├── Agglomerative code .ipynb
├── IoT Security Prediction system.ipynb
├── KNN Seed model.ipynb
├── KNN-Mean Algo.ipynb
├── Linear_Regression_Practice.ipynb
├── Naive Bayes Model.ipynb
├── README.md
├── Smart HAR System.ipynb
├── TRAFFIC - VIOLATION
    └── Traffic rules violation.ipynb
├── Traffic-Light-Detection-CNN
    └── traffic_cnn.ipynb
├── googlebrains-vpp-ver1.ipynb
└── handwritingrecognition.ipynb


/KNN Seed model.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "3a6e1e1a",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "#importing libraries\n",
 11 |     "import numpy as np\n",
 12 |     "import pandas as pd\n",
 13 |     "import matplotlib.pyplot as plt\n",
 14 |     "import matplotlib.axes as plt\n"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "code",
 19 |    "execution_count": 3,
 20 |    "id": "9c4d171e",
 21 |    "metadata": {},
 22 |    "outputs": [],
 23 |    "source": [
 24 |     "#visualising data and importing\n",
 25 |     "data = pd.read_csv(\"L:\\seed_data.csv\")"
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "code",
 30 |    "execution_count": 4,
 31 |    "id": "e47cb14f",
 32 |    "metadata": {},
 33 |    "outputs": [
 34 |     {
 35 |      "data": {
 36 |       "text/html": [
 37 |        "<div>\n",
 38 |        "<style scoped>\n",
 39 |        "    .dataframe tbody tr th:only-of-type {\n",
 40 |        "        vertical-align: middle;\n",
 41 |        "    }\n",
 42 |        "\n",
 43 |        "    .dataframe tbody tr th {\n",
 44 |        "        vertical-align: top;\n",
 45 |        "    }\n",
 46 |        "\n",
 47 |        "    .dataframe thead th {\n",
 48 |        "        text-align: right;\n",
 49 |        "    }\n",
 50 |        "</style>\n",
 51 |        "<table border=\"1\" class=\"dataframe\">\n",
 52 |        "  <thead>\n",
 53 |        "    <tr style=\"text-align: right;\">\n",
 54 |        "      <th></th>\n",
 55 |        "      <th>area</th>\n",
 56 |        "      <th>perimeter</th>\n",
 57 |        "      <th>compactness</th>\n",
 58 |        "      <th>length</th>\n",
 59 |        "      <th>width</th>\n",
 60 |        "      <th>asymmetry</th>\n",
 61 |        "      <th>length_kernel_groove</th>\n",
 62 |        "      <th>kernel</th>\n",
 63 |        "    </tr>\n",
 64 |        "  </thead>\n",
 65 |        "  <tbody>\n",
 66 |        "    <tr>\n",
 67 |        "      <th>0</th>\n",
 68 |        "      <td>15.26</td>\n",
 69 |        "      <td>14.84</td>\n",
 70 |        "      <td>0.8710</td>\n",
 71 |        "      <td>5.763</td>\n",
 72 |        "      <td>3.312</td>\n",
 73 |        "      <td>2.221</td>\n",
 74 |        "      <td>5.220</td>\n",
 75 |        "      <td>0</td>\n",
 76 |        "    </tr>\n",
 77 |        "    <tr>\n",
 78 |        "      <th>1</th>\n",
 79 |        "      <td>14.88</td>\n",
 80 |        "      <td>14.57</td>\n",
 81 |        "      <td>0.8811</td>\n",
 82 |        "      <td>5.554</td>\n",
 83 |        "      <td>3.333</td>\n",
 84 |        "      <td>1.018</td>\n",
 85 |        "      <td>4.956</td>\n",
 86 |        "      <td>0</td>\n",
 87 |        "    </tr>\n",
 88 |        "    <tr>\n",
 89 |        "      <th>2</th>\n",
 90 |        "      <td>14.29</td>\n",
 91 |        "      <td>14.09</td>\n",
 92 |        "      <td>0.9050</td>\n",
 93 |        "      <td>5.291</td>\n",
 94 |        "      <td>3.337</td>\n",
 95 |        "      <td>2.699</td>\n",
 96 |        "      <td>4.825</td>\n",
 97 |        "      <td>0</td>\n",
 98 |        "    </tr>\n",
 99 |        "    <tr>\n",
100 |        "      <th>3</th>\n",
101 |        "      <td>13.84</td>\n",
102 |        "      <td>13.94</td>\n",
103 |        "      <td>0.8955</td>\n",
104 |        "      <td>5.324</td>\n",
105 |        "      <td>3.379</td>\n",
106 |        "      <td>2.259</td>\n",
107 |        "      <td>4.805</td>\n",
108 |        "      <td>0</td>\n",
109 |        "    </tr>\n",
110 |        "    <tr>\n",
111 |        "      <th>4</th>\n",
112 |        "      <td>16.14</td>\n",
113 |        "      <td>14.99</td>\n",
114 |        "      <td>0.9034</td>\n",
115 |        "      <td>5.658</td>\n",
116 |        "      <td>3.562</td>\n",
117 |        "      <td>1.355</td>\n",
118 |        "      <td>5.175</td>\n",
119 |        "      <td>0</td>\n",
120 |        "    </tr>\n",
121 |        "    <tr>\n",
122 |        "      <th>5</th>\n",
123 |        "      <td>14.38</td>\n",
124 |        "      <td>14.21</td>\n",
125 |        "      <td>0.8951</td>\n",
126 |        "      <td>5.386</td>\n",
127 |        "      <td>3.312</td>\n",
128 |        "      <td>2.462</td>\n",
129 |        "      <td>4.956</td>\n",
130 |        "      <td>0</td>\n",
131 |        "    </tr>\n",
132 |        "    <tr>\n",
133 |        "      <th>6</th>\n",
134 |        "      <td>14.69</td>\n",
135 |        "      <td>14.49</td>\n",
136 |        "      <td>0.8799</td>\n",
137 |        "      <td>5.563</td>\n",
138 |        "      <td>3.259</td>\n",
139 |        "      <td>3.586</td>\n",
140 |        "      <td>5.219</td>\n",
141 |        "      <td>0</td>\n",
142 |        "    </tr>\n",
143 |        "    <tr>\n",
144 |        "      <th>7</th>\n",
145 |        "      <td>14.11</td>\n",
146 |        "      <td>14.10</td>\n",
147 |        "      <td>0.8911</td>\n",
148 |        "      <td>5.420</td>\n",
149 |        "      <td>3.302</td>\n",
150 |        "      <td>2.700</td>\n",
151 |        "      <td>5.000</td>\n",
152 |        "      <td>0</td>\n",
153 |        "    </tr>\n",
154 |        "    <tr>\n",
155 |        "      <th>8</th>\n",
156 |        "      <td>16.63</td>\n",
157 |        "      <td>15.46</td>\n",
158 |        "      <td>0.8747</td>\n",
159 |        "      <td>6.053</td>\n",
160 |        "      <td>3.465</td>\n",
161 |        "      <td>2.040</td>\n",
162 |        "      <td>5.877</td>\n",
163 |        "      <td>0</td>\n",
164 |        "    </tr>\n",
165 |        "    <tr>\n",
166 |        "      <th>9</th>\n",
167 |        "      <td>16.44</td>\n",
168 |        "      <td>15.25</td>\n",
169 |        "      <td>0.8880</td>\n",
170 |        "      <td>5.884</td>\n",
171 |        "      <td>3.505</td>\n",
172 |        "      <td>1.969</td>\n",
173 |        "      <td>5.533</td>\n",
174 |        "      <td>0</td>\n",
175 |        "    </tr>\n",
176 |        "    <tr>\n",
177 |        "      <th>10</th>\n",
178 |        "      <td>15.26</td>\n",
179 |        "      <td>14.85</td>\n",
180 |        "      <td>0.8696</td>\n",
181 |        "      <td>5.714</td>\n",
182 |        "      <td>3.242</td>\n",
183 |        "      <td>4.543</td>\n",
184 |        "      <td>5.314</td>\n",
185 |        "      <td>0</td>\n",
186 |        "    </tr>\n",
187 |        "    <tr>\n",
188 |        "      <th>11</th>\n",
189 |        "      <td>14.03</td>\n",
190 |        "      <td>14.16</td>\n",
191 |        "      <td>0.8796</td>\n",
192 |        "      <td>5.438</td>\n",
193 |        "      <td>3.201</td>\n",
194 |        "      <td>1.717</td>\n",
195 |        "      <td>5.001</td>\n",
196 |        "      <td>0</td>\n",
197 |        "    </tr>\n",
198 |        "    <tr>\n",
199 |        "      <th>12</th>\n",
200 |        "      <td>13.89</td>\n",
201 |        "      <td>14.02</td>\n",
202 |        "      <td>0.8880</td>\n",
203 |        "      <td>5.439</td>\n",
204 |        "      <td>3.199</td>\n",
205 |        "      <td>3.986</td>\n",
206 |        "      <td>4.738</td>\n",
207 |        "      <td>0</td>\n",
208 |        "    </tr>\n",
209 |        "    <tr>\n",
210 |        "      <th>13</th>\n",
211 |        "      <td>13.78</td>\n",
212 |        "      <td>14.06</td>\n",
213 |        "      <td>0.8759</td>\n",
214 |        "      <td>5.479</td>\n",
215 |        "      <td>3.156</td>\n",
216 |        "      <td>3.136</td>\n",
217 |        "      <td>4.872</td>\n",
218 |        "      <td>0</td>\n",
219 |        "    </tr>\n",
220 |        "    <tr>\n",
