Hugging Face's logo

Spaces:
therayz1
/
CarPricePredict
Sleeping

App Files Community
therayz1 commited on
Commit
e452f85
·
verified ·
1 Parent(s): 075a0b7

Upload 4 files

Browse files
Files changed (4) hide show
  1. CarPredict.ipynb +1352 -0
  2. app.py +196 -0
  3. cars.xls +0 -0
  4. requirements.txt.txt +4 -0
CarPredict.ipynb ADDED
@@ -0,0 +1,1352 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "markdown",
5
+ "id": "f191f617-72b5-4aa3-a4a1-08bc01ad0681",
6
+ "metadata": {},
7
+ "source": [
8
+ "## Car Predict ##\n",
9
+ "* second hand vehicle prices according to features "
10
+ ]
11
+ },
12
+ {
13
+ "cell_type": "code",
14
+ "execution_count": 11,
15
+ "id": "e9503d2a-396d-45e3-b59f-45a446b5bbc3",
16
+ "metadata": {},
17
+ "outputs": [],
18
+ "source": [
19
+ "import pandas as pd\n",
20
+ "from sklearn.model_selection import train_test_split\n",
21
+ "from sklearn.linear_model import LinearRegression\n",
22
+ "from sklearn.metrics import r2_score, mean_squared_error\n",
23
+ "from sklearn.compose import ColumnTransformer # Sütun Dönüşüm İşlemleri\n",
24
+ "from sklearn.preprocessing import OneHotEncoder, StandardScaler # kategori - sayısaş dönüşüm ve ölçeklendirme\n",
25
+ "from sklearn.pipeline import Pipeline # veri işleme hattı"
26
+ ]
27
+ },
28
+ {
29
+ "cell_type": "code",
30
+ "execution_count": 17,
31
+ "id": "e76a64dd-33b8-40a6-b9f0-3a0a58b5467a",
32
+ "metadata": {},
33
+ "outputs": [
34
+ {
35
+ "name": "stdout",
36
+ "output_type": "stream",
37
+ "text": [
38
+ "Requirement already satisfied: xlrd in c:\\users\\erayc\\anaconda3\\lib\\site-packages (2.0.1)\n"
39
+ ]
40
+ }
41
+ ],
42
+ "source": [
43
+ "!pip install xlrd"
44
+ ]
45
+ },
46
+ {
47
+ "cell_type": "code",
48
+ "execution_count": 19,
49
+ "id": "b5b062be-ff68-4ed3-810f-d4c9e92b3653",
50
+ "metadata": {
51
+ "scrolled": true
52
+ },
53
+ "outputs": [
54
+ {
55
+ "data": {
56
+ "text/html": [
57
+ "<div>\n",
58
+ "<style scoped>\n",
59
+ " .dataframe tbody tr th:only-of-type {\n",
60
+ " vertical-align: middle;\n",
61
+ " }\n",
62
+ "\n",
63
+ " .dataframe tbody tr th {\n",
64
+ " vertical-align: top;\n",
65
+ " }\n",
66
+ "\n",
67
+ " .dataframe thead th {\n",
68
+ " text-align: right;\n",
69
+ " }\n",
70
+ "</style>\n",
71
+ "<table border=\"1\" class=\"dataframe\">\n",
72
+ " <thead>\n",
73
+ " <tr style=\"text-align: right;\">\n",
74
+ " <th></th>\n",
75
+ " <th>Price</th>\n",
76
+ " <th>Mileage</th>\n",
77
+ " <th>Make</th>\n",
78
+ " <th>Model</th>\n",
79
+ " <th>Trim</th>\n",
80
+ " <th>Type</th>\n",
81
+ " <th>Cylinder</th>\n",
82
+ " <th>Liter</th>\n",
83
+ " <th>Doors</th>\n",
84
+ " <th>Cruise</th>\n",
85
+ " <th>Sound</th>\n",
86
+ " <th>Leather</th>\n",
87
+ " </tr>\n",
88
+ " </thead>\n",
89
+ " <tbody>\n",
90
+ " <tr>\n",
91
+ " <th>0</th>\n",
92
+ " <td>17314.103129</td>\n",
93
+ " <td>8221</td>\n",
94
+ " <td>Buick</td>\n",
95
+ " <td>Century</td>\n",
96
+ " <td>Sedan 4D</td>\n",
97
+ " <td>Sedan</td>\n",
98
+ " <td>6</td>\n",
99
+ " <td>3.1</td>\n",
100
+ " <td>4</td>\n",
101
+ " <td>1</td>\n",
102
+ " <td>1</td>\n",
103
+ " <td>1</td>\n",
104
+ " </tr>\n",
105
+ " <tr>\n",
106
+ " <th>1</th>\n",
107
+ " <td>17542.036083</td>\n",
108
+ " <td>9135</td>\n",
109
+ " <td>Buick</td>\n",
110
+ " <td>Century</td>\n",
111
+ " <td>Sedan 4D</td>\n",
112
+ " <td>Sedan</td>\n",
113
+ " <td>6</td>\n",
114
+ " <td>3.1</td>\n",
115
+ " <td>4</td>\n",
116
+ " <td>1</td>\n",
117
+ " <td>1</td>\n",
118
+ " <td>0</td>\n",
119
+ " </tr>\n",
120
+ " <tr>\n",
121
+ " <th>2</th>\n",
122
+ " <td>16218.847862</td>\n",
123
+ " <td>13196</td>\n",
124
+ " <td>Buick</td>\n",
125
+ " <td>Century</td>\n",
126
+ " <td>Sedan 4D</td>\n",
127
+ " <td>Sedan</td>\n",
128
+ " <td>6</td>\n",
129
+ " <td>3.1</td>\n",
130
+ " <td>4</td>\n",
131
+ " <td>1</td>\n",
132
+ " <td>1</td>\n",
133
+ " <td>0</td>\n",
134
+ " </tr>\n",
135
+ " <tr>\n",
136
+ " <th>3</th>\n",
137
+ " <td>16336.913140</td>\n",
138
+ " <td>16342</td>\n",
139
+ " <td>Buick</td>\n",
140
+ " <td>Century</td>\n",
141
+ " <td>Sedan 4D</td>\n",
142
+ " <td>Sedan</td>\n",
143
+ " <td>6</td>\n",
144
+ " <td>3.1</td>\n",
145
+ " <td>4</td>\n",
146
+ " <td>1</td>\n",
147
+ " <td>0</td>\n",
148
+ " <td>0</td>\n",
149
+ " </tr>\n",
150
+ " <tr>\n",
151
+ " <th>4</th>\n",
152
+ " <td>16339.170324</td>\n",
153
+ " <td>19832</td>\n",
154
+ " <td>Buick</td>\n",
155
+ " <td>Century</td>\n",
156
+ " <td>Sedan 4D</td>\n",
157
+ " <td>Sedan</td>\n",
158
+ " <td>6</td>\n",
159
+ " <td>3.1</td>\n",
160
+ " <td>4</td>\n",
161
+ " <td>1</td>\n",
162
+ " <td>0</td>\n",
163
+ " <td>1</td>\n",
164
+ " </tr>\n",
165
+ " <tr>\n",
166
+ " <th>...</th>\n",
167
+ " <td>...</td>\n",
168
+ " <td>...</td>\n",
169
+ " <td>...</td>\n",
170
+ " <td>...</td>\n",
171
+ " <td>...</td>\n",
172
+ " <td>...</td>\n",
173
+ " <td>...</td>\n",
174
+ " <td>...</td>\n",
175
+ " <td>...</td>\n",
176
+ " <td>...</td>\n",
177
+ " <td>...</td>\n",
178
+ " <td>...</td>\n",
179
+ " </tr>\n",
180
+ " <tr>\n",
181
+ " <th>799</th>\n",
182
+ " <td>16507.070267</td>\n",
183
+ " <td>16229</td>\n",
184
+ " <td>Saturn</td>\n",
185
+ " <td>L Series</td>\n",
186
+ " <td>L300 Sedan 4D</td>\n",
187
+ " <td>Sedan</td>\n",
188
+ " <td>6</td>\n",
189
+ " <td>3.0</td>\n",
190
+ " <td>4</td>\n",
191
+ " <td>1</td>\n",
192
+ " <td>0</td>\n",
193
+ " <td>0</td>\n",
194
+ " </tr>\n",
195
+ " <tr>\n",
196
+ " <th>800</th>\n",
197
+ " <td>16175.957604</td>\n",
198
+ " <td>19095</td>\n",
199
+ " <td>Saturn</td>\n",
200
+ " <td>L Series</td>\n",
201
+ " <td>L300 Sedan 4D</td>\n",
202
+ " <td>Sedan</td>\n",
203
+ " <td>6</td>\n",
204
+ " <td>3.0</td>\n",
205
+ " <td>4</td>\n",
206
+ " <td>1</td>\n",
207
+ " <td>1</td>\n",
208
+ " <td>0</td>\n",
209
+ " </tr>\n",
210
+ " <tr>\n",
211
+ " <th>801</th>\n",
212
+ " <td>15731.132897</td>\n",
213
+ " <td>20484</td>\n",
214
+ " <td>Saturn</td>\n",
215
+ " <td>L Series</td>\n",
216
+ " <td>L300 Sedan 4D</td>\n",
217
+ " <td>Sedan</td>\n",
218
+ " <td>6</td>\n",
219
+ " <td>3.0</td>\n",
220
+ " <td>4</td>\n",
221
+ " <td>1</td>\n",
222
+ " <td>1</td>\n",
223
+ " <td>0</td>\n",
224
+ " </tr>\n",
225
+ " <tr>\n",
226
+ " <th>802</th>\n",
227
+ " <td>15118.893228</td>\n",
228
+ " <td>25979</td>\n",
229
+ " <td>Saturn</td>\n",
230
+ " <td>L Series</td>\n",
231
+ " <td>L300 Sedan 4D</td>\n",
232
+ " <td>Sedan</td>\n",
233
+ " <td>6</td>\n",
234
+ " <td>3.0</td>\n",
235
+ " <td>4</td>\n",
236
+ " <td>1</td>\n",
237
+ " <td>1</td>\n",
238
+ " <td>0</td>\n",
239
+ " </tr>\n",
240
+ " <tr>\n",
241
+ " <th>803</th>\n",
242
+ " <td>13585.636802</td>\n",
243
+ " <td>35662</td>\n",
244
+ " <td>Saturn</td>\n",
245
+ " <td>L Series</td>\n",
246
+ " <td>L300 Sedan 4D</td>\n",
247
+ " <td>Sedan</td>\n",
248
+ " <td>6</td>\n",
249
+ " <td>3.0</td>\n",
250
+ " <td>4</td>\n",
251
+ " <td>1</td>\n",
252
+ " <td>0</td>\n",
253
+ " <td>0</td>\n",
254
+ " </tr>\n",
255
+ " </tbody>\n",
256
+ "</table>\n",
257
+ "<p>804 rows × 12 columns</p>\n",
258
+ "</div>"
259
+ ],
260
+ "text/plain": [
261
+ " Price Mileage Make Model Trim Type Cylinder \\\n",
262
+ "0 17314.103129 8221 Buick Century Sedan 4D Sedan 6 \n",
263
+ "1 17542.036083 9135 Buick Century Sedan 4D Sedan 6 \n",
264
+ "2 16218.847862 13196 Buick Century Sedan 4D Sedan 6 \n",
265
+ "3 16336.913140 16342 Buick Century Sedan 4D Sedan 6 \n",
266
+ "4 16339.170324 19832 Buick Century Sedan 4D Sedan 6 \n",
267
+ ".. ... ... ... ... ... ... ... \n",
268
+ "799 16507.070267 16229 Saturn L Series L300 Sedan 4D Sedan 6 \n",
269
+ "800 16175.957604 19095 Saturn L Series L300 Sedan 4D Sedan 6 \n",
270
+ "801 15731.132897 20484 Saturn L Series L300 Sedan 4D Sedan 6 \n",
271
+ "802 15118.893228 25979 Saturn L Series L300 Sedan 4D Sedan 6 \n",
272
+ "803 13585.636802 35662 Saturn L Series L300 Sedan 4D Sedan 6 \n",
273
+ "\n",
274
+ " Liter Doors Cruise Sound Leather \n",
275
+ "0 3.1 4 1 1 1 \n",
276
+ "1 3.1 4 1 1 0 \n",
277
+ "2 3.1 4 1 1 0 \n",
278
+ "3 3.1 4 1 0 0 \n",
279
+ "4 3.1 4 1 0 1 \n",
280
+ ".. ... ... ... ... ... \n",
281
+ "799 3.0 4 1 0 0 \n",
282
+ "800 3.0 4 1 1 0 \n",
283
+ "801 3.0 4 1 1 0 \n",
284
+ "802 3.0 4 1 1 0 \n",
285
+ "803 3.0 4 1 0 0 \n",
286
+ "\n",
287
+ "[804 rows x 12 columns]"
288
+ ]
289
+ },
290
+ "execution_count": 19,
291
+ "metadata": {},
292
+ "output_type": "execute_result"
293
+ }
294
+ ],
295
+ "source": [
296
+ "df = pd.read_excel('cars.xls')\n",
297
+ "df"
298
+ ]
299
+ },
300
+ {
301
+ "cell_type": "code",
302
+ "execution_count": 21,
303
+ "id": "1e110d3e-edf0-4c7b-a6b6-ac8f5930050d",
304
+ "metadata": {},
305
+ "outputs": [],
306
+ "source": [
307
+ "# Data preprocessing"
308
+ ]
309
+ },
310
+ {
311
+ "cell_type": "code",
312
+ "execution_count": 23,
313
+ "id": "edb61d8e-5c1c-4d87-b222-061e8010202d",
314
+ "metadata": {},
315
+ "outputs": [],
316
+ "source": [
317
+ "X = df.drop('Price', axis=1) # fiyata etki edenleri al\n",
318
+ "y = df['Price'] # tahmin"
319
+ ]
320
+ },
321
+ {
322
+ "cell_type": "code",
