
370 Public Transport
371 Public Transport
372 2Wheeler
373 Car
374 Public Transport
375 Public Transport
376 2Wheeler
377 2Wheeler
378 Public Transport
379 2Wheeler
380 Public Transport
381 Public Transport
382 2Wheeler
383 Car
384 Public Transport
385 2Wheeler
386 Public Transport
387 Public Transport
388 Public Transport
389 Public Transport
390 Public Transport
391 2Wheeler
392 Public Transport
393 2Wheeler
394 Public Transport
395 Public Transport
396 Car
397 Public Transport
398 2Wheeler
399 2Wheeler
400 2Wheeler
401 Car
402 2Wheeler
403 2Wheeler
404 Public Transport
405 Public Transport
406 Car
407 Public Transport
408 Public Transport
409 Public Transport
410 2Wheeler
411 Car
412 Car
413 Public Transport
414 Public Transport
415 2Wheeler
416 Public Transport
417 Public Transport
418 Car
419 Public Transport
420 Car
421 Car
422 2Wheeler
423 Public Transport
424 Car
425 Car
426 Car
427 Car
428 Car
429 Car
430 Car
431 Car
432 2Wheeler
433 Car
434 2Wheeler
435 Car
436 Car
437 Car
438 2Wheeler
439 Car
440 Car
441 Car
442 Car
443 Car
444 Car
summary(Cars)
Age Gender Engineer MBA
Min. :18.00 Female:128 Min. :0.0000 Min. :0.0000
1st Qu.:25.00 Male :316 1st Qu.:1.0000 1st Qu.:0.0000
Median :27.00 Median :1.0000 Median :0.0000
Mean :27.75 Mean :0.7545 Mean :0.2523
3rd Qu.:30.00 3rd Qu.:1.0000 3rd Qu.:1.0000
Max. :43.00 Max. :1.0000 Max. :1.0000
Work.Exp Salary Distance license
Min. : 0.0 Min. : 6.50 Min. : 3.20 Min. :0.0000
1st Qu.: 3.0 1st Qu.: 9.80 1st Qu.: 8.80 1st Qu.:0.0000
Median : 5.0 Median :13.60 Median :11.00 Median :0.0000
Mean : 6.3 Mean :16.24 Mean :11.32 Mean :0.2342
3rd Qu.: 8.0 3rd Qu.:15.72 3rd Qu.:13.43 3rd Qu.:0.0000
Max. :24.0 Max. :57.00 Max. :23.40 Max. :1.0000
Transport
2Wheeler : 83
Car : 61
Public Transport:300
str(Cars)
'data.frame': 444 obs. of 9 variables:
$ Age : int 28 23 29 28 27 26 28 26 22 27 ...
$ Gender : Factor w/ 2 levels "Female","Male": 2 1 2 1 2 2 2 1 2 2 ...
$ Engineer : int 0 1 1 1 1 1 1 1 1 1 ...
$ MBA : num 0 0 0 1 0 0 0 0 0 0 ...
$ Work.Exp : int 4 4 7 5 4 4 5 3 1 4 ...
$ Salary : num 14.3 8.3 13.4 13.4 13.4 12.3 14.4 10.5 7.5 13.5 ...
$ Distance : num 3.2 3.3 4.1 4.5 4.6 4.8 5.1 5.1 5.1 5.2 ...
$ license : int 0 0 0 0 0 1 0 0 0 0 ...
$ Transport: Factor w/ 3 levels "2Wheeler","Car",..: 3 3 3 3 3 3 1 3 3 3 ...
## Determine Levels
levels(Cars$Gender)
[1] "Female" "Male"
levels(Cars$Transport)
[1] "2Wheeler" "Car" "Public Transport"
#Define some dummies
Cars$Female<-ifelse(Cars$Gender=="Female",1,0)
Cars$Male<-ifelse(Cars$Gender=="Male",1,0)
View(Cars)
#Partitioning Data Sets
#Partition train and val
#We will use this throughout so that samples are comparable
set.seed(7)
pd<-sample(2,nrow(Cars),replace=TRUE, prob=c(0.7,0.3))
train<-Cars[pd==1,]
val<-Cars[pd==2,]
#K FOLD VALIDATIONS
names(Cars)
[1] "Age" "Gender" "Engineer" "MBA" "Work.Exp"
[6] "Salary" "Distance" "license" "Transport" "Female"
[11] "Male"
Carstrim<-Cars[,-2]
na.omit(Carstrim)
Age Engineer MBA Work.Exp Salary Distance license Transport
1 28 0 0 4 14.3 3.2 0 Public Transport
2 23 1 0 4 8.3 3.3 0 Public Transport
3 29 1 0 7 13.4 4.1 0 Public Transport
4 28 1 1 5 13.4 4.5 0 Public Transport
5 27 1 0 4 13.4 4.6 0 Public Transport
6 26 1 0 4 12.3 4.8 1 Public Transport
7 28 1 0 5 14.4 5.1 0 2Wheeler
8 26 1 0 3 10.5 5.1 0 Public Transport
9 22 1 0 1 7.5 5.1 0 Public Transport
10 27 1 0 4 13.5 5.2 0 Public Transport
11 25 1 0 4 11.5 5.2 0 Public Transport
12 27 1 0 4 13.5 5.3 1 Public Transport
13 24 1 0 2 8.5 5.4 0 Public Transport
14 27 1 0 4 13.4 5.5 1 Public Transport
15 32 1 0 9 15.5 5.5 0 Public Transport
16 25 1 1 4 11.5 5.6 0 Public Transport
17 34 1 0 13 16.5 5.9 0 Public Transport
18 26 1 0 4 12.3 5.9 0 Public Transport
19 23 1 0 2 8.5 6.1 0 Public Transport
20 23 0 0 2 8.6 6.1 0 Public Transport
21 26 1 0 5 11.4 6.1 0 Public Transport
22 24 1 0 6 10.6 6.1 0 2Wheeler
23 27 1 0 9 15.5 6.1 0 2Wheeler
24 30 0 0 8 14.6 6.1 0 Public Transport
25 24 0 0 2 8.5 6.2 0 Public Transport
26 26 0 0 3 9.5 6.2 0 Public Transport
27 28 1 1 7 13.6 6.3 0 Public Transport
28 25 0 0 1 7.6 6.3 0 2Wheeler
29 26 0 0 2 8.5 6.3 1 Public Transport
30 30 1 0 8 14.6 6.3 0 Public Transport
31 26 0 0 3 9.5 6.3 0 Public Transport
32 24 1 1 2 8.6 6.4 0 Public Transport
33 27 1 0 7 16.6 6.4 0 Public Transport
34 27 1 0 5 12.5 6.4 0 Public Transport
35 30 1 0 8 14.6 6.5 0 Public Transport
36 27 1 0 6 12.6 6.5 0 Public Transport
37 25 0 0 2 8.6 6.7 1 Public Transport
38 25 0 0 3 9.6 6.7 0 2Wheeler
39 26 0 0 3 9.5 6.8 0 Public Transport
40 22 1 0 3 8.4 6.8 0 Public Transport
41 28 1 0 6 13.6 6.9 1 Public Transport
42 25 1 0 4 11.5 7.0 0 Public Transport
43 20 1 0 2 8.5 7.0 0 Public Transport
44 25 1 0 3 10.5 7.1 0 Public Transport
45 33 0 0 13 36.6 7.1 1 Public Transport
46 23 1 1 4 8.4 7.1 0 Public Transport
47 21 0 0 3 9.5 7.1 0 2Wheeler
48 30 1 0 8 14.6 7.1 0 Public Transport
49 28 0 1 5 14.6 7.2 0 Public Transport
50 23 1 1 1 7.5 7.2 0 Public Transport
51 23 1 1 3 11.7 7.2 0 2Wheeler
52 30 1 0 8 14.4 7.2 0 Public Transport
53 30 0 1 7 14.4 7.3 0 Public Transport
54 23 0 0 0 6.5 7.3 0 2Wheeler
55 31 0 0 9 15.6 7.3 0 Public Transport
56 28 1 0 9 21.7 7.3 0 Public Transport
57 26 1 0 4 12.5 7.4 0 Public Transport
58 23 1 0 0 7.7 7.4 0 Public Transport
59 25 1 0 2 8.6 7.4 0 Public Transport
60 24 1 0 4 8.5 7.5 0 2Wheeler
61 28 1 0 6 13.7 7.5 1 2Wheeler
62 26 0 0 4 12.6 7.5 0 2Wheeler
63 23 1 0 1 7.5 7.5 0 Public Transport
64 28 1 0 5 13.6 7.5 0 Public Transport
65 22 1 0 0 6.5 7.6 0 Public Transport
66 29 1 0 5 15.4 7.6 1 Public Transport
67 31 1 0 9 15.6 7.6 0 Public Transport
68 24 0 0 2 8.7 7.6 0 Public Transport
69 29 1 0 6 14.6 7.6 0 Public Transport
70 26 1 1 4 12.4 7.6 0 Public Transport
71 21 0 1 3 10.6 7.7 0 2Wheeler
72 24 1 1 1 8.5 7.7 0 Public Transport
73 29 0 0 7 14.6 7.7 0 Public Transport
74 28 1 0 5 13.6 7.9 0 Public Transport
75 27 0 0 3 9.5 7.9 1 Public Transport
76 20 0 1 1 8.5 7.9 0 Public Transport
77 24 1 0 2 8.5 8.0 0 Public Transport
78 25 1 0 3 10.6 8.1 0 Public Transport
79 21 1 0 3 9.6 8.1 0 Public Transport
