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Commit
5f406a1
·
1 Parent(s): 188fbc5

train model and title and description

Browse files
Files changed (2) hide show
  1. app.py +2 -0
  2. train_model.ipynb +890 -0
app.py CHANGED
@@ -45,6 +45,8 @@ iface = gr.Interface(
45
  [7.4, 0.7, 0.0, 1.9, 0.076, 11.0, 34.0, 0.9978, 3.51, 0.56, 9.4],
46
  ],
47
  interpretation="default",
 
 
48
  )
49
 
50
  if __name__ == "__main__":
 
45
  [7.4, 0.7, 0.0, 1.9, 0.076, 11.0, 34.0, 0.9978, 3.51, 0.56, 9.4],
46
  ],
47
  interpretation="default",
48
+ title="Wine quality regressor",
49
+ description="Predict wine quality based on properties"
50
  )
51
 
52
  if __name__ == "__main__":
train_model.ipynb ADDED
@@ -0,0 +1,890 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "nbformat": 4,
3
+ "nbformat_minor": 0,
4
+ "metadata": {
5
+ "colab": {
6
+ "name": "train_model.ipynb",
7
+ "provenance": []
8
+ },
9
+ "kernelspec": {
10
+ "name": "python3",
11
+ "display_name": "Python 3"
12
+ },
13
+ "language_info": {
14
+ "name": "python"
15
+ }
16
+ },
17
+ "cells": [
18
+ {
19
+ "cell_type": "code",
20
+ "execution_count": 23,
21
+ "metadata": {
22
+ "id": "kFAHrl4RTtV4"
23
+ },
24
+ "outputs": [],
25
+ "source": [
26
+ "import pandas as pd\n",
27
+ "from sklearn.model_selection import train_test_split\n",
28
+ "from sklearn.ensemble import RandomForestRegressor\n"
29
+ ]
30
+ },
31
+ {
32
+ "cell_type": "code",
33
+ "source": [
34
+ "wine = pd.read_csv(\"winequality-red.csv\")"
35
+ ],
36
+ "metadata": {
37
+ "id": "PtRnEnZqUVz3"
38
+ },
39
+ "execution_count": 24,
40
+ "outputs": []
41
+ },
42
+ {
43
+ "cell_type": "code",
44
+ "source": [
45
+ "wine.describe()"
46
+ ],
47
+ "metadata": {
48
+ "colab": {
49
+ "base_uri": "https://localhost:8080/",
50
+ "height": 346
51
+ },
52
+ "id": "BEyAqxlzcY7K",
53
+ "outputId": "c9a6c736-27c4-4fcf-a70c-bb9f63dbb021"
54
+ },
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+ "execution_count": 43,
56
+ "outputs": [
57
+ {
58
+ "output_type": "execute_result",
59
+ "data": {
60
+ "text/html": [
61
+ "\n",
62
+ " <div id=\"df-0bdcf199-7a2a-4ca2-b770-b67e10da4e05\">\n",
63
+ " <div class=\"colab-df-container\">\n",
64
+ " <div>\n",
65
+ "<style scoped>\n",
66
+ " .dataframe tbody tr th:only-of-type {\n",
67
+ " vertical-align: middle;\n",
68
+ " }\n",
69
+ "\n",
70
+ " .dataframe tbody tr th {\n",
71
+ " vertical-align: top;\n",
72
+ " }\n",
73
+ "\n",
74
+ " .dataframe thead th {\n",
75
+ " text-align: right;\n",
76
+ " }\n",
77
+ "</style>\n",
78
+ "<table border=\"1\" class=\"dataframe\">\n",
79
+ " <thead>\n",
80
+ " <tr style=\"text-align: right;\">\n",
81
+ " <th></th>\n",
82
+ " <th>fixed acidity</th>\n",
83
+ " <th>volatile acidity</th>\n",
84
+ " <th>citric acid</th>\n",
85
+ " <th>residual sugar</th>\n",
86
+ " <th>chlorides</th>\n",
87
+ " <th>free sulfur dioxide</th>\n",
88
+ " <th>total sulfur dioxide</th>\n",
89
+ " <th>density</th>\n",
90
+ " <th>pH</th>\n",
91
+ " <th>sulphates</th>\n",
92
+ " <th>alcohol</th>\n",
93
+ " <th>quality</th>\n",
94
+ " </tr>\n",
95
+ " </thead>\n",
96
+ " <tbody>\n",
97
+ " <tr>\n",
98
+ " <th>count</th>\n",
99
+ " <td>1599.000000</td>\n",
100
+ " <td>1599.000000</td>\n",
101
+ " <td>1599.000000</td>\n",
102
+ " <td>1599.000000</td>\n",
103
+ " <td>1599.000000</td>\n",
104
+ " <td>1599.000000</td>\n",
105
+ " <td>1599.000000</td>\n",
106