323
+ "execution_count": 25,
324
+ "id": "51d6ae52-8e4d-4501-9bc6-5647187429bd",
325
+ "metadata": {},
326
+ "outputs": [],
327
+ "source": [
328
+ "X_train, X_test , y_train, y_test = train_test_split(X,y, test_size = 0.2 , random_state = 42)"
329
+ ]
330
+ },
331
+ {
332
+ "cell_type": "markdown",
333
+ "id": "8c8a6f88-e37c-490a-945d-8c2140ce3f2d",
334
+ "metadata": {},
335
+ "source": [
336
+ "# data preprocessing, standardization and with one hot encoding process automating"
337
+ ]
338
+ },
339
+ {
340
+ "cell_type": "code",
341
+ "execution_count": 30,
342
+ "id": "713b7032-0ce3-4ea7-9a21-dbe39e0794c3",
343
+ "metadata": {},
344
+ "outputs": [],
345
+ "source": [
346
+ "preprocess = ColumnTransformer(\n",
347
+ " transformers=[\n",
348
+ " ('num', StandardScaler(),['Mileage','Cylinder','Liter','Doors']),\n",
349
+ " ('cat', OneHotEncoder(),['Make','Model','Trim','Type'])\n",
350
+ " ]\n",
351
+ ")\n",
352
+ " "
353
+ ]
354
+ },
355
+ {
356
+ "cell_type": "code",
357
+ "execution_count": 34,
358
+ "id": "0bb76bfd-fed4-4beb-96ef-56e4a2e3092b",
359
+ "metadata": {},
360
+ "outputs": [],
361
+ "source": [
362
+ "#modeli tnaımladık\n",
363
+ "my_model = LinearRegression()"
364
+ ]
365
+ },
366
+ {
367
+ "cell_type": "code",
368
+ "execution_count": 36,
369
+ "id": "746038a4-71bb-46bb-870d-18061112c21b",
370
+ "metadata": {},
371
+ "outputs": [],
372
+ "source": [
373
+ "pipe = Pipeline(steps=[('preprocessor',preprocess),('model',my_model)])"
374
+ ]
375
+ },
376
+ {
377
+ "cell_type": "code",
378
+ "execution_count": 38,
379
+ "id": "4f60e30f-5955-4207-9544-a97be0246621",
380
+ "metadata": {},
381
+ "outputs": [
382
+ {
383
+ "data": {
384
+ "text/html": [
385
+ "<style>#sk-container-id-1 {\n",
386
+ " /* Definition of color scheme common for light and dark mode */\n",
387
+ " --sklearn-color-text: black;\n",
388
+ " --sklearn-color-line: gray;\n",
389
+ " /* Definition of color scheme for unfitted estimators */\n",
390
+ " --sklearn-color-unfitted-level-0: #fff5e6;\n",
391
+ " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
392
+ " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
393
+ " --sklearn-color-unfitted-level-3: chocolate;\n",
394
+ " /* Definition of color scheme for fitted estimators */\n",
395
+ " --sklearn-color-fitted-level-0: #f0f8ff;\n",
396
+ " --sklearn-color-fitted-level-1: #d4ebff;\n",
397
+ " --sklearn-color-fitted-level-2: #b3dbfd;\n",
398
+ " --sklearn-color-fitted-level-3: cornflowerblue;\n",
399
+ "\n",
400
+ " /* Specific color for light theme */\n",
401
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
402
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
403
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
404
+ " --sklearn-color-icon: #696969;\n",
405
+ "\n",
406
+ " @media (prefers-color-scheme: dark) {\n",
407
+ " /* Redefinition of color scheme for dark theme */\n",
408
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
409
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
410
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
411
+ " --sklearn-color-icon: #878787;\n",
412
+ " }\n",
413
+ "}\n",
414
+ "\n",
415
+ "#sk-container-id-1 {\n",
416
+ " color: var(--sklearn-color-text);\n",
417
+ "}\n",
418
+ "\n",
419
+ "#sk-container-id-1 pre {\n",
420
+ " padding: 0;\n",
421
+ "}\n",
422
+ "\n",
423
+ "#sk-container-id-1 input.sk-hidden--visually {\n",
424
+ " border: 0;\n",
425
+ " clip: rect(1px 1px 1px 1px);\n",
426
+ " clip: rect(1px, 1px, 1px, 1px);\n",
427
+ " height: 1px;\n",
428
+ " margin: -1px;\n",
429
+ " overflow: hidden;\n",
430
+ " padding: 0;\n",
431
+ " position: absolute;\n",
432
+ " width: 1px;\n",
433
+ "}\n",
434
+ "\n",
435
+ "#sk-container-id-1 div.sk-dashed-wrapped {\n",
436
+ " border: 1px dashed var(--sklearn-color-line);\n",
437
+ " margin: 0 0.4em 0.5em 0.4em;\n",
438
+ " box-sizing: border-box;\n",
439
+ " padding-bottom: 0.4em;\n",
440
+ " background-color: var(--sklearn-color-background);\n",
441
+ "}\n",
442
+ "\n",
443
+ "#sk-container-id-1 div.sk-container {\n",
444
+ " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
445
+ " but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
446
+ " so we also need the `!important` here to be able to override the\n",
447
+ " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
448
+ " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
449
+ " display: inline-block !important;\n",
450
+ " position: relative;\n",
451
+ "}\n",
452
+ "\n",
453
+ "#sk-container-id-1 div.sk-text-repr-fallback {\n",
454
+ " display: none;\n",
455
+ "}\n",
456
+ "\n",
457
+ "div.sk-parallel-item,\n",
458
+ "div.sk-serial,\n",
459
+ "div.sk-item {\n",
460
+ " /* draw centered vertical line to link estimators */\n",
461
+ " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
462
+ " background-size: 2px 100%;\n",
463
+ " background-repeat: no-repeat;\n",
464
+ " background-position: center center;\n",
465
+ "}\n",
466
+ "\n",
467
+ "/* Parallel-specific style estimator block */\n",
468
+ "\n",
469
+ "#sk-container-id-1 div.sk-parallel-item::after {\n",
470
+ " content: \"\";\n",
471
+ " width: 100%;\n",
472
+ " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
473
+ " flex-grow: 1;\n",
474
+ "}\n",
475
+ "\n",
476
+ "#sk-container-id-1 div.sk-parallel {\n",
477
+ " display: flex;\n",
478
+ " align-items: stretch;\n",
479
+ " justify-content: center;\n",
480
+ " background-color: var(--sklearn-color-background);\n",
481
+ " position: relative;\n",
482
+ "}\n",
483
+ "\n",
484
+ "#sk-container-id-1 div.sk-parallel-item {\n",
485
+ " display: flex;\n",
486
+ " flex-direction: column;\n",
487
+ "}\n",
488
+ "\n",
489
+ "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
490
+ " align-self: flex-end;\n",
491
+ " width: 50%;\n",
492
+ "}\n",
493
+ "\n",
494
+ "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
495
+ " align-self: flex-start;\n",
496
+ " width: 50%;\n",
497
+ "}\n",
498
+ "\n",
499
+ "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
500
+ " width: 0;\n",
501
+ "}\n",
502
+ "\n",
503
+ "/* Serial-specific style estimator block */\n",
504
+ "\n",
505
+ "#sk-container-id-1 div.sk-serial {\n",
506
+ " display: flex;\n",
507
+ " flex-direction: column;\n",
508
+ " align-items: center;\n",
509
+ " background-color: var(--sklearn-color-background);\n",
510
+ " padding-right: 1em;\n",
511
+ " padding-left: 1em;\n",
512
+ "}\n",
513
+ "\n",
514
+ "\n",
515
+ "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
516
+ "clickable and can be expanded/collapsed.\n",
517
+ "- Pipeline and ColumnTransformer use this feature and define the default style\n",
518
+ "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
519
+ "*/\n",
520
+ "\n",
521
+ "/* Pipeline and ColumnTransformer style (default) */\n",
522
+ "\n",
523
+ "#sk-container-id-1 div.sk-toggleable {\n",
524
+ " /* Default theme specific background. It is overwritten whether we have a\n",
525
+ " specific estimator or a Pipeline/ColumnTransformer */\n",
526
+ " background-color: var(--sklearn-color-background);\n",
527
+ "}\n",
528
+ "\n",
529
+ "/* Toggleable label */\n",
530
+ "#sk-container-id-1 label.sk-toggleable__label {\n",
531
+ " cursor: pointer;\n",
532
+ " display: block;\n",
533
+ " width: 100%;\n",
534
+ " margin-bottom: 0;\n",
535
+ " padding: 0.5em;\n",
536
+ " box-sizing: border-box;\n",
537
+ " text-align: center;\n",
538
+ "}\n",
539
+ "\n",
540
+ "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
541
+ " /* Arrow on the left of the label */\n",
542
+ " content: \"▸\";\n",
543
+ " float: left;\n",
544
+ " margin-right: 0.25em;\n",
545
+ " color: var(--sklearn-color-icon);\n",
546
+ "}\n",
547
+ "\n",
548
+ "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
549
+ " color: var(--sklearn-color-text);\n",
550
+ "}\n",
551
+ "\n",
552
+ "/* Toggleable content - dropdown */\n",
553
+ "\n",
554
+ "#sk-container-id-1 div.sk-toggleable__content {\n",
555
+ " max-height: 0;\n",
556
+ " max-width: 0;\n",
557
+ " overflow: hidden;\n",
558
+ " text-align: left;\n",
559
+ " /* unfitted */\n",
560
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
561
+ "}\n",
562
+ "\n",
563
+ "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
564
+ " /* fitted */\n",
565
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
566
+ "}\n",
567
+ "\n",
568
+ "#sk-container-id-1 div.sk-toggleable__content pre {\n",
569
+ " margin: 0.2em;\n",
570
+ " border-radius: 0.25em;\n",
571
+ " color: var(--sklearn-color-text);\n",
572
+ " /* unfitted */\n",
573
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
574
+ "}\n",
575
+ "\n",
576
+ "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
577
+ " /* unfitted */\n",
578
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
579
+ "}\n",
580
+ "\n",
581
+ "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
582
+ " /* Expand drop-down */\n",
583
+ " max-height: 200px;\n",
584
+ " max-width: 100%;\n",
585
+ " overflow: auto;\n",
586
+ "}\n",