80 26 1 1 8 21.6 8.1 1 Public Transport
81 22 1 0 2 11.7 8.1 0 Public Transport
82 29 0 0 7 14.6 8.1 0 Public Transport
83 19 1 0 1 7.5 8.1 0 Public Transport
84 22 1 0 2 8.5 8.1 0 2Wheeler
85 30 1 0 8 14.6 8.1 0 Public Transport
86 29 1 0 6 14.7 8.1 0 Public Transport
87 27 0 0 6 12.6 8.1 0 Public Transport
88 27 1 0 4 13.8 8.1 0 Public Transport
89 27 0 0 4 13.6 8.2 0 Public Transport
90 27 0 1 4 13.6 8.2 0 Public Transport
91 31 0 0 9 14.6 8.2 0 Public Transport
92 20 0 1 2 8.8 8.3 0 Public Transport
93 25 1 0 4 11.6 8.3 0 Public Transport
94 24 1 1 6 10.6 8.4 1 Public Transport
95 32 1 0 11 14.7 8.4 0 Public Transport
96 25 1 0 3 9.8 8.4 0 Public Transport
97 32 1 0 10 15.7 8.4 0 Public Transport
98 24 0 0 2 8.7 8.4 0 Public Transport
99 31 1 0 10 14.8 8.4 0 Public Transport
100 33 1 0 11 15.6 8.5 0 Public Transport
101 30 1 0 8 14.7 8.5 0 Public Transport
102 24 0 1 2 8.5 8.5 1 Public Transport
103 24 1 0 2 8.5 8.5 0 Public Transport
104 28 1 0 4 14.6 8.6 0 Public Transport
105 23 0 0 2 8.8 8.6 0 Public Transport
106 28 0 0 6 13.6 8.6 0 Public Transport
107 30 1 0 7 15.6 8.6 1 Public Transport
108 28 0 0 6 13.8 8.6 0 Public Transport
109 20 1 0 2 8.8 8.7 1 Public Transport
110 24 1 0 6 12.7 8.7 0 2Wheeler
111 27 1 1 5 13.5 8.8 0 Public Transport
112 26 1 0 5 12.8 8.8 0 Public Transport
113 25 1 0 3 10.6 8.8 0 Public Transport
114 25 1 0 7 17.8 8.8 0 Public Transport
115 26 1 0 3 10.8 8.9 0 Public Transport
116 29 1 0 9 22.8 8.9 0 Public Transport
117 24 1 0 6 10.5 8.9 0 Public Transport
118 21 1 0 3 9.8 8.9 0 Public Transport
119 28 1 0 6 13.7 8.9 0 Public Transport
120 27 0 1 8 15.6 9.0 0 2Wheeler
121 28 0 1 3 9.5 9.0 0 Public Transport
122 28 0 0 10 19.7 9.0 0 2Wheeler
123 26 1 0 3 10.8 9.0 0 Public Transport
124 26 1 0 4 12.7 9.0 1 Public Transport
125 24 1 0 4 10.9 9.0 0 Public Transport
126 28 0 1 10 20.7 9.0 0 Public Transport
127 39 1 1 19 38.9 9.0 1 Car
128 28 1 0 5 14.6 9.0 0 Public Transport
129 26 1 1 3 10.9 9.1 0 Public Transport
130 24 1 1 0 7.9 9.1 0 Public Transport
131 25 0 0 4 11.9 9.1 0 Public Transport
132 31 1 0 6 16.9 9.1 0 Car
133 27 0 0 7 12.5 9.1 0 Public Transport
134 29 1 1 11 25.9 9.1 0 Public Transport
135 23 1 0 2 8.8 9.2 1 2Wheeler
136 23 1 1 0 6.6 9.2 0 Public Transport
137 36 1 1 16 34.8 9.2 0 Public Transport
138 29 0 0 7 14.6 9.2 0 2Wheeler
139 31 1 1 12 28.8 9.3 0 Public Transport
140 28 1 0 5 13.6 9.3 0 Public Transport
141 27 1 0 5 12.5 9.3 0 Public Transport
142 23 0 0 0 6.8 9.3 0 Public Transport
143 27 1 0 3 10.6 9.3 0 Public Transport
144 33 1 1 11 15.6 9.3 0 Public Transport
145 28 0 0 6 13.7 9.4 0 Public Transport
146 25 1 0 1 8.6 9.4 0 Public Transport
147 25 1 0 1 8.6 9.4 0 Public Transport
148 26 0 0 3 9.9 9.4 0 Public Transport
149 26 0 0 4 12.9 9.4 0 Public Transport
150 29 1 0 9 23.8 9.4 0 2Wheeler
151 26 1 1 3 10.8 9.4 0 Public Transport
152 22 1 1 2 8.5 9.5 0 2Wheeler
153 26 1 0 2 9.6 9.5 0 Public Transport
154 27 1 1 6 12.9 9.5 0 Public Transport
155 24 0 0 2 8.6 9.5 1 Public Transport
156 28 1 0 6 13.9 9.5 0 Public Transport
157 24 1 1 0 7.6 9.5 0 Public Transport
158 26 1 0 4 12.9 9.6 0 Public Transport
159 26 1 0 2 9.5 9.6 0 Public Transport
160 26 1 0 3 10.6 9.6 0 Public Transport
161 28 1 1 5 14.8 9.7 0 Public Transport
162 26 1 0 3 10.5 9.7 1 Public Transport
163 25 1 0 1 8.6 9.7 0 Public Transport
164 28 1 0 6 13.6 9.7 1 Public Transport
165 31 0 1 7 15.9 9.7 0 Public Transport
166 27 1 0 5 12.8 9.7 0 2Wheeler
167 28 0 0 5 14.5 9.8 1 Public Transport
168 27 1 1 4 13.8 9.8 0 Public Transport
169 27 1 1 5 13.8 9.8 0 Public Transport
170 31 1 0 10 14.9 9.9 0 Public Transport
171 25 1 1 3 9.7 9.9 0 Public Transport
172 30 1 0 4 16.8 9.9 0 Car
173 26 0 0 3 9.6 9.9 0 Public Transport
174 24 1 0 0 7.6 10.0 0 Public Transport
175 26 0 0 3 9.8 10.0 0 Public Transport
176 39 1 0 21 39.9 10.0 1 Car
177 36 0 0 17 39.0 10.0 1 Car
178 26 1 1 4 12.9 10.0 0 Public Transport
179 30 0 1 8 14.6 10.0 0 Public Transport
180 27 0 1 5 13.9 10.0 0 Public Transport
181 25 1 0 6 11.6 10.1 0 2Wheeler
182 32 1 1 9 16.9 10.1 0 Car
183 24 0 1 2 8.7 10.1 0 Public Transport
184 29 1 0 6 14.8 10.1 0 Public Transport
185 27 1 0 4 13.8 10.1 0 Public Transport
186 29 0 0 6 14.6 10.1 0 Public Transport
187 35 1 0 16 28.7 10.2 0 Public Transport
188 25 1 0 2 8.7 10.2 0 Public Transport
189 29 1 1 6 14.6 10.2 1 Public Transport
190 24 1 0 0 7.6 10.2 0 Public Transport
191 28 1 0 3 10.8 10.2 1 Public Transport
192 34 1 1 14 36.9 10.4 1 2Wheeler
193 36 1 1 18 28.7 10.4 1 Public Transport
194 27 1 1 4 13.6 10.4 0 Public Transport
195 29 0 0 6 14.7 10.4 0 Public Transport
196 30 1 1 8 14.9 10.4 0 Public Transport
197 26 1 0 6 17.8 10.4 0 Public Transport
198 24 1 1 1 7.9 10.5 0 Public Transport
199 28 1 0 5 14.7 10.5 1 2Wheeler
200 28 1 0 6 13.9 10.5 0 Public Transport
201 30 1 0 7 14.6 10.5 0 Public Transport
202 29 0 0 5 15.9 10.5 0 Public Transport
203 30 1 1 8 14.6 10.6 0 Public Transport
204 27 1 0 5 12.9 10.6 0 Public Transport
205 27 0 1 8 20.7 10.7 0 Public Transport
206 28 0 0 6 13.9 10.7 0 Public Transport
207 26 1 0 2 9.8 10.7 0 2Wheeler
208 23 0 0 4 11.6 10.7 0 2Wheeler
209 30 1 0 6 15.8 10.7 0 Public Transport
210 24 1 1 0 7.8 10.7 0 Public Transport
211 25 1 1 7 13.6 10.7 0 2Wheeler
212 25 1 0 3 10.7 10.8 0 Public Transport
213 28 1 1 5 14.8 10.8 1 2Wheeler
214 26 1 1 4 12.8 10.8 0 Public Transport
215 30 0 0 8 14.6 10.9 1 Public Transport
216 33 1 1 14 34.9 10.9 0 Public Transport
217 32 1 0 12 15.7 10.9 0 Public Transport
218 29 0 1 7 14.6 10.9 0 Public Transport
219 33 1 0 11 16.7 10.9 1 Car
220 24 1 1 3 9.9 10.9 0 Public Transport
221 21 0 0 3 9.8 11.0 0 2Wheeler
222 26 1 0 4 12.6 11.0 0 2Wheeler
223 25 1 0 2 8.6 11.0 0 2Wheeler
224 26 1 0 2 8.6 11.0 1 Public Transport
225 24 1 0 0 8.0 11.0 1 2Wheeler
226 31 0 1 9 14.6 11.1 0 Public Transport
227 26 0 0 5 12.6 11.1 0 Public Transport
228 26 1 0 4 12.9 11.1 0 2Wheeler
229 39 1 0 19 47.0 11.2 1 Car
230 29 1 0 5 14.9 11.2 0 Public Transport
231 25 1 1 1 8.6 11.2 0 2Wheeler
232 27 1 1 6 12.8 11.3 0 Public Transport