221 |        "      <th>14</th>\n",
222 |        "      <td>13.74</td>\n",
223 |        "      <td>14.05</td>\n",
224 |        "      <td>0.8744</td>\n",
225 |        "      <td>5.482</td>\n",
226 |        "      <td>3.114</td>\n",
227 |        "      <td>2.932</td>\n",
228 |        "      <td>4.825</td>\n",
229 |        "      <td>0</td>\n",
230 |        "    </tr>\n",
231 |        "    <tr>\n",
232 |        "      <th>15</th>\n",
233 |        "      <td>14.59</td>\n",
234 |        "      <td>14.28</td>\n",
235 |        "      <td>0.8993</td>\n",
236 |        "      <td>5.351</td>\n",
237 |        "      <td>3.333</td>\n",
238 |        "      <td>4.185</td>\n",
239 |        "      <td>4.781</td>\n",
240 |        "      <td>0</td>\n",
241 |        "    </tr>\n",
242 |        "    <tr>\n",
243 |        "      <th>16</th>\n",
244 |        "      <td>13.99</td>\n",
245 |        "      <td>13.83</td>\n",
246 |        "      <td>0.9183</td>\n",
247 |        "      <td>5.119</td>\n",
248 |        "      <td>3.383</td>\n",
249 |        "      <td>5.234</td>\n",
250 |        "      <td>4.781</td>\n",
251 |        "      <td>0</td>\n",
252 |        "    </tr>\n",
253 |        "    <tr>\n",
254 |        "      <th>17</th>\n",
255 |        "      <td>15.69</td>\n",
256 |        "      <td>14.75</td>\n",
257 |        "      <td>0.9058</td>\n",
258 |        "      <td>5.527</td>\n",
259 |        "      <td>3.514</td>\n",
260 |        "      <td>1.599</td>\n",
261 |        "      <td>5.046</td>\n",
262 |        "      <td>0</td>\n",
263 |        "    </tr>\n",
264 |        "    <tr>\n",
265 |        "      <th>18</th>\n",
266 |        "      <td>14.70</td>\n",
267 |        "      <td>14.21</td>\n",
268 |        "      <td>0.9153</td>\n",
269 |        "      <td>5.205</td>\n",
270 |        "      <td>3.466</td>\n",
271 |        "      <td>1.767</td>\n",
272 |        "      <td>4.649</td>\n",
273 |        "      <td>0</td>\n",
274 |        "    </tr>\n",
275 |        "    <tr>\n",
276 |        "      <th>19</th>\n",
277 |        "      <td>12.72</td>\n",
278 |        "      <td>13.57</td>\n",
279 |        "      <td>0.8686</td>\n",
280 |        "      <td>5.226</td>\n",
281 |        "      <td>3.049</td>\n",
282 |        "      <td>4.102</td>\n",
283 |        "      <td>4.914</td>\n",
284 |        "      <td>0</td>\n",
285 |        "    </tr>\n",
286 |        "  </tbody>\n",
287 |        "</table>\n",
288 |        "</div>"
289 |       ],
290 |       "text/plain": [
291 |        "     area  perimeter  compactness  length  width  asymmetry  \\\n",
292 |        "0   15.26      14.84       0.8710   5.763  3.312      2.221   \n",
293 |        "1   14.88      14.57       0.8811   5.554  3.333      1.018   \n",
294 |        "2   14.29      14.09       0.9050   5.291  3.337      2.699   \n",
295 |        "3   13.84      13.94       0.8955   5.324  3.379      2.259   \n",
296 |        "4   16.14      14.99       0.9034   5.658  3.562      1.355   \n",
297 |        "5   14.38      14.21       0.8951   5.386  3.312      2.462   \n",
298 |        "6   14.69      14.49       0.8799   5.563  3.259      3.586   \n",
299 |        "7   14.11      14.10       0.8911   5.420  3.302      2.700   \n",
300 |        "8   16.63      15.46       0.8747   6.053  3.465      2.040   \n",
301 |        "9   16.44      15.25       0.8880   5.884  3.505      1.969   \n",
302 |        "10  15.26      14.85       0.8696   5.714  3.242      4.543   \n",
303 |        "11  14.03      14.16       0.8796   5.438  3.201      1.717   \n",
304 |        "12  13.89      14.02       0.8880   5.439  3.199      3.986   \n",
305 |        "13  13.78      14.06       0.8759   5.479  3.156      3.136   \n",
306 |        "14  13.74      14.05       0.8744   5.482  3.114      2.932   \n",
307 |        "15  14.59      14.28       0.8993   5.351  3.333      4.185   \n",
308 |        "16  13.99      13.83       0.9183   5.119  3.383      5.234   \n",
309 |        "17  15.69      14.75       0.9058   5.527  3.514      1.599   \n",
310 |        "18  14.70      14.21       0.9153   5.205  3.466      1.767   \n",
311 |        "19  12.72      13.57       0.8686   5.226  3.049      4.102   \n",
312 |        "\n",
313 |        "    length_kernel_groove  kernel  \n",
314 |        "0                  5.220       0  \n",
315 |        "1                  4.956       0  \n",
316 |        "2                  4.825       0  \n",
317 |        "3                  4.805       0  \n",
318 |        "4                  5.175       0  \n",
319 |        "5                  4.956       0  \n",
320 |        "6                  5.219       0  \n",
321 |        "7                  5.000       0  \n",
322 |        "8                  5.877       0  \n",
323 |        "9                  5.533       0  \n",
324 |        "10                 5.314       0  \n",
325 |        "11                 5.001       0  \n",
326 |        "12                 4.738       0  \n",
327 |        "13                 4.872       0  \n",
328 |        "14                 4.825       0  \n",
329 |        "15                 4.781       0  \n",
330 |        "16                 4.781       0  \n",
331 |        "17                 5.046       0  \n",
332 |        "18                 4.649       0  \n",
333 |        "19                 4.914       0  "
334 |       ]
335 |      },
336 |      "execution_count": 4,
337 |      "metadata": {},
338 |      "output_type": "execute_result"
339 |     }
340 |    ],
341 |    "source": [
342 |     "data.head(20)"
343 |    ]
344 |   },
345 |   {
346 |    "cell_type": "code",
347 |    "execution_count": 5,
348 |    "id": "d7162276",
349 |    "metadata": {},
350 |    "outputs": [
351 |     {
352 |      "name": "stdout",
353 |      "output_type": "stream",
354 |      "text": [
355 |       "<class 'pandas.core.frame.DataFrame'>\n",
356 |       "RangeIndex: 210 entries, 0 to 209\n",
357 |       "Data columns (total 8 columns):\n",
358 |       " #   Column                Non-Null Count  Dtype  \n",
359 |       "---  ------                --------------  -----  \n",
360 |       " 0   area                  210 non-null    float64\n",
361 |       " 1   perimeter             210 non-null    float64\n",
362 |       " 2   compactness           210 non-null    float64\n",
363 |       " 3   length                210 non-null    float64\n",
364 |       " 4   width                 210 non-null    float64\n",
365 |       " 5   asymmetry             210 non-null    float64\n",
366 |       " 6   length_kernel_groove  210 non-null    float64\n",
367 |       " 7   kernel                210 non-null    int64  \n",
368 |       "dtypes: float64(7), int64(1)\n",
369 |       "memory usage: 13.2 KB\n"
370 |      ]
371 |     }
372 |    ],
373 |    "source": [
374 |     "data.info()"
375 |    ]
376 |   },
377 |   {
378 |    "cell_type": "code",
379 |    "execution_count": 6,
380 |    "id": "4811f59a",
381 |    "metadata": {},
382 |    "outputs": [
383 |     {
384 |      "data": {
385 |       "text/html": [
386 |        "<div>\n",
387 |        "<style scoped>\n",
388 |        "    .dataframe tbody tr th:only-of-type {\n",
389 |        "        vertical-align: middle;\n",
390 |        "    }\n",
391 |        "\n",
392 |        "    .dataframe tbody tr th {\n",
393 |        "        vertical-align: top;\n",
394 |        "    }\n",
395 |        "\n",
396 |        "    .dataframe thead th {\n",
397 |        "        text-align: right;\n",
398 |        "    }\n",
399 |        "</style>\n",
400 |        "<table border=\"1\" class=\"dataframe\">\n",
401 |        "  <thead>\n",
402 |        "    <tr style=\"text-align: right;\">\n",
403 |        "      <th></th>\n",
404 |        "      <th>area</th>\n",
405 |        "      <th>perimeter</th>\n",
406 |        "      <th>compactness</th>\n",
407 |        "      <th>length</th>\n",
408 |        "      <th>width</th>\n",
409 |        "      <th>asymmetry</th>\n",
410 |        "      <th>length_kernel_groove</th>\n",
