-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy path1b
549 lines (549 loc) · 130 KB
/
1b
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
{
"cells": [
{
"cell_type": "code",
"execution_count": 30,
"id": "d74a27bd",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.cluster import KMeans\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "e9d92813",
"metadata": {},
"outputs": [],
"source": [
"df=pd.read_csv(\"advertising.csv\", header=0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "79a3a17f",
"metadata": {},
"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>TV</th>\n",
" <th>Radio</th>\n",
" <th>Newspaper</th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>230.1</td>\n",
" <td>37.8</td>\n",
" <td>69.2</td>\n",
" <td>22.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>44.5</td>\n",
" <td>39.3</td>\n",
" <td>45.1</td>\n",
" <td>10.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>17.2</td>\n",
" <td>45.9</td>\n",
" <td>69.3</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>151.5</td>\n",
" <td>41.3</td>\n",
" <td>58.5</td>\n",
" <td>16.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>180.8</td>\n",
" <td>10.8</td>\n",
" <td>58.4</td>\n",
" <td>17.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TV Radio Newspaper Sales\n",
"0 230.1 37.8 69.2 22.1\n",
"1 44.5 39.3 45.1 10.4\n",
"2 17.2 45.9 69.3 12.0\n",
"3 151.5 41.3 58.5 16.5\n",
"4 180.8 10.8 58.4 17.9"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "aba51825",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['TV', 'Radio', 'Newspaper', 'Sales'], dtype='object')\n"
]
}
],
"source": [
"print(df.columns)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "0ba42679",
"metadata": {},
"outputs": [],
"source": [
"df.columns = df.columns.str.strip()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "0ed67b21",
"metadata": {},
"outputs": [],
"source": [
"features = ['TV', 'Radio', 'Newspaper']"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "46ae1a7b",
"metadata": {},
"outputs": [],
"source": [
"X = df[features]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "05be9d34",
"metadata": {},
"outputs": [],
"source": [
"scaler = StandardScaler()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "d2770737",
"metadata": {},
"outputs": [],
"source": [
"X_scaled = scaler.fit_transform(X)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "b966f1cb",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n"
]
}
],
"source": [
"inertia = []\n",
"K = range(1, 11)\n",
"for k in K:\n",
" kmeans = KMeans(n_clusters=k, random_state=42)\n",
" kmeans.fit(X_scaled)\n",
" inertia.append(kmeans.inertia_)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "13f072f4",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8,6))\n",
"plt.plot(K, inertia, 'bo-')\n",
"plt.xlabel('Number of clusters')\n",
"plt.ylabel('Inertia')\n",
"plt.title('Elbow method to determine optimal k')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "f7a6422c",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" super()._check_params_vs_input(X, default_n_init=10)\n",
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n"
]
}
],
"source": [
"optimal_k = 3\n",
"kmeans = KMeans(n_clusters=optimal_k, random_state=42)\n",
"df['Cluster'] = kmeans.fit_predict(X_scaled)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "4dbab124",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" TV Radio Newspaper Sales\n",
"Cluster \n",
"0 220.083077 18.413846 18.687692 18.469231\n",
"1 69.537333 15.933333 21.060000 10.045333\n",
"2 164.796667 37.681667 55.276667 17.870000\n"
]
}
],
"source": [
"print(df.groupby('Cluster').mean())"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "609c9274",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X_scaled[:, 0], X_scaled[:, 1], c=df['Cluster'], cmap='viridis')\n",
"plt.xlabel('Feature 1')\n",
"plt.ylabel('Feature 2')\n",
"plt.title('Audience Segmentation')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "5a25e622",
"metadata": {},
"outputs": [],
"source": [
"X = df[['TV', 'Radio', 'Newspaper']]\n",
"y = df['Sales']"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "2e254d9e",
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "80e591c2",
"metadata": {},
"outputs": [],
"source": [
"model = LinearRegression()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "1e97693a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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 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 sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
],
"text/plain": [
"LinearRegression()"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "347036da",
"metadata": {},
"outputs": [],
"source": [
"y_pred = model.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "4598b142",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean Squared Error: 2.9077569102710923\n"
]
}
],
"source": [
"mse = mean_squared_error(y_test, y_pred)\n",
"print(f\"Mean Squared Error: {mse}\")"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "857be42b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Actual Sales Predicted Sales\n",
"95 16.9 17.034772\n",
"15 22.4 20.409740\n",
"30 21.4 23.723989\n",
"158 7.3 9.272785\n",
"128 24.7 21.682719\n",
"115 12.6 12.569402\n",
"69 22.3 21.081195\n",
"170 8.4 8.690350\n",
"174 16.5 17.237013\n",
"45 16.1 16.666575\n",
"66 11.0 8.923965\n",
"182 8.7 8.481734\n",
"165 16.9 18.207512\n",
"78 5.3 8.067507\n",
"186 10.3 12.645510\n",
"177 16.7 14.931628\n",
"56 5.5 8.128146\n",
"152 16.6 17.898766\n",
"82 11.3 11.008806\n",
"68 18.9 20.478328\n",
"124 19.7 20.806318\n",
"16 12.5 12.598833\n",
"148 10.9 10.905183\n",
"93 22.2 22.388548\n",
"65 11.3 9.417961\n",
"60 8.1 7.925067\n",
"84 21.7 20.839085\n",
"67 13.4 13.815209\n",
"125 10.6 10.770809\n",
"132 5.7 7.926825\n",
"9 15.6 15.959474\n",
"18 11.3 10.634909\n",
"55 23.7 20.802920\n",
"75 8.7 10.434342\n",
"150 16.1 21.578475\n",
"104 20.7 21.183645\n",
"135 11.6 12.128218\n",
"137 20.8 22.809533\n",
"164 11.9 12.609928\n",
"76 6.9 6.464413\n"
]
}
],
"source": [
"results = pd.DataFrame({'Actual Sales': y_test, 'Predicted Sales': y_pred})\n",
"print(results)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "8ff2f8a2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Sales for new data: 15.544250492282014\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\bhoom\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
" warnings.warn(\n"
]
}
],
"source": [
"new_data = [[150, 25, 30]]\n",
"predicted_sales = model.predict(new_data)\n",
"print(f\"Predicted Sales for new data: {predicted_sales[0]}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "564ef085",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}