-
Notifications
You must be signed in to change notification settings - Fork 0
/
Machine Learning Movie Recommendation Service with AWS EC2 Server.htm
608 lines (571 loc) · 19.4 KB
/
Machine Learning Movie Recommendation Service with AWS EC2 Server.htm
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
<html>
<head>
<meta http-equiv=Content-Type content="text/html; charset=windows-1252">
<meta name=Generator content="Microsoft Word 15 (filtered)">
<style>
<!--
/* Font Definitions */
@font-face
{font-family:Wingdings;
panose-1:5 0 0 0 0 0 0 0 0 0;}
@font-face
{font-family:"Cambria Math";
panose-1:2 4 5 3 5 4 6 3 2 4;}
@font-face
{font-family:"Century Gothic";}
@font-face
{font-family:HYGothic-Medium;
panose-1:0 0 0 0 0 0 0 0 0 0;}
@font-face
{font-family:"\@HYGothic-Medium";
panose-1:0 0 0 0 0 0 0 0 0 0;}
/* Style Definitions */
p.MsoNormal, li.MsoNormal, div.MsoNormal
{margin-top:0in;
margin-right:0in;
margin-bottom:8.0pt;
margin-left:0in;
line-height:107%;
font-size:11.0pt;
font-family:"Century Gothic",sans-serif;}
h1
{mso-style-link:"Heading 1 Char";
margin-top:20.0pt;
margin-right:0in;
margin-bottom:2.0pt;
margin-left:0in;
page-break-after:avoid;
font-size:18.0pt;
font-family:"Century Gothic",sans-serif;
color:#021730;
font-weight:normal;}
h2
{mso-style-link:"Heading 2 Char";
margin-top:2.0pt;
margin-right:0in;
margin-bottom:0in;
margin-left:0in;
margin-bottom:.0001pt;
page-break-after:avoid;
font-size:16.0pt;
font-family:"Century Gothic",sans-serif;
color:#032348;
font-weight:normal;}
h3
{mso-style-link:"Heading 3 Char";
margin-top:2.0pt;
margin-right:0in;
margin-bottom:0in;
margin-left:0in;
margin-bottom:.0001pt;
page-break-after:avoid;
font-size:14.0pt;
font-family:"Century Gothic",sans-serif;
color:#032348;
font-weight:normal;}
h4
{mso-style-link:"Heading 4 Char";
margin-top:2.0pt;
margin-right:0in;
margin-bottom:0in;
margin-left:0in;
margin-bottom:.0001pt;
line-height:107%;
page-break-after:avoid;
font-size:12.0pt;
font-family:"Century Gothic",sans-serif;
color:#032348;
font-weight:normal;}
h5
{mso-style-link:"Heading 5 Char";
margin-top:2.0pt;
margin-right:0in;
margin-bottom:0in;
margin-left:0in;
margin-bottom:.0001pt;
line-height:107%;
page-break-after:avoid;
font-size:11.0pt;
font-family:"Century Gothic",sans-serif;
color:#032348;
text-transform:uppercase;
font-weight:normal;}
h6
{mso-style-link:"Heading 6 Char";
margin-top:2.0pt;
margin-right:0in;
margin-bottom:0in;
margin-left:0in;
margin-bottom:.0001pt;
line-height:107%;
page-break-after:avoid;
font-size:11.0pt;
font-family:"Century Gothic",sans-serif;
color:#021730;
text-transform:uppercase;
font-weight:normal;
font-style:italic;}
p.MsoHeading7, li.MsoHeading7, div.MsoHeading7
{mso-style-link:"Heading 7 Char";
margin-top:2.0pt;
margin-right:0in;
margin-bottom:0in;
margin-left:0in;
margin-bottom:.0001pt;
line-height:107%;
page-break-after:avoid;
font-size:11.0pt;
font-family:"Century Gothic",sans-serif;
color:#021730;
font-weight:bold;}
p.MsoHeading8, li.MsoHeading8, div.MsoHeading8
{mso-style-link:"Heading 8 Char";
margin-top:2.0pt;
margin-right:0in;
margin-bottom:0in;
margin-left:0in;
margin-bottom:.0001pt;
line-height:107%;
page-break-after:avoid;
font-size:11.0pt;
font-family:"Century Gothic",sans-serif;
color:#021730;
font-weight:bold;
