-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathExercise: Explore Your Data
711 lines (711 loc) · 23 KB
/
Exercise: Explore Your Data
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
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
{
"cells": [
{
"cell_type": "markdown",
"id": "678d6bac",
"metadata": {
"papermill": {
"duration": 0.003752,
"end_time": "2024-06-04T15:41:57.886480",
"exception": false,
"start_time": "2024-06-04T15:41:57.882728",
"status": "completed"
},
"tags": []
},
"source": [
"**[Machine Learning Course Home Page](https://www.kaggle.com/learn/machine-learning)**\n",
"\n",
"---\n"
]
},
{
"cell_type": "markdown",
"id": "9b76aa27",
"metadata": {
"papermill": {
"duration": 0.003258,
"end_time": "2024-06-04T15:41:57.893895",
"exception": false,
"start_time": "2024-06-04T15:41:57.890637",
"status": "completed"
},
"tags": []
},
"source": [
"This exercise will test your ability to read a data file and understand statistics about the data.\n",
"\n",
"In later exercises, you will apply techniques to filter the data, build a machine learning model, and iteratively improve your model.\n",
"\n",
"The course examples use data from Melbourne. To ensure you can apply these techniques on your own, you will have to apply them to a new dataset (with house prices from Iowa).\n",
"\n",
"The exercises use a \"notebook\" coding environment. In case you are unfamiliar with notebooks, we have a [90-second intro video](https://www.youtube.com/watch?v=4C2qMnaIKL4).\n",
"\n",
"# Exercises\n",
"\n",
"Run the following cell to set up code-checking, which will verify your work as you go."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "6deedc36",
"metadata": {
"execution": {
"iopub.execute_input": "2024-06-04T15:41:57.902580Z",
"iopub.status.busy": "2024-06-04T15:41:57.901661Z",
"iopub.status.idle": "2024-06-04T15:41:58.686686Z",
"shell.execute_reply": "2024-06-04T15:41:58.685618Z"
},
"papermill": {
"duration": 0.791856,
"end_time": "2024-06-04T15:41:58.688950",
"exception": false,
"start_time": "2024-06-04T15:41:57.897094",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Setup Complete\n"
]
}
],
"source": [
"# Set up code checking\n",
"from learntools.core import binder\n",
"binder.bind(globals())\n",
"from learntools.machine_learning.ex2 import *\n",
"print(\"Setup Complete\")"
]
},
{
"cell_type": "markdown",
"id": "81c1604d",
"metadata": {
"papermill": {
"duration": 0.003119,
"end_time": "2024-06-04T15:41:58.695471",
"exception": false,
"start_time": "2024-06-04T15:41:58.692352",
"status": "completed"
},
"tags": []
},
"source": [
"## Step 1: Loading Data\n",
"Read the Iowa data file into a Pandas DataFrame called `home_data`."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "589c3514",
"metadata": {
"execution": {
"iopub.execute_input": "2024-06-04T15:41:58.703580Z",
"iopub.status.busy": "2024-06-04T15:41:58.703112Z",
"iopub.status.idle": "2024-06-04T15:41:58.752767Z",
"shell.execute_reply": "2024-06-04T15:41:58.751779Z"
},
"papermill": {
"duration": 0.056394,
"end_time": "2024-06-04T15:41:58.755067",
"exception": false,
"start_time": "2024-06-04T15:41:58.698673",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"application/javascript": [
"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.5, \"interactionType\": 1, \"questionType\": 1, \"questionId\": \"1_LoadHomeData\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"<span style=\"color:#33cc33\">Correct</span>"
],
"text/plain": [
"Correct"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Path of the file to read\n",
"iowa_file_path = '../input/home-data-for-ml-course/train.csv'\n",
"\n",
"# Fill in the line below to read the file into a variable home_data\n",
"home_data = pd.read_csv(iowa_file_path) \n",
"\n",
"# Call line below with no argument to check that you've loaded the data correctly\n",