411 |        "      <th>kernel</th>\n",
412 |        "    </tr>\n",
413 |        "  </thead>\n",
414 |        "  <tbody>\n",
415 |        "    <tr>\n",
416 |        "      <th>count</th>\n",
417 |        "      <td>210.000000</td>\n",
418 |        "      <td>210.000000</td>\n",
419 |        "      <td>210.000000</td>\n",
420 |        "      <td>210.000000</td>\n",
421 |        "      <td>210.000000</td>\n",
422 |        "      <td>210.000000</td>\n",
423 |        "      <td>210.000000</td>\n",
424 |        "      <td>210.000000</td>\n",
425 |        "    </tr>\n",
426 |        "    <tr>\n",
427 |        "      <th>mean</th>\n",
428 |        "      <td>14.847524</td>\n",
429 |        "      <td>14.559286</td>\n",
430 |        "      <td>0.870999</td>\n",
431 |        "      <td>5.628533</td>\n",
432 |        "      <td>3.258605</td>\n",
433 |        "      <td>3.700201</td>\n",
434 |        "      <td>5.408071</td>\n",
435 |        "      <td>1.000000</td>\n",
436 |        "    </tr>\n",
437 |        "    <tr>\n",
438 |        "      <th>std</th>\n",
439 |        "      <td>2.909699</td>\n",
440 |        "      <td>1.305959</td>\n",
441 |        "      <td>0.023629</td>\n",
442 |        "      <td>0.443063</td>\n",
443 |        "      <td>0.377714</td>\n",
444 |        "      <td>1.503557</td>\n",
445 |        "      <td>0.491480</td>\n",
446 |        "      <td>0.818448</td>\n",
447 |        "    </tr>\n",
448 |        "    <tr>\n",
449 |        "      <th>min</th>\n",
450 |        "      <td>10.590000</td>\n",
451 |        "      <td>12.410000</td>\n",
452 |        "      <td>0.808100</td>\n",
453 |        "      <td>4.899000</td>\n",
454 |        "      <td>2.630000</td>\n",
455 |        "      <td>0.765100</td>\n",
456 |        "      <td>4.519000</td>\n",
457 |        "      <td>0.000000</td>\n",
458 |        "    </tr>\n",
459 |        "    <tr>\n",
460 |        "      <th>25%</th>\n",
461 |        "      <td>12.270000</td>\n",
462 |        "      <td>13.450000</td>\n",
463 |        "      <td>0.856900</td>\n",
464 |        "      <td>5.262250</td>\n",
465 |        "      <td>2.944000</td>\n",
466 |        "      <td>2.561500</td>\n",
467 |        "      <td>5.045000</td>\n",
468 |        "      <td>0.000000</td>\n",
469 |        "    </tr>\n",
470 |        "    <tr>\n",
471 |        "      <th>50%</th>\n",
472 |        "      <td>14.355000</td>\n",
473 |        "      <td>14.320000</td>\n",
474 |        "      <td>0.873450</td>\n",
475 |        "      <td>5.523500</td>\n",
476 |        "      <td>3.237000</td>\n",
477 |        "      <td>3.599000</td>\n",
478 |        "      <td>5.223000</td>\n",
479 |        "      <td>1.000000</td>\n",
480 |        "    </tr>\n",
481 |        "    <tr>\n",
482 |        "      <th>75%</th>\n",
483 |        "      <td>17.305000</td>\n",
484 |        "      <td>15.715000</td>\n",
485 |        "      <td>0.887775</td>\n",
486 |        "      <td>5.979750</td>\n",
487 |        "      <td>3.561750</td>\n",
488 |        "      <td>4.768750</td>\n",
489 |        "      <td>5.877000</td>\n",
490 |        "      <td>2.000000</td>\n",
491 |        "    </tr>\n",
492 |        "    <tr>\n",
493 |        "      <th>max</th>\n",
494 |        "      <td>21.180000</td>\n",
495 |        "      <td>17.250000</td>\n",
496 |        "      <td>0.918300</td>\n",
497 |        "      <td>6.675000</td>\n",
498 |        "      <td>4.033000</td>\n",
499 |        "      <td>8.456000</td>\n",
500 |        "      <td>6.550000</td>\n",
501 |        "      <td>2.000000</td>\n",
502 |        "    </tr>\n",
503 |        "  </tbody>\n",
504 |        "</table>\n",
505 |        "</div>"
506 |       ],
507 |       "text/plain": [
508 |        "             area   perimeter  compactness      length       width  \\\n",
509 |        "count  210.000000  210.000000   210.000000  210.000000  210.000000   \n",
510 |        "mean    14.847524   14.559286     0.870999    5.628533    3.258605   \n",
511 |        "std      2.909699    1.305959     0.023629    0.443063    0.377714   \n",
512 |        "min     10.590000   12.410000     0.808100    4.899000    2.630000   \n",
513 |        "25%     12.270000   13.450000     0.856900    5.262250    2.944000   \n",
514 |        "50%     14.355000   14.320000     0.873450    5.523500    3.237000   \n",
515 |        "75%     17.305000   15.715000     0.887775    5.979750    3.561750   \n",
516 |        "max     21.180000   17.250000     0.918300    6.675000    4.033000   \n",
517 |        "\n",
518 |        "        asymmetry  length_kernel_groove      kernel  \n",
519 |        "count  210.000000            210.000000  210.000000  \n",
520 |        "mean     3.700201              5.408071    1.000000  \n",
521 |        "std      1.503557              0.491480    0.818448  \n",
522 |        "min      0.765100              4.519000    0.000000  \n",
523 |        "25%      2.561500              5.045000    0.000000  \n",
524 |        "50%      3.599000              5.223000    1.000000  \n",
525 |        "75%      4.768750              5.877000    2.000000  \n",
526 |        "max      8.456000              6.550000    2.000000  "
527 |       ]
528 |      },
529 |      "execution_count": 6,
530 |      "metadata": {},
531 |      "output_type": "execute_result"
532 |     }
533 |    ],
534 |    "source": [
535 |     "data.describe()"
536 |    ]
537 |   },
538 |   {
539 |    "cell_type": "code",
540 |    "execution_count": 8,
541 |    "id": "1637198e",
542 |    "metadata": {},
543 |    "outputs": [
544 |     {
545 |      "data": {
546 |       "text/plain": [
547 |        "0    70\n",
548 |        "1    70\n",
549 |        "2    70\n",
550 |        "Name: kernel, dtype: int64"
551 |       ]
552 |      },
553 |      "execution_count": 8,
554 |      "metadata": {},
555 |      "output_type": "execute_result"
556 |     }
557 |    ],
558 |    "source": [
559 |     "data['kernel'].value_counts()"
560 |    ]
561 |   },
562 |   {
563 |    "cell_type": "code",
564 |    "execution_count": 18,
565 |    "id": "9d2a7915",
566 |    "metadata": {},
567 |    "outputs": [],
568 |    "source": [
569 |     "# data-splitting and standard scaling\n",
570 |     "x = data.drop(data.columns[[7]], axis=1)\n",
571 |     "y = data['kernel']\n",
572 |     "from sklearn.model_selection import train_test_split\n",
573 |     "xtrain,xtest,ytrain,ytest = train_test_split(x,y, test_size = 0.25, random_state= -0)"
574 |    ]
575 |   },
576 |   {
577 |    "cell_type": "code",
578 |    "execution_count": 15,
579 |    "id": "ac4c2544",
580 |    "metadata": {},
581 |    "outputs": [
582 |     {
583 |      "data": {
584 |       "text/html": [
585 |        "<div>\n",
586 |        "<style scoped>\n",
587 |        "    .dataframe tbody tr th:only-of-type {\n",
588 |        "        vertical-align: middle;\n",
589 |        "    }\n",
590 |        "\n",
591 |        "    .dataframe tbody tr th {\n",
592 |        "        vertical-align: top;\n",
593 |        "    }\n",
594 |        "\n",
595 |        "    .dataframe thead th {\n",
596 |        "        text-align: right;\n",
597 |        "    }\n",
598 |        "</style>\n",
599 |        "<table border=\"1\" class=\"dataframe\">\n",
600 |        "  <thead>\n",
601 |        "    <tr style=\"text-align: right;\">\n",
602 |        "      <th></th>\n",
603 |        "      <th>area</th>\n",
604 |        "      <th>perimeter</th>\n",
605 |        "      <th>compactness</th>\n",
606 |        "      <th>length</th>\n",
607 |        "      <th>width</th>\n",
608 |        "      <th>asymmetry</th>\n",
609 |        "      <th>length_kernel_groove</th>\n",
610 |        "    </tr>\n",
611 |        "  </thead>\n",
612 |        "  <tbody>\n",
613 |        "    <tr>\n",
614 |        "      <th>0</th>\n",
615 |        "      <td>15.26</td>\n",
616 |        "      <td>14.84</td>\n",
617 |        "      <td>0.8710</td>\n",
618 |        "      <td>5.763</td>\n",