233 28 1 0 5 13.7 11.3 0 Public Transport
234 29 1 0 11 22.7 11.3 1 2Wheeler
235 24 1 1 6 11.6 11.3 1 Public Transport
236 24 1 1 0 7.7 11.3 1 Public Transport
237 30 1 0 10 13.8 11.4 0 Public Transport
238 29 1 0 9 13.7 11.4 0 Public Transport
239 30 1 0 8 14.7 11.4 1 2Wheeler
240 23 1 0 4 10.6 11.4 0 2Wheeler
241 28 0 0 9 23.8 11.4 0 Public Transport
242 27 1 0 6 12.7 11.5 0 Public Transport
243 26 0 0 4 12.6 11.5 1 Public Transport
244 26 1 1 7 20.9 11.6 0 Public Transport
245 26 1 0 4 12.8 11.6 0 Public Transport
246 33 1 0 9 17.0 11.6 1 Car
247 30 0 0 6 15.6 11.6 0 Public Transport
248 26 1 0 8 14.6 11.6 0 Public Transport
249 32 1 0 9 16.9 11.7 1 Car
250 23 1 0 0 6.9 11.7 0 2Wheeler
251 26 1 1 6 11.7 11.7 0 Public Transport
252 29 1 0 7 14.8 11.7 0 Public Transport
253 24 1 0 4 12.7 11.7 0 2Wheeler
254 27 1 0 3 10.7 11.7 0 Public Transport
255 23 1 0 0 7.7 11.7 0 2Wheeler
256 29 0 0 7 13.6 11.7 0 Public Transport
257 27 1 0 5 12.8 11.8 0 2Wheeler
258 32 1 0 10 15.6 11.8 0 Car
259 21 1 1 3 9.9 11.8 0 Public Transport
260 26 1 0 6 11.8 11.9 0 Public Transport
261 30 1 0 6 15.7 11.9 1 Public Transport
262 30 1 1 10 28.8 11.9 1 2Wheeler
263 38 1 0 18 36.9 11.9 1 Car
264 30 1 0 6 15.6 11.9 0 Public Transport
265 40 1 0 21 54.0 11.9 1 Car
266 28 1 1 6 13.7 11.9 0 Public Transport
267 30 1 0 8 14.8 12.0 0 Public Transport
268 22 1 1 2 12.7 12.0 0 Public Transport
269 28 1 1 7 13.9 12.1 0 Public Transport
270 30 0 0 10 29.9 12.1 0 Public Transport
271 25 1 1 4 11.7 12.1 1 Public Transport
272 25 1 0 5 17.8 12.1 0 Public Transport
273 31 1 0 10 14.9 12.1 0 Public Transport
274 24 1 1 0 7.7 12.1 0 Public Transport
275 34 1 1 16 34.9 12.2 0 Public Transport
276 28 1 0 5 13.9 12.2 1 2Wheeler
277 33 1 0 13 36.0 12.2 1 Car
278 25 1 0 5 18.9 12.2 0 2Wheeler
279 32 0 0 9 16.9 12.2 1 Car
280 24 0 0 0 6.8 12.2 0 Public Transport
281 26 1 0 2 9.8 12.2 0 2Wheeler
282 26 0 0 3 9.8 12.2 1 Public Transport
283 26 1 0 3 10.7 12.2 1 Public Transport
284 18 1 0 0 6.8 12.2 0 Public Transport
285 26 1 1 5 12.7 12.2 0 Public Transport
286 27 1 0 8 20.7 12.2 0 2Wheeler
287 33 0 0 11 17.0 12.3 1 Car
288 29 0 0 7 14.7 12.3 0 Public Transport
289 33 1 0 10 16.9 12.3 0 Car
290 24 1 0 1 7.7 12.4 1 Public Transport
291 31 1 0 7 17.0 12.4 1 Car
292 27 1 0 4 13.8 12.4 0 Public Transport
293 26 1 1 5 12.7 12.5 0 Public Transport
294 28 0 0 5 14.9 12.5 1 2Wheeler
295 24 1 1 1 8.8 12.6 1 2Wheeler
296 27 1 1 5 13.7 12.6 0 Public Transport
297 30 1 1 8 14.7 12.6 0 Public Transport
298 24 1 1 2 8.7 12.6 0 2Wheeler
299 27 1 0 8 20.7 12.6 0 Public Transport
300 38 0 0 19 44.0 12.6 1 Car
301 25 0 0 5 13.7 12.7 1 2Wheeler
302 24 1 0 1 8.9 12.7 0 Public Transport
303 21 0 0 3 9.8 12.7 0 Public Transport
304 22 1 0 0 6.8 12.7 0 Public Transport
305 25 0 1 4 11.8 12.8 0 Public Transport
306 34 1 1 11 17.0 12.8 0 Car
307 27 1 0 6 12.8 12.8 0 Public Transport
308 24 1 1 1 8.8 12.8 0 Public Transport
309 26 1 0 4 12.7 12.8 0 Public Transport
310 25 1 0 3 10.8 12.8 0 Public Transport
311 28 1 0 6 13.8 12.9 0 Public Transport
312 30 1 0 6 15.8 12.9 0 Public Transport
313 34 1 1 15 37.0 12.9 1 2Wheeler
314 30 1 1 9 14.9 12.9 0 Public Transport
315 26 1 0 5 11.7 13.0 0 Public Transport
316 30 1 0 9 14.8 13.0 0 Public Transport
317 27 1 0 8 24.9 13.0 0 Public Transport
318 31 1 0 12 43.0 13.0 1 Car
319 22 1 1 0 6.9 13.0 0 2Wheeler
320 28 1 0 4 14.8 13.0 0 Public Transport
321 18 0 0 0 6.7 13.0 0 2Wheeler
322 23 1 1 2 8.8 13.1 0 2Wheeler
323 24 1 0 1 7.8 13.1 0 Public Transport
324 25 0 0 2 8.9 13.2 0 2Wheeler
325 26 0 0 5 12.8 13.2 0 2Wheeler
326 22 1 0 0 6.9 13.2 0 2Wheeler
327 24 1 0 4 13.8 13.2 0 Public Transport
328 26 0 0 4 12.7 13.3 0 Public Transport
329 27 1 1 4 13.7 13.3 0 Public Transport
330 27 1 0 6 12.9 13.3 0 Public Transport
331 24 0 1 2 8.9 13.4 0 Public Transport
332 27 1 1 8 21.8 13.4 0 Public Transport
333 26 1 0 8 20.9 13.4 0 2Wheeler
334 22 1 0 0 7.0 13.5 0 2Wheeler
335 35 1 0 15 37.0 13.5 1 Car
336 26 1 0 4 12.8 13.6 1 2Wheeler
337 38 1 0 19 54.0 13.6 1 Car
338 36 1 0 18 44.0 13.6 1 Car
339 27 1 0 1 8.9 13.6 0 Public Transport
340 28 1 0 7 12.7 13.6 0 Public Transport
341 28 1 0 3 10.9 13.6 0 Public Transport
342 28 1 0 4 14.9 13.7 0 Public Transport
343 31 1 0 7 15.9 13.7 0 Public Transport
344 23 0 0 0 6.9 13.7 0 2Wheeler
345 24 1 1 2 8.9 13.8 0 Public Transport
346 24 1 0 2 8.9 13.8 0 2Wheeler
347 34 1 0 14 34.0 13.8 0 Car
348 26 0 0 7 18.8 13.8 0 Public Transport
349 26 0 1 5 12.8 13.9 0 Public Transport
350 30 1 0 7 14.9 14.0 0 Public Transport
351 32 1 0 11 15.8 14.1 1 Car
352 27 0 0 9 23.9 14.1 0 Public Transport
353 38 1 0 19 48.0 14.1 1 Car
354 38 1 1 20 42.0 14.1 1 Car
355 40 1 0 22 51.0 14.1 1 Car
356 27 1 0 4 13.9 14.2 0 Public Transport
357 26 1 0 4 12.8 14.2 0 Public Transport
358 24 0 0 2 9.0 14.2 0 2Wheeler
359 30 1 0 8 14.8 14.3 0 Public Transport
360 28 1 0 4 14.9 14.3 0 Public Transport
361 30 1 0 6 15.8 14.3 0 Public Transport
362 27 1 0 7 23.8 14.4 0 2Wheeler
363 36 1 0 16 45.0 14.4 1 Car
364 31 1 0 12 34.0 14.4 1 Car
365 22 1 0 0 6.8 14.5 0 Public Transport
366 25 0 0 3 9.9 14.6 0 Public Transport
367 32 0 0 10 15.9 14.6 0 Car
368 32 1 1 10 15.8 14.6 1 Car
369 26 1 0 5 12.8 15.0 0 Public Transport
370 34 0 0 14 28.8 15.0 0 Public Transport
371 27 1 0 9 20.8 15.1 0 Public Transport
372 24 1 0 2 9.0 15.1 0 2Wheeler
373 34 1 0 14 45.0 15.1 1 Car
374 28 1 0 6 13.9 15.1 0 Public Transport
375 27 0 0 5 13.8 15.2 0 Public Transport
376 22 0 0 0 6.8 15.2 1 2Wheeler
377 25 1 0 2 8.8 15.2 0 2Wheeler
378 23 1 0 1 7.9 15.2 0 Public Transport
379 24 0 0 0 6.9 15.3 0 2Wheeler
380 21 1 1 3 9.8 15.3 0 Public Transport
381 29 0 0 5 14.8 15.4 0 Public Transport
382 30 1 0 8 15.0 15.5 1 2Wheeler
383 40 1 0 20 42.9 15.5 0 Car
384 28 1 0 5 13.8 15.5 0 Public Transport
385 30 1 0 8 14.9 15.5 1 2Wheeler
386 28 0 1 9 23.8 15.5 0 Public Transport
387 27 1 1 6 12.9 15.6 0 Public Transport
388 26 1 0 6 18.8 15.6 0 Public Transport
389 26 1 0 3 10.8 15.6 0 Public Transport
390 27 0 0 3 9.8 15.7 1 Public Transport
391 26 0 0 7 18.8 15.7 0 2Wheeler
