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  1. Simple_Linear_Regression.ipynb +1133 -0
  2. Simple_Linear_regression_app.py +37 -0
  3. hw.csv +17 -0
Simple_Linear_Regression.ipynb ADDED
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+ "cell_type": "code",
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+ "execution_count": 2,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "import pandas as pd\n",
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+ "import matplotlib.pyplot as plt\n",
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+ "import numpy as np\n",
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+ "import seaborn as sns\n",
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+ "\n",
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+ "import warnings\n",
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+ "warnings.filterwarnings(\"ignore\")"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 3,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "df = pd.read_csv(\"hw.csv\")"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 4,
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+ ],
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+ " weight Height\n",
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+ "df"
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+ "cell_type": "code",
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+ "execution_count": 5,
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+ "metadata": {},
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+ {
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+ "data": {
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",
179
+ "text/plain": [
180
+ "<Figure size 640x480 with 1 Axes>"
181
+ ]
182
+ },
183
+ "metadata": {},
184
+ "output_type": "display_data"
185
+ }
186
+ ],
187
+ "source": [
188
+ "# Scatter plot\n",
189
+ "plt.scatter(df['weight'],df['Height'])\n",
190
+ "plt.xlabel(\"Weight\")\n",
191
+ "plt.ylabel(\"Height\")\n",
192
+ "plt.show()"
193
+ ]
194
+ },
195
+ {
196
+ "cell_type": "code",
197
+ "execution_count": 6,
198
+ "metadata": {},
199
+ "outputs": [
200
+ {
201
+ "data": {
202
+ "text/html": [
203
+ "<div>\n",
204
+ "<style scoped>\n",
205
+ " .dataframe tbody tr th:only-of-type {\n",
206
+ " vertical-align: middle;\n",
207
+ " }\n",
208
+ "\n",
209
+ " .dataframe tbody tr th {\n",
210
+ " vertical-align: top;\n",
211
+ " }\n",
212
+ "\n",
213
+ " .dataframe thead th {\n",
214
+ " text-align: right;\n",
215
+ " }\n",
216
+ "</style>\n",
217
+ "<table border=\"1\" class=\"dataframe\">\n",
218
+ " <thead>\n",
219
+ " <tr style=\"text-align: right;\">\n",
220
+ " <th></th>\n",
221
+ " <th>weight</th>\n",
222
+ " <th>Height</th>\n",
223
+ " </tr>\n",
224
+ " </thead>\n",
225
+ " <tbody>\n",
226
+ " <tr>\n",
227
+ " <th>weight</th>\n",
228
+ " <td>1.000000</td>\n",
229
+ " <td>0.896534</td>\n",
230
+ " </tr>\n",
231
+ " <tr>\n",
232
+ " <th>Height</th>\n",
233
+ " <td>0.896534</td>\n",
234
+ " <td>1.000000</td>\n",
235
+ " </tr>\n",
236
+ " </tbody>\n",
237
+ "</table>\n",
238
+ "</div>"
239
+ ],
240
+ "text/plain": [
241
+ " weight Height\n",
242
+ "weight 1.000000 0.896534\n",
243
+ "Height 0.896534 1.000000"
244
+ ]
245
+ },
246
+ "execution_count": 6,
247
+ "metadata": {},
248
+ "output_type": "execute_result"
249
+ }
250
+ ],
251
+ "source": [
252
+ "# Correlation\n",
253
+ "df.corr()"
254
+ ]
255
+ },
256
+ {
257
+ "cell_type": "code",
258
+ "execution_count": 7,
259
+ "metadata": {},
260
+ "outputs": [
261
+ {
262
+ "data": {
263
+ "text/plain": [
264
+ "<seaborn.axisgrid.PairGrid at 0x22430e731a0>"
265
+ ]
266
+ },
267
+ "execution_count": 7,
268
+ "metadata": {},
269
+ "output_type": "execute_result"
270
+ },
271
+ {
272
+ "data": {
273