587
+ "\n",
588
+ "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
589
+ " content: \"▾\";\n",
590
+ "}\n",
591
+ "\n",
592
+ "/* Pipeline/ColumnTransformer-specific style */\n",
593
+ "\n",
594
+ "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
595
+ " color: var(--sklearn-color-text);\n",
596
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
597
+ "}\n",
598
+ "\n",
599
+ "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
600
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
601
+ "}\n",
602
+ "\n",
603
+ "/* Estimator-specific style */\n",
604
+ "\n",
605
+ "/* Colorize estimator box */\n",
606
+ "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
607
+ " /* unfitted */\n",
608
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
609
+ "}\n",
610
+ "\n",
611
+ "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
612
+ " /* fitted */\n",
613
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
614
+ "}\n",
615
+ "\n",
616
+ "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
617
+ "#sk-container-id-1 div.sk-label label {\n",
618
+ " /* The background is the default theme color */\n",
619
+ " color: var(--sklearn-color-text-on-default-background);\n",
620
+ "}\n",
621
+ "\n",
622
+ "/* On hover, darken the color of the background */\n",
623
+ "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
624
+ " color: var(--sklearn-color-text);\n",
625
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
626
+ "}\n",
627
+ "\n",
628
+ "/* Label box, darken color on hover, fitted */\n",
629
+ "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
630
+ " color: var(--sklearn-color-text);\n",
631
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
632
+ "}\n",
633
+ "\n",
634
+ "/* Estimator label */\n",
635
+ "\n",
636
+ "#sk-container-id-1 div.sk-label label {\n",
637
+ " font-family: monospace;\n",
638
+ " font-weight: bold;\n",
639
+ " display: inline-block;\n",
640
+ " line-height: 1.2em;\n",
641
+ "}\n",
642
+ "\n",
643
+ "#sk-container-id-1 div.sk-label-container {\n",
644
+ " text-align: center;\n",
645
+ "}\n",
646
+ "\n",
647
+ "/* Estimator-specific */\n",
648
+ "#sk-container-id-1 div.sk-estimator {\n",
649
+ " font-family: monospace;\n",
650
+ " border: 1px dotted var(--sklearn-color-border-box);\n",
651
+ " border-radius: 0.25em;\n",
652
+ " box-sizing: border-box;\n",
653
+ " margin-bottom: 0.5em;\n",
654
+ " /* unfitted */\n",
655
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
656
+ "}\n",
657
+ "\n",
658
+ "#sk-container-id-1 div.sk-estimator.fitted {\n",
659
+ " /* fitted */\n",
660
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
661
+ "}\n",
662
+ "\n",
663
+ "/* on hover */\n",
664
+ "#sk-container-id-1 div.sk-estimator:hover {\n",
665
+ " /* unfitted */\n",
666
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
667
+ "}\n",
668
+ "\n",
669
+ "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
670
+ " /* fitted */\n",
671
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
672
+ "}\n",
673
+ "\n",
674
+ "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
675
+ "\n",
676
+ "/* Common style for \"i\" and \"?\" */\n",
677
+ "\n",
678
+ ".sk-estimator-doc-link,\n",
679
+ "a:link.sk-estimator-doc-link,\n",
680
+ "a:visited.sk-estimator-doc-link {\n",
681
+ " float: right;\n",
682
+ " font-size: smaller;\n",
683
+ " line-height: 1em;\n",
684
+ " font-family: monospace;\n",
685
+ " background-color: var(--sklearn-color-background);\n",
686
+ " border-radius: 1em;\n",
687
+ " height: 1em;\n",
688
+ " width: 1em;\n",
689
+ " text-decoration: none !important;\n",
690
+ " margin-left: 1ex;\n",
691
+ " /* unfitted */\n",
692
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
693
+ " color: var(--sklearn-color-unfitted-level-1);\n",
694
+ "}\n",
695
+ "\n",
696
+ ".sk-estimator-doc-link.fitted,\n",
697
+ "a:link.sk-estimator-doc-link.fitted,\n",
698
+ "a:visited.sk-estimator-doc-link.fitted {\n",
699
+ " /* fitted */\n",
700
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
701
+ " color: var(--sklearn-color-fitted-level-1);\n",
702
+ "}\n",
703
+ "\n",
704
+ "/* On hover */\n",
705
+ "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
706
+ ".sk-estimator-doc-link:hover,\n",
707
+ "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
708
+ ".sk-estimator-doc-link:hover {\n",
709
+ " /* unfitted */\n",
710
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
711
+ " color: var(--sklearn-color-background);\n",
712
+ " text-decoration: none;\n",
713
+ "}\n",
714
+ "\n",
715
+ "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
716
+ ".sk-estimator-doc-link.fitted:hover,\n",
717
+ "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
718
+ ".sk-estimator-doc-link.fitted:hover {\n",
719
+ " /* fitted */\n",
720
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
721
+ " color: var(--sklearn-color-background);\n",
722
+ " text-decoration: none;\n",
723
+ "}\n",
724
+ "\n",
725
+ "/* Span, style for the box shown on hovering the info icon */\n",
726
+ ".sk-estimator-doc-link span {\n",
727
+ " display: none;\n",
728
+ " z-index: 9999;\n",
729
+ " position: relative;\n",
730
+ " font-weight: normal;\n",
731
+ " right: .2ex;\n",
732
+ " padding: .5ex;\n",
733
+ " margin: .5ex;\n",
734
+ " width: min-content;\n",
735
+ " min-width: 20ex;\n",
736
+ " max-width: 50ex;\n",
737
+ " color: var(--sklearn-color-text);\n",
738
+ " box-shadow: 2pt 2pt 4pt #999;\n",
739
+ " /* unfitted */\n",
740
+ " background: var(--sklearn-color-unfitted-level-0);\n",
741
+ " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
742
+ "}\n",
743
+ "\n",
744
+ ".sk-estimator-doc-link.fitted span {\n",
745
+ " /* fitted */\n",
746
+ " background: var(--sklearn-color-fitted-level-0);\n",
747
+ " border: var(--sklearn-color-fitted-level-3);\n",
748
+ "}\n",
749
+ "\n",
750
+ ".sk-estimator-doc-link:hover span {\n",
751
+ " display: block;\n",
752
+ "}\n",
753
+ "\n",
754
+ "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
755
+ "\n",
756
+ "#sk-container-id-1 a.estimator_doc_link {\n",
757
+ " float: right;\n",
758
+ " font-size: 1rem;\n",
759
+ " line-height: 1em;\n",
760
+ " font-family: monospace;\n",
761
+ " background-color: var(--sklearn-color-background);\n",
762
+ " border-radius: 1rem;\n",
763
+ " height: 1rem;\n",
764
+ " width: 1rem;\n",
765
+ " text-decoration: none;\n",
766
+ " /* unfitted */\n",
767
+ " color: var(--sklearn-color-unfitted-level-1);\n",
768
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
769
+ "}\n",
770
+ "\n",
771
+ "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
772
+ " /* fitted */\n",
773
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
774
+ " color: var(--sklearn-color-fitted-level-1);\n",
775
+ "}\n",
776
+ "\n",
777
+ "/* On hover */\n",
778
+ "#sk-container-id-1 a.estimator_doc_link:hover {\n",
779
+ " /* unfitted */\n",
780
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
781
+ " color: var(--sklearn-color-background);\n",
782
+ " text-decoration: none;\n",
783
+ "}\n",
784
+ "\n",
785
+ "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
786
+ " /* fitted */\n",
787
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
788
+ "}\n",
789
+ "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
790
+ " ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
791
+ " [&#x27;Mileage&#x27;, &#x27;Cylinder&#x27;,\n",
792
+ " &#x27;Liter&#x27;, &#x27;Doors&#x27;]),\n",
793
+ " (&#x27;cat&#x27;, OneHotEncoder(),\n",
794
+ " [&#x27;Make&#x27;, &#x27;Model&#x27;, &#x27;Trim&#x27;,\n",
795
+ " &#x27;Type&#x27;])])),\n",
796
+ " (&#x27;model&#x27;, 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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
797
+ " ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
798
+ " [&#x27;Mileage&#x27;, &#x27;Cylinder&#x27;,\n",
799
+ " &#x27;Liter&#x27;, &#x27;Doors&#x27;]),\n",
800
+ " (&#x27;cat&#x27;, OneHotEncoder(),\n",
801
+ " [&#x27;Make&#x27;, &#x27;Model&#x27;, &#x27;Trim&#x27;,\n",
802
+ " &#x27;Type&#x27;])])),\n",
803
+ " (&#x27;model&#x27;, LinearRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
804
+ " [&#x27;Mileage&#x27;, &#x27;Cylinder&#x27;, &#x27;Liter&#x27;, &#x27;Doors&#x27;]),\n",
805
+ " (&#x27;cat&#x27;, OneHotEncoder(),\n",
806
+ " [&#x27;Make&#x27;, &#x27;Model&#x27;, &#x27;Trim&#x27;, &#x27;Type&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Mileage&#x27;, &#x27;Cylinder&#x27;, &#x27;Liter&#x27;, &#x27;Doors&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Make&#x27;, &#x27;Model&#x27;, &#x27;Trim&#x27;, &#x27;Type&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&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></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div></div></div>"
807
+ ],
808
+ "text/plain": [
809
+ "Pipeline(steps=[('preprocessor',\n",
810
+ " ColumnTransformer(transformers=[('num', StandardScaler(),\n",
811
+ " ['Mileage', 'Cylinder',\n",
812
+ " 'Liter', 'Doors']),\n",