392 22 1 1 0 6.9 15.7 0 Public Transport
393 24 1 0 1 8.8 15.8 0 2Wheeler
394 23 1 1 2 8.9 15.8 0 Public Transport
395 25 1 0 2 8.9 15.8 0 Public Transport
396 37 1 1 18 41.0 15.9 1 Car
397 25 0 0 3 9.9 15.9 0 Public Transport
398 23 1 0 0 8.0 15.9 0 2Wheeler
399 20 1 0 2 9.0 16.2 0 2Wheeler
400 22 0 0 1 7.9 16.3 1 2Wheeler
401 39 1 0 21 40.9 16.3 0 Car
402 26 1 0 6 23.0 16.3 0 2Wheeler
403 26 1 0 2 10.0 16.4 1 2Wheeler
404 28 1 0 5 13.9 16.4 0 Public Transport
405 31 1 0 8 15.9 16.4 0 Public Transport
406 32 1 0 14 30.9 16.5 0 Car
407 34 1 0 12 16.9 16.6 0 Public Transport
408 32 1 1 9 15.9 16.6 0 Public Transport
409 24 1 0 2 8.9 16.8 0 Public Transport
410 25 1 0 1 8.9 16.8 0 2Wheeler
411 40 1 1 20 41.9 16.9 1 Car
412 38 1 0 20 43.0 17.0 1 Car
413 29 1 0 6 14.9 17.0 0 Public Transport
414 29 1 1 8 13.9 17.1 0 Public Transport
415 24 1 0 0 7.9 17.1 0 2Wheeler
416 25 1 0 3 9.9 17.2 0 Public Transport
417 27 0 0 4 13.9 17.3 0 Public Transport
418 33 1 0 14 33.0 17.3 0 Car
419 26 1 1 2 9.9 17.7 0 Public Transport
420 34 1 1 16 36.0 17.8 1 Car
421 31 0 0 11 33.0 17.8 1 Car
422 23 1 1 2 9.0 17.9 0 2Wheeler
423 23 0 0 3 9.9 17.9 0 Public Transport
424 36 1 0 17 38.0 18.0 1 Car
425 39 1 0 21 46.0 18.1 1 Car
426 38 1 0 18 45.0 18.1 1 Car
427 40 1 0 20 48.0 18.2 1 Car
428 30 1 1 11 35.0 18.3 1 Car
429 39 0 0 21 51.0 18.6 1 Car
430 38 1 0 19 51.0 18.8 1 Car
431 42 1 0 22 55.0 19.0 1 Car
432 29 0 1 7 15.0 19.0 1 2Wheeler
433 33 1 1 10 17.0 19.1 0 Car
434 26 1 0 4 13.0 19.1 1 2Wheeler
435 40 1 0 22 45.0 19.8 1 Car
436 37 0 0 19 42.0 20.7 1 Car
437 43 1 1 24 52.0 20.8 1 Car
438 28 1 1 7 13.0 21.0 1 2Wheeler
439 34 1 0 14 38.0 21.3 1 Car
440 40 1 0 20 57.0 21.4 1 Car
441 38 1 0 19 44.0 21.5 1 Car
442 37 1 0 19 45.0 21.5 1 Car
443 37 0 0 19 47.0 22.8 1 Car
444 39 1 1 21 50.0 23.4 1 Car
Female Male
1 0 1
2 1 0
3 0 1
4 1 0
5 0 1
6 0 1
7 0 1
8 1 0
9 0 1
10 0 1
11 1 0
12 0 1
13 0 1
14 0 1
15 0 1
16 0 1
17 0 1
18 1 0
19 0 1
20 0 1
21 0 1
22 0 1
23 1 0
24 0 1
25 0 1
26 1 0
27 0 1
28 0 1
29 0 1
30 1 0
31 1 0
32 0 1
33 0 1
34 1 0
35 1 0
36 0 1
37 0 1
38 1 0
39 0 1
40 0 1
41 0 1
42 1 0
43 0 1
44 0 1
45 0 1
46 1 0
47 0 1
48 0 1
49 1 0
50 1 0
51 0 1
52 1 0
53 0 1
54 0 1
55 0 1
56 1 0
57 0 1
58 1 0
59 0 1
60 0 1
61 0 1
62 0 1
63 0 1
64 1 0
65 1 0
66 0 1
67 0 1
68 0 1
69 0 1
70 0 1
71 0 1
72 0 1
73 1 0
74 0 1
75 0 1
76 1 0
77 0 1
78 0 1
79 1 0
80 0 1
81 1 0
82 0 1
83 1 0
84 1 0
85 1 0
86 0 1
87 0 1
88 1 0
89 1 0
90 1 0
91 1 0
92 0 1
93 0 1
94 0 1
95 0 1
96 0 1
97 0 1
98 0 1
99 0 1
100 0 1
101 1 0
102 0 1
103 0 1
104 0 1
105 0 1
106 1 0
107 0 1
108 0 1
109 0 1
110 0 1
111 0 1
112 0 1
113 1 0
114 0 1
115 1 0
116 0 1
117 1 0
118 1 0
119 0 1
120 0 1
121 0 1
122 1 0
123 1 0
124 0 1
125 0 1
126 0 1
127 0 1
128 1 0
129 1 0
130 0 1
131 0 1
132 1 0
133 0 1
134 0 1
135 0 1
136 0 1
137 0 1
138 1 0
139 0 1
140 0 1
141 0 1
142 1 0
143 0 1
144 0 1
145 1 0
146 0 1
147 1 0
148 0 1
149 1 0
150 0 1
151 0 1
152 1 0
153 0 1
154 0 1
155 0 1
156 0 1
157 0 1
158 0 1
159 0 1
160 0 1
161 0 1
162 0 1
163 0 1
164 0 1
165 0 1
166 1 0
167 0 1
168 1 0
169 1 0
170 1 0
171 0 1
172 0 1
173 1 0
174 0 1
175 0 1
176 0 1
177 0 1
178 0 1
179 0 1
180 0 1
181 1 0
182 1 0
183 0 1
184 0 1
185 0 1
186 0 1
187 1 0
188 0 1
189 0 1
190 0 1
191 0 1
192 0 1
193 0 1
194 0 1
195 0 1
196 0 1
197 1 0
198 0 1
199 0 1
200 1 0
201 1 0
202 0 1
203 0 1
204 1 0
205 0 1
206 0 1
207 1 0
208 1 0
209 0 1
210 0 1
211 0 1
212 0 1
213 0 1
214 1 0
215 0 1
216 0 1
217 0 1
218 1 0
219 0 1
220 0 1
221 1 0
222 1 0
223 1 0
224 0 1
225 0 1
226 1 0
227 0 1
228 1 0
229 0 1
230 0 1
231 1 0
232 0 1
233 1 0
234 0 1
235 0 1
236 0 1
237 0 1
238 0 1
239 1 0
240 0 1
241 1 0
242 0 1
243 0 1
244 0 1
245 0 1
246 0 1
247 1 0
248 1 0
249 0 1
250 0 1
251 0 1
252 1 0
253 0 1
254 1 0
255 0 1
256 1 0
257 1 0
258 1 0
259 0 1
260 1 0
261 0 1
262 0 1
263 0 1
264 1 0
265 0 1
266 0 1
267 0 1
268 0 1
269 0 1
270 0 1
271 0 1
272 1 0
273 0 1
274 0 1
275 0 1
276 0 1
277 1 0
278 1 0
279 0 1
280 1 0
281 1 0
282 0 1
283 0 1
284 0 1
285 0 1
286 0 1
287 0 1
288 0 1
289 0 1
290 0 1
291 0 1
292 0 1
293 0 1
294 0 1
295 1 0
296 0 1
297 0 1
298 1 0
299 0 1
300 0 1
301 0 1
302 0 1
303 0 1
304 0 1
305 0 1
306 0 1
307 0 1
308 0 1
309 0 1
310 0 1
311 0 1
312 1 0
313 0 1
314 0 1
315 0 1
316 0 1
317 1 0
318 0 1
319 0 1
320 0 1
321 0 1
322 1 0
323 0 1
324 1 0
325 1 0
326 0 1
327 0 1
328 0 1
329 1 0
330 0 1
331 0 1
332 0 1
333 1 0
334 0 1
335 1 0
336 1 0
337 0 1
338 1 0
339 0 1
340 0 1
341 0 1
342 0 1
343 0 1
344 0 1
345 0 1
346 1 0
347 1 0
348 0 1
349 0 1
350 0 1
351 0 1
352 0 1
353 0 1
354 0 1
355 0 1
356 0 1
357 0 1
358 1 0
359 0 1
360 0 1
361 0 1
362 1 0
363 0 1
364 0 1
365 1 0
366 1 0
367 1 0
368 1 0
369 0 1
370 1 0
371 0 1
372 1 0
373 0 1
374 1 0
375 0 1
376 0 1
377 1 0
378 0 1
379 0 1
380 0 1
381 0 1
382 0 1
383 1 0
384 0 1
385 0 1
386 1 0
387 0 1
388 0 1
389 1 0
390 0 1
391 1 0
392 0 1
393 1 0
394 0 1
395 0 1
396 0 1
397 0 1
398 0 1
399 1 0
400 0 1
401 0 1
402 1 0
403 0 1
404 0 1
405 0 1
406 1 0
407 0 1
408 1 0
409 0 1
410 1 0
411 0 1
412 1 0
413 1 0
414 0 1
415 0 1
416 0 1
417 1 0
418 0 1
419 0 1
420 0 1
421 0 1
422 1 0
423 0 1
424 1 0
425 0 1
426 0 1
427 0 1
428 0 1
429 0 1
430 0 1
431 0 1
432 0 1
433 0 1
434 0 1
435 0 1
436 0 1
437 0 1
438 1 0
439 0 1
440 0 1
441 0 1
442 0 1
443 0 1
444 0 1
set.seed(7)
pd<-sample(2,nrow(Carstrim),replace=TRUE, prob=c(0.7,0.3))
traintrim<-Carstrim[pd==1,]
valtrim<-Carstrim[pd==2,]
set.seed(7)
folds<-createFolds(Cars$Transport,k=10)
str(folds)
List of 10
$ Fold01: int [1:46] 29 33 45 56 69 90 104 109 119 120 ...
$ Fold02: int [1:44] 30 78 92 94 99 100 103 121 130 135 ...
$ Fold03: int [1:44] 14 15 47 52 59 71 75 87 88 95 ...
$ Fold04: int [1:45] 9 16 36 42 49 50 54 57 64 72 ...
$ Fold05: int [1:44] 8 11 13 23 24 26 35 37 41 68 ...
$ Fold06: int [1:44] 1 5 7 18 32 34 44 51 63 65 ...
$ Fold07: int [1:44] 38 58 84 85 86 113 127 136 141 161 ...
$ Fold08: int [1:44] 3 12 20 21 27 28 31 48 60 61 ...
$ Fold09: int [1:44] 4 10 19 25 40 46 53 67 77 80 ...
$ Fold10: int [1:45] 2 6 17 22 39 43 55 70 73 97 ...
######
Eq.2 <- Transport ~ .