+ "image/png": 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",
274
+ "text/plain": [
275
+ "<Figure size 500x500 with 6 Axes>"
276
+ ]
277
+ },
278
+ "metadata": {},
279
+ "output_type": "display_data"
280
+ }
281
+ ],
282
+ "source": [
283
+ "sns.pairplot(df)"
284
+ ]
285
+ },
286
+ {
287
+ "cell_type": "code",
288
+ "execution_count": 8,
289
+ "metadata": {},
290
+ "outputs": [
291
+ {
292
+ "data": {
293
+ "text/html": [
294
+ "<div>\n",
295
+ "<style scoped>\n",
296
+ " .dataframe tbody tr th:only-of-type {\n",
297
+ " vertical-align: middle;\n",
298
+ " }\n",
299
+ "\n",
300
+ " .dataframe tbody tr th {\n",
301
+ " vertical-align: top;\n",
302
+ " }\n",
303
+ "\n",
304
+ " .dataframe thead th {\n",
305
+ " text-align: right;\n",
306
+ " }\n",
307
+ "</style>\n",
308
+ "<table border=\"1\" class=\"dataframe\">\n",
309
+ " <thead>\n",
310
+ " <tr style=\"text-align: right;\">\n",
311
+ " <th></th>\n",
312
+ " <th>weight</th>\n",
313
+ " <th>Height</th>\n",
314
+ " </tr>\n",
315
+ " </thead>\n",
316
+ " <tbody>\n",
317
+ " <tr>\n",
318
+ " <th>0</th>\n",
319
+ " <td>50</td>\n",
320
+ " <td>100</td>\n",
321
+ " </tr>\n",
322
+ " <tr>\n",
323
+ " <th>1</th>\n",
324
+ " <td>57</td>\n",
325
+ " <td>105</td>\n",
326
+ " </tr>\n",
327
+ " <tr>\n",
328
+ " <th>2</th>\n",
329
+ " <td>62</td>\n",
330
+ " <td>110</td>\n",
331
+ " </tr>\n",
332
+ " <tr>\n",
333
+ " <th>3</th>\n",
334
+ " <td>67</td>\n",
335
+ " <td>120</td>\n",
336
+ " </tr>\n",
337
+ " <tr>\n",
338
+ " <th>4</th>\n",
339
+ " <td>75</td>\n",
340
+ " <td>130</td>\n",
341
+ " </tr>\n",
342
+ " </tbody>\n",
343
+ "</table>\n",
344
+ "</div>"
345
+ ],
346
+ "text/plain": [
347
+ " weight Height\n",
348
+ "0 50 100\n",
349
+ "1 57 105\n",
350
+ "2 62 110\n",
351
+ "3 67 120\n",
352
+ "4 75 130"
353
+ ]
354
+ },
355
+ "execution_count": 8,
356
+ "metadata": {},
357
+ "output_type": "execute_result"
358
+ }
359
+ ],
360
+ "source": [
361
+ "# Independent and Dependent feature\n",
362
+ "df.head()"
363
+ ]
364
+ },
365
+ {
366
+ "cell_type": "code",
367
+ "execution_count": 9,
368
+ "metadata": {},
369
+ "outputs": [
370
+ {
371
+ "data": {
372
+ "text/plain": [
373
+ "(16, 1)"
374
+ ]
375
+ },
376
+ "execution_count": 9,
377
+ "metadata": {},
378
+ "output_type": "execute_result"
379
+ }
380
+ ],
381
+ "source": [
382
+ "X = df[['weight']] # Independent should be Data Frame or 2D array\n",
383
+ "type(X)\n",
384
+ "np.array(X).shape"
385
+ ]
386
+ },
387
+ {
388
+ "cell_type": "code",
389
+ "execution_count": 10,
390
+ "metadata": {},
391
+ "outputs": [
392
+ {
393
+ "data": {
394
+ "text/plain": [
395
+ "(16,)"
396
+ ]
397
+ },
398
+ "execution_count": 10,
399
+ "metadata": {},
400
+ "output_type": "execute_result"
401
+ }
402
+ ],
403
+ "source": [
404
+ "X_Series = df['weight']\n",
405
+ "type(X_Series)\n",
406
+ "np.array(X_Series).shape # it is one 1D array"
407
+ ]
408
+ },
409
+ {
410
+ "cell_type": "code",
411
+ "execution_count": 11,
412
+ "metadata": {},
413
+ "outputs": [],
414
+ "source": [
415
+ "y = df['Height'] # this variable can be in series or array"
416
+ ]
417
+ },
418
+ {
419
+ "cell_type": "code",
420
+ "execution_count": 12,
421
+ "metadata": {},
422
+ "outputs": [
423
+ {
424
+ "data": {
425
+ "text/plain": [
426
+ "(16,)"
427
+ ]
428
+ },
429
+ "execution_count": 12,
430
+ "metadata": {},
431
+ "output_type": "execute_result"
432
+ }
433
+ ],
434
+ "source": [
435
+ "np.array(y).shape"
436
+ ]
437
+ },
438
+ {
439
+ "cell_type": "code",
440
+ "execution_count": 13,
441
+ "metadata": {},
442
+ "outputs": [],
443
+ "source": [
444
+ "# Train Test Split\n",
445
+ "from sklearn.model_selection import train_test_split\n",
446
+ "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.25,random_state=42)"
447
+ ]
448
+ },
449
+ {
450
+ "cell_type": "code",