813
+ " ('cat', OneHotEncoder(),\n",
814
+ " ['Make', 'Model', 'Trim',\n",
815
+ " 'Type'])])),\n",
816
+ " ('model', LinearRegression())])"
817
+ ]
818
+ },
819
+ "execution_count": 38,
820
+ "metadata": {},
821
+ "output_type": "execute_result"
822
+ }
823
+ ],
824
+ "source": [
825
+ "pipe.fit(X_train,y_train)"
826
+ ]
827
+ },
828
+ {
829
+ "cell_type": "code",
830
+ "execution_count": 40,
831
+ "id": "ed17767e-513f-43d2-b60b-27748d0a2836",
832
+ "metadata": {},
833
+ "outputs": [
834
+ {
835
+ "name": "stdout",
836
+ "output_type": "stream",
837
+ "text": [
838
+ "MSE 835.100716728648\n",
839
+ "R2 0.9912072828879327\n"
840
+ ]
841
+ }
842
+ ],
843
+ "source": [
844
+ "y_pred = pipe.predict(X_test)\n",
845
+ "print('MSE',mean_squared_error(y_test,y_pred)**0.5)\n",
846
+ "print('R2', r2_score(y_test,y_pred))\n"
847
+ ]
848
+ },
849
+ {
850
+ "cell_type": "code",
851
+ "execution_count": 50,
852
+ "id": "2636e539-d6b3-4249-89c7-42b0413e70ed",
853
+ "metadata": {},
854
+ "outputs": [],
855
+ "source": [
856
+ "#istersek veri setinin tamamıyla tekrar eğitim yapabiliriz.\n",
857
+ "#pipe.fit(X,y)"
858
+ ]
859
+ },
860
+ {
861
+ "cell_type": "markdown",
862
+ "id": "30719e98-9aad-4f7f-9baa-88ea94894e7e",
863
+ "metadata": {},
864
+ "source": [
865
+ "# streamlit ile modeli Deploy etme / Yayma / Kullanıma Sunma/ Mlops"
866
+ ]
867
+ },
868
+ {
869
+ "cell_type": "code",
870
+ "execution_count": 45,
871
+ "id": "54b5657e-b40c-484c-a1d2-e1c52154a7fe",
872
+ "metadata": {
873
+ "collapsed": true,
874
+ "jupyter": {
875
+ "outputs_hidden": true
876
+ }
877
+ },
878
+ "outputs": [
879
+ {
880
+ "name": "stdout",
881
+ "output_type": "stream",
882
+ "text": [
883
+ "Collecting streamlit\n",
884
+ " Obtaining dependency information for streamlit from https://files.pythonhosted.org/packages/0e/86/69fdac2ec6852304bda08e5af5b72dfa6e74dc0b3cef0d7c1e19994388ae/streamlit-1.35.0-py2.py3-none-any.whl.metadata\n",
885
+ " Downloading streamlit-1.35.0-py2.py3-none-any.whl.metadata (8.5 kB)\n",
886
+ "Requirement already satisfied: altair<6,>=4.0 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from streamlit) (5.3.0)\n",
887
+ "Collecting blinker<2,>=1.0.0 (from streamlit)\n",
888
+ " Obtaining dependency information for blinker<2,>=1.0.0 from https://files.pythonhosted.org/packages/bb/2a/10164ed1f31196a2f7f3799368a821765c62851ead0e630ab52b8e14b4d0/blinker-1.8.2-py3-none-any.whl.metadata\n",
889
+ " Using cached blinker-1.8.2-py3-none-any.whl.metadata (1.6 kB)\n",
890
+ "Collecting cachetools<6,>=4.0 (from streamlit)\n",
891
+ " Obtaining dependency information for cachetools<6,>=4.0 from https://files.pythonhosted.org/packages/fb/2b/a64c2d25a37aeb921fddb929111413049fc5f8b9a4c1aefaffaafe768d54/cachetools-5.3.3-py3-none-any.whl.metadata\n",
892
+ " Downloading cachetools-5.3.3-py3-none-any.whl.metadata (5.3 kB)\n",
893
+ "Requirement already satisfied: click<9,>=7.0 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from streamlit) (8.1.7)\n",
894
+ "Requirement already satisfied: numpy<2,>=1.19.3 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from streamlit) (1.26.4)\n",
895
+ "Requirement already satisfied: packaging<25,>=16.8 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from streamlit) (23.2)\n",
896
+ "Requirement already satisfied: pandas<3,>=1.3.0 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from streamlit) (2.0.3)\n",
897
+ "Requirement already satisfied: pillow<11,>=7.1.0 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from streamlit) (10.3.0)\n",
898
+ "Requirement already satisfied: protobuf<5,>=3.20 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from streamlit) (4.21.12)\n",
899
+ "Requirement already satisfied: pyarrow>=7.0 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from streamlit) (14.0.2)\n",
900
+ "Requirement already satisfied: requests<3,>=2.27 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from streamlit) (2.32.2)\n",
901
+ "Collecting rich<14,>=10.14.0 (from streamlit)\n",
902
+ " Obtaining dependency information for rich<14,>=10.14.0 from https://files.pythonhosted.org/packages/87/67/a37f6214d0e9fe57f6ae54b2956d550ca8365857f42a1ce0392bb21d9410/rich-13.7.1-py3-none-any.whl.metadata\n",
903
+ " Downloading rich-13.7.1-py3-none-any.whl.metadata (18 kB)\n",
904
+ "Collecting tenacity<9,>=8.1.0 (from streamlit)\n",
905
+ " Obtaining dependency information for tenacity<9,>=8.1.0 from https://files.pythonhosted.org/packages/61/a1/6bb0cbebefb23641f068bb58a2bc56da9beb2b1c550242e3c540b37698f3/tenacity-8.3.0-py3-none-any.whl.metadata\n",
906
+ " Downloading tenacity-8.3.0-py3-none-any.whl.metadata (1.2 kB)\n",
907
+ "Requirement already satisfied: toml<2,>=0.10.1 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from streamlit) (0.10.2)\n",
908
+ "Requirement already satisfied: typing-extensions<5,>=4.3.0 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from streamlit) (4.11.0)\n",
909
+ "Collecting gitpython!=3.1.19,<4,>=3.0.7 (from streamlit)\n",
910
+ " Obtaining dependency information for gitpython!=3.1.19,<4,>=3.0.7 from https://files.pythonhosted.org/packages/e9/bd/cc3a402a6439c15c3d4294333e13042b915bbeab54edc457c723931fed3f/GitPython-3.1.43-py3-none-any.whl.metadata\n",
911
+ " Downloading GitPython-3.1.43-py3-none-any.whl.metadata (13 kB)\n",
912
+ "Collecting pydeck<1,>=0.8.0b4 (from streamlit)\n",
913
+ " Obtaining dependency information for pydeck<1,>=0.8.0b4 from https://files.pythonhosted.org/packages/ab/4c/b888e6cf58bd9db9c93f40d1c6be8283ff49d88919231afe93a6bcf61626/pydeck-0.9.1-py2.py3-none-any.whl.metadata\n",
914
+ " Downloading pydeck-0.9.1-py2.py3-none-any.whl.metadata (4.1 kB)\n",
915
+ "Requirement already satisfied: tornado<7,>=6.0.3 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from streamlit) (6.3.3)\n",
916
+ "Collecting watchdog>=2.1.5 (from streamlit)\n",
917
+ " Obtaining dependency information for watchdog>=2.1.5 from https://files.pythonhosted.org/packages/85/e0/2a9f43008902427b5f074c497705d6ef8f815c85d4bc25fbf83f720a6159/watchdog-4.0.1-py3-none-win_amd64.whl.metadata\n",
918
+ " Downloading watchdog-4.0.1-py3-none-win_amd64.whl.metadata (37 kB)\n",
919
+ "Requirement already satisfied: jinja2 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from altair<6,>=4.0->streamlit) (3.1.4)\n",
920
+ "Requirement already satisfied: jsonschema>=3.0 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from altair<6,>=4.0->streamlit) (4.19.2)\n",
921
+ "Requirement already satisfied: toolz in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from altair<6,>=4.0->streamlit) (0.12.0)\n",
922
+ "Requirement already satisfied: colorama in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from click<9,>=7.0->streamlit) (0.4.6)\n",
923
+ "Collecting gitdb<5,>=4.0.1 (from gitpython!=3.1.19,<4,>=3.0.7->streamlit)\n",
924
+ " Obtaining dependency information for gitdb<5,>=4.0.1 from https://files.pythonhosted.org/packages/fd/5b/8f0c4a5bb9fd491c277c21eff7ccae71b47d43c4446c9d0c6cff2fe8c2c4/gitdb-4.0.11-py3-none-any.whl.metadata\n",
925
+ " Downloading gitdb-4.0.11-py3-none-any.whl.metadata (1.2 kB)\n",
926
+ "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from pandas<3,>=1.3.0->streamlit) (2.9.0.post0)\n",
927
+ "Requirement already satisfied: pytz>=2020.1 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from pandas<3,>=1.3.0->streamlit) (2024.1)\n",
928
+ "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from pandas<3,>=1.3.0->streamlit) (2023.3)\n",
929
+ "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from requests<3,>=2.27->streamlit) (2.0.4)\n",
930
+ "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from requests<3,>=2.27->streamlit) (3.7)\n",
931
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from requests<3,>=2.27->streamlit) (2.0.3)\n",
932
+ "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from requests<3,>=2.27->streamlit) (2024.6.2)\n",
933
+ "Collecting markdown-it-py>=2.2.0 (from rich<14,>=10.14.0->streamlit)\n",
934
+ " Obtaining dependency information for markdown-it-py>=2.2.0 from https://files.pythonhosted.org/packages/42/d7/1ec15b46af6af88f19b8e5ffea08fa375d433c998b8a7639e76935c14f1f/markdown_it_py-3.0.0-py3-none-any.whl.metadata\n",
935
+ " Downloading markdown_it_py-3.0.0-py3-none-any.whl.metadata (6.9 kB)\n",
936
+ "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from rich<14,>=10.14.0->streamlit) (2.15.1)\n",
937
+ "Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->gitpython!=3.1.19,<4,>=3.0.7->streamlit)\n",
938
+ " Obtaining dependency information for smmap<6,>=3.0.1 from https://files.pythonhosted.org/packages/a7/a5/10f97f73544edcdef54409f1d839f6049a0d79df68adbc1ceb24d1aaca42/smmap-5.0.1-py3-none-any.whl.metadata\n",
939
+ " Downloading smmap-5.0.1-py3-none-any.whl.metadata (4.3 kB)\n",
940
+ "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from jinja2->altair<6,>=4.0->streamlit) (2.1.3)\n",
941