font-style:italic;}
p.MsoHeading9, li.MsoHeading9, div.MsoHeading9
{mso-style-link:"Heading 9 Char";
margin-top:2.0pt;
margin-right:0in;
margin-bottom:0in;
margin-left:0in;
margin-bottom:.0001pt;
line-height:107%;
page-break-after:avoid;
font-size:11.0pt;
font-family:"Century Gothic",sans-serif;
color:#021730;
font-style:italic;}
p.MsoToc1, li.MsoToc1, div.MsoToc1
{margin-top:0in;
margin-right:0in;
margin-bottom:5.0pt;
margin-left:0in;
line-height:107%;
font-size:11.0pt;
font-family:"Century Gothic",sans-serif;}
p.MsoToc2, li.MsoToc2, div.MsoToc2
{margin-top:0in;
margin-right:0in;
margin-bottom:5.0pt;
margin-left:11.0pt;
line-height:107%;
font-size:11.0pt;
font-family:"Century Gothic",sans-serif;}
p.MsoHeader, li.MsoHeader, div.MsoHeader
{mso-style-link:"Header Char";
margin:0in;
margin-bottom:.0001pt;
font-size:11.0pt;
font-family:"Century Gothic",sans-serif;}
p.MsoFooter, li.MsoFooter, div.MsoFooter
{mso-style-link:"Footer Char";
margin:0in;
margin-bottom:.0001pt;
font-size:11.0pt;
font-family:"Century Gothic",sans-serif;}
p.MsoCaption, li.MsoCaption, div.MsoCaption
{margin-top:0in;
margin-right:0in;
margin-bottom:8.0pt;
margin-left:0in;
font-size:11.0pt;
font-family:"Century Gothic",sans-serif;
font-variant:small-caps;
color:#146194;
font-weight:bold;}
p.MsoTitle, li.MsoTitle, div.MsoTitle
{mso-style-link:"Title Char";
margin:0in;
margin-bottom:.0001pt;
line-height:85%;
font-size:36.0pt;
font-family:"Century Gothic",sans-serif;
color:#146194;
text-transform:uppercase;
letter-spacing:-.75pt;}
p.MsoTitleCxSpFirst, li.MsoTitleCxSpFirst, div.MsoTitleCxSpFirst
{mso-style-link:"Title Char";
margin:0in;
margin-bottom:.0001pt;
line-height:85%;
font-size:36.0pt;
font-family:"Century Gothic",sans-serif;
color:#146194;
text-transform:uppercase;
letter-spacing:-.75pt;}
p.MsoTitleCxSpMiddle, li.MsoTitleCxSpMiddle, div.MsoTitleCxSpMiddle
{mso-style-link:"Title Char";
margin:0in;
margin-bottom:.0001pt;
line-height:85%;
font-size:36.0pt;
font-family:"Century Gothic",sans-serif;
color:#146194;
text-transform:uppercase;
letter-spacing:-.75pt;}
p.MsoTitleCxSpLast, li.MsoTitleCxSpLast, div.MsoTitleCxSpLast
{mso-style-link:"Title Char";
margin:0in;
margin-bottom:.0001pt;
line-height:85%;
font-size:36.0pt;
font-family:"Century Gothic",sans-serif;
color:#146194;
text-transform:uppercase;
letter-spacing:-.75pt;}
p.MsoSubtitle, li.MsoSubtitle, div.MsoSubtitle
{mso-style-link:"Subtitle Char";
margin-top:0in;
margin-right:0in;
margin-bottom:12.0pt;
margin-left:0in;
font-size:14.0pt;
font-family:"Century Gothic",sans-serif;
color:#052F61;}
a:link, span.MsoHyperlink
{color:#0D2E46;
text-decoration:underline;}
a:visited, span.MsoHyperlinkFollowed
{color:#356A95;
text-decoration:underline;}
code
{font-family:"Courier New";}
pre
{mso-style-link:"HTML Preformatted Char";
margin:0in;
margin-bottom:.0001pt;
font-size:10.0pt;
font-family:"Courier New";}
p.MsoNoSpacing, li.MsoNoSpacing, div.MsoNoSpacing
{margin:0in;
margin-bottom:.0001pt;
font-size:11.0pt;
font-family:"Century Gothic",sans-serif;}
p.MsoListParagraph, li.MsoListParagraph, div.MsoListParagraph
{margin-top:0in;
margin-right:0in;
margin-bottom:8.0pt;
margin-left:.5in;
line-height:107%;
font-size:11.0pt;
font-family:"Century Gothic",sans-serif;}