451
+ "execution_count": 14,
452
+ "metadata": {},
453
+ "outputs": [
454
+ {
455
+ "data": {
456
+ "text/plain": [
457
+ "(12, 1)"
458
+ ]
459
+ },
460
+ "execution_count": 14,
461
+ "metadata": {},
462
+ "output_type": "execute_result"
463
+ }
464
+ ],
465
+ "source": [
466
+ "X_train.shape"
467
+ ]
468
+ },
469
+ {
470
+ "cell_type": "code",
471
+ "execution_count": 15,
472
+ "metadata": {},
473
+ "outputs": [],
474
+ "source": [
475
+ "# Standardization\n",
476
+ "from sklearn.preprocessing import StandardScaler\n",
477
+ "scaler = StandardScaler()\n",
478
+ "X_train = scaler.fit_transform(X_train)"
479
+ ]
480
+ },
481
+ {
482
+ "cell_type": "code",
483
+ "execution_count": 16,
484
+ "metadata": {},
485
+ "outputs": [],
486
+ "source": [
487
+ "X_test = scaler.transform(X_test) # I don't use fit_tranform because of data leakage. In the above shell i used fit_tranform, if i use the same command here data leakage may happen."
488
+ ]
489
+ },
490
+ {
491
+ "cell_type": "code",
492
+ "execution_count": 17,
493
+ "metadata": {},
494
+ "outputs": [
495
+ {
496
+ "data": {
497
+ "text/plain": [
498
+ "array([[-2.23200719],\n",
499
+ " [-1.8253074 ],\n",
500
+ " [ 0.09199162],\n",
501
+ " [ 0.44059144]])"
502
+ ]
503
+ },
504
+ "execution_count": 17,
505
+ "metadata": {},
506
+ "output_type": "execute_result"
507
+ }
508
+ ],
509
+ "source": [
510
+ "X_test"
511
+ ]
512
+ },
513
+ {
514
+ "cell_type": "code",
515
+ "execution_count": 18,
516
+ "metadata": {},
517
+ "outputs": [
518
+ {
519
+ "data": {
520
+ "text/html": [
521
+ "<style>#sk-container-id-1 {\n",
522
+ " /* Definition of color scheme common for light and dark mode */\n",
523
+ " --sklearn-color-text: black;\n",
524
+ " --sklearn-color-line: gray;\n",
525
+ " /* Definition of color scheme for unfitted estimators */\n",
526
+ " --sklearn-color-unfitted-level-0: #fff5e6;\n",
527
+ " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
528
+ " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
529
+ " --sklearn-color-unfitted-level-3: chocolate;\n",
530
+ " /* Definition of color scheme for fitted estimators */\n",
531
+ " --sklearn-color-fitted-level-0: #f0f8ff;\n",
532
+ " --sklearn-color-fitted-level-1: #d4ebff;\n",
533
+ " --sklearn-color-fitted-level-2: #b3dbfd;\n",
534
+ " --sklearn-color-fitted-level-3: cornflowerblue;\n",
535
+ "\n",
536
+ " /* Specific color for light theme */\n",
537
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
538
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
539
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
540
+ " --sklearn-color-icon: #696969;\n",
541
+ "\n",
542
+ " @media (prefers-color-scheme: dark) {\n",
543
+ " /* Redefinition of color scheme for dark theme */\n",
544
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
545
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
546
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
547
+ " --sklearn-color-icon: #878787;\n",
548
+ " }\n",
549
+ "}\n",
550
+ "\n",
551
+ "#sk-container-id-1 {\n",
552
+ " color: var(--sklearn-color-text);\n",
553
+ "}\n",
554
+ "\n",
555
+ "#sk-container-id-1 pre {\n",
556
+ " padding: 0;\n",
557
+ "}\n",
558
+ "\n",
559
+ "#sk-container-id-1 input.sk-hidden--visually {\n",
560
+ " border: 0;\n",
561
+ " clip: rect(1px 1px 1px 1px);\n",
562
+ " clip: rect(1px, 1px, 1px, 1px);\n",
563
+ " height: 1px;\n",
564
+ " margin: -1px;\n",
565
+ " overflow: hidden;\n",
566
+ " padding: 0;\n",
567
+ " position: absolute;\n",
568
+ " width: 1px;\n",
569
+ "}\n",
570
+ "\n",
571
+ "#sk-container-id-1 div.sk-dashed-wrapped {\n",
572
+ " border: 1px dashed var(--sklearn-color-line);\n",
573
+ " margin: 0 0.4em 0.5em 0.4em;\n",
574
+ " box-sizing: border-box;\n",
575
+ " padding-bottom: 0.4em;\n",
576