+ " <td>1599.000000</td>\n",
107
+ " <td>1599.000000</td>\n",
108
+ " <td>1599.000000</td>\n",
109
+ " <td>1599.000000</td>\n",
110
+ " <td>1599.000000</td>\n",
111
+ " </tr>\n",
112
+ " <tr>\n",
113
+ " <th>mean</th>\n",
114
+ " <td>8.319637</td>\n",
115
+ " <td>0.527821</td>\n",
116
+ " <td>0.270976</td>\n",
117
+ " <td>2.538806</td>\n",
118
+ " <td>0.087467</td>\n",
119
+ " <td>15.874922</td>\n",
120
+ " <td>46.467792</td>\n",
121
+ " <td>0.996747</td>\n",
122
+ " <td>3.311113</td>\n",
123
+ " <td>0.658149</td>\n",
124
+ " <td>10.422983</td>\n",
125
+ " <td>5.636023</td>\n",
126
+ " </tr>\n",
127
+ " <tr>\n",
128
+ " <th>std</th>\n",
129
+ " <td>1.741096</td>\n",
130
+ " <td>0.179060</td>\n",
131
+ " <td>0.194801</td>\n",
132
+ " <td>1.409928</td>\n",
133
+ " <td>0.047065</td>\n",
134
+ " <td>10.460157</td>\n",
135
+ " <td>32.895324</td>\n",
136
+ " <td>0.001887</td>\n",
137
+ " <td>0.154386</td>\n",
138
+ " <td>0.169507</td>\n",
139
+ " <td>1.065668</td>\n",
140
+ " <td>0.807569</td>\n",
141
+ " </tr>\n",
142
+ " <tr>\n",
143
+ " <th>min</th>\n",
144
+ " <td>4.600000</td>\n",
145
+ " <td>0.120000</td>\n",
146
+ " <td>0.000000</td>\n",
147
+ " <td>0.900000</td>\n",
148
+ " <td>0.012000</td>\n",
149
+ " <td>1.000000</td>\n",
150
+ " <td>6.000000</td>\n",
151
+ " <td>0.990070</td>\n",
152
+ " <td>2.740000</td>\n",
153
+ " <td>0.330000</td>\n",
154
+ " <td>8.400000</td>\n",
155
+ " <td>3.000000</td>\n",
156
+ " </tr>\n",
157
+ " <tr>\n",
158
+ " <th>25%</th>\n",
159
+ " <td>7.100000</td>\n",
160
+ " <td>0.390000</td>\n",
161
+ " <td>0.090000</td>\n",
162
+ " <td>1.900000</td>\n",
163
+ " <td>0.070000</td>\n",
164
+ " <td>7.000000</td>\n",
165
+ " <td>22.000000</td>\n",
166
+ " <td>0.995600</td>\n",
167
+ " <td>3.210000</td>\n",
168
+ " <td>0.550000</td>\n",
169
+ " <td>9.500000</td>\n",
170
+ " <td>5.000000</td>\n",
171
+ " </tr>\n",
172
+ " <tr>\n",
173
+ " <th>50%</th>\n",
174
+ " <td>7.900000</td>\n",
175
+ " <td>0.520000</td>\n",
176
+ " <td>0.260000</td>\n",
177
+ " <td>2.200000</td>\n",
178
+ " <td>0.079000</td>\n",
179
+ " <td>14.000000</td>\n",
180
+ " <td>38.000000</td>\n",
181
+ " <td>0.996750</td>\n",
182
+ " <td>3.310000</td>\n",
183
+ " <td>0.620000</td>\n",
184
+ " <td>10.200000</td>\n",
185
+ " <td>6.000000</td>\n",
186
+ " </tr>\n",
187
+ " <tr>\n",
188
+ " <th>75%</th>\n",
189
+ " <td>9.200000</td>\n",
190
+ " <td>0.640000</td>\n",
191
+ " <td>0.420000</td>\n",
192
+ " <td>2.600000</td>\n",
193
+ " <td>0.090000</td>\n",
194
+ " <td>21.000000</td>\n",
195
+ " <td>62.000000</td>\n",
196
+ " <td>0.997835</td>\n",
197
+ " <td>3.400000</td>\n",
198
+ " <td>0.730000</td>\n",
199
+ " <td>11.100000</td>\n",
200
+ " <td>6.000000</td>\n",
201
+ " </tr>\n",
202
+ " <tr>\n",
203
+ " <th>max</th>\n",
204
+ " <td>15.900000</td>\n",
205
+ " <td>1.580000</td>\n",
206
+ " <td>1.000000</td>\n",
207
+ " <td>15.500000</td>\n",
208
+ " <td>0.611000</td>\n",
209
+ " <td>72.000000</td>\n",
210
+ " <td>289.000000</td>\n",
211
+ " <td>1.003690</td>\n",
212
+ " <td>4.010000</td>\n",
213
+ " <td>2.000000</td>\n",
214
+ " <td>14.900000</td>\n",
215
+ " <td>8.000000</td>\n",
216
+ " </tr>\n",
217
+ " </tbody>\n",
218
+ "</table>\n",
219
+ "</div>\n",
220
+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0bdcf199-7a2a-4ca2-b770-b67e10da4e05')\"\n",
221
+ " title=\"Convert this dataframe to an interactive table.\"\n",
222
+ " style=\"display:none;\">\n",