p.MsoListParagraphCxSpFirst, li.MsoListParagraphCxSpFirst, div.MsoListParagraphCxSpFirst
{margin-top:0in;
margin-right:0in;
margin-bottom:0in;
margin-left:.5in;
margin-bottom:.0001pt;
line-height:107%;
font-size:11.0pt;
font-family:"Century Gothic",sans-serif;}
p.MsoListParagraphCxSpMiddle, li.MsoListParagraphCxSpMiddle, div.MsoListParagraphCxSpMiddle
{margin-top:0in;
margin-right:0in;
margin-bottom:0in;
margin-left:.5in;
margin-bottom:.0001pt;
line-height:107%;
font-size:11.0pt;
font-family:"Century Gothic",sans-serif;}
p.MsoListParagraphCxSpLast, li.MsoListParagraphCxSpLast, div.MsoListParagraphCxSpLast
{margin-top:0in;
margin-right:0in;
margin-bottom:8.0pt;
margin-left:.5in;
line-height:107%;
font-size:11.0pt;
font-family:"Century Gothic",sans-serif;}
p.MsoQuote, li.MsoQuote, div.MsoQuote
{mso-style-link:"Quote Char";
margin-top:6.0pt;
margin-right:0in;
margin-bottom:6.0pt;
margin-left:.5in;
line-height:107%;
font-size:12.0pt;
font-family:"Century Gothic",sans-serif;
color:#146194;}
p.MsoIntenseQuote, li.MsoIntenseQuote, div.MsoIntenseQuote
{mso-style-link:"Intense Quote Char";
margin-right:0in;
margin-bottom:12.0pt;
margin-left:.5in;
text-align:center;
font-size:16.0pt;
font-family:"Century Gothic",sans-serif;
color:#146194;
letter-spacing:-.3pt;}
span.MsoSubtleEmphasis
{color:#595959;
font-style:italic;}
span.MsoIntenseEmphasis
{font-weight:bold;
font-style:italic;}
span.MsoSubtleReference
{font-variant:small-caps;
color:#595959;
border:none;
text-decoration:none;}
span.MsoIntenseReference
{font-variant:small-caps;
color:#146194;
font-weight:bold;
text-decoration:underline;}
span.MsoBookTitle
{font-variant:small-caps;
letter-spacing:.5pt;
font-weight:bold;}
p.MsoTocHeading, li.MsoTocHeading, div.MsoTocHeading
{margin-top:20.0pt;
margin-right:0in;
margin-bottom:2.0pt;
margin-left:0in;
page-break-after:avoid;
font-size:18.0pt;
font-family:"Century Gothic",sans-serif;
color:#021730;}
span.HTMLPreformattedChar
{mso-style-name:"HTML Preformatted Char";
mso-style-link:"HTML Preformatted";
font-family:"Courier New";}
span.Heading1Char
{mso-style-name:"Heading 1 Char";
mso-style-link:"Heading 1";
font-family:"Century Gothic",sans-serif;
color:#021730;}
span.apple-converted-space
{mso-style-name:apple-converted-space;}
span.cm-keyword
{mso-style-name:cm-keyword;}
span.hljs-string
{mso-style-name:hljs-string;}
span.hljs-number
{mso-style-name:hljs-number;}
span.Heading2Char
{mso-style-name:"Heading 2 Char";
mso-style-link:"Heading 2";
font-family:"Century Gothic",sans-serif;
color:#032348;}
span.HeaderChar
{mso-style-name:"Header Char";
mso-style-link:Header;}
span.FooterChar
{mso-style-name:"Footer Char";
mso-style-link:Footer;}
span.Heading3Char
{mso-style-name:"Heading 3 Char";
mso-style-link:"Heading 3";
font-family:"Century Gothic",sans-serif;
color:#032348;}
span.Heading4Char
{mso-style-name:"Heading 4 Char";
mso-style-link:"Heading 4";
font-family:"Century Gothic",sans-serif;
color:#032348;}
span.Heading5Char
{mso-style-name:"Heading 5 Char";
mso-style-link:"Heading 5";
font-family:"Century Gothic",sans-serif;
color:#032348;
text-transform:uppercase;}
span.Heading6Char
{mso-style-name:"Heading 6 Char";
mso-style-link:"Heading 6";
font-family:"Century Gothic",sans-serif;
color:#021730;
text-transform:uppercase;