+ " background-color: var(--sklearn-color-background);\n",
577
+ "}\n",
578
+ "\n",
579
+ "#sk-container-id-1 div.sk-container {\n",
580
+ " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
581
+ " but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
582
+ " so we also need the `!important` here to be able to override the\n",
583
+ " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
584
+ " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
585
+ " display: inline-block !important;\n",
586
+ " position: relative;\n",
587
+ "}\n",
588
+ "\n",
589
+ "#sk-container-id-1 div.sk-text-repr-fallback {\n",
590
+ " display: none;\n",
591
+ "}\n",
592
+ "\n",
593
+ "div.sk-parallel-item,\n",
594
+ "div.sk-serial,\n",
595
+ "div.sk-item {\n",
596
+ " /* draw centered vertical line to link estimators */\n",
597
+ " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
598
+ " background-size: 2px 100%;\n",
599
+ " background-repeat: no-repeat;\n",
600
+ " background-position: center center;\n",
601
+ "}\n",
602
+ "\n",
603
+ "/* Parallel-specific style estimator block */\n",
604
+ "\n",
605
+ "#sk-container-id-1 div.sk-parallel-item::after {\n",
606
+ " content: \"\";\n",
607
+ " width: 100%;\n",
608
+ " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
609
+ " flex-grow: 1;\n",
610
+ "}\n",
611
+ "\n",
612
+ "#sk-container-id-1 div.sk-parallel {\n",
613
+ " display: flex;\n",
614
+ " align-items: stretch;\n",
615
+ " justify-content: center;\n",
616
+ " background-color: var(--sklearn-color-background);\n",
617
+ " position: relative;\n",
618
+ "}\n",
619
+ "\n",
620
+ "#sk-container-id-1 div.sk-parallel-item {\n",
621
+ " display: flex;\n",
622
+ " flex-direction: column;\n",
623
+ "}\n",
624
+ "\n",
625
+ "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
626
+ " align-self: flex-end;\n",
627
+ " width: 50%;\n",
628
+ "}\n",
629
+ "\n",
630
+ "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
631
+ " align-self: flex-start;\n",
632
+ " width: 50%;\n",
633
+ "}\n",
634
+ "\n",
635
+ "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
636
+ " width: 0;\n",
637
+ "}\n",
638
+ "\n",
639
+ "/* Serial-specific style estimator block */\n",
640
+ "\n",
641
+ "#sk-container-id-1 div.sk-serial {\n",
642
+ " display: flex;\n",
643
+ " flex-direction: column;\n",
644
+ " align-items: center;\n",
645
+ " background-color: var(--sklearn-color-background);\n",
646
+ " padding-right: 1em;\n",
647
+ " padding-left: 1em;\n",
648
+ "}\n",
649
+ "\n",
650
+ "\n",
651
+ "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
652
+ "clickable and can be expanded/collapsed.\n",
653
+ "- Pipeline and ColumnTransformer use this feature and define the default style\n",
654
+ "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
655
+ "*/\n",
656
+ "\n",
657
+ "/* Pipeline and ColumnTransformer style (default) */\n",
658
+ "\n",
659
+ "#sk-container-id-1 div.sk-toggleable {\n",
660
+ " /* Default theme specific background. It is overwritten whether we have a\n",
661
+ " specific estimator or a Pipeline/ColumnTransformer */\n",
662
+ " background-color: var(--sklearn-color-background);\n",
663
+ "}\n",
664
+ "\n",
665
+ "/* Toggleable label */\n",
666
+ "#sk-container-id-1 label.sk-toggleable__label {\n",
667
+ " cursor: pointer;\n",
668
+ " display: block;\n",
669
+ " width: 100%;\n",
670
+ " margin-bottom: 0;\n",
671
+ " padding: 0.5em;\n",
672
+ " box-sizing: border-box;\n",
673
+ " text-align: center;\n",
674
+ "}\n",
675
+ "\n",
676
+ "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
677
+ " /* Arrow on the left of the label */\n",
678
+ " content: \"▸\";\n",
679
+ " float: left;\n",
680
+ " margin-right: 0.25em;\n",
681
+ " color: var(--sklearn-color-icon);\n",
682
+ "}\n",
683
+ "\n",
684
+ "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