223
+ " \n",
224
+ " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
225
+ " width=\"24px\">\n",
226
+ " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
227
+ " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
228
+ " </svg>\n",
229
+ " </button>\n",
230
+ " \n",
231
+ " <style>\n",
232
+ " .colab-df-container {\n",
233
+ " display:flex;\n",
234
+ " flex-wrap:wrap;\n",
235
+ " gap: 12px;\n",
236
+ " }\n",
237
+ "\n",
238
+ " .colab-df-convert {\n",
239
+ " background-color: #E8F0FE;\n",
240
+ " border: none;\n",
241
+ " border-radius: 50%;\n",
242
+ " cursor: pointer;\n",
243
+ " display: none;\n",
244
+ " fill: #1967D2;\n",
245
+ " height: 32px;\n",
246
+ " padding: 0 0 0 0;\n",
247
+ " width: 32px;\n",
248
+ " }\n",
249
+ "\n",
250
+ " .colab-df-convert:hover {\n",
251
+ " background-color: #E2EBFA;\n",
252
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
253
+ " fill: #174EA6;\n",
254
+ " }\n",
255
+ "\n",
256
+ " [theme=dark] .colab-df-convert {\n",
257
+ " background-color: #3B4455;\n",
258
+ " fill: #D2E3FC;\n",
259
+ " }\n",
260
+ "\n",
261
+ " [theme=dark] .colab-df-convert:hover {\n",
262
+ " background-color: #434B5C;\n",
263
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
264
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
265
+ " fill: #FFFFFF;\n",
266
+ " }\n",
267
+ " </style>\n",
268
+ "\n",
269
+ " <script>\n",
270
+ " const buttonEl =\n",
271
+ " document.querySelector('#df-0bdcf199-7a2a-4ca2-b770-b67e10da4e05 button.colab-df-convert');\n",
272
+ " buttonEl.style.display =\n",
273
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
274
+ "\n",
275
+ " async function convertToInteractive(key) {\n",
276
+ " const element = document.querySelector('#df-0bdcf199-7a2a-4ca2-b770-b67e10da4e05');\n",
277
+ " const dataTable =\n",
278
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
279
+ " [key], {});\n",
280
+ " if (!dataTable) return;\n",
281
+ "\n",
282
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
283
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
284
+ " + ' to learn more about interactive tables.';\n",
285
+ " element.innerHTML = '';\n",
286
+ " dataTable['output_type'] = 'display_data';\n",
287
+ " await google.colab.output.renderOutput(dataTable, element);\n",
288
+ " const docLink = document.createElement('div');\n",
289
+ " docLink.innerHTML = docLinkHtml;\n",
290
+ " element.appendChild(docLink);\n",
291
+ " }\n",
292
+ " </script>\n",
293
+ " </div>\n",
294
+ " </div>\n",
295
+ " "
296
+ ],
297
+ "text/plain": [
298
+ " fixed acidity volatile acidity ... alcohol quality\n",
299
+ "count 1599.000000 1599.000000 ... 1599.000000 1599.000000\n",
300
+ "mean 8.319637 0.527821 ... 10.422983 5.636023\n",
301
+ "std 1.741096 0.179060 ... 1.065668 0.807569\n",
302
+ "min 4.600000 0.120000 ... 8.400000 3.000000\n",
303
+ "25% 7.100000 0.390000 ... 9.500000 5.000000\n",
304
+ "50% 7.900000 0.520000 ... 10.200000 6.000000\n",
305
+ "75% 9.200000 0.640000 ... 11.100000 6.000000\n",
306
+ "max 15.900000 1.580000 ... 14.900000 8.000000\n",
307
+ "\n",
308
+ "[8 rows x 12 columns]"
309
+ ]
310
+ },
311
+ "metadata": {},
312
+ "execution_count": 43
313
+ }
314
+ ]
315
+ },
316
+ {
317
+ "cell_type": "code",
318
+ "source": [
319
+ "wine.head()"
320
+ ],
321
+ "metadata": {
322
+ "id": "xuERkgD3UZlx",
323
+ "colab": {
324
+ "base_uri": "https://localhost:8080/",