"step_1.check()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "75ee5aa3",
"metadata": {
"execution": {
"iopub.execute_input": "2024-06-04T15:41:58.764098Z",
"iopub.status.busy": "2024-06-04T15:41:58.763695Z",
"iopub.status.idle": "2024-06-04T15:41:58.768186Z",
"shell.execute_reply": "2024-06-04T15:41:58.767160Z"
},
"papermill": {
"duration": 0.011632,
"end_time": "2024-06-04T15:41:58.770287",
"exception": false,
"start_time": "2024-06-04T15:41:58.758655",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# Lines below will give you a hint or solution code\n",
"#step_1.hint()\n",
"#step_1.solution()"
]
},
{
"cell_type": "markdown",
"id": "d04b4874",
"metadata": {
"papermill": {
"duration": 0.003289,
"end_time": "2024-06-04T15:41:58.777284",
"exception": false,
"start_time": "2024-06-04T15:41:58.773995",
"status": "completed"
},
"tags": []
},
"source": [
"## Step 2: Review The Data\n",
"Use the command you learned to view summary statistics of the data. Then fill in variables to answer the following questions"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "774966a9",
"metadata": {
"execution": {
"iopub.execute_input": "2024-06-04T15:41:58.786007Z",
"iopub.status.busy": "2024-06-04T15:41:58.785271Z",
"iopub.status.idle": "2024-06-04T15:41:58.881351Z",
"shell.execute_reply": "2024-06-04T15:41:58.880458Z"
},
"papermill": {
"duration": 0.102991,
"end_time": "2024-06-04T15:41:58.883707",
"exception": false,
"start_time": "2024-06-04T15:41:58.780716",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Id</th>\n",
" <th>MSSubClass</th>\n",
" <th>LotFrontage</th>\n",
" <th>LotArea</th>\n",
" <th>OverallQual</th>\n",
" <th>OverallCond</th>\n",
" <th>YearBuilt</th>\n",
" <th>YearRemodAdd</th>\n",
" <th>MasVnrArea</th>\n",
" <th>BsmtFinSF1</th>\n",
" <th>...</th>\n",
" <th>WoodDeckSF</th>\n",
" <th>OpenPorchSF</th>\n",
" <th>EnclosedPorch</th>\n",
" <th>3SsnPorch</th>\n",
" <th>ScreenPorch</th>\n",
" <th>PoolArea</th>\n",
" <th>MiscVal</th>\n",
" <th>MoSold</th>\n",
" <th>YrSold</th>\n",
" <th>SalePrice</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1201.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1452.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>...</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>730.500000</td>\n",
" <td>56.897260</td>\n",
" <td>70.049958</td>\n",
" <td>10516.828082</td>\n",
" <td>6.099315</td>\n",
" <td>5.575342</td>\n",
" <td>1971.267808</td>\n",
" <td>1984.865753</td>\n",
" <td>103.685262</td>\n",
" <td>443.639726</td>\n",
" <td>...</td>\n",
" <td>94.244521</td>\n",
" <td>46.660274</td>\n",
" <td>21.954110</td>\n",
" <td>3.409589</td>\n",
" <td>15.060959</td>\n",
" <td>2.758904</td>\n",
" <td>43.489041</td>\n",
" <td>6.321918</td>\n",
" <td>2007.815753</td>\n",
" <td>180921.195890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>421.610009</td>\n",
" <td>42.300571</td>\n",
" <td>24.284752</td>\n",
" <td>9981.264932</td>\n",
" <td>1.382997</td>\n",
" <td>1.112799</td>\n",
" <td>30.202904</td>\n",
" <td>20.645407</td>\n",
" <td>181.066207</td>\n",
" <td>456.098091</td>\n",
" <td>...</td>\n",
" <td>125.338794</td>\n",
" <td>66.256028</td>\n",
" <td>61.119149</td>\n",
" <td>29.317331</td>\n",
" <td>55.757415</td>\n",
" <td>40.177307</td>\n",
" <td>496.123024</td>\n",
" <td>2.703626</td>\n",
" <td>1.328095</td>\n",
" <td>79442.502883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>20.000000</td>\n",
" <td>21.000000</td>\n",
" <td>1300.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1872.000000</td>\n",
" <td>1950.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2006.000000</td>\n",
" <td>34900.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>365.750000</td>\n",
" <td>20.000000</td>\n",
" <td>59.000000</td>\n",
" <td>7553.500000</td>\n",
" <td>5.000000</td>\n",
" <td>5.000000</td>\n",
" <td>1954.000000</td>\n",
" <td>1967.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>5.000000</td>\n",