+ "Requirement already satisfied: attrs>=22.2.0 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit) (23.1.0)\n",
942
+ "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit) (2023.7.1)\n",
943
+ "Requirement already satisfied: referencing>=0.28.4 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit) (0.30.2)\n",
944
+ "Requirement already satisfied: rpds-py>=0.7.1 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from jsonschema>=3.0->altair<6,>=4.0->streamlit) (0.10.6)\n",
945
+ "Collecting mdurl~=0.1 (from markdown-it-py>=2.2.0->rich<14,>=10.14.0->streamlit)\n",
946
+ " Obtaining dependency information for mdurl~=0.1 from https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl.metadata\n",
947
+ " Downloading mdurl-0.1.2-py3-none-any.whl.metadata (1.6 kB)\n",
948
+ "Requirement already satisfied: six>=1.5 in c:\\users\\erayc\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.2->pandas<3,>=1.3.0->streamlit) (1.16.0)\n",
949
+ "Downloading streamlit-1.35.0-py2.py3-none-any.whl (8.6 MB)\n",
950
+ " ---------------------------------------- 0.0/8.6 MB ? eta -:--:--\n",
951
+ " ---------------------------------------- 0.0/8.6 MB 1.4 MB/s eta 0:00:07\n",
952
+ " ---------------------------------------- 0.1/8.6 MB 919.0 kB/s eta 0:00:10\n",
953
+ " ---------------------------------------- 0.1/8.6 MB 919.0 kB/s eta 0:00:10\n",
954
+ " --------------------------------------- 0.2/8.6 MB 919.0 kB/s eta 0:00:10\n",
955
+ " --------------------------------------- 0.2/8.6 MB 958.4 kB/s eta 0:00:09\n",
956
+ " --------------------------------------- 0.2/8.6 MB 958.4 kB/s eta 0:00:09\n",
957
+ " - -------------------------------------- 0.2/8.6 MB 801.7 kB/s eta 0:00:11\n",
958
+ " - -------------------------------------- 0.3/8.6 MB 780.5 kB/s eta 0:00:11\n",
959
+ " - -------------------------------------- 0.3/8.6 MB 780.5 kB/s eta 0:00:11\n",
960
+ " - -------------------------------------- 0.3/8.6 MB 780.5 kB/s eta 0:00:11\n",
961
+ " - -------------------------------------- 0.3/8.6 MB 611.3 kB/s eta 0:00:14\n",
962
+ " - -------------------------------------- 0.3/8.6 MB 611.3 kB/s eta 0:00:14\n",
963
+ " - -------------------------------------- 0.3/8.6 MB 534.4 kB/s eta 0:00:16\n",
964
+ " - -------------------------------------- 0.3/8.6 MB 534.4 kB/s eta 0:00:16\n",
965
+ " - -------------------------------------- 0.4/8.6 MB 594.3 kB/s eta 0:00:14\n",
966
+ " -- ------------------------------------- 0.5/8.6 MB 640.9 kB/s eta 0:00:13\n",
967
+ " -- ------------------------------------- 0.5/8.6 MB 640.9 kB/s eta 0:00:13\n",
968
+ " -- ------------------------------------- 0.5/8.6 MB 581.3 kB/s eta 0:00:14\n",
969
+ " -- ------------------------------------- 0.5/8.6 MB 618.8 kB/s eta 0:00:14\n",
970
+ " -- ------------------------------------- 0.6/8.6 MB 610.8 kB/s eta 0:00:14\n",
971
+ " --- ------------------------------------ 0.7/8.6 MB 698.4 kB/s eta 0:00:12\n",
972
+ " --- ------------------------------------ 0.7/8.6 MB 686.1 kB/s eta 0:00:12\n",
973
+ " --- ------------------------------------ 0.7/8.6 MB 686.1 kB/s eta 0:00:12\n",
974
+ " --- ------------------------------------ 0.7/8.6 MB 684.6 kB/s eta 0:00:12\n",
975
+ " --- ------------------------------------ 0.8/8.6 MB 691.8 kB/s eta 0:00:12\n",
976
+ " --- ------------------------------------ 0.8/8.6 MB 691.8 kB/s eta 0:00:12\n",
977
+ " --- ------------------------------------ 0.8/8.6 MB 691.8 kB/s eta 0:00:12\n",
978
+ " ---- ----------------------------------- 0.9/8.6 MB 695.5 kB/s eta 0:00:12\n",
979
+ " ---- ----------------------------------- 0.9/8.6 MB 695.5 kB/s eta 0:00:12\n",
980
+ " ---- ----------------------------------- 1.0/8.6 MB 730.9 kB/s eta 0:00:11\n",
981
+ " ---- ----------------------------------- 1.1/8.6 MB 750.2 kB/s eta 0:00:11\n",
982
+ " ----- ---------------------------------- 1.1/8.6 MB 740.0 kB/s eta 0:00:11\n",
983
+ " ----- ---------------------------------- 1.1/8.6 MB 745.2 kB/s eta 0:00:11\n",
984
+ " ----- ---------------------------------- 1.1/8.6 MB 745.2 kB/s eta 0:00:11\n",
985
+ " ----- ---------------------------------- 1.1/8.6 MB 745.2 kB/s eta 0:00:11\n",
986
+ " ----- ---------------------------------- 1.1/8.6 MB 745.2 kB/s eta 0:00:11\n",
987
+ " ----- ---------------------------------- 1.1/8.6 MB 745.2 kB/s eta 0:00:11\n",
988
+ " ----- ---------------------------------- 1.1/8.6 MB 745.2 kB/s eta 0:00:11\n",
989
+ " ----- ---------------------------------- 1.1/8.6 MB 745.2 kB/s eta 0:00:11\n",
990
+ " ----- ---------------------------------- 1.1/8.6 MB 627.3 kB/s eta 0:00:12\n",
991
+ " ----- ---------------------------------- 1.1/8.6 MB 627.3 kB/s eta 0:00:12\n",
992
+ " ----- ---------------------------------- 1.2/8.6 MB 602.0 kB/s eta 0:00:13\n",
993
+ " ----- ---------------------------------- 1.2/8.6 MB 613.2 kB/s eta 0:00:13\n",
994
+ " ----- ---------------------------------- 1.2/8.6 MB 613.2 kB/s eta 0:00:13\n",
995
+ " ----- ---------------------------------- 1.3/8.6 MB 615.3 kB/s eta 0:00:12\n",
996
+ " ------ --------------------------------- 1.3/8.6 MB 607.2 kB/s eta 0:00:12\n",
997
+ " ------ --------------------------------- 1.4/8.6 MB 622.2 kB/s eta 0:00:12\n",
998
+ " ------ --------------------------------- 1.4/8.6 MB 646.1 kB/s eta 0:00:12\n",
999
+ " ------ --------------------------------- 1.4/8.6 MB 646.1 kB/s eta 0:00:12\n",
1000
+ " ------ --------------------------------- 1.4/8.6 MB 646.1 kB/s eta 0:00:12\n",
1001
+ " ------ --------------------------------- 1.5/8.6 MB 633.4 kB/s eta 0:00:12\n",
1002
+ " ------ --------------------------------- 1.5/8.6 MB 633.4 kB/s eta 0:00:12\n",
1003
+ " ------ --------------------------------- 1.5/8.6 MB 633.4 kB/s eta 0:00:12\n",
1004
+ " ------ --------------------------------- 1.5/8.6 MB 633.4 kB/s eta 0:00:12\n",
1005
+ " ------- -------------------------------- 1.5/8.6 MB 606.9 kB/s eta 0:00:12\n",
1006
+ " ------- -------------------------------- 1.6/8.6 MB 615.1 kB/s eta 0:00:12\n",
1007
+ " ------- -------------------------------- 1.6/8.6 MB 608.4 kB/s eta 0:00:12\n",
1008
+ " ------- -------------------------------- 1.6/8.6 MB 608.4 kB/s eta 0:00:12\n",
1009
+ " ------- -------------------------------- 1.6/8.6 MB 598.8 kB/s eta 0:00:12\n",
1010
+ " ------- -------------------------------- 1.7/8.6 MB 610.5 kB/s eta 0:00:12\n",
1011
+ " ------- -------------------------------- 1.7/8.6 MB 610.5 kB/s eta 0:00:12\n",
1012
+ " -------- ------------------------------- 1.7/8.6 MB 601.3 kB/s eta 0:00:12\n",
1013
+ " -------- ------------------------------- 1.7/8.6 MB 601.3 kB/s eta 0:00:12\n",
1014
+ " -------- ------------------------------- 1.7/8.6 MB 601.3 kB/s eta 0:00:12\n",
1015
+ " -------- ------------------------------- 1.7/8.6 MB 582.9 kB/s eta 0:00:12\n",
1016
+ " -------- ------------------------------- 1.8/8.6 MB 588.1 kB/s eta 0:00:12\n",
1017
+ " -------- ------------------------------- 1.8/8.6 MB 598.6 kB/s eta 0:00:12\n",
1018
+ " -------- ------------------------------- 1.9/8.6 MB 596.8 kB/s eta 0:00:12\n",
1019
+ " -------- ------------------------------- 1.9/8.6 MB 594.4 kB/s eta 0:00:12\n",
1020
+ " -------- ------------------------------- 1.9/8.6 MB 594.4 kB/s eta 0:00:12\n",
1021
+ " -------- ------------------------------- 1.9/8.6 MB 594.4 kB/s eta 0:00:12\n",
1022
+ " --------- ------------------------------ 2.0/8.6 MB 590.1 kB/s eta 0:00:12\n",
1023
+ " --------- ------------------------------ 2.0/8.6 MB 590.1 kB/s eta 0:00:12\n",
1024
+ " --------- ------------------------------ 2.0/8.6 MB 601.1 kB/s eta 0:00:11\n",
1025
+ " ---------- ----------------------------- 2.2/8.6 MB 628.7 kB/s eta 0:00:11\n",
1026
+ " ---------- ----------------------------- 2.2/8.6 MB 637.8 kB/s eta 0:00:10\n",
1027
+ " ---------- ----------------------------- 2.3/8.6 MB 641.1 kB/s eta 0:00:10\n",
1028
+ " ----------- ---------------------------- 2.4/8.6 MB 672.4 kB/s eta 0:00:10\n",
1029
+ " ----------- ---------------------------- 2.5/8.6 MB 678.1 kB/s eta 0:00:10\n",
1030
+ " ------------ --------------------------- 2.6/8.6 MB 708.2 kB/s eta 0:00:09\n",
1031
+ " ------------ --------------------------- 2.7/8.6 MB 719.0 kB/s eta 0:00:09\n",
1032
+ " ------------ --------------------------- 2.7/8.6 MB 719.0 kB/s eta 0:00:09\n",
1033
+ " ------------ --------------------------- 2.8/8.6 MB 716.9 kB/s eta 0:00:09\n",
1034
+ " ------------ --------------------------- 2.8/8.6 MB 716.9 kB/s eta 0:00:09\n",
1035
+ " ------------ --------------------------- 2.8/8.6 MB 716.9 kB/s eta 0:00:09\n",
1036
+ " ------------ --------------------------- 2.8/8.6 MB 716.9 kB/s eta 0:00:09\n",
1037
+ " ------------- -------------------------- 2.8/8.6 MB 703.9 kB/s eta 0:00:09\n",
1038
+ " ------------- -------------------------- 2.8/8.6 MB 703.9 kB/s eta 0:00:09\n",
1039
+ " ------------- -------------------------- 2.9/8.6 MB 707.8 kB/s eta 0:00:09\n",
1040
+ " ------------- -------------------------- 2.9/8.6 MB 707.8 kB/s eta 0:00:09\n",
1041