325
+ "height": 204
326
+ },
327
+ "outputId": "8376f32a-b09b-4c7a-be86-3413a770c3b5"
328
+ },
329
+ "execution_count": 25,
330
+ "outputs": [
331
+ {
332
+ "output_type": "execute_result",
333
+ "data": {
334
+ "text/html": [
335
+ "\n",
336
+ " <div id=\"df-9c66c3b7-b0e1-46aa-8330-3d03c87c9b1b\">\n",
337
+ " <div class=\"colab-df-container\">\n",
338
+ " <div>\n",
339
+ "<style scoped>\n",
340
+ " .dataframe tbody tr th:only-of-type {\n",
341
+ " vertical-align: middle;\n",
342
+ " }\n",
343
+ "\n",
344
+ " .dataframe tbody tr th {\n",
345
+ " vertical-align: top;\n",
346
+ " }\n",
347
+ "\n",
348
+ " .dataframe thead th {\n",
349
+ " text-align: right;\n",
350
+ " }\n",
351
+ "</style>\n",
352
+ "<table border=\"1\" class=\"dataframe\">\n",
353
+ " <thead>\n",
354
+ " <tr style=\"text-align: right;\">\n",
355
+ " <th></th>\n",
356
+ " <th>fixed acidity</th>\n",
357
+ " <th>volatile acidity</th>\n",
358
+ " <th>citric acid</th>\n",
359
+ " <th>residual sugar</th>\n",
360
+ " <th>chlorides</th>\n",
361
+ " <th>free sulfur dioxide</th>\n",
362
+ " <th>total sulfur dioxide</th>\n",
363
+ " <th>density</th>\n",
364
+ " <th>pH</th>\n",
365
+ " <th>sulphates</th>\n",
366
+ " <th>alcohol</th>\n",
367
+ " <th>quality</th>\n",
368
+ " </tr>\n",
369
+ " </thead>\n",
370
+ " <tbody>\n",
371
+ " <tr>\n",
372
+ " <th>0</th>\n",
373
+ " <td>7.4</td>\n",
374
+ " <td>0.70</td>\n",
375
+ " <td>0.00</td>\n",
376
+ " <td>1.9</td>\n",
377
+ " <td>0.076</td>\n",
378
+ " <td>11.0</td>\n",
379
+ " <td>34.0</td>\n",
380
+ " <td>0.9978</td>\n",
381
+ " <td>3.51</td>\n",
382
+ " <td>0.56</td>\n",
383
+ " <td>9.4</td>\n",
384
+ " <td>5</td>\n",
385
+ " </tr>\n",
386
+ " <tr>\n",
387
+ " <th>1</th>\n",
388
+ " <td>7.8</td>\n",
389
+ " <td>0.88</td>\n",
390
+ " <td>0.00</td>\n",
391
+ " <td>2.6</td>\n",
392
+ " <td>0.098</td>\n",
393
+ " <td>25.0</td>\n",
394
+ " <td>67.0</td>\n",
395
+ " <td>0.9968</td>\n",
396
+ " <td>3.20</td>\n",
397
+ " <td>0.68</td>\n",
398
+ " <td>9.8</td>\n",
399
+ " <td>5</td>\n",
400
+ " </tr>\n",
401
+ " <tr>\n",
402
+ " <th>2</th>\n",
403
+ " <td>7.8</td>\n",
404
+ " <td>0.76</td>\n",
405
+ " <td>0.04</td>\n",
406
+ " <td>2.3</td>\n",
407
+ " <td>0.092</td>\n",
408
+ " <td>15.0</td>\n",
409
+ " <td>54.0</td>\n",
410
+ " <td>0.9970</td>\n",
411
+ " <td>3.26</td>\n",
412
+ " <td>0.65</td>\n",
413
+ " <td>9.8</td>\n",
414
+ " <td>5</td>\n",
415
+ " </tr>\n",
416
+ " <tr>\n",
417
+ " <th>3</th>\n",
418
+ " <td>11.2</td>\n",
419
+ " <td>0.28</td>\n",
420
+ " <td>0.56</td>\n",
421
+ " <td>1.9</td>\n",
422
+ " <td>0.075</td>\n",
423
+ " <td>17.0</td>\n",
424
+ " <td>60.0</td>\n",
425
+ " <td>0.9980</td>\n",
426
+ " <td>3.16</td>\n",
427
+ " <td>0.58</td>\n",
428
+ " <td>9.8</td>\n",
429
+ " <td>6</td>\n",
430
+ " </tr>\n",
431
+ " <tr>\n",
432
+ " <th>4</th>\n",
433
+ " <td>7.4</td>\n",
434
+ " <td>0.70</td>\n",
435
+ " <td>0.00</td>\n",
436
+ " <td>1.9</td>\n",
437
+ " <td>0.076</td>\n",
438
+ " <td>11.0</td>\n",
439
+ " <td>34.0</td>\n",
440
+ " <td>0.9978</td>\n",
441
+ " <td>3.51</td>\n",
442
+ " <td>0.56</td>\n",
443
+ " <td>9.4</td>\n",
444
+ " <td>5</td>\n",
445
+ " </tr>\n",
446
+ " </tbody>\n",
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+ "</table>\n",
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+ "</div>\n",
449
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+ " </svg>\n",