619 |        "      <td>3.312</td>\n",
620 |        "      <td>2.221</td>\n",
621 |        "      <td>5.220</td>\n",
622 |        "    </tr>\n",
623 |        "    <tr>\n",
624 |        "      <th>1</th>\n",
625 |        "      <td>14.88</td>\n",
626 |        "      <td>14.57</td>\n",
627 |        "      <td>0.8811</td>\n",
628 |        "      <td>5.554</td>\n",
629 |        "      <td>3.333</td>\n",
630 |        "      <td>1.018</td>\n",
631 |        "      <td>4.956</td>\n",
632 |        "    </tr>\n",
633 |        "    <tr>\n",
634 |        "      <th>2</th>\n",
635 |        "      <td>14.29</td>\n",
636 |        "      <td>14.09</td>\n",
637 |        "      <td>0.9050</td>\n",
638 |        "      <td>5.291</td>\n",
639 |        "      <td>3.337</td>\n",
640 |        "      <td>2.699</td>\n",
641 |        "      <td>4.825</td>\n",
642 |        "    </tr>\n",
643 |        "    <tr>\n",
644 |        "      <th>3</th>\n",
645 |        "      <td>13.84</td>\n",
646 |        "      <td>13.94</td>\n",
647 |        "      <td>0.8955</td>\n",
648 |        "      <td>5.324</td>\n",
649 |        "      <td>3.379</td>\n",
650 |        "      <td>2.259</td>\n",
651 |        "      <td>4.805</td>\n",
652 |        "    </tr>\n",
653 |        "    <tr>\n",
654 |        "      <th>4</th>\n",
655 |        "      <td>16.14</td>\n",
656 |        "      <td>14.99</td>\n",
657 |        "      <td>0.9034</td>\n",
658 |        "      <td>5.658</td>\n",
659 |        "      <td>3.562</td>\n",
660 |        "      <td>1.355</td>\n",
661 |        "      <td>5.175</td>\n",
662 |        "    </tr>\n",
663 |        "  </tbody>\n",
664 |        "</table>\n",
665 |        "</div>"
666 |       ],
667 |       "text/plain": [
668 |        "    area  perimeter  compactness  length  width  asymmetry  \\\n",
669 |        "0  15.26      14.84       0.8710   5.763  3.312      2.221   \n",
670 |        "1  14.88      14.57       0.8811   5.554  3.333      1.018   \n",
671 |        "2  14.29      14.09       0.9050   5.291  3.337      2.699   \n",
672 |        "3  13.84      13.94       0.8955   5.324  3.379      2.259   \n",
673 |        "4  16.14      14.99       0.9034   5.658  3.562      1.355   \n",
674 |        "\n",
675 |        "   length_kernel_groove  \n",
676 |        "0                 5.220  \n",
677 |        "1                 4.956  \n",
678 |        "2                 4.825  \n",
679 |        "3                 4.805  \n",
680 |        "4                 5.175  "
681 |       ]
682 |      },
683 |      "execution_count": 15,
684 |      "metadata": {},
685 |      "output_type": "execute_result"
686 |     }
687 |    ],
688 |    "source": [
689 |     "x.head()"
690 |    ]
691 |   },
692 |   {
693 |    "cell_type": "code",
694 |    "execution_count": 16,
695 |    "id": "ad8fae54",
696 |    "metadata": {},
697 |    "outputs": [
698 |     {
699 |      "data": {
700 |       "text/plain": [
701 |        "0    0\n",
702 |        "1    0\n",
703 |        "2    0\n",
704 |        "3    0\n",
705 |        "4    0\n",
706 |        "Name: kernel, dtype: int64"
707 |       ]
708 |      },
709 |      "execution_count": 16,
710 |      "metadata": {},
711 |      "output_type": "execute_result"
712 |     }
713 |    ],
714 |    "source": [
715 |     "y.head()"
716 |    ]
717 |   },
718 |   {
719 |    "cell_type": "code",
720 |    "execution_count": 19,
721 |    "id": "2ce79d8b",
722 |    "metadata": {},
723 |    "outputs": [],
724 |    "source": [
725 |     "#scaling data using standard scaler\n",
726 |     "from sklearn.preprocessing import StandardScaler\n",
727 |     "sc = StandardScaler()\n",
728 |     "xtrain = sc.fit_transform(xtrain)\n",
729 |     "xtest = sc.fit_transform(xtest)"
730 |    ]
731 |   },
732 |   {
733 |    "cell_type": "code",
734 |    "execution_count": 20,
735 |    "id": "f629be8c",
736 |    "metadata": {},
737 |    "outputs": [
738 |     {
739 |      "data": {
740 |       "text/plain": [
741 |        "KNeighborsClassifier(n_neighbors=3)"
742 |       ]
743 |      },
744 |      "execution_count": 20,
745 |      "metadata": {},
746 |      "output_type": "execute_result"
747 |     }
748 |    ],
749 |    "source": [
750 |     "#training model using K Nearest Neighbor\n",
751 |     "from sklearn.neighbors import KNeighborsClassifier\n",
752 |     "knn = KNeighborsClassifier(n_neighbors = 3,p=2)\n",
753 |     "knn.fit(xtrain,ytrain)"
754 |    ]
755 |   },
756 |   {
757 |    "cell_type": "code",
758 |    "execution_count": 21,
759 |    "id": "e2052b85",
760 |    "metadata": {},
761 |    "outputs": [],
762 |    "source": [
763 |     "#predicting \n",
764 |     "y_p = knn.predict(xtest)"
765 |    ]
766 |   },
767 |   {
768 |    "cell_type": "code",
769 |    "execution_count": 23,
770 |    "id": "e5811ff7",
771 |    "metadata": {},
772 |    "outputs": [
773 |     {
774 |      "data": {
775 |       "text/plain": [
776 |        "array([[14,  0,  3],\n",
777 |        "       [ 3, 18,  0],\n",
778 |        "       [ 0,  0, 15]], dtype=int64)"
779 |       ]
780 |      },
781 |      "execution_count": 23,
782 |      "metadata": {},
783 |      "output_type": "execute_result"
784 |     }
785 |    ],
786 |    "source": [
787 |     "#Results\n",
788 |     "from sklearn.metrics import confusion_matrix , accuracy_score\n",
789 |     "confusion_matrix(ytest,y_p)\n"
790 |    ]
791 |   },
792 |   {
793 |    "cell_type": "code",
794 |    "execution_count": 24,
795 |    "id": "4c0be3a7",
796 |    "metadata": {},
797 |    "outputs": [
798 |     {
799 |      "data": {
800 |       "text/plain": [
801 |        "0.8867924528301887"
802 |       ]
803 |      },
804 |      "execution_count": 24,
805 |      "metadata": {},
806 |      "output_type": "execute_result"
807 |     }
808 |    ],
809 |    "source": [
810 |     "accuracy_score(ytest,y_p)"
811 |    ]
812 |   },
813 |   {
814 |    "cell_type": "code",
815 |    "execution_count": null,
816 |    "id": "97fe214b",
817 |    "metadata": {},
818 |    "outputs": [],
819 |    "source": []
820 |   }
821 |  ],
822 |  "metadata": {
823 |   "kernelspec": {
824 |    "display_name": "Python 3",
825 |    "language": "python",
826 |    "name": "python3"
827 |   },
828 |   "language_info": {
829 |    "codemirror_mode": {
830 |     "name": "ipython",
831 |     "version": 3
832 |    },
833 |    "file_extension": ".py",
834 |    "mimetype": "text/x-python",
835 |    "name": "python",
836 |    "nbconvert_exporter": "python",
837 |    "pygments_lexer": "ipython3",
838 |    "version": "3.8.8"
839 |   }
840 |  },
841 |  "nbformat": 4,
842 |  "nbformat_minor": 5
843 | }
844 | 


--------------------------------------------------------------------------------
/KNN-Mean Algo.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 5,
  6 |    "id": "43f66314",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "#import libraries\n",
 11 |     "import numpy as np\n",
 12 |     "import pandas as pd\n",
 13 |     "import matplotlib.pyplot as plt\n",
 14 |     "import seaborn as sns\n",
 15 |     "sns.set()"
 16 |    ]
 17 |   },
 18 |   {
 19 |    "cell_type": "code",
 20 |    "execution_count": 6,
 21 |    "id": "174b06d7",
 22 |    "metadata": {},
 23 |    "outputs": [],
 24 |    "source": [
 25 |     "#importing data\n",
 26 |     "data = pd.read_csv(\"E:\\Desktop\\Machine-Learning-Collection\\Data Folder\\market_data.csv\")"
 27 |    ]
 28 |   },
 29 |   {
 30 |    "cell_type": "code",
 31 |    "execution_count": 7,
 32 |    "id": "ed8f399a",
 33 |    "metadata": {},
 34 |    "outputs": [
 35 |     {
 36 |      "data": {
 37 |       "text/html": [
 38 |        "<div>\n",
 39 |        "<style scoped>\n",
 40 |        "    .dataframe tbody tr th:only-of-type {\n",
 41 |        "        vertical-align: middle;\n",
 42 |        "    }\n",
 43 |        "\n",
 44 |        "    .dataframe tbody tr th {\n",
 45 |        "        vertical-align: top;\n",
 46 |        "    }\n",
 47 |        "\n",
 48 |        "    .dataframe thead th {\n",