#10 Fold validation with LPM
cv_LPM<-lapply(folds,function(x){
train<-traintrim[x,]
test<-valtrim[-x,]
LPM.1<-lm(Eq.2, train)
LPM1.pred<-predict(LPM.1, test)
tab.LPM<-table(test$Transport, LPM1.pred>0.5)
sum(diag(tab.LPM))/sum(tab.LPM)
})
str(cv_LPM)
List of 10
$ Fold01: num 0.145
$ Fold02: num 0.133
$ Fold03: num 0.145
$ Fold04: num 0.116
$ Fold05: num 0.145
$ Fold06: num 0.142
$ Fold07: num 0.147
$ Fold08: num 0.12
$ Fold09: num 0.159
$ Fold10: num 0.148
fit.LPM<-mean(unlist(cv_LPM))
fit.LPM
[1] 0.1401046
##########
## Accuracy of LPM is 14.01%
#########
#10 Vold Validation with NB
cv_NB<-lapply(folds,function(x){
train.NB.kval<-traintrim[x,]
test.NB.kval<-valtrim[-x,]
NB.kval<-naiveBayes(x=train.NB.kval[-1], y=train.NB.kval$Transport)
y_pred.NB.kval<-predict( NB.kval,newdata=test.NB.kval[-1])
cm.NB.kval=table(test.NB.kval[,1],y_pred.NB.kval)
sum(diag(cm.NB.kval))/sum(cm.NB.kval)
})
str(cv_NB)
List of 10
$ Fold01: num 0.0229
$ Fold02: num 0.00741
$ Fold03: num 0.0305
$ Fold04: num 0.0155
$ Fold05: num 0.0382
$ Fold06: num 0.0157
$ Fold07: num 0.0368
$ Fold08: num 0.032
$ Fold09: num 0.0152
$ Fold10: num 0.0156
fit.NB<-mean(unlist(cv_NB))
fit.NB
[1] 0.02298036
#####################
##Accuracy of NB is 2.2%
#####################
#10 Fold with LDA
library(MASS)
library(ISLR)
cv_LDA<-lapply(folds,function(x){
train<-traintrim[x,]
test<-valtrim[-x,]
lda_1<-lda(Eq.2 , train)
lda1.pred<-predict(lda_1, newdata=test)
ldapredclass<-lda1.pred$class
tab.LDA<-table(ldapredclass,test$Transport)
sum(diag(tab.LDA))/sum(tab.LDA)
})
str(cv_LDA)
List of 10
$ Fold01: num 0.626
$ Fold02: num 0.733
$ Fold03: num 0.779
$ Fold04: num 0.775
$ Fold05: num 0.763
$ Fold06: num 0.78
$ Fold07: num 0.721
$ Fold08: num 0.768
$ Fold09: num 0.742
$ Fold10: num 0.695
fit.LDA<-mean(unlist(cv_LDA))
fit.LDA
[1] 0.7382319
#########
##Accuracy of LDA is 73.82%
#########
#10 Fold on Decision Trees
cv_DT<-lapply(folds,function(x){
train<-traintrim[x,]
test<-valtrim[-x,]
DT<-rpart(Eq.2, method="class",train)
pred = predict(DT, type="class",newdata=test)
tabDT<-table( pred,test$Transport)
sum(diag(tabDT))/sum(tabDT)
})
str(cv_DT)
List of 10
$ Fold01: num 0.718
$ Fold02: num 0.778
$ Fold03: num 0.702
$ Fold04: num 0.543
$ Fold05: num 0.756
$ Fold06: num 0.606
$ Fold07: num 0.647
$ Fold08: num 0.584
$ Fold09: num 0.523
$ Fold10: num 0.734
fit.DT<-mean(unlist(cv_DT))
fit.DT
[1] 0.6590446
########
##Accuracy of Decision trees is 65.90%
########
#####SMOTE
library(DMwR)
train_SMOTE<-traintrim[,-2]
qplot(Distance,Salary,color=Transport, data=traintrim)
table(train_SMOTE$Transport)
2Wheeler Car Public Transport
63 39 197
#SMOTE
#Two factors to see the plot
train_SMOTE$target <- as.factor(traintrim$Transport)
table(train_SMOTE$target)
2Wheeler Car Public Transport
63 39 197
trainSplit <- SMOTE(target ~ ., train_SMOTE, perc.over = 200, perc.under=300)
print(prop.table(table(trainSplit$target)))
2Wheeler Car Public Transport
0.1509972 0.3333333 0.5156695
table(trainSplit$target)
2Wheeler Car Public Transport
53 117 181
qplot(Distance,Salary,color=Transport, data=trainSplit)
train_SMOTE_new<-trainSplit
train_SMOTE_new <- SMOTE(Transport ~ ., train_SMOTE_new, perc.over = 100, perc.under=100)
train_SMOTE_new$target <- as.factor(train_SMOTE_new$Transport)
trainSplit <- SMOTE(target ~ ., train_SMOTE_new, perc.over = 100, perc.under=100)
trainSplit$target <- as.numeric(trainSplit$target)
trainSplit$target<-ifelse(trainSplit$target==1,1,0)
print(prop.table(table(trainSplit$target)))
0 1
0.7708333 0.2291667
write.csv(train_SMOTE, "SMOTE.csv", row.names = FALSE)
#####################
Cars1<-read.csv("SMOTE.csv", header=T)
dim(Cars1)
[1] 299 10
names(Cars1)
[1] "Age" "MBA" "Work.Exp" "Salary" "Distance"
[6] "license" "Transport" "Female" "Male" "target"
Cars1<-Cars1[,-7]
View(Cars1)
na.omit(Cars1)
Age MBA Work.Exp Salary Distance license Female Male target
1 23 0 4 8.3 3.3 0 1 0 Public Transport
2 29 0 7 13.4 4.1 0 0 1 Public Transport
3 28 1 5 13.4 4.5 0 1 0 Public Transport
4 27 0 4 13.4 4.6 0 0 1 Public Transport
5 28 0 5 14.4 5.1 0 0 1 2Wheeler
6 22 0 1 7.5 5.1 0 0 1 Public Transport
7 27 0 4 13.5 5.2 0 0 1 Public Transport
8 25 0 4 11.5 5.2 0 1 0 Public Transport
9 27 0 4 13.5 5.3 1 0 1 Public Transport
10 27 0 4 13.4 5.5 1 0 1 Public Transport
11 32 0 9 15.5 5.5 0 0 1 Public Transport
12 25 1 4 11.5 5.6 0 0 1 Public Transport
13 34 0 13 16.5 5.9 0 0 1 Public Transport
14 26 0 4 12.3 5.9 0 1 0 Public Transport
15 23 0 2 8.6 6.1 0 0 1 Public Transport
16 26 0 5 11.4 6.1 0 0 1 Public Transport
17 24 0 6 10.6 6.1 0 0 1 2Wheeler
18 26 0 3 9.5 6.2 0 1 0 Public Transport
19 28 1 7 13.6 6.3 0 0 1 Public Transport
20 25 0 1 7.6 6.3 0 0 1 2Wheeler
21 30 0 8 14.6 6.3 0 1 0 Public Transport
22 26 0 3 9.5 6.3 0 1 0 Public Transport
23 24 1 2 8.6 6.4 0 0 1 Public Transport
24 27 0 7 16.6 6.4 0 0 1 Public Transport
25 27 0 5 12.5 6.4 0 1 0 Public Transport
26 30 0 8 14.6 6.5 0 1 0 Public Transport
27 25 0 2 8.6 6.7 1 0 1 Public Transport
28 22 0 3 8.4 6.8 0 0 1 Public Transport
29 25 0 4 11.5 7.0 0 1 0 Public Transport
30 25 0 3 10.5 7.1 0 0 1 Public Transport
31 23 1 4 8.4 7.1 0 1 0 Public Transport
32 21 0 3 9.5 7.1 0 0 1 2Wheeler
33 23 1 3 11.7 7.2 0 0 1 2Wheeler
34 23 0 0 6.5 7.3 0 0 1 2Wheeler
35 28 0 9 21.7 7.3 0 1 0 Public Transport
36 26 0 4 12.5 7.4 0 0 1 Public Transport
37 23 0 0 7.7 7.4 0 1 0 Public Transport
38 25 0 2 8.6 7.4 0 0 1 Public Transport
39 24 0 4 8.5 7.5 0 0 1 2Wheeler
40 28 0 6 13.7 7.5 1 0 1 2Wheeler
41 26 0 4 12.6 7.5 0 0 1 2Wheeler
42 28 0 5 13.6 7.5 0 1 0 Public Transport
43 22 0 0 6.5 7.6 0 1 0 Public Transport
44 29 0 5 15.4 7.6 1 0 1 Public Transport
45 31 0 9 15.6 7.6 0 0 1 Public Transport
46 24 0 2 8.7 7.6 0 0 1 Public Transport
47 29 0 6 14.6 7.6 0 0 1 Public Transport
48 26 1 4 12.4 7.6 0 0 1 Public Transport
49 21 1 3 10.6 7.7 0 0 1 2Wheeler
50 24 1 1 8.5 7.7 0 0 1 Public Transport
51 28 0 5 13.6 7.9 0 0 1 Public Transport
52 27 0 3 9.5 7.9 1 0 1 Public Transport
53 20 1 1 8.5 7.9 0 1 0 Public Transport
54 24 0 2 8.5 8.0 0 0 1 Public Transport
55 25 0 3 10.6 8.1 0 0 1 Public Transport
56 21 0 3 9.6 8.1 0 1 0 Public Transport
57 19 0 1 7.5 8.1 0 1 0 Public Transport
58 30 0 8 14.6 8.1 0 1 0 Public Transport
59 29 0 6 14.7 8.1 0 0 1 Public Transport
60 27 0 6 12.6 8.1 0 0 1 Public Transport
61 27 0 4 13.6 8.2 0 1 0 Public Transport
62 27 1 4 13.6 8.2 0 1 0 Public Transport
63 31 0 9 14.6 8.2 0 1 0 Public Transport
64 20 1 2 8.8 8.3 0 0 1 Public Transport
65 24 1 6 10.6 8.4 1 0 1 Public Transport
66 25 0 3 9.8 8.4 0 0 1 Public Transport
67 32 0 10 15.7 8.4 0 0 1 Public Transport
68 24 0 2 8.7 8.4 0 0 1 Public Transport
69 33 0 11 15.6 8.5 0 0 1 Public Transport
70 30 0 8 14.7 8.5 0 1 0 Public Transport
71 24 1 2 8.5 8.5 1 0 1 Public Transport
72 24 0 2 8.5 8.5 0 0 1 Public Transport
73 28 0 4 14.6 8.6 0 0 1 Public Transport
74 23 0 2 8.8 8.6 0 0 1 Public Transport
75 28 0 6 13.6 8.6 0 1 0 Public Transport
76 28 0 6 13.8 8.6 0 0 1 Public Transport
77 24 0 6 12.7 8.7 0 0 1 2Wheeler
78 27 1 5 13.5 8.8 0 0 1 Public Transport
79 26 0 5 12.8 8.8 0 0 1 Public Transport
80 25 0 3 10.6 8.8 0 1 0 Public Transport
81 26 0 3 10.8 8.9 0 1 0 Public Transport