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+ " </button>\n",
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+ " \n",
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+ " <style>\n",
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+ " .colab-df-container {\n",
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+ " display:flex;\n",
463
+ " flex-wrap:wrap;\n",
464
+ " gap: 12px;\n",
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+ " }\n",
466
+ "\n",
467
+ " .colab-df-convert {\n",
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+ " background-color: #E8F0FE;\n",
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+ " border: none;\n",
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+ " border-radius: 50%;\n",
471
+ " cursor: pointer;\n",
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+ " display: none;\n",
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+ " fill: #1967D2;\n",
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+ " height: 32px;\n",
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+ " padding: 0 0 0 0;\n",
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+ " width: 32px;\n",
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+ " }\n",
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+ "\n",
479
+ " .colab-df-convert:hover {\n",
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+ " background-color: #E2EBFA;\n",
481
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
482
+ " fill: #174EA6;\n",
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+ " }\n",
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+ "\n",
485
+ " [theme=dark] .colab-df-convert {\n",
486
+ " background-color: #3B4455;\n",
487
+ " fill: #D2E3FC;\n",
488
+ " }\n",
489
+ "\n",
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+ " [theme=dark] .colab-df-convert:hover {\n",
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+ " background-color: #434B5C;\n",
492
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
493
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+ " fill: #FFFFFF;\n",
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+ " }\n",
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+ " </style>\n",
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+ "\n",
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+ " <script>\n",
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+ " const buttonEl =\n",
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+ " document.querySelector('#df-9c66c3b7-b0e1-46aa-8330-3d03c87c9b1b button.colab-df-convert');\n",
501
+ " buttonEl.style.display =\n",
502
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
503
+ "\n",
504
+ " async function convertToInteractive(key) {\n",
505
+ " const element = document.querySelector('#df-9c66c3b7-b0e1-46aa-8330-3d03c87c9b1b');\n",
506
+ " const dataTable =\n",
507
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
508
+ " [key], {});\n",
509
+ " if (!dataTable) return;\n",
510
+ "\n",
511
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
512
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
513
+ " + ' to learn more about interactive tables.';\n",
514
+ " element.innerHTML = '';\n",
515
+ " dataTable['output_type'] = 'display_data';\n",
516
+ " await google.colab.output.renderOutput(dataTable, element);\n",
517
+ " const docLink = document.createElement('div');\n",
518