+ " ------------- -------------------------- 3.0/8.6 MB 711.4 kB/s eta 0:00:08\n",
1042
+ " -------------- ------------------------- 3.0/8.6 MB 716.0 kB/s eta 0:00:08\n",
1043
+ " -------------- ------------------------- 3.0/8.6 MB 716.0 kB/s eta 0:00:08\n",
1044
+ " -------------- ------------------------- 3.0/8.6 MB 716.0 kB/s eta 0:00:08\n",
1045
+ " -------------- ------------------------- 3.1/8.6 MB 704.4 kB/s eta 0:00:08\n",
1046
+ " -------------- ------------------------- 3.1/8.6 MB 704.4 kB/s eta 0:00:08\n",
1047
+ " -------------- ------------------------- 3.2/8.6 MB 703.4 kB/s eta 0:00:08\n",
1048
+ " --------------- ------------------------ 3.3/8.6 MB 718.7 kB/s eta 0:00:08\n",
1049
+ " --------------- ------------------------ 3.3/8.6 MB 718.7 kB/s eta 0:00:08\n",
1050
+ " --------------- ------------------------ 3.4/8.6 MB 728.9 kB/s eta 0:00:08\n",
1051
+ " --------------- ------------------------ 3.4/8.6 MB 728.0 kB/s eta 0:00:08\n",
1052
+ " ---------------- ----------------------- 3.5/8.6 MB 736.3 kB/s eta 0:00:07\n",
1053
+ " ---------------- ----------------------- 3.6/8.6 MB 754.5 kB/s eta 0:00:07\n",
1054
+ " ---------------- ----------------------- 3.6/8.6 MB 754.5 kB/s eta 0:00:07\n",
1055
+ " ----------------- ---------------------- 3.8/8.6 MB 773.4 kB/s eta 0:00:07\n",
1056
+ " ----------------- ---------------------- 3.8/8.6 MB 781.4 kB/s eta 0:00:07\n",
1057
+ " ----------------- ---------------------- 3.8/8.6 MB 781.4 kB/s eta 0:00:07\n",
1058
+ " ----------------- ---------------------- 3.9/8.6 MB 775.3 kB/s eta 0:00:07\n",
1059
+ " ------------------ --------------------- 3.9/8.6 MB 777.5 kB/s eta 0:00:06\n",
1060
+ " ------------------ --------------------- 4.0/8.6 MB 787.2 kB/s eta 0:00:06\n",
1061
+ " ------------------ --------------------- 4.1/8.6 MB 791.7 kB/s eta 0:00:06\n",
1062
+ " ------------------ --------------------- 4.1/8.6 MB 791.7 kB/s eta 0:00:06\n",
1063
+ " ------------------ --------------------- 4.1/8.6 MB 791.7 kB/s eta 0:00:06\n",
1064
+ " ------------------- -------------------- 4.2/8.6 MB 789.6 kB/s eta 0:00:06\n",
1065
+ " -------------------- ------------------- 4.3/8.6 MB 813.5 kB/s eta 0:00:06\n",
1066
+ " -------------------- ------------------- 4.4/8.6 MB 823.6 kB/s eta 0:00:06\n",
1067
+ " -------------------- ------------------- 4.4/8.6 MB 824.9 kB/s eta 0:00:06\n",
1068
+ " --------------------- ------------------ 4.5/8.6 MB 825.7 kB/s eta 0:00:05\n",
1069
+ " --------------------- ------------------ 4.6/8.6 MB 832.8 kB/s eta 0:00:05\n",
1070
+ " --------------------- ------------------ 4.6/8.6 MB 832.8 kB/s eta 0:00:05\n",
1071
+ " --------------------- ------------------ 4.6/8.6 MB 827.4 kB/s eta 0:00:05\n",
1072
+ " --------------------- ------------------ 4.6/8.6 MB 827.4 kB/s eta 0:00:05\n",
1073
+ " --------------------- ------------------ 4.6/8.6 MB 827.4 kB/s eta 0:00:05\n",
1074
+ " --------------------- ------------------ 4.7/8.6 MB 810.3 kB/s eta 0:00:05\n",
1075
+ " --------------------- ------------------ 4.7/8.6 MB 817.4 kB/s eta 0:00:05\n",
1076
+ " --------------------- ------------------ 4.7/8.6 MB 817.4 kB/s eta 0:00:05\n",
1077
+ " ---------------------- ----------------- 4.8/8.6 MB 807.0 kB/s eta 0:00:05\n",
1078
+ " ---------------------- ----------------- 4.8/8.6 MB 808.4 kB/s eta 0:00:05\n",
1079
+ " ---------------------- ----------------- 4.9/8.6 MB 811.4 kB/s eta 0:00:05\n",
1080
+ " ---------------------- ----------------- 4.9/8.6 MB 808.6 kB/s eta 0:00:05\n",
1081
+ " ----------------------- ---------------- 5.0/8.6 MB 826.0 kB/s eta 0:00:05\n",
1082
+ " ----------------------- ---------------- 5.1/8.6 MB 836.4 kB/s eta 0:00:05\n",
1083
+ " ------------------------ --------------- 5.2/8.6 MB 850.6 kB/s eta 0:00:04\n",
1084
+ " ------------------------ --------------- 5.2/8.6 MB 850.6 kB/s eta 0:00:04\n",
1085
+ " ------------------------ --------------- 5.3/8.6 MB 847.6 kB/s eta 0:00:04\n",
1086
+ " ------------------------- -------------- 5.5/8.6 MB 870.0 kB/s eta 0:00:04\n",
1087
+ " ------------------------- -------------- 5.5/8.6 MB 872.2 kB/s eta 0:00:04\n",
1088
+ " ------------------------- -------------- 5.5/8.6 MB 872.2 kB/s eta 0:00:04\n",
1089
+ " -------------------------- ------------- 5.6/8.6 MB 871.8 kB/s eta 0:00:04\n",
1090
+ " -------------------------- ------------- 5.7/8.6 MB 880.7 kB/s eta 0:00:04\n",
1091
+ " -------------------------- ------------- 5.8/8.6 MB 886.9 kB/s eta 0:00:04\n",
1092
+ " --------------------------- ------------ 5.9/8.6 MB 888.9 kB/s eta 0:00:04\n",
1093
+ " --------------------------- ------------ 5.9/8.6 MB 890.4 kB/s eta 0:00:04\n",
1094
+ " ---------------------------- ----------- 6.0/8.6 MB 903.1 kB/s eta 0:00:03\n",
1095
+ " ---------------------------- ----------- 6.1/8.6 MB 901.3 kB/s eta 0:00:03\n",
1096
+ " ---------------------------- ----------- 6.1/8.6 MB 899.6 kB/s eta 0:00:03\n",
1097
+ " ---------------------------- ----------- 6.2/8.6 MB 908.0 kB/s eta 0:00:03\n",
1098
+ " ---------------------------- ----------- 6.2/8.6 MB 908.0 kB/s eta 0:00:03\n",
1099
+ " ----------------------------- ---------- 6.3/8.6 MB 911.5 kB/s eta 0:00:03\n",
1100
+ " ----------------------------- ---------- 6.4/8.6 MB 918.2 kB/s eta 0:00:03\n",
1101
+ " ----------------------------- ---------- 6.4/8.6 MB 919.3 kB/s eta 0:00:03\n",
1102
+ " ------------------------------ --------- 6.5/8.6 MB 926.3 kB/s eta 0:00:03\n",
1103
+ " ------------------------------- -------- 6.7/8.6 MB 946.2 kB/s eta 0:00:02\n",
1104
+ " ------------------------------- -------- 6.8/8.6 MB 957.9 kB/s eta 0:00:02\n",
1105
+ " ------------------------------- -------- 6.8/8.6 MB 957.9 kB/s eta 0:00:02\n",
1106
+ " ------------------------------- -------- 6.8/8.6 MB 957.9 kB/s eta 0:00:02\n",
1107
+ " ------------------------------- -------- 6.8/8.6 MB 957.9 kB/s eta 0:00:02\n",
1108
+ " -------------------------------- ------- 6.9/8.6 MB 944.4 kB/s eta 0:00:02\n",
1109
+ " -------------------------------- ------- 7.0/8.6 MB 945.4 kB/s eta 0:00:02\n",
1110
+ " -------------------------------- ------- 7.0/8.6 MB 948.5 kB/s eta 0:00:02\n",
1111
+ " --------------------------------- ------ 7.1/8.6 MB 953.4 kB/s eta 0:00:02\n",
1112
+ " --------------------------------- ------ 7.1/8.6 MB 953.4 kB/s eta 0:00:02\n",
1113
+ " --------------------------------- ------ 7.1/8.6 MB 943.0 kB/s eta 0:00:02\n",
1114
+ " --------------------------------- ------ 7.2/8.6 MB 950.6 kB/s eta 0:00:02\n",
1115
+ " --------------------------------- ------ 7.2/8.6 MB 946.8 kB/s eta 0:00:02\n",
1116
+ " --------------------------------- ------ 7.3/8.6 MB 947.1 kB/s eta 0:00:02\n",
1117
+ " ---------------------------------- ----- 7.3/8.6 MB 949.8 kB/s eta 0:00:02\n",
1118
+ " ---------------------------------- ----- 7.3/8.6 MB 949.8 kB/s eta 0:00:02\n",
1119
+ " ---------------------------------- ----- 7.3/8.6 MB 949.8 kB/s eta 0:00:02\n",
1120
+ " ---------------------------------- ----- 7.4/8.6 MB 942.0 kB/s eta 0:00:02\n",
1121
+ " ---------------------------------- ----- 7.5/8.6 MB 941.6 kB/s eta 0:00:02\n",
1122
+ " ----------------------------------- ---- 7.5/8.6 MB 946.4 kB/s eta 0:00:02\n",
1123
+ " ----------------------------------- ---- 7.5/8.6 MB 946.4 kB/s eta 0:00:02\n",
1124
+ " ----------------------------------- ---- 7.6/8.6 MB 944.2 kB/s eta 0:00:02\n",
1125
+ " ----------------------------------- ---- 7.6/8.6 MB 944.4 kB/s eta 0:00:01\n",
1126
+ " ------------------------------------ --- 7.7/8.6 MB 947.9 kB/s eta 0:00:01\n",
1127
+ " ------------------------------------ --- 7.8/8.6 MB 957.3 kB/s eta 0:00:01\n",
1128
+ " ------------------------------------ --- 7.8/8.6 MB 957.3 kB/s eta 0:00:01\n",
1129
+ " ------------------------------------ --- 7.9/8.6 MB 956.5 kB/s eta 0:00:01\n",
1130
+ " ------------------------------------ --- 7.9/8.6 MB 951.7 kB/s eta 0:00:01\n",
1131
+ " ------------------------------------- -- 8.0/8.6 MB 951.8 kB/s eta 0:00:01\n",
1132
+ " ------------------------------------- -- 8.2/8.6 MB 964.9 kB/s eta 0:00:01\n",
1133
+ " --------------------------------------- 8.4/8.6 MB 986.1 kB/s eta 0:00:01\n",
1134
+ " --------------------------------------- 8.5/8.6 MB 994.5 kB/s eta 0:00:01\n",
1135
+ " --------------------------------------- 8.5/8.6 MB 993.9 kB/s eta 0:00:01\n",
1136
+ " --------------------------------------- 8.5/8.6 MB 993.9 kB/s eta 0:00:01\n",
1137
+ " --------------------------------------- 8.6/8.6 MB 987.8 kB/s eta 0:00:01\n",
1138
+ " --------------------------------------- 8.6/8.6 MB 984.8 kB/s eta 0:00:01\n",
1139
+ " ---------------------------------------- 8.6/8.6 MB 983.0 kB/s eta 0:00:00\n",
1140
+ "Using cached blinker-1.8.2-py3-none-any.whl (9.5 kB)\n",
1141
+ "Downloading cachetools-5.3.3-py3-none-any.whl (9.3 kB)\n",
1142
+ "Downloading GitPython-3.1.43-py3-none-any.whl (207 kB)\n",
1143
+ " ---------------------------------------- 0.0/207.3 kB ? eta -:--:--\n",