 49 |        "        text-align: right;\n",
 50 |        "    }\n",
 51 |        "</style>\n",
 52 |        "<table border=\"1\" class=\"dataframe\">\n",
 53 |        "  <thead>\n",
 54 |        "    <tr style=\"text-align: right;\">\n",
 55 |        "      <th></th>\n",
 56 |        "      <th>Satisfaction</th>\n",
 57 |        "      <th>Loyalty</th>\n",
 58 |        "    </tr>\n",
 59 |        "  </thead>\n",
 60 |        "  <tbody>\n",
 61 |        "    <tr>\n",
 62 |        "      <th>0</th>\n",
 63 |        "      <td>4</td>\n",
 64 |        "      <td>-1.33</td>\n",
 65 |        "    </tr>\n",
 66 |        "    <tr>\n",
 67 |        "      <th>1</th>\n",
 68 |        "      <td>6</td>\n",
 69 |        "      <td>-0.28</td>\n",
 70 |        "    </tr>\n",
 71 |        "    <tr>\n",
 72 |        "      <th>2</th>\n",
 73 |        "      <td>5</td>\n",
 74 |        "      <td>-0.99</td>\n",
 75 |        "    </tr>\n",
 76 |        "    <tr>\n",
 77 |        "      <th>3</th>\n",
 78 |        "      <td>7</td>\n",
 79 |        "      <td>-0.29</td>\n",
 80 |        "    </tr>\n",
 81 |        "    <tr>\n",
 82 |        "      <th>4</th>\n",
 83 |        "      <td>4</td>\n",
 84 |        "      <td>1.06</td>\n",
 85 |        "    </tr>\n",
 86 |        "  </tbody>\n",
 87 |        "</table>\n",
 88 |        "</div>"
 89 |       ],
 90 |       "text/plain": [
 91 |        "   Satisfaction  Loyalty\n",
 92 |        "0             4    -1.33\n",
 93 |        "1             6    -0.28\n",
 94 |        "2             5    -0.99\n",
 95 |        "3             7    -0.29\n",
 96 |        "4             4     1.06"
 97 |       ]
 98 |      },
 99 |      "execution_count": 7,
100 |      "metadata": {},
101 |      "output_type": "execute_result"
102 |     }
103 |    ],
104 |    "source": [
105 |     "data.head()"
106 |    ]
107 |   },
108 |   {
109 |    "cell_type": "code",
110 |    "execution_count": 8,
111 |    "id": "e43ee217",
112 |    "metadata": {},
113 |    "outputs": [
114 |     {
115 |      "name": "stdout",
116 |      "output_type": "stream",
117 |      "text": [
118 |       "<class 'pandas.core.frame.DataFrame'>\n",
119 |       "RangeIndex: 30 entries, 0 to 29\n",
120 |       "Data columns (total 2 columns):\n",
121 |       " #   Column        Non-Null Count  Dtype  \n",
122 |       "---  ------        --------------  -----  \n",
123 |       " 0   Satisfaction  30 non-null     int64  \n",
124 |       " 1   Loyalty       30 non-null     float64\n",
125 |       "dtypes: float64(1), int64(1)\n",
126 |       "memory usage: 608.0 bytes\n"
127 |      ]
128 |     }
129 |    ],
130 |    "source": [
131 |     "data.info()"
132 |    ]
133 |   },
134 |   {
135 |    "cell_type": "code",
136 |    "execution_count": 10,
137 |    "id": "a632a1fe",
138 |    "metadata": {},
139 |    "outputs": [
140 |     {
141 |      "data": {
142 |       "text/plain": [
143 |        "Text(0, 0.5, 'Loyalty')"
144 |       ]
145 |      },
146 |      "execution_count": 10,
147 |      "metadata": {},
148 |      "output_type": "execute_result"
149 |     },
150 |     {
151 |      "data": {
152 |       "image/png": 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\n",
153 |       "text/plain": [
154 |        "<Figure size 432x288 with 1 Axes>"
155 |       ]
156 |      },
157 |      "metadata": {},
158 |      "output_type": "display_data"
159 |     }
160 |    ],
161 |    "source": [
162 |     "#plotting data\n",
163 |     "plt.scatter(data['Satisfaction'],data[\"Loyalty\"])\n",
164 |     "plt.xlabel(\"Satisfaction\")\n",
165 |     "plt.ylabel(\"Loyalty\")"
166 |    ]
167 |   },
168 |   {
169 |    "cell_type": "code",
170 |    "execution_count": 11,
171 |    "id": "c37fffb9",
172 |    "metadata": {},
173 |    "outputs": [
174 |     {
175 |      "data": {
176 |       "text/plain": [
177 |        "array([[-0.93138063, -1.3318111 ],\n",
178 |        "       [-0.15523011, -0.28117124],\n",
179 |        "       [-0.54330537, -0.99160391],\n",
180 |        "       [ 0.23284516, -0.29117733],\n",
181 |        "       [-0.93138063,  1.05964534],\n",
182 |        "       [-2.09560642, -1.6620122 ],\n",
183 |        "       [ 1.39707095, -0.97159172],\n",
184 |        "       [ 0.62092042, -0.32119561],\n",
185 |        "       [ 0.62092042,  1.01962097],\n",
186 |        "       [ 0.62092042,  0.67941378],\n",
187 |        "       [ 1.39707095, -0.3412078 ],\n",
188 |        "       [-0.54330537,  0.38923705],\n",
189 |        "       [-0.54330537, -1.69203048],\n",
190 |        "       [-1.70753116,  0.66940768],\n",
191 |        "       [ 0.23284516,  0.26916393],\n",
192 |        "       [ 1.00899568,  1.35982816],\n",
193 |        "       [ 0.62092042,  1.37984035],\n",
194 |        "       [ 0.23284516,  1.35982816],\n",
195 |        "       [ 0.23284516, -0.3412078 ],\n",
196 |        "       [ 1.00899568,  0.66940768],\n",
197 |        "       [ 1.39707095,  1.17971847],\n",
198 |        "       [-1.31945589, -1.69203048],\n",
199 |        "       [-0.93138063,  1.03963316],\n",
200 |        "       [-1.31945589, -0.96158562],\n",
201 |        "       [-0.15523011,  1.02962706],\n",
202 |        "       [ 1.00899568, -0.99160391],\n",
203 |        "       [ 1.39707095,  0.36922486],\n",
204 |        "       [ 1.00899568,  0.02901767],\n",
205 |        "       [-1.31945589, -1.36182938],\n",
206 |        "       [-0.54330537,  0.72944425]])"
207 |       ]
208 |      },
209 |      "execution_count": 11,
210 |      "metadata": {},
211 |      "output_type": "execute_result"
212 |     }
213 |    ],
214 |    "source": [
215 |     "from sklearn import preprocessing\n",
216 |     "ds = data.copy()\n",
217 |     "ds=preprocessing.scale(data)\n",
218 |     "ds"
219 |    ]
220 |   },
221 |   {
222 |    "cell_type": "code",
223 |    "execution_count": 17,
224 |    "id": "97384ce9",
225 |    "metadata": {},
226 |    "outputs": [],
227 |    "source": [
228 |     "from sklearn.cluster import KMeans\n"
229 |    ]
230 |   },
231 |   {
232 |    "cell_type": "code",
233 |    "execution_count": 18,
234 |    "id": "f228f5dd",
235 |    "metadata": {},
236 |    "outputs": [
237 |     {
238 |      "name": "stderr",
239 |      "output_type": "stream",
240 |      "text": [
241 |       "C:\\Users\\aryan\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:881: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
242 |       "  warnings.warn(\n"
243 |      ]
244 |     }
245 |    ],
246 |    "source": [
247 |     "w=  []\n",
248 |     "for i in range(1,8 ):\n",
249 |     "    kmeans= KMeans(n_clusters=i,random_state=0)\n",
250 |     "    kmeans.fit(ds)\n",
251 |     "    w.append(kmeans.inertia_)"
252 |    ]
253 |   },
254 |   {
255 |    "cell_type": "code",
256 |    "execution_count": 19,
257 |    "id": "9d107d08",
258 |    "metadata": {},
259 |    "outputs": [
260 |     {
261 |      "data": {
262 |       "image/png": 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\n",
263 |       "text/plain": [
264 |        "<Figure size 432x288 with 1 Axes>"
265 |       ]
266 |      },
267 |      "metadata": {},
268 |      "output_type": "display_data"
269 |     }
270 |    ],
271 |    "source": [
272 |     "plt.plot(range(1,8),w)\n",
273 |     "plt.xlabel(\"clusterno.\")\n",
274 |     "plt.ylabel(\"value\")\n",
275 |     "plt.show()"
276 |    ]
277 |   },
278 |   {
279 |    "cell_type": "code",
280 |    "execution_count": 21,
281 |    "id": "7809db79",
282 |    "metadata": {},
283 |    "outputs": [
284 |     {
285 |      "data": {
286 |       "text/plain": [
287 |        "KMeans(n_clusters=4)"
288 |       ]
289 |      },
290 |      "execution_count": 21,
291 |      "metadata": {},
292 |      "output_type": "execute_result"
293 |     }
294 |    ],
295 |    "source": [
296 |     "#training\n",
297 |     "k = KMeans(4)\n",
298 |     "k.fit(ds)"
299 |    ]
300 |   },
301 |   {
302 |    "cell_type": "code",
303 |    "execution_count": 23,
304 |    "id": "26d00895",
305 |    "metadata": {},