font-style:italic;}
span.Heading7Char
{mso-style-name:"Heading 7 Char";
mso-style-link:"Heading 7";
font-family:"Century Gothic",sans-serif;
color:#021730;
font-weight:bold;}
span.Heading8Char
{mso-style-name:"Heading 8 Char";
mso-style-link:"Heading 8";
font-family:"Century Gothic",sans-serif;
color:#021730;
font-weight:bold;
font-style:italic;}
span.Heading9Char
{mso-style-name:"Heading 9 Char";
mso-style-link:"Heading 9";
font-family:"Century Gothic",sans-serif;
color:#021730;
font-style:italic;}
span.TitleChar
{mso-style-name:"Title Char";
mso-style-link:Title;
font-family:"Century Gothic",sans-serif;
color:#146194;
text-transform:uppercase;
letter-spacing:-.75pt;}
span.SubtitleChar
{mso-style-name:"Subtitle Char";
mso-style-link:Subtitle;
font-family:"Century Gothic",sans-serif;
color:#052F61;}
span.QuoteChar
{mso-style-name:"Quote Char";
mso-style-link:Quote;
color:#146194;}
span.IntenseQuoteChar
{mso-style-name:"Intense Quote Char";
mso-style-link:"Intense Quote";
font-family:"Century Gothic",sans-serif;
color:#146194;
letter-spacing:-.3pt;}
.MsoChpDefault
{font-family:"Century Gothic",sans-serif;}
.MsoPapDefault
{margin-bottom:8.0pt;
line-height:107%;}
/* Page Definitions */
@page WordSection1
{size:8.5in 11.0in;
margin:.5in .5in .5in .5in;}
div.WordSection1
{page:WordSection1;}
/* List Definitions */
ol
{margin-bottom:0in;}
ul
{margin-bottom:0in;}
-->
</style>
</head>
<body lang=EN-US link="#0D2E46" vlink="#356A95">
<div class=WordSection1>
<p class=MsoTocHeading>By Afsar Ali</p>
<p class=MsoToc1><span class=MsoHyperlink><a href="#_Toc525334386"><b>Machine
Learning Movie Recommendation Service with AWS EC2 Server</b><span
style='display:none;text-decoration:none'> </span><span
style='display:none;text-decoration:none'>1</span></a></span></p>
<p class=MsoToc2><span class=MsoHyperlink><a href="#_Toc525334387">1.<span
style='text-decoration:none'> </span>Discuss the nature of Datasets used in
this example<span style='display:none;text-decoration:none'>. </span><span style='display:none;text-decoration:none'>1</span></a></span></p>
<p class=MsoToc2><span class=MsoHyperlink><a href="#_Toc525334388">2.<span
style='text-decoration:none'> </span>Describe the Data ingestion<span
style='display:none;text-decoration:none'>. </span><span
style='display:none;text-decoration:none'>1</span></a></span></p>
<p class=MsoToc2><span class=MsoHyperlink><a href="#_Toc525334389">3.<span
style='text-decoration:none'> </span>Describe the Data process<span
style='display:none;text-decoration:none'> </span><span
style='display:none;text-decoration:none'>1</span></a></span></p>
<p class=MsoToc2><span class=MsoHyperlink><a href="#_Toc525334390">4.<span
style='text-decoration:none'> </span>Summarize the recommendation service
with datasets<span style='display:none;text-decoration:none'> </span><span style='display:none;text-decoration:none'>1</span></a></span></p>
<p class=MsoToc2><span class=MsoHyperlink><a href="#_Toc525334391">5.<span
style='text-decoration:none'> </span>Enter your ratings and evaluate the
recommendations. Does this fit to your preferences? Why can it produces your
preferences correctly/not correctly?<span style='display:none;text-decoration:
none'>. </span><span
style='display:none;text-decoration:none'>1</span></a></span></p>