+ " docLink.innerHTML = docLinkHtml;\n",
519
+ " element.appendChild(docLink);\n",
520
+ " }\n",
521
+ " </script>\n",
522
+ " </div>\n",
523
+ " </div>\n",
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+ " "
525
+ ],
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+ "text/plain": [
527
+ " fixed acidity volatile acidity citric acid ... sulphates alcohol quality\n",
528
+ "0 7.4 0.70 0.00 ... 0.56 9.4 5\n",
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+ "1 7.8 0.88 0.00 ... 0.68 9.8 5\n",
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+ "\n",
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+ "[5 rows x 12 columns]"
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+ ]
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+ },
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+ "metadata": {},
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+ "execution_count": 25
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+ }
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+ ]
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+ },
542
+ {
543
+ "cell_type": "code",
544
+ "source": [
545
+ "X = wine.drop('quality', axis = 1)\n",
546
+ "y = wine['quality']"
547
+ ],
548
+ "metadata": {
549
+ "id": "G2XlFDL1UuGU"
550
+ },
551
+ "execution_count": 26,
552
+ "outputs": []
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+ },
554
+ {
555
+ "cell_type": "code",
556
+ "source": [
557
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
558
+ ],
559
+ "metadata": {
560
+ "id": "fwQiTgseUZ8t"
561
+ },
562
+ "execution_count": 27,
563
+ "outputs": []
564
+ },
565
+ {
566
+ "cell_type": "code",
567
+ "source": [
568
+ "rfc = RandomForestRegressor(n_estimators=200)\n",
569
+ "rfc.fit(X_train, y_train)\n",
570
+ "rfc.score(X_test, y_test)"
571
+ ],
572
+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/"
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+ },
576
+ "id": "gfaWbU0XU8qx",
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+ "outputId": "c1360af2-235a-441b-89dc-ed8a1a3cd652"
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579
+ "execution_count": 28,
580
+ "outputs": [
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+ {
582
+ "output_type": "execute_result",
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+ "data": {
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+ "0.452940101720804"
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+ }
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
596
+ "preds = rfc.predict(X_test)"
597
+ ],
598
+ "metadata": {
599
+ "id": "RS8QjOk6W3eW"
600
+ },
601
+ "execution_count": 31,
602
+ "outputs": []
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+ },
604
+ {
605
+ "cell_type": "code",
606
+ "source": [
607
+ "df.iloc[0,:]"
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+ ],
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+ "metadata": {
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+ "base_uri": "https://localhost:8080/"
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+ },
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+ "outputId": "27042b35-0070-4a8f-dc21-10e30ea4f03a"
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+ },
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+ "execution_count": 34,
617
+ "outputs": [
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+ {
619