306 |    "outputs": [],
307 |    "source": [
308 |     "cs = data.copy()\n",
309 |     "cs['cp'] = k.fit_predict(ds)"
310 |    ]
311 |   },
312 |   {
313 |    "cell_type": "code",
314 |    "execution_count": 25,
315 |    "id": "169a321a",
316 |    "metadata": {},
317 |    "outputs": [
318 |     {
319 |      "data": {
320 |       "text/html": [
321 |        "<div>\n",
322 |        "<style scoped>\n",
323 |        "    .dataframe tbody tr th:only-of-type {\n",
324 |        "        vertical-align: middle;\n",
325 |        "    }\n",
326 |        "\n",
327 |        "    .dataframe tbody tr th {\n",
328 |        "        vertical-align: top;\n",
329 |        "    }\n",
330 |        "\n",
331 |        "    .dataframe thead th {\n",
332 |        "        text-align: right;\n",
333 |        "    }\n",
334 |        "</style>\n",
335 |        "<table border=\"1\" class=\"dataframe\">\n",
336 |        "  <thead>\n",
337 |        "    <tr style=\"text-align: right;\">\n",
338 |        "      <th></th>\n",
339 |        "      <th>Satisfaction</th>\n",
340 |        "      <th>Loyalty</th>\n",
341 |        "      <th>cp</th>\n",
342 |        "    </tr>\n",
343 |        "  </thead>\n",
344 |        "  <tbody>\n",
345 |        "    <tr>\n",
346 |        "      <th>0</th>\n",
347 |        "      <td>4</td>\n",
348 |        "      <td>-1.33</td>\n",
349 |        "      <td>1</td>\n",
350 |        "    </tr>\n",
351 |        "    <tr>\n",
352 |        "      <th>1</th>\n",
353 |        "      <td>6</td>\n",
354 |        "      <td>-0.28</td>\n",
355 |        "      <td>2</td>\n",
356 |        "    </tr>\n",
357 |        "    <tr>\n",
358 |        "      <th>2</th>\n",
359 |        "      <td>5</td>\n",
360 |        "      <td>-0.99</td>\n",
361 |        "      <td>1</td>\n",
362 |        "    </tr>\n",
363 |        "    <tr>\n",
364 |        "      <th>3</th>\n",
365 |        "      <td>7</td>\n",
366 |        "      <td>-0.29</td>\n",
367 |        "      <td>2</td>\n",
368 |        "    </tr>\n",
369 |        "    <tr>\n",
370 |        "      <th>4</th>\n",
371 |        "      <td>4</td>\n",
372 |        "      <td>1.06</td>\n",
373 |        "      <td>3</td>\n",
374 |        "    </tr>\n",
375 |        "    <tr>\n",
376 |        "      <th>5</th>\n",
377 |        "      <td>1</td>\n",
378 |        "      <td>-1.66</td>\n",
379 |        "      <td>1</td>\n",
380 |        "    </tr>\n",
381 |        "    <tr>\n",
382 |        "      <th>6</th>\n",
383 |        "      <td>10</td>\n",
384 |        "      <td>-0.97</td>\n",
385 |        "      <td>2</td>\n",
386 |        "    </tr>\n",
387 |        "    <tr>\n",
388 |        "      <th>7</th>\n",
389 |        "      <td>8</td>\n",
390 |        "      <td>-0.32</td>\n",
391 |        "      <td>2</td>\n",
392 |        "    </tr>\n",
393 |        "    <tr>\n",
394 |        "      <th>8</th>\n",
395 |        "      <td>8</td>\n",
396 |        "      <td>1.02</td>\n",
397 |        "      <td>0</td>\n",
398 |        "    </tr>\n",
399 |        "    <tr>\n",
400 |        "      <th>9</th>\n",
401 |        "      <td>8</td>\n",
402 |        "      <td>0.68</td>\n",
403 |        "      <td>0</td>\n",
404 |        "    </tr>\n",
405 |        "    <tr>\n",
406 |        "      <th>10</th>\n",
407 |        "      <td>10</td>\n",
408 |        "      <td>-0.34</td>\n",
409 |        "      <td>2</td>\n",
410 |        "    </tr>\n",
411 |        "    <tr>\n",
412 |        "      <th>11</th>\n",
413 |        "      <td>5</td>\n",
414 |        "      <td>0.39</td>\n",
415 |        "      <td>3</td>\n",
416 |        "    </tr>\n",
417 |        "    <tr>\n",
418 |        "      <th>12</th>\n",
419 |        "      <td>5</td>\n",
420 |        "      <td>-1.69</td>\n",
421 |        "      <td>1</td>\n",
422 |        "    </tr>\n",
423 |        "    <tr>\n",
424 |        "      <th>13</th>\n",
425 |        "      <td>2</td>\n",
426 |        "      <td>0.67</td>\n",
427 |        "      <td>3</td>\n",
428 |        "    </tr>\n",
429 |        "    <tr>\n",
430 |        "      <th>14</th>\n",
431 |        "      <td>7</td>\n",
432 |        "      <td>0.27</td>\n",
433 |        "      <td>2</td>\n",
434 |        "    </tr>\n",
435 |        "    <tr>\n",
436 |        "      <th>15</th>\n",
437 |        "      <td>9</td>\n",
438 |        "      <td>1.36</td>\n",
439 |        "      <td>0</td>\n",
440 |        "    </tr>\n",
441 |        "    <tr>\n",
442 |        "      <th>16</th>\n",
443 |        "      <td>8</td>\n",
444 |        "      <td>1.38</td>\n",
445 |        "      <td>0</td>\n",
446 |        "    </tr>\n",
447 |        "    <tr>\n",
448 |        "      <th>17</th>\n",
449 |        "      <td>7</td>\n",
450 |        "      <td>1.36</td>\n",
451 |        "      <td>0</td>\n",
452 |        "    </tr>\n",
453 |        "    <tr>\n",
454 |        "      <th>18</th>\n",
455 |        "      <td>7</td>\n",
456 |        "      <td>-0.34</td>\n",
457 |        "      <td>2</td>\n",
458 |        "    </tr>\n",
459 |        "    <tr>\n",
460 |        "      <th>19</th>\n",
461 |        "      <td>9</td>\n",
462 |        "      <td>0.67</td>\n",
463 |        "      <td>0</td>\n",
464 |        "    </tr>\n",
465 |        "    <tr>\n",
466 |        "      <th>20</th>\n",
467 |        "      <td>10</td>\n",
468 |        "      <td>1.18</td>\n",
469 |        "      <td>0</td>\n",
470 |        "    </tr>\n",
471 |        "    <tr>\n",
472 |        "      <th>21</th>\n",
473 |        "      <td>3</td>\n",
474 |        "      <td>-1.69</td>\n",
475 |        "      <td>1</td>\n",
476 |        "    </tr>\n",
477 |        "    <tr>\n",
478 |        "      <th>22</th>\n",
479 |        "      <td>4</td>\n",
480 |        "      <td>1.04</td>\n",
481 |        "      <td>3</td>\n",
482 |        "    </tr>\n",
483 |        "    <tr>\n",
484 |        "      <th>23</th>\n",
485 |        "      <td>3</td>\n",
486 |        "      <td>-0.96</td>\n",
487 |        "      <td>1</td>\n",
488 |        "    </tr>\n",
489 |        "    <tr>\n",
490 |        "      <th>24</th>\n",
491 |        "      <td>6</td>\n",
492 |        "      <td>1.03</td>\n",
493 |        "      <td>3</td>\n",
494 |        "    </tr>\n",
495 |        "    <tr>\n",
496 |        "      <th>25</th>\n",
497 |        "      <td>9</td>\n",
498 |        "      <td>-0.99</td>\n",
499 |        "      <td>2</td>\n",
500 |        "    </tr>\n",
501 |        "    <tr>\n",
502 |        "      <th>26</th>\n",
503 |        "      <td>10</td>\n",
504 |        "      <td>0.37</td>\n",
505 |        "      <td>0</td>\n",
506 |        "    </tr>\n",
507 |        "    <tr>\n",
508 |        "      <th>27</th>\n",
509 |        "      <td>9</td>\n",
510 |        "      <td>0.03</td>\n",
511 |        "      <td>2</td>\n",
512 |        "    </tr>\n",
513 |        "    <tr>\n",
514 |        "      <th>28</th>\n",
515 |        "      <td>3</td>\n",
516 |        "      <td>-1.36</td>\n",
517 |        "      <td>1</td>\n",
518 |        "    </tr>\n",
519 |        "    <tr>\n",
520 |        "      <th>29</th>\n",
521 |        "      <td>5</td>\n",
522 |        "      <td>0.73</td>\n",
523 |        "      <td>3</td>\n",
524 |        "    </tr>\n",
525 |        "  </tbody>\n",
526 |        "</table>\n",
527 |        "</div>"
528 |       ],
529 |       "text/plain": [
530 |        "    Satisfaction  Loyalty  cp\n",
531 |        "0              4    -1.33   1\n",
532 |        "1              6    -0.28   2\n",
533 |        "2              5    -0.99   1\n",
534 |        "3              7    -0.29   2\n",
535 |        "4              4     1.06   3\n",
536 |        "5              1    -1.66   1\n",
537 |        "6             10    -0.97   2\n",
538 |        "7              8    -0.32   2\n",
539 |        "8              8     1.02   0\n",
540 |        "9              8     0.68   0\n",
541 |        "10            10    -0.34   2\n",
542 |        "11             5     0.39   3\n",
543 |        "12             5    -1.69   1\n",