1144
+ " ----------------- ---------------------- 92.2/207.3 kB 5.5 MB/s eta 0:00:01\n",
1145
+ " ----------------------- ---------------- 122.9/207.3 kB 1.4 MB/s eta 0:00:01\n",
1146
+ " ---------------------------------------- 207.3/207.3 kB 1.8 MB/s eta 0:00:00\n",
1147
+ "Downloading pydeck-0.9.1-py2.py3-none-any.whl (6.9 MB)\n",
1148
+ " ---------------------------------------- 0.0/6.9 MB ? eta -:--:--\n",
1149
+ " --------------------------------------- 0.1/6.9 MB 2.8 MB/s eta 0:00:03\n",
1150
+ " -- ------------------------------------- 0.4/6.9 MB 4.0 MB/s eta 0:00:02\n",
1151
+ " -- ------------------------------------- 0.4/6.9 MB 3.9 MB/s eta 0:00:02\n",
1152
+ " -- ------------------------------------- 0.4/6.9 MB 3.9 MB/s eta 0:00:02\n",
1153
+ " -- ------------------------------------- 0.4/6.9 MB 3.9 MB/s eta 0:00:02\n",
1154
+ " -- ------------------------------------- 0.4/6.9 MB 3.9 MB/s eta 0:00:02\n",
1155
+ " --- ------------------------------------ 0.6/6.9 MB 1.7 MB/s eta 0:00:04\n",
1156
+ " ---- ----------------------------------- 0.8/6.9 MB 2.3 MB/s eta 0:00:03\n",
1157
+ " ---- ----------------------------------- 0.8/6.9 MB 2.3 MB/s eta 0:00:03\n",
1158
+ " ----- ---------------------------------- 0.9/6.9 MB 2.0 MB/s eta 0:00:03\n",
1159
+ " ------ --------------------------------- 1.2/6.9 MB 2.3 MB/s eta 0:00:03\n",
1160
+ " ------- -------------------------------- 1.2/6.9 MB 2.3 MB/s eta 0:00:03\n",
1161
+ " ------- -------------------------------- 1.3/6.9 MB 2.2 MB/s eta 0:00:03\n",
1162
+ " ------- -------------------------------- 1.4/6.9 MB 2.2 MB/s eta 0:00:03\n",
1163
+ " ------- -------------------------------- 1.4/6.9 MB 2.2 MB/s eta 0:00:03\n",
1164
+ " -------- ------------------------------- 1.4/6.9 MB 1.9 MB/s eta 0:00:03\n",
1165
+ " -------- ------------------------------- 1.4/6.9 MB 1.9 MB/s eta 0:00:03\n",
1166
+ " --------- ------------------------------ 1.6/6.9 MB 1.9 MB/s eta 0:00:03\n",
1167
+ " ---------- ----------------------------- 1.7/6.9 MB 2.0 MB/s eta 0:00:03\n",
1168
+ " ---------- ----------------------------- 1.8/6.9 MB 2.0 MB/s eta 0:00:03\n",
1169
+ " ---------- ----------------------------- 1.8/6.9 MB 2.0 MB/s eta 0:00:03\n",
1170
+ " ---------- ----------------------------- 1.8/6.9 MB 2.0 MB/s eta 0:00:03\n",
1171
+ " ---------- ----------------------------- 1.9/6.9 MB 1.8 MB/s eta 0:00:03\n",
1172
+ " ---------- ----------------------------- 1.9/6.9 MB 1.7 MB/s eta 0:00:03\n",
1173
+ " ----------- ---------------------------- 2.0/6.9 MB 1.7 MB/s eta 0:00:03\n",
1174
+ " ------------ --------------------------- 2.1/6.9 MB 1.7 MB/s eta 0:00:03\n",
1175
+ " ------------ --------------------------- 2.1/6.9 MB 1.7 MB/s eta 0:00:03\n",
1176
+ " -------------- ------------------------- 2.4/6.9 MB 1.8 MB/s eta 0:00:03\n",
1177
+ " -------------- ------------------------- 2.5/6.9 MB 1.9 MB/s eta 0:00:03\n",
1178
+ " --------------- ------------------------ 2.7/6.9 MB 1.9 MB/s eta 0:00:03\n",
1179
+ " --------------- ------------------------ 2.7/6.9 MB 1.9 MB/s eta 0:00:03\n",
1180
+ " ---------------- ----------------------- 2.8/6.9 MB 1.9 MB/s eta 0:00:03\n",
1181
+ " ----------------- ---------------------- 3.1/6.9 MB 2.0 MB/s eta 0:00:02\n",
1182
+ " ------------------- -------------------- 3.3/6.9 MB 2.1 MB/s eta 0:00:02\n",
1183
+ " ------------------- -------------------- 3.4/6.9 MB 2.1 MB/s eta 0:00:02\n",
1184
+ " -------------------- ------------------- 3.5/6.9 MB 2.1 MB/s eta 0:00:02\n",
1185
+ " -------------------- ------------------- 3.5/6.9 MB 2.1 MB/s eta 0:00:02\n",
1186
+ " -------------------- ------------------- 3.5/6.9 MB 2.1 MB/s eta 0:00:02\n",
1187
+ " -------------------- ------------------- 3.6/6.9 MB 2.0 MB/s eta 0:00:02\n",
1188
+ " -------------------- ------------------- 3.6/6.9 MB 2.0 MB/s eta 0:00:02\n",
1189
+ " --------------------- ------------------ 3.6/6.9 MB 1.9 MB/s eta 0:00:02\n",
1190
+ " --------------------- ------------------ 3.7/6.9 MB 1.9 MB/s eta 0:00:02\n",
1191
+ " --------------------- ------------------ 3.7/6.9 MB 1.9 MB/s eta 0:00:02\n",
1192
+ " --------------------- ------------------ 3.7/6.9 MB 1.9 MB/s eta 0:00:02\n",
1193
+ " --------------------- ------------------ 3.7/6.9 MB 1.9 MB/s eta 0:00:02\n",
1194
+ " --------------------- ------------------ 3.7/6.9 MB 1.7 MB/s eta 0:00:02\n",
1195
+ " ----------------------- ---------------- 4.1/6.9 MB 1.9 MB/s eta 0:00:02\n",
1196
+ " ------------------------ --------------- 4.2/6.9 MB 1.9 MB/s eta 0:00:02\n",
1197
+ " ------------------------- -------------- 4.4/6.9 MB 1.9 MB/s eta 0:00:02\n",
1198
+ " ------------------------- -------------- 4.5/6.9 MB 1.9 MB/s eta 0:00:02\n",
1199
+ " -------------------------- ------------- 4.5/6.9 MB 1.9 MB/s eta 0:00:02\n",
1200
+ " -------------------------- ------------- 4.5/6.9 MB 1.9 MB/s eta 0:00:02\n",
1201
+ " -------------------------- ------------- 4.6/6.9 MB 1.9 MB/s eta 0:00:02\n",
1202
+ " -------------------------- ------------- 4.6/6.9 MB 1.9 MB/s eta 0:00:02\n",
1203
+ " ---------------------------- ----------- 4.9/6.9 MB 1.9 MB/s eta 0:00:02\n",
1204
+ " ---------------------------- ----------- 5.0/6.9 MB 1.9 MB/s eta 0:00:02\n",
1205
+ " ---------------------------- ----------- 5.0/6.9 MB 1.9 MB/s eta 0:00:02\n",
1206
+ " ---------------------------- ----------- 5.0/6.9 MB 1.9 MB/s eta 0:00:02\n",
1207
+ " ----------------------------- ---------- 5.1/6.9 MB 1.9 MB/s eta 0:00:01\n",
1208
+ " ------------------------------ --------- 5.2/6.9 MB 1.9 MB/s eta 0:00:01\n",
1209
+ " ------------------------------ --------- 5.2/6.9 MB 1.9 MB/s eta 0:00:01\n",
1210
+ " ------------------------------ --------- 5.3/6.9 MB 1.8 MB/s eta 0:00:01\n",
1211
+ " ------------------------------ --------- 5.3/6.9 MB 1.8 MB/s eta 0:00:01\n",
1212
+ " ------------------------------- -------- 5.4/6.9 MB 1.8 MB/s eta 0:00:01\n",
1213
+ " ------------------------------- -------- 5.5/6.9 MB 1.8 MB/s eta 0:00:01\n",
1214
+ " -------------------------------- ------- 5.5/6.9 MB 1.8 MB/s eta 0:00:01\n",
1215
+ " -------------------------------- ------- 5.6/6.9 MB 1.8 MB/s eta 0:00:01\n",
1216
+ " -------------------------------- ------- 5.7/6.9 MB 1.8 MB/s eta 0:00:01\n",
1217
+ " --------------------------------- ------ 5.7/6.9 MB 1.8 MB/s eta 0:00:01\n",
1218
+ " --------------------------------- ------ 5.7/6.9 MB 1.8 MB/s eta 0:00:01\n",
1219
+ " ---------------------------------- ----- 5.9/6.9 MB 1.8 MB/s eta 0:00:01\n",
1220
+ " ---------------------------------- ----- 5.9/6.9 MB 1.8 MB/s eta 0:00:01\n",
1221
+ " ---------------------------------- ----- 5.9/6.9 MB 1.8 MB/s eta 0:00:01\n",
1222
+ " ---------------------------------- ----- 6.0/6.9 MB 1.8 MB/s eta 0:00:01\n",
1223
+ " ----------------------------------- ---- 6.1/6.9 MB 1.7 MB/s eta 0:00:01\n",
1224
+ " ----------------------------------- ---- 6.1/6.9 MB 1.7 MB/s eta 0:00:01\n",
1225
+ " ----------------------------------- ---- 6.2/6.9 MB 1.7 MB/s eta 0:00:01\n",
1226
+ " ----------------------------------- ---- 6.2/6.9 MB 1.7 MB/s eta 0:00:01\n",
1227
+ " ------------------------------------ --- 6.2/6.9 MB 1.7 MB/s eta 0:00:01\n",
1228
+ " ------------------------------------ --- 6.3/6.9 MB 1.7 MB/s eta 0:00:01\n",
1229
+ " ------------------------------------- -- 6.5/6.9 MB 1.7 MB/s eta 0:00:01\n",
1230
+ " ------------------------------------- -- 6.6/6.9 MB 1.7 MB/s eta 0:00:01\n",
1231
+ " -------------------------------------- - 6.6/6.9 MB 1.7 MB/s eta 0:00:01\n",
1232
+ " -------------------------------------- - 6.7/6.9 MB 1.7 MB/s eta 0:00:01\n",
1233
+ " --------------------------------------- 6.8/6.9 MB 1.7 MB/s eta 0:00:01\n",
1234
+ " --------------------------------------- 6.9/6.9 MB 1.7 MB/s eta 0:00:01\n",
1235
+ " ---------------------------------------- 6.9/6.9 MB 1.7 MB/s eta 0:00:00\n",
1236
+ "Downloading rich-13.7.1-py3-none-any.whl (240 kB)\n",
1237
+ " ---------------------------------------- 0.0/240.7 kB ? eta -:--:--\n",
1238
+ " --------------- ------------------------ 92.2/240.7 kB 2.6 MB/s eta 0:00:01\n",
1239
+ " --------------------------- ------------ 163.8/240.7 kB 2.4 MB/s eta 0:00:01\n",
1240
+ " -------------------------------- ------- 194.6/240.7 kB 1.7 MB/s eta 0:00:01\n",
1241
+ " ---------------------------------------- 240.7/240.7 kB 1.5 MB/s eta 0:00:00\n",
1242
+ "Downloading tenacity-8.3.0-py3-none-any.whl (25 kB)\n",
1243
+ "Downloading watchdog-4.0.1-py3-none-win_amd64.whl (83 kB)\n",
1244
+ " ---------------------------------------- 0.0/83.0 kB ? eta -:--:--\n",
1245
+ " ---------------------------------------- 83.0/83.0 kB 1.5 MB/s eta 0:00:00\n",
1246
+ "Downloading gitdb-4.0.11-py3-none-any.whl (62 kB)\n",
1247
+ " ---------------------------------------- 0.0/62.7 kB ? eta -:--:--\n",
1248
+ " ---------------------------------------- 62.7/62.7 kB ? eta 0:00:00\n",