" <td>2007.000000</td>\n",
" <td>129975.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>730.500000</td>\n",
" <td>50.000000</td>\n",
" <td>69.000000</td>\n",
" <td>9478.500000</td>\n",
" <td>6.000000</td>\n",
" <td>5.000000</td>\n",
" <td>1973.000000</td>\n",
" <td>1994.000000</td>\n",
" <td>0.000000</td>\n",
" <td>383.500000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>25.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>6.000000</td>\n",
" <td>2008.000000</td>\n",
" <td>163000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1095.250000</td>\n",
" <td>70.000000</td>\n",
" <td>80.000000</td>\n",
" <td>11601.500000</td>\n",
" <td>7.000000</td>\n",
" <td>6.000000</td>\n",
" <td>2000.000000</td>\n",
" <td>2004.000000</td>\n",
" <td>166.000000</td>\n",
" <td>712.250000</td>\n",
" <td>...</td>\n",
" <td>168.000000</td>\n",
" <td>68.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>8.000000</td>\n",
" <td>2009.000000</td>\n",
" <td>214000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1460.000000</td>\n",
" <td>190.000000</td>\n",
" <td>313.000000</td>\n",
" <td>215245.000000</td>\n",
" <td>10.000000</td>\n",
" <td>9.000000</td>\n",
" <td>2010.000000</td>\n",
" <td>2010.000000</td>\n",
" <td>1600.000000</td>\n",
" <td>5644.000000</td>\n",
" <td>...</td>\n",
" <td>857.000000</td>\n",
" <td>547.000000</td>\n",
" <td>552.000000</td>\n",
" <td>508.000000</td>\n",
" <td>480.000000</td>\n",
" <td>738.000000</td>\n",
" <td>15500.000000</td>\n",
" <td>12.000000</td>\n",
" <td>2010.000000</td>\n",
" <td>755000.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Id MSSubClass LotFrontage LotArea OverallQual \\\n",
"count 1460.000000 1460.000000 1201.000000 1460.000000 1460.000000 \n",
"mean 730.500000 56.897260 70.049958 10516.828082 6.099315 \n",
"std 421.610009 42.300571 24.284752 9981.264932 1.382997 \n",
"min 1.000000 20.000000 21.000000 1300.000000 1.000000 \n",
"25% 365.750000 20.000000 59.000000 7553.500000 5.000000 \n",
"50% 730.500000 50.000000 69.000000 9478.500000 6.000000 \n",
"75% 1095.250000 70.000000 80.000000 11601.500000 7.000000 \n",
"max 1460.000000 190.000000 313.000000 215245.000000 10.000000 \n",
"\n",
" OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 ... \\\n",
"count 1460.000000 1460.000000 1460.000000 1452.000000 1460.000000 ... \n",
"mean 5.575342 1971.267808 1984.865753 103.685262 443.639726 ... \n",
"std 1.112799 30.202904 20.645407 181.066207 456.098091 ... \n",
"min 1.000000 1872.000000 1950.000000 0.000000 0.000000 ... \n",
"25% 5.000000 1954.000000 1967.000000 0.000000 0.000000 ... \n",
"50% 5.000000 1973.000000 1994.000000 0.000000 383.500000 ... \n",
"75% 6.000000 2000.000000 2004.000000 166.000000 712.250000 ... \n",
"max 9.000000 2010.000000 2010.000000 1600.000000 5644.000000 ... \n",
"\n",
" WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch \\\n",
"count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 \n",
"mean 94.244521 46.660274 21.954110 3.409589 15.060959 \n",
"std 125.338794 66.256028 61.119149 29.317331 55.757415 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"50% 0.000000 25.000000 0.000000 0.000000 0.000000 \n",
"75% 168.000000 68.000000 0.000000 0.000000 0.000000 \n",
"max 857.000000 547.000000 552.000000 508.000000 480.000000 \n",
"\n",
" PoolArea MiscVal MoSold YrSold SalePrice \n",
"count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 \n",
"mean 2.758904 43.489041 6.321918 2007.815753 180921.195890 \n",
"std 40.177307 496.123024 2.703626 1.328095 79442.502883 \n",
"min 0.000000 0.000000 1.000000 2006.000000 34900.000000 \n",
"25% 0.000000 0.000000 5.000000 2007.000000 129975.000000 \n",
"50% 0.000000 0.000000 6.000000 2008.000000 163000.000000 \n",
"75% 0.000000 0.000000 8.000000 2009.000000 214000.000000 \n",
"max 738.000000 15500.000000 12.000000 2010.000000 755000.000000 \n",