544 |        "13             2     0.67   3\n",
545 |        "14             7     0.27   2\n",
546 |        "15             9     1.36   0\n",
547 |        "16             8     1.38   0\n",
548 |        "17             7     1.36   0\n",
549 |        "18             7    -0.34   2\n",
550 |        "19             9     0.67   0\n",
551 |        "20            10     1.18   0\n",
552 |        "21             3    -1.69   1\n",
553 |        "22             4     1.04   3\n",
554 |        "23             3    -0.96   1\n",
555 |        "24             6     1.03   3\n",
556 |        "25             9    -0.99   2\n",
557 |        "26            10     0.37   0\n",
558 |        "27             9     0.03   2\n",
559 |        "28             3    -1.36   1\n",
560 |        "29             5     0.73   3"
561 |       ]
562 |      },
563 |      "execution_count": 25,
564 |      "metadata": {},
565 |      "output_type": "execute_result"
566 |     }
567 |    ],
568 |    "source": [
569 |     "cs"
570 |    ]
571 |   },
572 |   {
573 |    "cell_type": "code",
574 |    "execution_count": 27,
575 |    "id": "7609d2b0",
576 |    "metadata": {},
577 |    "outputs": [
578 |     {
579 |      "data": {
580 |       "text/plain": [
581 |        "Text(0, 0.5, 'Loyalty')"
582 |       ]
583 |      },
584 |      "execution_count": 27,
585 |      "metadata": {},
586 |      "output_type": "execute_result"
587 |     },
588 |     {
589 |      "data": {
590 |       "image/png": 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\n",
591 |       "text/plain": [
592 |        "<Figure size 432x288 with 1 Axes>"
593 |       ]
594 |      },
595 |      "metadata": {},
596 |      "output_type": "display_data"
597 |     }
598 |    ],
599 |    "source": [
600 |     "#visualisation\n",
601 |     "plt.scatter(data[\"Satisfaction\"],data['Loyalty'],c= cs['cp'])\n",
602 |     "plt.xlabel('Satisfaction')\n",
603 |     "plt.ylabel('Loyalty')"
604 |    ]
605 |   },
606 |   {
607 |    "cell_type": "code",
608 |    "execution_count": 28,
609 |    "id": "a71c46b5",
610 |    "metadata": {},
611 |    "outputs": [],
612 |    "source": [
613 |     "#-------------------------------------------------------------------------------\n"
614 |    ]
615 |   },
616 |   {
617 |    "cell_type": "code",
618 |    "execution_count": null,
619 |    "id": "6891d596",
620 |    "metadata": {},
621 |    "outputs": [],
622 |    "source": []
623 |   }
624 |  ],
625 |  "metadata": {
626 |   "kernelspec": {
627 |    "display_name": "Python 3",
628 |    "language": "python",
629 |    "name": "python3"
630 |   },
631 |   "language_info": {
632 |    "codemirror_mode": {
633 |     "name": "ipython",
634 |     "version": 3
635 |    },
636 |    "file_extension": ".py",
637 |    "mimetype": "text/x-python",
638 |    "name": "python",
639 |    "nbconvert_exporter": "python",
640 |    "pygments_lexer": "ipython3",
641 |    "version": "3.8.8"
642 |   }
643 |  },
644 |  "nbformat": 4,
645 |  "nbformat_minor": 5
646 | }
647 | 


--------------------------------------------------------------------------------
/Linear_Regression_Practice.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 3,
  6 |    "id": "3e903d11",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "#importing libs\n",
 11 |     "import numpy as np\n",
 12 |     "import pandas as pd\n",
 13 |     "import matplotlib.pyplot as plt"
 14 |    ]
 15 |   },
 16 |   {
 17 |    "cell_type": "code",
 18 |    "execution_count": 5,
 19 |    "id": "065ad21f",
 20 |    "metadata": {},
 21 |    "outputs": [],
 22 |    "source": [
 23 |     "#importing data to notebook\n",
 24 |     "d= pd.read_csv(\"L:/train.csv\")"
 25 |    ]
 26 |   },
 27 |   {
 28 |    "cell_type": "code",
 29 |    "execution_count": 6,
 30 |    "id": "0f10b84e",
 31 |    "metadata": {},
 32 |    "outputs": [
 33 |     {
 34 |      "data": {
 35 |       "text/html": [
 36 |        "<div>\n",
 37 |        "<style scoped>\n",
 38 |        "    .dataframe tbody tr th:only-of-type {\n",
 39 |        "        vertical-align: middle;\n",
 40 |        "    }\n",
 41 |        "\n",
 42 |        "    .dataframe tbody tr th {\n",
 43 |        "        vertical-align: top;\n",
 44 |        "    }\n",
 45 |        "\n",
 46 |        "    .dataframe thead th {\n",
 47 |        "        text-align: right;\n",
 48 |        "    }\n",
 49 |        "</style>\n",
 50 |        "<table border=\"1\" class=\"dataframe\">\n",
 51 |        "  <thead>\n",
 52 |        "    <tr style=\"text-align: right;\">\n",
 53 |        "      <th></th>\n",
 54 |        "      <th>x</th>\n",
 55 |        "      <th>y</th>\n",
 56 |        "    </tr>\n",
 57 |        "  </thead>\n",
 58 |        "  <tbody>\n",
 59 |        "    <tr>\n",
 60 |        "      <th>0</th>\n",
 61 |        "      <td>24.0</td>\n",
 62 |        "      <td>21.549452</td>\n",
 63 |        "    </tr>\n",
 64 |        "    <tr>\n",
 65 |        "      <th>1</th>\n",
 66 |        "      <td>50.0</td>\n",
 67 |        "      <td>47.464463</td>\n",
 68 |        "    </tr>\n",
 69 |        "    <tr>\n",
 70 |        "      <th>2</th>\n",
 71 |        "      <td>15.0</td>\n",
 72 |        "      <td>17.218656</td>\n",
 73 |        "    </tr>\n",
 74 |        "    <tr>\n",
 75 |        "      <th>3</th>\n",
 76 |        "      <td>38.0</td>\n",
 77 |        "      <td>36.586398</td>\n",
 78 |        "    </tr>\n",
 79 |        "    <tr>\n",
 80 |        "      <th>4</th>\n",
 81 |        "      <td>87.0</td>\n",
 82 |        "      <td>87.288984</td>\n",
 83 |        "    </tr>\n",
 84 |        "    <tr>\n",
 85 |        "      <th>5</th>\n",
 86 |        "      <td>36.0</td>\n",
 87 |        "      <td>32.463875</td>\n",
 88 |        "    </tr>\n",
 89 |        "    <tr>\n",
 90 |        "      <th>6</th>\n",
 91 |        "      <td>12.0</td>\n",
 92 |        "      <td>10.780897</td>\n",
 93 |        "    </tr>\n",
 94 |        "    <tr>\n",
 95 |        "      <th>7</th>\n",
 96 |        "      <td>81.0</td>\n",
 97 |        "      <td>80.763399</td>\n",
 98 |        "    </tr>\n",
 99 |        "    <tr>\n",
100 |        "      <th>8</th>\n",
101 |        "      <td>25.0</td>\n",
102 |        "      <td>24.612151</td>\n",
103 |        "    </tr>\n",
104 |        "    <tr>\n",
105 |        "      <th>9</th>\n",
106 |        "      <td>5.0</td>\n",
107 |        "      <td>6.963319</td>\n",
108 |        "    </tr>\n",
109 |        "  </tbody>\n",
110 |        "</table>\n",
111 |        "</div>"
112 |       ],
113 |       "text/plain": [
114 |        "      x          y\n",
115 |        "0  24.0  21.549452\n",
116 |        "1  50.0  47.464463\n",
117 |        "2  15.0  17.218656\n",
118 |        "3  38.0  36.586398\n",
119 |        "4  87.0  87.288984\n",
120 |        "5  36.0  32.463875\n",
121 |        "6  12.0  10.780897\n",
122 |        "7  81.0  80.763399\n",
123 |        "8  25.0  24.612151\n",
124 |        "9   5.0   6.963319"
125 |       ]
126 |      },
127 |      "execution_count": 6,
128 |      "metadata": {},
129 |      "output_type": "execute_result"
130 |     }
131 |    ],
132 |    "source": [
133 |     "d.head(10)"
134 |    ]
135 |   },
136 |   {
137 |    "cell_type": "code",
138 |    "execution_count": 7,
139 |    "id": "2af2f56a",
140 |    "metadata": {},
141 |    "outputs": [
142 |     {
143 |      "name": "stdout",
144 |      "output_type": "stream",
145 |      "text": [
146 |       "<class 'pandas.core.frame.DataFrame'>\n",
147 |       "RangeIndex: 700 entries, 0 to 699\n",
148 |       "Data columns (total 2 columns):\n",
149 |       " #   Column  Non-Null Count  Dtype  \n",
150 |       "---  ------  --------------  -----  \n",
151 |       " 0   x       700 non-null    float64\n",
152 |       " 1   y       699 non-null    float64\n",
153 |       "dtypes: float64(2)\n",
154 |       "memory usage: 11.1 KB\n"
155 |      ]
156 |     }
157 |    ],
158 |    "source": [
159 |     "d.info()"
160 |    ]