<p class=MsoToc1><span class=MsoHyperlink><a href="#_Toc525334392">Screen shot
of Jupyter Notebook with the ratings and its recommendations.<span
style='display:none;text-decoration:none'> </span><span
style='display:none;text-decoration:none'>1</span></a></span></p>
<p class=MsoNormal> </p>
<h1><a name="_Toc525334386"></a><a name="_Toc525332349"><b><span
style='font-size:14.0pt'>Machine Learning Movie Recommendation Service with AWS
EC2 Server</span></b></a></h1>
<p class=MsoNormal style='margin-bottom:0in;margin-bottom:.0001pt;line-height:
normal'> </p>
<p class=MsoListParagraphCxSpFirst style='margin-bottom:0in;margin-bottom:.0001pt;
text-indent:-.25in;line-height:normal'><a name="_Toc525334387">1.<span
style='font:7.0pt "Times New Roman"'> </span>Discuss
the nature of Datasets used in this example</a></p>
<p class=MsoListParagraphCxSpMiddle style='margin-top:0in;margin-right:0in;
margin-bottom:0in;margin-left:1.0in;margin-bottom:.0001pt;line-height:normal'>The
nature of the dataset (ml-20m) in this example is rating activity from
MovieLens, a movie recommendation service. MovieLens helps users find movies they
would like. It contains 20000263 ratings and 465564 tag applications across
27278 movies. These data were created by 138493 users between January 09, 1995
and March 31, 2015. This dataset was generated on October 17, 2016. MLlib is
Sparks scalable machine learning library consisting of common learning
algorithms and utilities, including classification, regression, clustering,
collaborative filtering, dimensionality reduction, as well as underlying
optimization primitives. As user rate movies to build a custom taste profile,
the machine learning algorithm recommends movies for the users to watch. Users were
selected at random for inclusion. There are no demographic information. The
user is represented by an id only. </p>
<p class=MsoListParagraphCxSpMiddle style='margin-top:0in;margin-right:0in;
margin-bottom:0in;margin-left:1.0in;margin-bottom:.0001pt;line-height:normal'> </p>
<p class=MsoListParagraphCxSpMiddle style='margin-bottom:0in;margin-bottom:
.0001pt;text-indent:-.25in;line-height:normal'><a name="_Toc525334388">2.<span
style='font:7.0pt "Times New Roman"'> </span>Describe
the Data ingestion</a></p>
<p class=MsoListParagraphCxSpMiddle style='margin-top:0in;margin-right:0in;
margin-bottom:0in;margin-left:1.0in;margin-bottom:.0001pt;line-height:normal'>Data
ingestion is the process of obtaining and importing data for immediate use or
storage in a database. To ingest something is to "take something in or
absorb something." Data can be streamed in real time or ingested in
batches. In this case we are importing the data from Movie Lens Dataset in
batch. Group Lens research has collected and made available this rating data
sets from the Movie Lens web site. The data sets were collected over various
periods of time.</p>
<p class=MsoListParagraphCxSpMiddle style='margin-top:0in;margin-right:0in;
margin-bottom:0in;margin-left:1.0in;margin-bottom:.0001pt;line-height:normal'> </p>
<p class=MsoListParagraphCxSpMiddle style='margin-bottom:0in;margin-bottom:
.0001pt;text-indent:-.25in;line-height:normal'><a name="_Toc525334389">3.<span
style='font:7.0pt "Times New Roman"'> </span>Describe
