-
Notifications
You must be signed in to change notification settings - Fork 0
/
Task-1.lyx
638 lines (422 loc) · 9.44 KB
/
Task-1.lyx
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
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
#LyX 2.3 created this file. For more info see http://www.lyx.org/
\lyxformat 544
\begin_document
\begin_header
\save_transient_properties true
\origin unavailable
\textclass article
\use_default_options true
\begin_modules
knitr
\end_modules
\maintain_unincluded_children false
\language english
\language_package default
\inputencoding auto
\fontencoding global
\font_roman "default" "default"
\font_sans "default" "default"
\font_typewriter "default" "default"
\font_math "auto" "auto"
\font_default_family default
\use_non_tex_fonts false
\font_sc false
\font_osf false
\font_sf_scale 100 100
\font_tt_scale 100 100
\use_microtype false
\use_dash_ligatures true
\graphics default
\default_output_format default
\output_sync 0
\bibtex_command default
\index_command default
\paperfontsize default
\spacing single
\use_hyperref false
\papersize default
\use_geometry false
\use_package amsmath 1
\use_package amssymb 1
\use_package cancel 1
\use_package esint 1
\use_package mathdots 1
\use_package mathtools 1
\use_package mhchem 1
\use_package stackrel 1
\use_package stmaryrd 1
\use_package undertilde 1
\cite_engine basic
\cite_engine_type default
\biblio_style plain
\use_bibtopic false
\use_indices false
\paperorientation portrait
\suppress_date false
\justification true
\use_refstyle 1
\use_minted 0
\index Index
\shortcut idx
\color #008000
\end_index
\secnumdepth 3
\tocdepth 3
\paragraph_separation indent
\paragraph_indentation default
\is_math_indent 0
\math_numbering_side default
\quotes_style english
\dynamic_quotes 0
\papercolumns 1
\papersides 1
\paperpagestyle default
\tracking_changes false
\output_changes false
\html_math_output 0
\html_css_as_file 0
\html_be_strict false
\end_header
\begin_body
\begin_layout Title
GRIP Task 1: Prediction using Supervised ML
\end_layout
\begin_layout Author
Name: Debartha Paul
\end_layout
\begin_layout Section*
Importing libraries and visualising the data
\end_layout
\begin_layout Standard
We first load the libraries required for our work and then read the dataset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
#Importing the necessary libraries
\end_layout
\begin_layout Plain Layout
library(Metrics)
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
#Reading the dataset
\end_layout
\begin_layout Plain Layout
s_data<-read.csv('http://bit.ly/w-data',header=T)
\end_layout
\begin_layout Plain Layout
dim(s_data)#the dimensions of the dataset
\end_layout
\begin_layout Plain Layout
head(s_data,n=5)#a brief preview of the dataset
\end_layout
\begin_layout Plain Layout
names(s_data)#column names of the dataset
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
Now we plot the data points on a 2-D graph to check if there's any visible
correlation between the variables
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
#Plotting the distribution of the scores
\end_layout
\begin_layout Plain Layout
par(bg='#CCFFFF')
\end_layout
\begin_layout Plain Layout
plot(s_data$Hours,s_data$Scores,pch=16,col='dark blue',
\end_layout
\begin_layout Plain Layout
xlab='Hours studied',ylab='Precentage Score',
\end_layout
\begin_layout Plain Layout
main='Hours vs.
Percentage')
\end_layout
\begin_layout Plain Layout
legend(x=1,y=95,'Scores',pch=16,col='dark blue')
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
From the graph above, we can clearly see that there is a positive linear
relation between the number of hours studied and percentage of score.
\end_layout
\begin_layout Section*
Preparing the data
\end_layout
\begin_layout Standard
We now divide the data into attributes (inputs or
\family typewriter
x
\family default
) and labels (outputs or
\family typewriter
y
\family default
)
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
X<-s_data$Hours;Y<-s_data$Scores
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
Then, we split our data into training and test sets.
We do this using the
\family typewriter
sample()
\family default
method in
\family typewriter
R
\family default
.
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
t_sample<-sample(nrow(s_data),floor(0.8*nrow(s_data)),replace=F)
\end_layout
\begin_layout Plain Layout
X_train<-X[t_sample];Y_train<-Y[t_sample]
\end_layout
\begin_layout Plain Layout
X_test<-X[-t_sample];Y_test<-Y[-t_sample]
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Section*
Training the Algorithm
\end_layout
\begin_layout Standard
We have split our data into training and testing sets.
Now we train our algorithm and then plot the regression line along with
the observed marks.
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
model<-lm(Y_train~X_train)
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
#Plotting the regression line and the test data
\end_layout
\begin_layout Plain Layout
par(bg='#CCFFCC')
\end_layout
\begin_layout Plain Layout
plot(s_data$Hours,s_data$Scores,pch=16,col='dark blue',
\end_layout
\begin_layout Plain Layout
xlab='Hours studied',ylab='Precentage Score',
\end_layout
\begin_layout Plain Layout
main='Hours vs.
Percentage')
\end_layout
\begin_layout Plain Layout
abline(model,col='red',lwd=2)
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Section*
Making Predictions
\end_layout
\begin_layout Standard
Now, it's time to make some predictions using our trained agorithm.
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
X_test#Testing data
\end_layout
\begin_layout Plain Layout
Y_predicted<-predict(model,newdata=data.frame(X_train=X_test))#Predicting
the scores
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
#Comparing Actual vs.
Predicted values
\end_layout
\begin_layout Plain Layout
df<-data.frame('Hours'=X_test,'Actual score'=Y_test,'Predicted score'=Y_predicted
)
\end_layout
\begin_layout Plain Layout
df
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
Predicting the score of a student who studied for 9.25 hours:
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
pred<-predict(model,newdata=data.frame(X_train=9.25))
\end_layout
\begin_layout Plain Layout
pr_data<-data.frame('Hours'=9.25,'Predicted Score'=pred)
\end_layout
\begin_layout Plain Layout
pr_data
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Section*
Evaluating the model
\end_layout
\begin_layout Standard
Finally, we evaluate the performance of the model.
We chose the mean absolute error as the measure of performance of the algorithm
(lower is better):
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
mae(df$Actual,df$Predicted)
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\end_body
\end_document