685
+ " color: var(--sklearn-color-text);\n",
686
+ "}\n",
687
+ "\n",
688
+ "/* Toggleable content - dropdown */\n",
689
+ "\n",
690
+ "#sk-container-id-1 div.sk-toggleable__content {\n",
691
+ " max-height: 0;\n",
692
+ " max-width: 0;\n",
693
+ " overflow: hidden;\n",
694
+ " text-align: left;\n",
695
+ " /* unfitted */\n",
696
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
697
+ "}\n",
698
+ "\n",
699
+ "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
700
+ " /* fitted */\n",
701
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
702
+ "}\n",
703
+ "\n",
704
+ "#sk-container-id-1 div.sk-toggleable__content pre {\n",
705
+ " margin: 0.2em;\n",
706
+ " border-radius: 0.25em;\n",
707
+ " color: var(--sklearn-color-text);\n",
708
+ " /* unfitted */\n",
709
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
710
+ "}\n",
711
+ "\n",
712
+ "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
713
+ " /* unfitted */\n",
714
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
715
+ "}\n",
716
+ "\n",
717
+ "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
718
+ " /* Expand drop-down */\n",
719
+ " max-height: 200px;\n",
720
+ " max-width: 100%;\n",
721
+ " overflow: auto;\n",
722
+ "}\n",
723
+ "\n",
724
+ "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
725
+ " content: \"▾\";\n",
726
+ "}\n",
727
+ "\n",
728
+ "/* Pipeline/ColumnTransformer-specific style */\n",
729
+ "\n",
730
+ "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
731
+ " color: var(--sklearn-color-text);\n",
732
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
733
+ "}\n",
734
+ "\n",
735
+ "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
736
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
737
+ "}\n",
738
+ "\n",
739
+ "/* Estimator-specific style */\n",
740
+ "\n",
741
+ "/* Colorize estimator box */\n",
742
+ "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
743
+ " /* unfitted */\n",
744
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
745
+ "}\n",
746
+ "\n",
747
+ "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
748
+ " /* fitted */\n",
749
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
750
+ "}\n",
751
+ "\n",
752
+ "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
753
+ "#sk-container-id-1 div.sk-label label {\n",
754
+ " /* The background is the default theme color */\n",
755
+ " color: var(--sklearn-color-text-on-default-background);\n",
756
+ "}\n",
757
+ "\n",
758
+ "/* On hover, darken the color of the background */\n",
759
+ "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
760
+ " color: var(--sklearn-color-text);\n",
761
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
762
+ "}\n",
763
+ "\n",
764
+ "/* Label box, darken color on hover, fitted */\n",
765
+ "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
766
+ " color: var(--sklearn-color-text);\n",
767
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
768
+ "}\n",
769
+ "\n",
770
+ "/* Estimator label */\n",
771
+ "\n",
772
+ "#sk-container-id-1 div.sk-label label {\n",
773
+ " font-family: monospace;\n",
774
+ " font-weight: bold;\n",
775
+ " display: inline-block;\n",
776
+ " line-height: 1.2em;\n",
777
+ "}\n",
778
+ "\n",
779
+ "#sk-container-id-1 div.sk-label-container {\n",
780
+ " text-align: center;\n",
781
+ "}\n",
782
+ "\n",
783
+ "/* Estimator-specific */\n",
784
+ "#sk-container-id-1 div.sk-estimator {\n",
785
+ " font-family: monospace;\n",
786
+ " border: 1px dotted var(--sklearn-color-border-box);\n",
787
+ " border-radius: 0.25em;\n",
788
+ " box-sizing: border-box;\n",
789
+ " margin-bottom: 0.5em;\n",
790
+ " /* unfitted */\n",
791
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
792
+ "}\n",
793
+ "\n",
794
+ "#sk-container-id-1 div.sk-estimator.fitted {\n",