the Data process</a></p>
<p class=MsoListParagraphCxSpMiddle style='margin-top:0in;margin-right:0in;
margin-bottom:0in;margin-left:1.0in;margin-bottom:.0001pt;line-height:normal'>Data
processing is, generally, "the collection and manipulation of items of
data to produce meaningful information." In order to build to our movie
recommendation using Spark, we need to preprocess our data by loading and
parsing the dataset, persisting the resulting RDD for later use. Then building
the recommender model using the complete dataset and persisting the dataset for
later use.</p>
<p class=MsoListParagraphCxSpMiddle style='margin-top:0in;margin-right:0in;
margin-bottom:0in;margin-left:1.0in;margin-bottom:.0001pt;line-height:normal'> </p>
<p class=MsoListParagraphCxSpMiddle style='margin-bottom:0in;margin-bottom:
.0001pt;text-indent:-.25in;line-height:normal'><a name="_Toc525334390">4.<span
style='font:7.0pt "Times New Roman"'> </span>Summarize
the recommendation service with datasets</a></p>
<p class=MsoListParagraphCxSpMiddle style='margin-left:1.0in'>Our
Recommendation model is using Spark's MLlib library provides scalable data
analytics. We used distributed computation engine to perform model computation
using real-world recommendation engine. We had to reduce dataset and select
parameters for our ALS model. As the new user rates movies the model predicts
the ratings for new recommendation. Basically the model uses the new user
preferences in order to compare them with other users in the dataset. And the
recommender needs to be trained every time there is new user rating.</p>
<p class=MsoListParagraphCxSpMiddle style='margin-left:1.0in'> </p>
<p class=MsoListParagraphCxSpMiddle style='margin-bottom:0in;margin-bottom:
.0001pt;text-indent:-.25in;line-height:normal'><a name="_Toc525334391">5.<span
style='font:7.0pt "Times New Roman"'> </span>Enter
your ratings and evaluate the recommendations. Does this fit to your
preferences? Why can it produces your preferences correctly/not correctly?</a></p>
<p class=MsoListParagraphCxSpMiddle style='margin-left:1.0in'>It does fit my
preferences. It can produce my preferences correctly because the data set was
large and using Alternating Least Squares and Collaborative Filtering and many
users habits and likes, using all of this information and matching my
Recommendations to output most likely movies I would like. </p>
<p class=MsoListParagraphCxSpLast style='margin-top:0in;margin-right:0in;
margin-bottom:0in;margin-left:1.25in;margin-bottom:.0001pt;line-height:normal'> </p>
<p class=MsoNormal style='margin-bottom:0in;margin-bottom:.0001pt;line-height:
normal'> </p>
<h1><a name="_Toc525334392">Screen shot of Jupyter Notebook with the ratings
and its recommendations.</a></h1>
<p class=MsoNormal style='margin-bottom:0in;margin-bottom:.0001pt;line-height:
normal'><img width=720 height=437 id="Picture 3"
src="Machine%20Learning%20Movie%20Recommendation%20Service%20with%20AWS%20EC2%20Server_files/image001.jpg"></p>
<p class=MsoNormal style='margin-bottom:0in;margin-bottom:.0001pt;line-height:
normal'><img width=522 height=439 id="Picture 1"
src="Machine%20Learning%20Movie%20Recommendation%20Service%20with%20AWS%20EC2%20Server_files/image002.jpg"></p>
</div>
</body>
</html>