82 29 0 9 22.8 8.9 0 0 1 Public Transport
83 24 0 6 10.5 8.9 0 1 0 Public Transport
84 28 0 6 13.7 8.9 0 0 1 Public Transport
85 28 1 3 9.5 9.0 0 0 1 Public Transport
86 28 0 10 19.7 9.0 0 1 0 2Wheeler
87 26 0 3 10.8 9.0 0 1 0 Public Transport
88 26 0 4 12.7 9.0 1 0 1 Public Transport
89 28 1 10 20.7 9.0 0 0 1 Public Transport
90 28 0 5 14.6 9.0 0 1 0 Public Transport
91 26 1 3 10.9 9.1 0 1 0 Public Transport
92 24 1 0 7.9 9.1 0 0 1 Public Transport
93 29 1 11 25.9 9.1 0 0 1 Public Transport
94 23 1 0 6.6 9.2 0 0 1 Public Transport
95 27 0 5 12.5 9.3 0 0 1 Public Transport
96 23 0 0 6.8 9.3 0 1 0 Public Transport
97 27 0 3 10.6 9.3 0 0 1 Public Transport
98 33 1 11 15.6 9.3 0 0 1 Public Transport
99 28 0 6 13.7 9.4 0 1 0 Public Transport
100 25 0 1 8.6 9.4 0 1 0 Public Transport
101 26 0 4 12.9 9.4 0 1 0 Public Transport
102 26 1 3 10.8 9.4 0 0 1 Public Transport
103 26 0 2 9.6 9.5 0 0 1 Public Transport
104 27 1 6 12.9 9.5 0 0 1 Public Transport
105 24 0 2 8.6 9.5 1 0 1 Public Transport
106 24 1 0 7.6 9.5 0 0 1 Public Transport
107 28 1 5 14.8 9.7 0 0 1 Public Transport
108 25 0 1 8.6 9.7 0 0 1 Public Transport
109 28 0 6 13.6 9.7 1 0 1 Public Transport
110 31 1 7 15.9 9.7 0 0 1 Public Transport
111 27 0 5 12.8 9.7 0 1 0 2Wheeler
112 28 0 5 14.5 9.8 1 0 1 Public Transport
113 27 1 5 13.8 9.8 0 1 0 Public Transport
114 31 0 10 14.9 9.9 0 1 0 Public Transport
115 25 1 3 9.7 9.9 0 0 1 Public Transport
116 26 0 3 9.6 9.9 0 1 0 Public Transport
117 26 0 3 9.8 10.0 0 0 1 Public Transport
118 36 0 17 39.0 10.0 1 0 1 Car
119 26 1 4 12.9 10.0 0 0 1 Public Transport
120 32 1 9 16.9 10.1 0 1 0 Car
121 24 1 2 8.7 10.1 0 0 1 Public Transport
122 35 0 16 28.7 10.2 0 1 0 Public Transport
123 25 0 2 8.7 10.2 0 0 1 Public Transport
124 24 0 0 7.6 10.2 0 0 1 Public Transport
125 28 0 3 10.8 10.2 1 0 1 Public Transport
126 29 0 6 14.7 10.4 0 0 1 Public Transport
127 30 1 8 14.9 10.4 0 0 1 Public Transport
128 26 0 6 17.8 10.4 0 1 0 Public Transport
129 28 0 5 14.7 10.5 1 0 1 2Wheeler
130 28 0 6 13.9 10.5 0 1 0 Public Transport
131 30 0 7 14.6 10.5 0 1 0 Public Transport
132 27 0 5 12.9 10.6 0 1 0 Public Transport
133 27 1 8 20.7 10.7 0 0 1 Public Transport
134 26 0 2 9.8 10.7 0 1 0 2Wheeler
135 23 0 4 11.6 10.7 0 1 0 2Wheeler
136 30 0 6 15.8 10.7 0 0 1 Public Transport
137 24 1 0 7.8 10.7 0 0 1 Public Transport
138 25 1 7 13.6 10.7 0 0 1 2Wheeler
139 25 0 3 10.7 10.8 0 0 1 Public Transport
140 26 1 4 12.8 10.8 0 1 0 Public Transport
141 33 1 14 34.9 10.9 0 0 1 Public Transport
142 33 0 11 16.7 10.9 1 0 1 Car
143 24 1 3 9.9 10.9 0 0 1 Public Transport
144 21 0 3 9.8 11.0 0 1 0 2Wheeler
145 26 0 4 12.6 11.0 0 1 0 2Wheeler
146 25 0 2 8.6 11.0 0 1 0 2Wheeler
147 31 1 9 14.6 11.1 0 1 0 Public Transport
148 26 0 4 12.9 11.1 0 1 0 2Wheeler
149 39 0 19 47.0 11.2 1 0 1 Car
150 28 0 5 13.7 11.3 0 1 0 Public Transport
151 29 0 11 22.7 11.3 1 0 1 2Wheeler
152 24 1 0 7.7 11.3 1 0 1 Public Transport
153 29 0 9 13.7 11.4 0 0 1 Public Transport
154 30 0 8 14.7 11.4 1 1 0 2Wheeler
155 23 0 4 10.6 11.4 0 0 1 2Wheeler
156 28 0 9 23.8 11.4 0 1 0 Public Transport
157 27 0 6 12.7 11.5 0 0 1 Public Transport
158 33 0 9 17.0 11.6 1 0 1 Car
159 30 0 6 15.6 11.6 0 1 0 Public Transport
160 23 0 0 6.9 11.7 0 0 1 2Wheeler
161 29 0 7 14.8 11.7 0 1 0 Public Transport
162 24 0 4 12.7 11.7 0 0 1 2Wheeler
163 23 0 0 7.7 11.7 0 0 1 2Wheeler
164 29 0 7 13.6 11.7 0 1 0 Public Transport
165 27 0 5 12.8 11.8 0 1 0 2Wheeler
166 32 0 10 15.6 11.8 0 1 0 Car
167 26 0 6 11.8 11.9 0 1 0 Public Transport
168 30 0 6 15.7 11.9 1 0 1 Public Transport
169 30 1 10 28.8 11.9 1 0 1 2Wheeler
170 30 0 6 15.6 11.9 0 1 0 Public Transport
171 28 1 6 13.7 11.9 0 0 1 Public Transport
172 30 0 8 14.8 12.0 0 0 1 Public Transport
173 22 1 2 12.7 12.0 0 0 1 Public Transport
174 30 0 10 29.9 12.1 0 0 1 Public Transport
175 25 1 4 11.7 12.1 1 0 1 Public Transport
176 31 0 10 14.9 12.1 0 0 1 Public Transport
177 24 1 0 7.7 12.1 0 0 1 Public Transport
178 33 0 13 36.0 12.2 1 1 0 Car
179 25 0 5 18.9 12.2 0 1 0 2Wheeler
180 24 0 0 6.8 12.2 0 1 0 Public Transport
181 26 0 2 9.8 12.2 0 1 0 2Wheeler
182 26 0 3 10.7 12.2 1 0 1 Public Transport
183 18 0 0 6.8 12.2 0 0 1 Public Transport
184 26 1 5 12.7 12.2 0 0 1 Public Transport
185 27 0 8 20.7 12.2 0 0 1 2Wheeler
186 33 0 11 17.0 12.3 1 0 1 Car
187 29 0 7 14.7 12.3 0 0 1 Public Transport
188 33 0 10 16.9 12.3 0 0 1 Car
189 24 0 1 7.7 12.4 1 0 1 Public Transport
190 31 0 7 17.0 12.4 1 0 1 Car
191 27 0 4 13.8 12.4 0 0 1 Public Transport
192 26 1 5 12.7 12.5 0 0 1 Public Transport
193 28 0 5 14.9 12.5 1 0 1 2Wheeler
194 24 1 1 8.8 12.6 1 1 0 2Wheeler
195 30 1 8 14.7 12.6 0 0 1 Public Transport
196 24 1 2 8.7 12.6 0 1 0 2Wheeler
197 27 0 8 20.7 12.6 0 0 1 Public Transport
198 38 0 19 44.0 12.6 1 0 1 Car
199 25 0 5 13.7 12.7 1 0 1 2Wheeler
200 24 0 1 8.9 12.7 0 0 1 Public Transport
201 22 0 0 6.8 12.7 0 0 1 Public Transport
202 25 1 4 11.8 12.8 0 0 1 Public Transport
203 34 1 11 17.0 12.8 0 0 1 Car
204 27 0 6 12.8 12.8 0 0 1 Public Transport
205 24 1 1 8.8 12.8 0 0 1 Public Transport
206 25 0 3 10.8 12.8 0 0 1 Public Transport
207 28 0 6 13.8 12.9 0 0 1 Public Transport
208 34 1 15 37.0 12.9 1 0 1 2Wheeler
209 31 0 12 43.0 13.0 1 0 1 Car
210 22 1 0 6.9 13.0 0 0 1 2Wheeler
211 18 0 0 6.7 13.0 0 0 1 2Wheeler
212 24 0 1 7.8 13.1 0 0 1 Public Transport
213 25 0 2 8.9 13.2 0 1 0 2Wheeler
214 26 0 5 12.8 13.2 0 1 0 2Wheeler
215 26 0 4 12.7 13.3 0 0 1 Public Transport
216 27 0 6 12.9 13.3 0 0 1 Public Transport
217 24 1 2 8.9 13.4 0 0 1 Public Transport
218 26 0 8 20.9 13.4 0 1 0 2Wheeler
219 22 0 0 7.0 13.5 0 0 1 2Wheeler
220 26 0 4 12.8 13.6 1 1 0 2Wheeler
221 38 0 19 54.0 13.6 1 0 1 Car
222 36 0 18 44.0 13.6 1 1 0 Car
223 27 0 1 8.9 13.6 0 0 1 Public Transport
224 28 0 7 12.7 13.6 0 0 1 Public Transport
225 28 0 3 10.9 13.6 0 0 1 Public Transport
226 28 0 4 14.9 13.7 0 0 1 Public Transport
227 31 0 7 15.9 13.7 0 0 1 Public Transport
228 23 0 0 6.9 13.7 0 0 1 2Wheeler
229 24 0 2 8.9 13.8 0 1 0 2Wheeler
230 34 0 14 34.0 13.8 0 1 0 Car
231 26 0 7 18.8 13.8 0 0 1 Public Transport
232 30 0 7 14.9 14.0 0 0 1 Public Transport
233 32 0 11 15.8 14.1 1 0 1 Car
234 27 0 9 23.9 14.1 0 0 1 Public Transport
235 38 1 20 42.0 14.1 1 0 1 Car
236 27 0 4 13.9 14.2 0 0 1 Public Transport
237 26 0 4 12.8 14.2 0 0 1 Public Transport
238 24 0 2 9.0 14.2 0 1 0 2Wheeler
239 30 0 8 14.8 14.3 0 0 1 Public Transport
240 28 0 4 14.9 14.3 0 0 1 Public Transport
241 31 0 12 34.0 14.4 1 0 1 Car
242 32 0 10 15.9 14.6 0 1 0 Car
243 32 1 10 15.8 14.6 1 1 0 Car
244 26 0 5 12.8 15.0 0 0 1 Public Transport
245 34 0 14 28.8 15.0 0 1 0 Public Transport
246 27 0 9 20.8 15.1 0 0 1 Public Transport
247 24 0 2 9.0 15.1 0 1 0 2Wheeler
248 28 0 6 13.9 15.1 0 1 0 Public Transport
249 27 0 5 13.8 15.2 0 0 1 Public Transport
250 22 0 0 6.8 15.2 1 0 1 2Wheeler
251 25 0 2 8.8 15.2 0 1 0 2Wheeler
252 23 0 1 7.9 15.2 0 0 1 Public Transport
253 24 0 0 6.9 15.3 0 0 1 2Wheeler
254 21 1 3 9.8 15.3 0 0 1 Public Transport
255 29 0 5 14.8 15.4 0 0 1 Public Transport
256 30 0 8 15.0 15.5 1 0 1 2Wheeler
257 30 0 8 14.9 15.5 1 0 1 2Wheeler
258 28 1 9 23.8 15.5 0 1 0 Public Transport
259 27 1 6 12.9 15.6 0 0 1 Public Transport