795
+ " /* fitted */\n",
796
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
797
+ "}\n",
798
+ "\n",
799
+ "/* on hover */\n",
800
+ "#sk-container-id-1 div.sk-estimator:hover {\n",
801
+ " /* unfitted */\n",
802
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
803
+ "}\n",
804
+ "\n",
805
+ "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
806
+ " /* fitted */\n",
807
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
808
+ "}\n",
809
+ "\n",
810
+ "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
811
+ "\n",
812
+ "/* Common style for \"i\" and \"?\" */\n",
813
+ "\n",
814
+ ".sk-estimator-doc-link,\n",
815
+ "a:link.sk-estimator-doc-link,\n",
816
+ "a:visited.sk-estimator-doc-link {\n",
817
+ " float: right;\n",
818
+ " font-size: smaller;\n",
819
+ " line-height: 1em;\n",
820
+ " font-family: monospace;\n",
821
+ " background-color: var(--sklearn-color-background);\n",
822
+ " border-radius: 1em;\n",
823
+ " height: 1em;\n",
824
+ " width: 1em;\n",
825
+ " text-decoration: none !important;\n",
826
+ " margin-left: 1ex;\n",
827
+ " /* unfitted */\n",
828
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
829
+ " color: var(--sklearn-color-unfitted-level-1);\n",
830
+ "}\n",
831
+ "\n",
832
+ ".sk-estimator-doc-link.fitted,\n",
833
+ "a:link.sk-estimator-doc-link.fitted,\n",
834
+ "a:visited.sk-estimator-doc-link.fitted {\n",
835
+ " /* fitted */\n",
836
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
837
+ " color: var(--sklearn-color-fitted-level-1);\n",
838
+ "}\n",
839
+ "\n",
840
+ "/* On hover */\n",
841
+ "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
842
+ ".sk-estimator-doc-link:hover,\n",
843
+ "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
844
+ ".sk-estimator-doc-link:hover {\n",
845
+ " /* unfitted */\n",
846
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
847
+ " color: var(--sklearn-color-background);\n",
848
+ " text-decoration: none;\n",
849
+ "}\n",
850
+ "\n",
851
+ "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
852
+ ".sk-estimator-doc-link.fitted:hover,\n",
853
+ "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
854
+ ".sk-estimator-doc-link.fitted:hover {\n",
855
+ " /* fitted */\n",
856
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
857
+ " color: var(--sklearn-color-background);\n",
858
+ " text-decoration: none;\n",
859
+ "}\n",
860
+ "\n",
861
+ "/* Span, style for the box shown on hovering the info icon */\n",
862
+ ".sk-estimator-doc-link span {\n",
863
+ " display: none;\n",
864
+ " z-index: 9999;\n",
865
+ " position: relative;\n",
866
+ " font-weight: normal;\n",
867
+ " right: .2ex;\n",
868
+ " padding: .5ex;\n",
869
+ " margin: .5ex;\n",
870
+ " width: min-content;\n",
871
+ " min-width: 20ex;\n",
872
+ " max-width: 50ex;\n",
873
+ " color: var(--sklearn-color-text);\n",
874
+ " box-shadow: 2pt 2pt 4pt #999;\n",
875
+ " /* unfitted */\n",
876
+ " background: var(--sklearn-color-unfitted-level-0);\n",
877
+ " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
878
+ "}\n",
879
+ "\n",
880
+ ".sk-estimator-doc-link.fitted span {\n",
881
+ " /* fitted */\n",
882
+ " background: var(--sklearn-color-fitted-level-0);\n",
883
+ " border: var(--sklearn-color-fitted-level-3);\n",
884
+ "}\n",
885
+ "\n",
886
+ ".sk-estimator-doc-link:hover span {\n",
887
+ " display: block;\n",
888
+ "}\n",
889
+ "\n",
890
+ "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
891
+ "\n",
892
+ "#sk-container-id-1 a.estimator_doc_link {\n",
893
+ " float: right;\n",
894
+ " font-size: 1rem;\n",
895
+ " line-height: 1em;\n",
896
+ " font-family: monospace;\n",
897
+ " background-color: var(--sklearn-color-background);\n",
898
+ " border-radius: 1rem;\n",
899
+ " height: 1rem;\n",
900
+ " width: 1rem;\n",
901
+ " text-decoration: none;\n",