+ "output_type": "execute_result",
620
+ "data": {
621
+ "text/plain": [
622
+ "fixed acidity 7.4000\n",
623
+ "volatile acidity 0.7000\n",
624
+ "citric acid 0.0000\n",
625
+ "residual sugar 1.9000\n",
626
+ "chlorides 0.0760\n",
627
+ "free sulfur dioxide 11.0000\n",
628
+ "total sulfur dioxide 34.0000\n",
629
+ "density 0.9978\n",
630
+ "pH 3.5100\n",
631
+ "sulphates 0.5600\n",
632
+ "alcohol 9.4000\n",
633
+ "quality 5.0000\n",
634
+ "Name: 0, dtype: float64"
635
+ ]
636
+ },
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+ "metadata": {},
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+ "execution_count": 34
639
+ }
640
+ ]
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+ },
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+ {
643
+ "cell_type": "code",
644
+ "source": [
645
+ "df_pred = pd.DataFrame.from_dict({\n",
646
+ " 'fixed acidity': 7.4, \n",
647
+ " 'volatile acidity': 0.7, \n",
648
+ " 'citric acid': 0, \n",
649
+ " 'residual sugar': 1.9,\n",
650
+ " 'chlorides': 0.076, \n",
651
+ " 'free sulfur dioxide': 11, \n",
652
+ " 'total sulfur dioxide': 34, \n",
653
+ " 'density':0.9978,\n",
654
+ " 'pH': 3.51, \n",
655
+ " 'sulphates': 0.56, \n",
656
+ " 'alcohol':9.4\n",
657
+ "}, orient='index').T"
658
+ ],
659
+ "metadata": {
660
+ "id": "YYRmAoyJYGKR"
661
+ },
662
+ "execution_count": 40,
663
+ "outputs": []
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+ },
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+ "df_pred"
669
+ ],
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+ "<table border=\"1\" class=\"dataframe\">\n",
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+ " <thead>\n",
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+ " <tr style=\"text-align: right;\">\n",
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+ " <th></th>\n",
705
+ " <th>fixed acidity</th>\n",
706
+ " <th>volatile acidity</th>\n",
707
+ " <th>citric acid</th>\n",
708
+ " <th>residual sugar</th>\n",
709
+ " <th>chlorides</th>\n",
710
+ " <th>free sulfur dioxide</th>\n",
711
+ " <th>total sulfur dioxide</th>\n",
712
+ " <th>density</th>\n",
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+ " <th>pH</th>\n",
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+ " <th>sulphates</th>\n",
715
+ " <th>alcohol</th>\n",
716
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717
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718
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+ " <tr>\n",
720
+ " <th>0</th>\n",
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+ " <td>0.076</td>\n",
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+ " <td>34.0</td>\n",
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+ " <td>0.9978</td>\n",
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+ " <td>3.51</td>\n",
730
+ " <td>0.56</td>\n",
731
+ " <td>9.4</td>\n",
732
+ " </tr>\n",
733
+ " </tbody>\n",
734
+ "</table>\n",
735
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736
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+ " background-color: #E8F0FE;\n",
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+ " border: none;\n",
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765
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766
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767
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768