1249
+ "Downloading markdown_it_py-3.0.0-py3-none-any.whl (87 kB)\n",
1250
+ " ---------------------------------------- 0.0/87.5 kB ? eta -:--:--\n",
1251
+ " ---------------------------------------- 87.5/87.5 kB 4.8 MB/s eta 0:00:00\n",
1252
+ "Downloading mdurl-0.1.2-py3-none-any.whl (10.0 kB)\n",
1253
+ "Downloading smmap-5.0.1-py3-none-any.whl (24 kB)\n",
1254
+ "Installing collected packages: watchdog, tenacity, smmap, mdurl, cachetools, blinker, pydeck, markdown-it-py, gitdb, rich, gitpython, streamlit\n",
1255
+ "Successfully installed blinker-1.8.2 cachetools-5.3.3 gitdb-4.0.11 gitpython-3.1.43 markdown-it-py-3.0.0 mdurl-0.1.2 pydeck-0.9.1 rich-13.7.1 smmap-5.0.1 streamlit-1.35.0 tenacity-8.3.0 watchdog-4.0.1\n"
1256
+ ]
1257
+ }
1258
+ ],
1259
+ "source": [
1260
+ "!pip install streamlit"
1261
+ ]
1262
+ },
1263
+ {
1264
+ "cell_type": "markdown",
1265
+ "id": "f408d44e-72f6-4096-961c-a0747cf79061",
1266
+ "metadata": {},
1267
+ "source": [
1268
+ "# python ile yapılan çalışmaların hızlı bir şekilde deployment süreçleri - HTML Rendering"
1269
+ ]
1270
+ },
1271
+ {
1272
+ "cell_type": "code",
1273
+ "execution_count": 56,
1274
+ "id": "30f657b3-cb9e-4e08-9179-2eaf3ebcc51b",
1275
+ "metadata": {},
1276
+ "outputs": [
1277
+ {
1278
+ "name": "stderr",
1279
+ "output_type": "stream",
1280
+ "text": [
1281
+ "2024-06-11 20:10:16.914 Session state does not function when running a script without `streamlit run`\n"
1282
+ ]
1283
+ }
1284
+ ],
1285
+ "source": [
1286
+ "import streamlit as st\n",
1287
+ "#price tahmin fonksiyonu tanımla\n",
1288
+ "def price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather):\n",
1289
+ " input_data=pd.DataFrame({'Make':[make],\n",
1290
+ " 'Model':[model],\n",
1291
+ " 'Trim':[trim],\n",
1292
+ " 'Mileage':[mileage],\n",
1293
+ " 'Type':[car_type],\n",
1294
+ " 'Cylinder':[cylinder],\n",
1295
+ " 'Liter':[liter],\n",
1296
+ " 'Doors':[doors],\n",
1297
+ " 'Cruise':[cruise],\n",
1298
+ " 'Sound':[sound],\n",
1299
+ " 'Leather':[leather]})\n",
1300
+ " prediction = pipe.predict(input_data)[0]\n",
1301
+ " return prediction\n",
1302
+ "\n",
1303
+ "\n",
1304
+ "st.title(\"Car Price Prediction: red_car: @ErayCoşkunAI\")\n",
1305
+ "st.write('Select feature of the car')\n",
1306
+ "make = st.selectbox(\"Brand of Car\",df['Make'].unique())\n",
1307
+ "model = st.selectbox(\"Model of Car\",df[df['Make']==make]['Model'].unique())\n",
1308
+ "trim = st.selectbox('Trim Of Car', df[(df['Make']==make) & (df['Model']==model)]['Trim'].unique())\n",
1309
+ "mileage = st.number_input('Kilometer of Car',100,200000)\n",
1310
+ "car_type = st.selectbox('Type Of Car', df['Type'].unique())\n",
1311
+ "cylinder = st.selectbox('Cylinder of Car',df['Cylinder'].unique())\n",
1312
+ "liter = st.number_input('Liter value of car',1,10)\n",
1313
+ "doors = st.selectbox('Count of Door',df['Doors'].unique())\n",
1314
+ "cruise = st.radio('Hız sbt', [True,False])\n",
1315
+ "sound = st.radio('Sound System',[True,False])\n",
1316
+ "leather = st.radio('Deri Döşeme',[True,False])\n",
1317
+ "if st.button('Tahmin'):\n",
1318
+ " pred = price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather)\n",
1319
+ " st.write('Price:$',round(pred[0],2))\n"
1320
+ ]
1321
+ },
1322
+ {
1323
+ "cell_type": "code",
1324
+ "execution_count": null,
1325
+ "id": "2676981b-51c0-4d72-8897-011bdc45724a",
1326
+ "metadata": {},
1327
+ "outputs": [],
1328
+ "source": []
1329
+ }
1330
+ ],
1331
+ "metadata": {
1332
+ "kernelspec": {
1333
+ "display_name": "Python 3 (ipykernel)",
1334
+ "language": "python",
1335
+ "name": "python3"
1336
+ },
1337
+ "language_info": {
1338
+ "codemirror_mode": {
1339
+ "name": "ipython",
1340
+ "version": 3
1341
+ },
1342
+ "file_extension": ".py",
1343
+ "mimetype": "text/x-python",
1344
+ "name": "python",
1345
+ "nbconvert_exporter": "python",
1346
+ "pygments_lexer": "ipython3",
1347
+ "version": "3.11.9"
1348
+ }
1349
+ },
1350
+ "nbformat": 4,
1351
+ "nbformat_minor": 5
1352
+ }
app.py ADDED
@@ -0,0 +1,196 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python
2
+ # coding: utf-8
3
+
4
+ # # Car Prediction #
5
+ # İkinci el araç fiyatlarını (özelliklerine göre) tahmin eden modeller oluşturma ve MLOPs ile Hugging Face üzerinden yayımlayacağız.
6
+ #
7
+
8
+ # In[1]:
9
+
10
+
11
+ import pandas as pd
12
+ from sklearn.model_selection import train_test_split #veri setini bölme işlemleri
13
+ from sklearn.linear_model import LinearRegression #Doğrusal regresyon
14
+ from sklearn.metrics import r2_score,mean_squared_error #modelimizin performansını ölçmek için
15
+ from sklearn.compose import ColumnTransformer #Sütun dönüşüm işlemleri
16
+ from sklearn.preprocessing import OneHotEncoder, StandardScaler # kategori - sayısal dönüşüm ve ölçeklendirme
17
+ from sklearn.pipeline import Pipeline #Veri işleme hattı
18
+
19
+
20
+ # In[ ]:
21
+
22
+
23
+ #Excell dosyalarını okumak için
24
+
25
+
26
+ # In[2]:
27
+
28
+
29
+ get_ipython().system('pip install xldr')
30
+
31
+
32
+ # ## Veri dosyasını yükle
33
+
34
+ # In[3]:
35
+
36
+
37
+ ls
38
+
39
+
40
+ # In[5]:
41
+
42
+
43
+ df=pd.read_excel('cars.xls')
44
+ df
45
+
46
+
47
+ # In[10]:
48
+
49
+
50
+ df.info()
51
+
52
+
53
+ # In[6]:
54
+
55
+
56
+ # Veri ön işleme
57
+
58
+
59
+ # In[7]:
60
+
61
+
62
+ X=df.drop('Price',axis=1) #fiyat sütunu çıkar fiyata etki edenler kalsın
63
+ y=df['Price'] #tahmin
64
+
65
+
66
+ # In[9]:
67
+
68
+
69
+ X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)
70
+
71
+
72
+ # #### Veri ön işleme, standartlaştırma ve OHE işlemlerini otomatikleştiriyoruz (standarlaştırıyoruz). Artık preprocess kullanarak kullanıcında arayüz aracılığıyla gelen veriyi mdoelimize uygun hale çevirebiliriz.
73
+ #
74
+
75
+ # In[11]:
76
+
77
+
78
+ preprocess=ColumnTransformer(
79
+ transformers=[
80
+ ('num',StandardScaler(),['Mileage', 'Cylinder','Liter','Doors']),
81
+ ('cat',OneHotEncoder(),['Make','Model','Trim','Type'])
82
+ ]
83
+ )
84
+
85
+
86
+ # In[12]:
87
+
88
+
89
+ my_model=LinearRegression()
90
+
91
+
92
+ # In[13]:
93
+
94
+
95
+ #pipeline ı tanımla
96
+ pipe=Pipeline(steps=[('preprocessor',preprocess),('model',my_model)])
97
+
98
+
99
+ # In[14]:
100
+
101
+
102
+ #pipeline fit
103
+ pipe.fit(X_train,y_train)
104
+
105
+
106
+ # In[16]:
107
+
108
+
109
+ y_pred=pipe.predict(X_test)
110
+ print('RMSE',mean_squared_error(y_test,y_pred)**0.5)
111
+ print('R2',r2_score(y_test,y_pred))
112
+
113
+
114
+ # In[ ]:
115
+
116
+
117
+ #isterseniz veri setinin tammamıyla tekrar eğitim yapabilirsiniz.
118
+ #pipe.fit(X,y)
119
+
120
+
121
+ # ## Streamlit ile modeli yayma/deploy/kullanıma sunma
122
+
123
+ # In[17]:
124
+
125
+
126
+ get_ipython().system('pip install streamlit')
127
+
128
+
129
+ # In[18]:
130
+
131
+
132
+ df['Mileage'].max()
133
+
134
+
135
+ # In[19]:
136
+
137
+
138
+ df['Type'].unique()
139
+
140
+
141
+ # In[20]:
142
+
143
+
144
+ df['Liter'].max()
145
+
146
+
147
+ # #### Python ile yapılan çalışmnalrın hızlı bir şekilde deploy edilmesi için HTML render arayüzler tasarlamanızı sağlar.
148
+
149
+ # In[21]:
150
+
151
+
152
+ import streamlit as st
153
+ #price tahmin fonksiyonu tanımla
154
+ def price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather):
155
+ input_data=pd.DataFrame({'Make':[make],
156
+ 'Model':[model],
157
+ 'Trim':[trim],
158
+ 'Mileage':[mileage],
159
+ 'Type':[car_type],
160
+ 'Cylinder':[cylinder],
161
+ 'Liter':[liter],
162
+ 'Doors':[doors],
163
+ 'Cruise':[cruise],
164
+ 'Sound':[sound],
165
+ 'Leather':[leather]})
166
+ prediction=pipe.predict(input_data)[0]
167
+ return prediction
168
+ st.title("II. El Araba Fiyatı Tahmin:red_car: @drmurataltun")
169
+ st.write('Arabanın özelliklerini seçiniz')
170
+ make=st.selectbox('Marka',df['Make'].unique())
171
+ model=st.selectbox('Model',df[df['Make']==make]['Model'].unique())
172
+ trim=st.selectbox('Trim',df[(df['Make']==make) &(df['Model']==model)]['Trim'].unique())
173
+ mileage=st.number_input('Kilometre',100,200000)
174
+ car_type=st.selectbox('Araç Tipi',df[(df['Make']==make) &(df['Model']==model)&(df['Trim']==trim)]['Type'].unique())
175
+ cylinder=st.selectbox('Cylinder',df['Cylinder'].unique())
176
+ liter=st.number_input('Yakıt hacmi',1,10)
177
+ doors=st.selectbox('Kapı sayısı',df['Doors'].unique())
178
+ cruise=st.radio('Hız Sbt.',[True,False])
179
+ sound=st.radio('Ses Sis.',[True,False])
180
+ leather=st.radio('Deri döşeme.',[True,False])
181
+ if st.button('Tahmin'):
182
+ pred=price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather)
183
+ st.write('Fiyat:$', round(pred[0],2))
184
+
185
+
186
+ # In[25]:
187
+
188
+
189
+ #streamlit run C:\ProgramData\anaconda3\Lib\site-packages\ipykernel_launcher.py
190
+
191
+
192
+ # In[ ]:
193
+
194
+
195
+
196
+
cars.xls ADDED
Binary file (142 kB). View file
 
requirements.txt.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ streamlit==1.31.1
2
+ scikit-learn==1.4.1.post1
3
+ pandas==2.1.0
4
+ xlrd == 2.0.1