902
+ " /* unfitted */\n",
903
+ " color: var(--sklearn-color-unfitted-level-1);\n",
904
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
905
+ "}\n",
906
+ "\n",
907
+ "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
908
+ " /* fitted */\n",
909
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
910
+ " color: var(--sklearn-color-fitted-level-1);\n",
911
+ "}\n",
912
+ "\n",
913
+ "/* On hover */\n",
914
+ "#sk-container-id-1 a.estimator_doc_link:hover {\n",
915
+ " /* unfitted */\n",
916
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
917
+ " color: var(--sklearn-color-background);\n",
918
+ " text-decoration: none;\n",
919
+ "}\n",
920
+ "\n",
921
+ "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
922
+ " /* fitted */\n",
923
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
924
+ "}\n",
925
+ "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression(n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression(n_jobs=-1)</pre></div> </div></div></div></div>"
926
+ ],
927
+ "text/plain": [
928
+ "LinearRegression(n_jobs=-1)"
929
+ ]
930
+ },
931
+ "execution_count": 18,
932
+ "metadata": {},
933
+ "output_type": "execute_result"
934
+ }
935
+ ],
936
+ "source": [
937
+ "# Simple Linear Regression\n",
938
+ "from sklearn.linear_model import LinearRegression\n",
939
+ "regression = LinearRegression(n_jobs=-1) # n_jobs=-1 is use to utilise all the processors in your system\n",
940
+ "regression.fit(X_train,y_train)"
941
+ ]
942
+ },
943
+ {
944
+ "cell_type": "code",
945
+ "execution_count": 19,
946
+ "metadata": {},
947
+ "outputs": [
948
+ {
949
+ "name": "stdout",
950
+ "output_type": "stream",
951
+ "text": [
952
+ "Coefficient or slope: [12.89900036]\n",
953
+ "Intercept : 138.16666666666666\n"
954
+ ]
955
+ }
956
+ ],
957
+ "source": [
958
+ "print(\"Coefficient or slope: \",regression.coef_)\n",
959
+ "print(\"Intercept :\", regression.intercept_)"
960
+ ]
961
+ },
962
+ {
963
+ "cell_type": "code",
964
+ "execution_count": 20,
965
+ "metadata": {},
966
+ "outputs": [
967
+ {
968
+ "data": {
969
+ "image/png": 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",
970
+ "text/plain": [
971
+ "<Figure size 640x480 with 1 Axes>"
972
+ ]
973
+ },
974
+ "metadata": {},
975
+ "output_type": "display_data"
976
+ }
977
+ ],
978
+ "source": [
979
+ "# plot best fit line\n",
980
+ "plt.scatter(X_train,y_train)\n",
981
+ "plt.plot(X_train,regression.predict(X_train))\n",
982
+ "plt.savefig(\"Best_fit_line.png\")\n",
983
+ "plt.show()"
984
+ ]
985
+ },
986
+ {
987
+ "cell_type": "markdown",
988
+ "metadata": {},
989
+ "source": [
990
+ "##### Prediction of test data\n",
991
+ "prediction height output = intercept_ + coef_(Weight)"
992
+ ]
993
+ },
994
+ {
995
+ "cell_type": "code",
996
+ "execution_count": 21,
997
+ "metadata": {},
998
+ "outputs": [
999
+ {
1000
+ "data": {
1001
+ "text/plain": [
1002
+ "array([109.37600506, 114.62202583, 139.3532666 , 143.84985583])"
1003
+ ]
1004
+ },
1005
+ "execution_count": 21,
1006
+ "metadata": {},
1007
+ "output_type": "execute_result"
1008
+ }
1009
+ ],
1010
+ "source": [
1011
+ "# prediction for the test data\n",
1012
+ "y_predict = regression.predict(X_test)\n",
1013
+ "y_predict"
1014
+ ]
1015
+ },
1016
+ {
1017
+ "cell_type": "code",
1018
+ "execution_count": 22,
1019
+ "metadata": {},
1020
+ "outputs": [
1021
+ {
1022
+ "name": "stdout",
1023
+ "output_type": "stream",
1024
+ "text": [
1025
+ "9.123655031763516\n",
1026
+ "9.123655031763516\n",
1027
+ "9.229928345473649\n"
1028
+ ]
1029
+ }
1030
+ ],
1031
+ "source": [
1032
+ "# Performance Metrics\n",
1033
+ "from sklearn.metrics import mean_absolute_error, mean_squared_error, root_mean_squared_error, r2_score\n",
1034
+ "mse = mean_absolute_error(y_test,y_predict)\n",
1035