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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+ " fill: #174EA6;\n",
770
+ " }\n",
771
+ "\n",
772
+ " [theme=dark] .colab-df-convert {\n",
773
+ " background-color: #3B4455;\n",
774
+ " fill: #D2E3FC;\n",
775
+ " }\n",
776
+ "\n",
777
+ " [theme=dark] .colab-df-convert:hover {\n",
778
+ " background-color: #434B5C;\n",
779
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
780
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
781
+ " fill: #FFFFFF;\n",
782
+ " }\n",
783
+ " </style>\n",
784
+ "\n",
785
+ " <script>\n",
786
+ " const buttonEl =\n",
787
+ " document.querySelector('#df-8ac8e971-1853-44d2-995f-b7f382067827 button.colab-df-convert');\n",
788
+ " buttonEl.style.display =\n",
789
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
790
+ "\n",
791
+ " async function convertToInteractive(key) {\n",
792
+ " const element = document.querySelector('#df-8ac8e971-1853-44d2-995f-b7f382067827');\n",
793
+ " const dataTable =\n",
794
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
795
+ " [key], {});\n",
796
+ " if (!dataTable) return;\n",
797
+ "\n",
798
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
799
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
800
+ " + ' to learn more about interactive tables.';\n",
801
+ " element.innerHTML = '';\n",
802
+ " dataTable['output_type'] = 'display_data';\n",
803
+ " await google.colab.output.renderOutput(dataTable, element);\n",
804
+ " const docLink = document.createElement('div');\n",
805
+ " docLink.innerHTML = docLinkHtml;\n",
806
+ " element.appendChild(docLink);\n",
807
+ " }\n",
808
+ " </script>\n",
809
+ " </div>\n",
810
+ " </div>\n",
811
+ " "
812
+ ],
813
+ "text/plain": [
814
+ " fixed acidity volatile acidity citric acid ... pH sulphates alcohol\n",
815
+ "0 7.4 0.7 0.0 ... 3.51 0.56 9.4\n",
816
+ "\n",
817
+ "[1 rows x 11 columns]"
818
+ ]
819
+ },
820
+ "metadata": {},
821
+ "execution_count": 41
822
+ }
823
+ ]
824
+ },
825
+ {
826
+ "cell_type": "code",
827
+ "source": [
828
+ "rfc.predict(df_pred)"
829
+ ],
830
+ "metadata": {
831
+ "colab": {
832
+ "base_uri": "https://localhost:8080/"
833
+ },
834
+ "id": "TbLBRotEYBOf",
835
+ "outputId": "6ac50cb8-147b-4c96-bc39-2261b736973e"
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+ },
837
+ "execution_count": 42,
838
+ "outputs": [
839
+ {
840
+ "output_type": "execute_result",
841
+ "data": {
842
+ "text/plain": [
843
+ "array([5.025])"
844
+ ]
845
+ },
846
+ "metadata": {},
847
+ "execution_count": 42
848
+ }
849
+ ]
850
+ },
851
+ {
852
+ "cell_type": "code",
853
+ "source": [
854
+ "from joblib import dump, load\n",
855
+ "dump(rfc, 'wine_pred.joblib') "
856
+ ],
857
+ "metadata": {
858
+ "colab": {
859
+ "base_uri": "https://localhost:8080/"
860
+ },
861
+ "id": "Wh5wXQqbWHNK",
862
+ "outputId": "84d59d4f-811b-4e1d-d182-267bbde56414"
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+ },
864
+ "execution_count": 32,
865
+ "outputs": [
866
+ {
867
+ "output_type": "execute_result",
868
+ "data": {
869
+ "text/plain": [
870
+ "['wine_pred.joblib']"
871
+ ]
872
+ },
873
+ "metadata": {},
874
+ "execution_count": 32
875
+ }
876
+ ]
877
+ },
878
+ {
879
+ "cell_type": "code",
880
+ "source": [
881
+ ""
882
+ ],
883
+ "metadata": {
884
+ "id": "BkfTMO4AXi4o"
885
+ },
886
+ "execution_count": null,
887
+ "outputs": []
888
+ }
889
+ ]
890
+ }