260 26 0 6 18.8 15.6 0 0 1 Public Transport
261 26 0 3 10.8 15.6 0 1 0 Public Transport
262 26 0 7 18.8 15.7 0 1 0 2Wheeler
263 22 1 0 6.9 15.7 0 0 1 Public Transport
264 24 0 1 8.8 15.8 0 1 0 2Wheeler
265 23 1 2 8.9 15.8 0 0 1 Public Transport
266 25 0 2 8.9 15.8 0 0 1 Public Transport
267 37 1 18 41.0 15.9 1 0 1 Car
268 25 0 3 9.9 15.9 0 0 1 Public Transport
269 23 0 0 8.0 15.9 0 0 1 2Wheeler
270 22 0 1 7.9 16.3 1 0 1 2Wheeler
271 26 0 6 23.0 16.3 0 1 0 2Wheeler
272 31 0 8 15.9 16.4 0 0 1 Public Transport
273 32 0 14 30.9 16.5 0 1 0 Car
274 32 1 9 15.9 16.6 0 1 0 Public Transport
275 25 0 1 8.9 16.8 0 1 0 2Wheeler
276 40 1 20 41.9 16.9 1 0 1 Car
277 29 0 6 14.9 17.0 0 1 0 Public Transport
278 24 0 0 7.9 17.1 0 0 1 2Wheeler
279 25 0 3 9.9 17.2 0 0 1 Public Transport
280 27 0 4 13.9 17.3 0 1 0 Public Transport
281 33 0 14 33.0 17.3 0 0 1 Car
282 31 0 11 33.0 17.8 1 0 1 Car
283 23 0 3 9.9 17.9 0 0 1 Public Transport
284 39 0 21 46.0 18.1 1 0 1 Car
285 40 0 20 48.0 18.2 1 0 1 Car
286 39 0 21 51.0 18.6 1 0 1 Car
287 29 1 7 15.0 19.0 1 0 1 2Wheeler
288 33 1 10 17.0 19.1 0 0 1 Car
289 26 0 4 13.0 19.1 1 0 1 2Wheeler
290 40 0 22 45.0 19.8 1 0 1 Car
291 37 0 19 42.0 20.7 1 0 1 Car
292 43 1 24 52.0 20.8 1 0 1 Car
293 28 1 7 13.0 21.0 1 1 0 2Wheeler
294 34 0 14 38.0 21.3 1 0 1 Car
295 40 0 20 57.0 21.4 1 0 1 Car
296 38 0 19 44.0 21.5 1 0 1 Car
297 37 0 19 45.0 21.5 1 0 1 Car
298 37 0 19 47.0 22.8 1 0 1 Car
299 39 1 21 50.0 23.4 1 0 1 Car
summary(Cars1)
Age MBA Work.Exp Salary
Min. :18.00 Min. :0.0000 Min. : 0.000 Min. : 6.50
1st Qu.:25.00 1st Qu.:0.0000 1st Qu.: 3.000 1st Qu.: 9.55
Median :27.00 Median :0.0000 Median : 5.000 Median :12.90
Mean :27.49 Mean :0.2408 Mean : 5.967 Mean :15.61
3rd Qu.:30.00 3rd Qu.:0.0000 3rd Qu.: 8.000 3rd Qu.:15.60
Max. :43.00 Max. :1.0000 Max. :24.000 Max. :57.00
Distance license Female Male
Min. : 3.30 Min. :0.000 Min. :0.0000 Min. :0.0000
1st Qu.: 8.60 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.0000
Median :11.30 Median :0.000 Median :0.0000 Median :1.0000
Mean :11.44 Mean :0.214 Mean :0.3144 Mean :0.6856
3rd Qu.:13.60 3rd Qu.:0.000 3rd Qu.:1.0000 3rd Qu.:1.0000
Max. :23.40 Max. :1.000 Max. :1.0000 Max. :1.0000
target
2Wheeler : 63
Car : 39
Public Transport:197
str(Cars1)
'data.frame': 299 obs. of 9 variables:
$ Age : int 23 29 28 27 28 22 27 25 27 27 ...
$ MBA : int 0 0 1 0 0 0 0 0 0 0 ...
$ Work.Exp: int 4 7 5 4 5 1 4 4 4 4 ...
$ Salary : num 8.3 13.4 13.4 13.4 14.4 7.5 13.5 11.5 13.5 13.4 ...
$ Distance: num 3.3 4.1 4.5 4.6 5.1 5.1 5.2 5.2 5.3 5.5 ...
$ license : int 0 0 0 0 0 0 0 0 1 1 ...
$ Female : int 1 0 1 0 0 0 0 1 0 0 ...
$ Male : int 0 1 0 1 1 1 1 0 1 1 ...
$ target : Factor w/ 3 levels "2Wheeler","Car",..: 3 3 3 3 1 3 3 3 3 3 ...
attach(Cars1)
set.seed(777)
pd<-sample(2,nrow(Cars1),replace=TRUE, prob=c(0.7,0.3))
train1<-Cars1[pd==1,]
val1<-Cars1[pd==2,]
attach(train1)
attach(val1)
#10 Fold with LDA
library(MASS)
library(ISLR)
Eq.3 <- target~.
cv_LDA<-lapply(folds,function(x){
train<-train1[x,]
test<-val1[-x,]
lda_1<-lda(Eq.3 , train1)
lda1.pred<-predict(lda_1, newdata=test)
ldapredclass<-lda1.pred$class
tab.LDA<-table(ldapredclass,test$target)
sum(diag(tab.LDA))/sum(tab.LDA)
})
str(cv_LDA)
List of 10
$ Fold01: num 0.84
$ Fold02: num 0.833
$ Fold03: num 0.835
$ Fold04: num 0.842
$ Fold05: num 0.827
$ Fold06: num 0.808
$ Fold07: num 0.84
$ Fold08: num 0.847
$ Fold09: num 0.855
$ Fold10: num 0.844
fit.LDA<-mean(unlist(cv_LDA))
fit.LDA
[1] 0.8371421
#########
##Accuracy of LDA post Smote is 83.71%
#########
#10 Fold on Decision Trees post SMOTE
cv_DT<-lapply(folds,function(x){
train<-train1[x,]
test<-val1[-x,]
DT<-rpart(Eq.3, method="class",train)
pred = predict(DT, type="class",newdata=test)
tabDT<-table( pred,test$target)
sum(diag(tabDT))/sum(tabDT)
})
str(cv_DT)
List of 10
$ Fold01: num 0.593
$ Fold02: num 0.631
$ Fold03: num 0.633
$ Fold04: num 0.526
$ Fold05: num 0.507
$ Fold06: num 0.616
$ Fold07: num 0.654
$ Fold08: num 0.639
$ Fold09: num 0.763
$ Fold10: num 0.636
fit.DT<-mean(unlist(cv_DT))
fit.DT
[1] 0.6198609
########
##Accuracy of Decision trees is 61.98%
########
#10 Vold Validation with NB
cv_NB<-lapply(folds,function(x){
train.NB.kval<-train1[x,]
test.NB.kval<-val1[-x,]
NB.kval<-naiveBayes(x=train.NB.kval[-1], y=train.NB.kval$target)
y_pred.NB.kval<-predict( NB.kval,newdata=test.NB.kval[-1])
cm.NB.kval=table(test.NB.kval[,1],y_pred.NB.kval)
sum(diag(cm.NB.kval))/sum(cm.NB.kval)
})
str(cv_NB)
List of 10
$ Fold01: num 0.0247
$ Fold02: num 0.0357
$ Fold03: num 0.0253
$ Fold04: num 0.0263
$ Fold05: num 0.0133
$ Fold06: num 0.0411
$ Fold07: num 0.0123
$ Fold08: num 0.111
$ Fold09: num 0.0132
$ Fold10: num 0.026
fit.NB<-mean(unlist(cv_NB))
fit.NB
[1] 0.03290558
#####################
##Accuracy of NB is 3.9%
#####################
###SVM
####2_D PLOT
library(rpart)
svm.2<-svm(target~., data=train1, kernel="linear")
summary(svm.2)
Call:
svm(formula = target ~ ., data = train1, kernel = "linear")
Parameters:
SVM-Type: C-classification
SVM-Kernel: linear
cost: 1
gamma: 0.125
Number of Support Vectors: 109
( 52 46 11 )
Number of Classes: 3
Levels:
2Wheeler Car Public Transport
##Confusion matrix
svm.full<-svm(target~., data=train1, kernel="radial")
summary(svm.full)
Call:
svm(formula = target ~ ., data = train1, kernel = "radial")
Parameters:
SVM-Type: C-classification
SVM-Kernel: radial
cost: 1
gamma: 0.125
Number of Support Vectors: 126
( 63 45 18 )
Number of Classes: 3
Levels:
2Wheeler Car Public Transport
y_pred.svm.full<-predict(svm.full,newdata=val1[,-9])
y_pred.svm.full
4 9 11 15
Public Transport Public Transport Public Transport Public Transport
18 26 29 30
Public Transport Public Transport Public Transport Public Transport
31 32 33 37
Public Transport Public Transport Public Transport Public Transport
46 47 48 49
Public Transport Public Transport Public Transport Public Transport
58 61 63 65
Public Transport Public Transport Public Transport Public Transport
69 72 73 76
Public Transport Public Transport Public Transport Public Transport
89 90 91 94
Public Transport Public Transport Public Transport Public Transport
96 99 100 101
Public Transport Public Transport Public Transport Public Transport
105 106 112 118
Public Transport Public Transport Public Transport Car
119 120 126 129
Public Transport Public Transport Public Transport Public Transport
132 141 142 151
Public Transport Public Transport Car Car
154 161 164 165
2Wheeler Public Transport Public Transport Public Transport
174 176 177 185
Public Transport Public Transport Public Transport Public Transport
188 189 190 192
Public Transport Public Transport Public Transport Public Transport
195 201 209 210
Public Transport Public Transport Car Public Transport
211 214 221 223
Public Transport 2Wheeler Car Public Transport
224 230 234 244
Public Transport Public Transport Public Transport Public Transport
246 250 251 255
Public Transport Public Transport 2Wheeler Public Transport
257 259 265 267
2Wheeler Public Transport Public Transport Car
270 273 275 276
Public Transport Public Transport 2Wheeler Car
277 286 287 290
2Wheeler Car 2Wheeler Car
291 294
Car Car
Levels: 2Wheeler Car Public Transport
#Confusion matrix