+ "mae = mean_absolute_error(y_test,y_predict)\n",
1036
+ "rmse = root_mean_squared_error(y_test,y_predict)\n",
1037
+ "print(mse)\n",
1038
+ "print(mae)\n",
1039
+ "print(rmse)"
1040
+ ]
1041
+ },
1042
+ {
1043
+ "cell_type": "code",
1044
+ "execution_count": 23,
1045
+ "metadata": {},
1046
+ "outputs": [
1047
+ {
1048
+ "data": {
1049
+ "text/plain": [
1050
+ "0.8083429082956627"
1051
+ ]
1052
+ },
1053
+ "execution_count": 23,
1054
+ "metadata": {},
1055
+ "output_type": "execute_result"
1056
+ }
1057
+ ],
1058
+ "source": [
1059
+ "score = r2_score(y_test,y_predict)\n",
1060
+ "score"
1061
+ ]
1062
+ },
1063
+ {
1064
+ "cell_type": "code",
1065
+ "execution_count": 26,
1066
+ "metadata": {},
1067
+ "outputs": [
1068
+ {
1069
+ "data": {
1070
+ "text/plain": [
1071
+ "array([142.35099276])"
1072
+ ]
1073
+ },
1074
+ "execution_count": 26,
1075
+ "metadata": {},
1076
+ "output_type": "execute_result"
1077
+ }
1078
+ ],
1079
+ "source": [
1080
+ "regression.predict(scaler.transform([[94]]))"
1081
+ ]
1082
+ },
1083
+ {
1084
+ "cell_type": "code",
1085
+ "execution_count": null,
1086
+ "metadata": {},
1087
+ "outputs": [],
1088
+ "source": []
1089
+ },
1090
+ {
1091
+ "cell_type": "code",
1092
+ "execution_count": null,
1093
+ "metadata": {},
1094
+ "outputs": [],
1095
+ "source": []
1096
+ },
1097
+ {
1098
+ "cell_type": "code",
1099
+ "execution_count": null,
1100
+ "metadata": {},
1101
+ "outputs": [],
1102
+ "source": []
1103
+ },
1104
+ {
1105
+ "cell_type": "code",
1106
+ "execution_count": null,
1107
+ "metadata": {},
1108
+ "outputs": [],
1109
+ "source": []
1110
+ }
1111
+ ],
1112
+ "metadata": {
1113
+ "kernelspec": {
1114
+ "display_name": "ml",
1115
+ "language": "python",
1116
+ "name": "python3"
1117
+ },
1118
+ "language_info": {
1119
+ "codemirror_mode": {
1120
+ "name": "ipython",
1121
+ "version": 3
1122
+ },
1123
+ "file_extension": ".py",
1124
+ "mimetype": "text/x-python",
1125
+ "name": "python",
1126
+ "nbconvert_exporter": "python",
1127
+ "pygments_lexer": "ipython3",
1128
+ "version": "3.12.2"
1129
+ }
1130
+ },
1131
+ "nbformat": 4,
1132
+ "nbformat_minor": 2
1133
+ }
Simple_Linear_regression_app.py ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import streamlit as st
2
+ import pandas as pd
3
+ from sklearn.preprocessing import StandardScaler
4
+ from sklearn.linear_model import LinearRegression
5
+ import numpy as np
6
+
7
+ # Load the dataset
8
+ df = pd.read_csv("hw.csv")
9
+
10
+ # Data preparation
11
+ X = df[['weight']]
12
+ y = df['Height']
13
+ from sklearn.model_selection import train_test_split
14
+ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
15
+
16
+ # Scaling
17
+ scaler = StandardScaler()
18
+ X_train = scaler.fit_transform(X_train)
19
+ X_test = scaler.transform(X_test)
20
+
21
+ # Model training
22
+ regression = LinearRegression(n_jobs=-1)
23
+ regression.fit(X_train, y_train)
24
+
25
+ # Streamlit application
26
+ st.title("Height Prediction App")
27
+ st.write("This app predicts the height based on the given weight using a Linear Regression model.")
28
+
29
+ # User input
30
+ user_weight = st.number_input("Enter weight (in kg):", min_value=0.0, max_value=300.0, step=0.1)
31
+
32
+ # Predict button
33
+ if st.button("Predict Height"):
34
+ # Transform user input and make a prediction
35
+ scaled_weight = scaler.transform([[user_weight]])
36
+ predicted_height = regression.predict(scaled_weight)
37
+ st.success(f"The predicted height for a weight of {user_weight} kg is {predicted_height[0]:.2f} cm.")
hw.csv ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ weight,Height
2
+ 50,100
3
+ 57,105
4
+ 62,110
5
+ 67,120
6
+ 75,130
7
+ 90,150
8
+ 97,135
9
+ 100,145
10
+ 105,155
11
+ 107,157
12
+ 103,132
13
+ 69,128
14
+ 73,133
15
+ 91,153
16
+ 96,137
17
+ 112,160