"\n",
"[8 rows x 38 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Print summary statistics in next line\n",
"home_data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a952d56f",
"metadata": {
"execution": {
"iopub.execute_input": "2024-06-04T15:41:58.893841Z",
"iopub.status.busy": "2024-06-04T15:41:58.893171Z",
"iopub.status.idle": "2024-06-04T15:41:58.902379Z",
"shell.execute_reply": "2024-06-04T15:41:58.901359Z"
},
"papermill": {
"duration": 0.016766,
"end_time": "2024-06-04T15:41:58.904609",
"exception": false,
"start_time": "2024-06-04T15:41:58.887843",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"application/javascript": [
"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.5, \"interactionType\": 1, \"questionType\": 1, \"questionId\": \"2_HomeDescription\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"<span style=\"color:#33cc33\">Correct</span>"
],
"text/plain": [
"Correct"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# What is the average lot size (rounded to nearest integer)?\n",
"avg_lot_size = 10517\n",
"\n",
"# As of today, how old is the newest home (current year - the date in which it was built)\n",
"newest_home_age = 14\n",
"\n",
"# Checks your answers\n",
"step_2.check()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8d21e50a",
"metadata": {
"execution": {
"iopub.execute_input": "2024-06-04T15:41:58.916529Z",
"iopub.status.busy": "2024-06-04T15:41:58.915633Z",
"iopub.status.idle": "2024-06-04T15:41:58.919841Z",
"shell.execute_reply": "2024-06-04T15:41:58.918985Z"
},
"papermill": {
"duration": 0.012421,
"end_time": "2024-06-04T15:41:58.921907",
"exception": false,
"start_time": "2024-06-04T15:41:58.909486",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"#step_2.hint()\n",
"#step_2.solution()"
]
},
{
"cell_type": "markdown",
"id": "352ababb",
"metadata": {
"papermill": {
"duration": 0.00404,
"end_time": "2024-06-04T15:41:58.930231",
"exception": false,
"start_time": "2024-06-04T15:41:58.926191",
"status": "completed"
},
"tags": []
},
"source": [
"## Think About Your Data\n",
"\n",
"The newest house in your data isn't that new. A few potential explanations for this:\n",
"1. They haven't built new houses where this data was collected.\n",
"1. The data was collected a long time ago. Houses built after the data publication wouldn't show up.\n",
"\n",
"If the reason is explanation #1 above, does that affect your trust in the model you build with this data? What about if it is reason #2?\n",
"\n",
"How could you dig into the data to see which explanation is more plausible?\n",
"\n",
"Check out this **[discussion thread](https://www.kaggle.com/learn-forum/60581)** to see what others think or to add your ideas.\n",
"\n",
"# Keep Going\n",
"\n",
"You are ready for **[Your First Machine Learning Model](https://www.kaggle.com/dansbecker/your-first-machine-learning-model).**\n"
]
},
{
"cell_type": "markdown",
"id": "0ea9377a",
"metadata": {
"papermill": {
"duration": 0.003906,
"end_time": "2024-06-04T15:41:58.938414",
"exception": false,
"start_time": "2024-06-04T15:41:58.934508",
"status": "completed"
},
"tags": []
},
"source": [
"---\n",
"**[Machine Learning Course Home Page](https://www.kaggle.com/learn/machine-learning)**\n",
"\n"
]
}
],
"metadata": {
"kaggle": {
"accelerator": "none",
"dataSources": [
{
"datasetId": 2709,
"sourceId": 38454,
"sourceType": "datasetVersion"
},
{
"datasetId": 108980,
"sourceId": 260251,
"sourceType": "datasetVersion"
}
],
"isGpuEnabled": false,
"isInternetEnabled": false,
"language": "python",
"sourceType": "notebook"
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
},
"papermill": {
"default_parameters": {},
"duration": 4.133713,
"end_time": "2024-06-04T15:41:59.363167",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2024-06-04T15:41:55.229454",
"version": "2.5.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}