head(val1)
Age MBA Work.Exp Salary Distance license Female Male target
4 27 0 4 13.4 4.6 0 0 1 Public Transport
9 27 0 4 13.5 5.3 1 0 1 Public Transport
11 32 0 9 15.5 5.5 0 0 1 Public Transport
15 23 0 2 8.6 6.1 0 0 1 Public Transport
18 26 0 3 9.5 6.2 0 1 0 Public Transport
26 30 0 8 14.6 6.5 0 1 0 Public Transport
head(train1)
Age MBA Work.Exp Salary Distance license Female Male target
1 23 0 4 8.3 3.3 0 1 0 Public Transport
2 29 0 7 13.4 4.1 0 0 1 Public Transport
3 28 1 5 13.4 4.5 0 1 0 Public Transport
5 28 0 5 14.4 5.1 0 0 1 2Wheeler
6 22 0 1 7.5 5.1 0 0 1 Public Transport
7 27 0 4 13.5 5.2 0 0 1 Public Transport
cm.SVMB.full=table(val1[,9],y_pred.svm.full)
cm.SVMB.full
y_pred.svm.full
2Wheeler Car Public Transport
2Wheeler 6 1 10
Car 0 10 5
Public Transport 1 0 53
accuracy.svm.full<-sum(diag(cm.SVMB.full))/sum(cm.SVMB.full)
accuracy.svm.full
[1] 0.8023256
##
##Accuracy of SVM post SMOTE is 80.23%
###3
#Tune
set.seed(77)
tune.svm<-tune(svm, target~.,data=train1,ranges=list(epsilon=seq(0,1,0.1), cost=2^(2:9)))
summary(tune.svm)
Parameter tuning of 'svm':
- sampling method: 10-fold cross validation
- best parameters:
epsilon cost
0 64
- best performance: 0.2010823
- Detailed performance results:
epsilon cost error dispersion
1 0.0 4 0.2064935 0.05921759
2 0.1 4 0.2064935 0.05921759
3 0.2 4 0.2064935 0.05921759
4 0.3 4 0.2064935 0.05921759
5 0.4 4 0.2064935 0.05921759
6 0.5 4 0.2064935 0.05921759
7 0.6 4 0.2064935 0.05921759
8 0.7 4 0.2064935 0.05921759
9 0.8 4 0.2064935 0.05921759
10 0.9 4 0.2064935 0.05921759
11 1.0 4 0.2064935 0.05921759
12 0.0 8 0.2153680 0.08001342
13 0.1 8 0.2153680 0.08001342
14 0.2 8 0.2153680 0.08001342
15 0.3 8 0.2153680 0.08001342
16 0.4 8 0.2153680 0.08001342
17 0.5 8 0.2153680 0.08001342
18 0.6 8 0.2153680 0.08001342
19 0.7 8 0.2153680 0.08001342
20 0.8 8 0.2153680 0.08001342
21 0.9 8 0.2153680 0.08001342
22 1.0 8 0.2153680 0.08001342
23 0.0 16 0.2058442 0.08064332
24 0.1 16 0.2058442 0.08064332
25 0.2 16 0.2058442 0.08064332
26 0.3 16 0.2058442 0.08064332
27 0.4 16 0.2058442 0.08064332
28 0.5 16 0.2058442 0.08064332
29 0.6 16 0.2058442 0.08064332
30 0.7 16 0.2058442 0.08064332
31 0.8 16 0.2058442 0.08064332
32 0.9 16 0.2058442 0.08064332
33 1.0 16 0.2058442 0.08064332
34 0.0 32 0.2012987 0.10149338
35 0.1 32 0.2012987 0.10149338
36 0.2 32 0.2012987 0.10149338
37 0.3 32 0.2012987 0.10149338
38 0.4 32 0.2012987 0.10149338
39 0.5 32 0.2012987 0.10149338
40 0.6 32 0.2012987 0.10149338
41 0.7 32 0.2012987 0.10149338
42 0.8 32 0.2012987 0.10149338
43 0.9 32 0.2012987 0.10149338
44 1.0 32 0.2012987 0.10149338
45 0.0 64 0.2010823 0.09343058
46 0.1 64 0.2010823 0.09343058
47 0.2 64 0.2010823 0.09343058
48 0.3 64 0.2010823 0.09343058
49 0.4 64 0.2010823 0.09343058
50 0.5 64 0.2010823 0.09343058
51 0.6 64 0.2010823 0.09343058
52 0.7 64 0.2010823 0.09343058
53 0.8 64 0.2010823 0.09343058
54 0.9 64 0.2010823 0.09343058
55 1.0 64 0.2010823 0.09343058
56 0.0 128 0.2194805 0.09422258
57 0.1 128 0.2194805 0.09422258
58 0.2 128 0.2194805 0.09422258
59 0.3 128 0.2194805 0.09422258
60 0.4 128 0.2194805 0.09422258
61 0.5 128 0.2194805 0.09422258
62 0.6 128 0.2194805 0.09422258
63 0.7 128 0.2194805 0.09422258
64 0.8 128 0.2194805 0.09422258
65 0.9 128 0.2194805 0.09422258
66 1.0 128 0.2194805 0.09422258
67 0.0 256 0.2142857 0.11591712
68 0.1 256 0.2142857 0.11591712
69 0.2 256 0.2142857 0.11591712
70 0.3 256 0.2142857 0.11591712
71 0.4 256 0.2142857 0.11591712
72 0.5 256 0.2142857 0.11591712
73 0.6 256 0.2142857 0.11591712
74 0.7 256 0.2142857 0.11591712
75 0.8 256 0.2142857 0.11591712
76 0.9 256 0.2142857 0.11591712
77 1.0 256 0.2142857 0.11591712
78 0.0 512 0.2285714 0.10580360
79 0.1 512 0.2285714 0.10580360
80 0.2 512 0.2285714 0.10580360
81 0.3 512 0.2285714 0.10580360
82 0.4 512 0.2285714 0.10580360
83 0.5 512 0.2285714 0.10580360
84 0.6 512 0.2285714 0.10580360
85 0.7 512 0.2285714 0.10580360
86 0.8 512 0.2285714 0.10580360
87 0.9 512 0.2285714 0.10580360
88 1.0 512 0.2285714 0.10580360
best.svm<-tune.svm$best.model
summary(best.svm)
Call:
best.tune(method = svm, train.x = target ~ ., data = train1,
ranges = list(epsilon = seq(0, 1, 0.1), cost = 2^(2:9)))
Parameters:
SVM-Type: C-classification
SVM-Kernel: radial
cost: 64
gamma: 0.125
Number of Support Vectors: 96
( 47 34 15 )
Number of Classes: 3
Levels:
2Wheeler Car Public Transport
best.par<-tune.svm$best.parameters
summary(best.par)
epsilon cost
Min. :0 Min. :64
1st Qu.:0 1st Qu.:64
Median :0 Median :64
Mean :0 Mean :64
3rd Qu.:0 3rd Qu.:64
Max. :0 Max. :64
#CM
y_pred.svm.best<-predict(best.svm,newdata=val1[-9])
#Confusion matrix
cm.SVMB.best=table(val1[,9],y_pred.svm.best)
cm.SVMB.best
y_pred.svm.best
2Wheeler Car Public Transport
2Wheeler 6 2 9
Car 0 10 5
Public Transport 1 0 53
accuracy.svm.best<-sum(diag(cm.SVMB.best))/sum(cm.SVMB.best)
accuracy.svm.best
[1] 0.8023256
#################
## Accuracy after tuning SVM is 80.23%
################
#K- nearest neighbour
names(Cars1)
[1] "Age" "MBA" "Work.Exp" "Salary" "Distance" "license"
[7] "Female" "Male" "target"
kneig = knn(train = Cars1[,-9],test = Cars1[,-9],cl= Cars1$target)
cm.knn= table(Actual=Cars1$target, Predicted = kneig)
accuracy.knn<-sum(diag(cm.knn))/sum(cm.knn)
accuracy.knn
[1] 1
#
#Predicting the 2 rows using various methods
Cars2=read.csv("Actual_Cars_data.csv", header=T)
Cars2 = na.omit(Cars2)
predictdata = read.csv("cars.test.csv",header = T)
Cars2$Gender <- as.numeric(Cars2$Gender)
predictdata$Gender <- as.numeric(predictdata$Gender)
predictdata$Engineer <- as.numeric(predictdata$Engineer)
predictdata$MBA <- as.numeric(predictdata$MBA)
predictdata$license <- as.numeric(predictdata$license)
names(Cars2)
[1] "Age" "Gender" "Engineer" "MBA" "Work.Exp" "Salary"
[7] "Distance" "license" "Transport"
names(predictdata)
[1] "Age" "Gender" "Engineer" "MBA" "Work.Exp" "Salary"
[7] "Distance" "license"
#Using KNN
knn.test = knn(train= Cars2[,-9], test= predictdata[,-9], cl=Cars2[,9], k=3)
predictdata$Transport.KNN = knn.test
predictdata
Age Gender Engineer MBA Work.Exp Salary Distance license
1 25 2 0 0 2 10 5 1
2 25 1 1 0 2 10 5 0
Transport.KNN
1 Public Transport
2 Public Transport
##Predicting is PUBLIC TRANSPORT
names(predictdata)
[1] "Age" "Gender" "Engineer" "MBA"
[5] "Work.Exp" "Salary" "Distance" "license"
[9] "Transport.KNN"
#Lets us use LDA
lda.predidct = lda(Cars2$Transport~., data=Cars2[,-9])
a = predict(lda.predidct, newdata = predictdata[,-9])
predictdata$Transport.LDA = a$class
predictdata
Age Gender Engineer MBA Work.Exp Salary Distance license
1 25 2 0 0 2 10 5 1
2 25 1 1 0 2 10 5 0
Transport.KNN Transport.LDA
1 Public Transport Public Transport
2 Public Transport Public Transport
#Even LDA predicts Public Transport
# Lets us use SVM
predict.svm = svm(Cars2$Transport~., data=Cars2[,-9], kernel= "polynomial")
p.svm = predict(predict.svm, newdata = predictdata[,-c(9,10)])
predictdata$Transport.SVM = p.svm
predictdata[,c(9,10,11)]
Transport.KNN Transport.LDA Transport.SVM
1 Public Transport Public Transport Public Transport
2 Public Transport Public Transport Public Transport
#Prediction is Public Transport using all the methods
####################################