161 |   },
162 |   {
163 |    "cell_type": "code",
164 |    "execution_count": 8,
165 |    "id": "4fdcfde4",
166 |    "metadata": {},
167 |    "outputs": [],
168 |    "source": [
169 |     "d= d.dropna()"
170 |    ]
171 |   },
172 |   {
173 |    "cell_type": "code",
174 |    "execution_count": 9,
175 |    "id": "721bace4",
176 |    "metadata": {},
177 |    "outputs": [
178 |     {
179 |      "data": {
180 |       "text/plain": [
181 |        "(699, 2)"
182 |       ]
183 |      },
184 |      "execution_count": 9,
185 |      "metadata": {},
186 |      "output_type": "execute_result"
187 |     }
188 |    ],
189 |    "source": [
190 |     "d.shape"
191 |    ]
192 |   },
193 |   {
194 |    "cell_type": "code",
195 |    "execution_count": 18,
196 |    "id": "a693a65e",
197 |    "metadata": {},
198 |    "outputs": [],
199 |    "source": [
200 |     "#Splitting train Data \n",
201 |     "traini = np.array(d.x[0:500]).reshape(500,1)\n",
202 |     "traino = np.array(d.y[0:500]).reshape(500,1)"
203 |    ]
204 |   },
205 |   {
206 |    "cell_type": "code",
207 |    "execution_count": 20,
208 |    "id": "44eac590",
209 |    "metadata": {},
210 |    "outputs": [],
211 |    "source": [
212 |     "#splitting test data\n",
213 |     "testi = np.array(d.x[500:700]).reshape(199,1)\n",
214 |     "testo = np.array(d.y[500:700]).reshape(199,1)"
215 |    ]
216 |   },
217 |   {
218 |    "cell_type": "code",
219 |    "execution_count": 23,
220 |    "id": "f0b80847",
221 |    "metadata": {},
222 |    "outputs": [
223 |     {
224 |      "data": {
225 |       "text/plain": [
226 |        "LinearRegression()"
227 |       ]
228 |      },
229 |      "execution_count": 23,
230 |      "metadata": {},
231 |      "output_type": "execute_result"
232 |     }
233 |    ],
234 |    "source": [
235 |     "#importing sklearn linear regression library and training\n",
236 |     "from sklearn.linear_model import LinearRegression\n",
237 |     "l = LinearRegression()\n",
238 |     "l.fit(traini,traino)"
239 |    ]
240 |   },
241 |   {
242 |    "cell_type": "code",
243 |    "execution_count": 24,
244 |    "id": "d78a7cea",
245 |    "metadata": {},
246 |    "outputs": [],
247 |    "source": [
248 |     "#predicting\n",
249 |     "pdict=l.predict(testi)"
250 |    ]
251 |   },
252 |   {
253 |    "cell_type": "code",
254 |    "execution_count": 25,
255 |    "id": "3dc0c6ea",
256 |    "metadata": {},
257 |    "outputs": [],
258 |    "source": [
259 |     "#importing metrics library and accuracy of model\n",
260 |     "from sklearn.metrics import mean_squared_error\n",
261 |     "error = mean_squared_error(testo,pdict)"
262 |    ]
263 |   },
264 |   {
265 |    "cell_type": "code",
266 |    "execution_count": 26,
267 |    "id": "3c7ab567",
268 |    "metadata": {},
269 |    "outputs": [
270 |     {
271 |      "data": {
272 |       "text/plain": [
273 |        "8.030031591835371"
274 |       ]
275 |      },
276 |      "execution_count": 26,
277 |      "metadata": {},
278 |      "output_type": "execute_result"
279 |     }
280 |    ],
281 |    "source": [
282 |     "error"
283 |    ]
284 |   },
285 |   {
286 |    "cell_type": "code",
287 |    "execution_count": 27,
288 |    "id": "65802b47",
289 |    "metadata": {},
290 |    "outputs": [
291 |     {
292 |      "data": {
293 |       "image/png": 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\n",
294 |       "text/plain": [
295 |        "<Figure size 432x288 with 1 Axes>"
296 |       ]
297 |      },
298 |      "metadata": {
299 |       "needs_background": "light"
300 |      },
301 |      "output_type": "display_data"
302 |     }
303 |    ],
304 |    "source": [
305 |     "# Visualising model\n",
306 |     "plt.plot(testi,testo,\"+\",color=\"green\")\n",
307 |     "plt.plot(testi,pdict,\"*\",color=\"red\")\n",
308 |     "plt.xlabel('input')\n",
309 |     "plt.ylabel(\"output\")\n",
310 |     "plt.show()"
311 |    ]
312 |   },
313 |   {
314 |    "cell_type": "code",
315 |    "execution_count": null,
316 |    "id": "dc4f9a7c",
317 |    "metadata": {},
318 |    "outputs": [],
319 |    "source": []
320 |   }
321 |  ],
322 |  "metadata": {
323 |   "kernelspec": {
324 |    "display_name": "Python 3",
325 |    "language": "python",
326 |    "name": "python3"
327 |   },
328 |   "language_info": {
329 |    "codemirror_mode": {
330 |     "name": "ipython",
331 |     "version": 3
332 |    },
333 |    "file_extension": ".py",
334 |    "mimetype": "text/x-python",
335 |    "name": "python",
336 |    "nbconvert_exporter": "python",
337 |    "pygments_lexer": "ipython3",
338 |    "version": "3.8.8"
339 |   }
340 |  },
341 |  "nbformat": 4,
342 |  "nbformat_minor": 5
343 | }
344 | 


--------------------------------------------------------------------------------
/Naive Bayes Model.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "cb9af7fb",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import numpy as np\n",
 11 |     "import pandas as pd\n",
 12 |     "import matplotlib.pyplot as plt\n"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 2,
 18 |    "id": "637425f0",
 19 |    "metadata": {},
 20 |    "outputs": [],
 21 |    "source": [
 22 |     "#load data\n",
 23 |     "data = pd.read_csv(\"E:\\Desktop\\Machine-Learning-Collection\\Data Folder\\study_data.csv\")"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "code",
 28 |    "execution_count": 3,
 29 |    "id": "b4840518",
 30 |    "metadata": {},
 31 |    "outputs": [
 32 |     {
 33 |      "data": {
 34 |       "text/html": [
 35 |        "<div>\n",
 36 |        "<style scoped>\n",
 37 |        "    .dataframe tbody tr th:only-of-type {\n",
 38 |        "        vertical-align: middle;\n",
 39 |        "    }\n",
 40 |        "\n",
 41 |        "    .dataframe tbody tr th {\n",
 42 |        "        vertical-align: top;\n",
 43 |        "    }\n",
 44 |        "\n",
 45 |        "    .dataframe thead th {\n",
 46 |        "        text-align: right;\n",
 47 |        "    }\n",
 48 |        "</style>\n",
 49 |        "<table border=\"1\" class=\"dataframe\">\n",
 50 |        "  <thead>\n",
 51 |        "    <tr style=\"text-align: right;\">\n",
 52 |        "      <th></th>\n",
 53 |        "      <th>repetition_time</th>\n",
 54 |        "      <th>study_time</th>\n",
 55 |        "      <th>knowledge_level</th>\n",
 56 |        "    </tr>\n",
 57 |        "  </thead>\n",
 58 |        "  <tbody>\n",
 59 |        "    <tr>\n",
 60 |        "      <th>0</th>\n",
 61 |        "      <td>0.00</td>\n",
 62 |        "      <td>0.00</td>\n",
 63 |        "      <td>Low</td>\n",
 64 |        "    </tr>\n",
 65 |        "    <tr>\n",
 66 |        "      <th>1</th>\n",
 67 |        "      <td>0.24</td>\n",
 68 |        "      <td>0.90</td>\n",
 69 |        "      <td>High</td>\n",
 70 |        "    </tr>\n",
 71 |        "    <tr>\n",
 72 |        "      <th>2</th>\n",
 73 |        "      <td>0.25</td>\n",
 74 |        "      <td>0.33</td>\n",
 75 |        "      <td>Low</td>\n",
 76 |        "    </tr>\n",
 77 |        "    <tr>\n",
 78 |        "      <th>3</th>\n",
 79 |        "      <td>0.65</td>\n",
 80 |        "      <td>0.30</td>\n",
 81 |        "      <td>High</td>\n",
 82 |        "    </tr>\n",
 83 |        "    <tr>\n",
 84 |        "      <th>4</th>\n",
 85 |        "      <td>0.98</td>\n",
 86 |        "      <td>0.24</td>\n",
 87 |        "      <td>Low</td>\n",
 88 |        "    </tr>\n",
 89 |        "    <tr>\n",
 90 |        "      <th>5</th>\n",
 91 |        "      <td>0.10</td>\n",
 92 |        "      <td>0.66</td>\n",
 93 |        "      <td>High</td>\n",
 94 |        "    </tr>\n",
 95 |        "    <tr>\n",
 96 |        "      <th>6</th>\n",
 97 |        "      <td>0.29</td>\n",
 98 |        "      <td>0.56</td>\n",
 99 |        "      <td>High</td>\n",
100 |        "    </tr>\n",
101 |        "    <tr>\n",
102 |        "      <th>7</th>\n",
103 |        "      <td>0.40</td>\n",
104 |        "      <td>0.01</td>\n",
105 |        "      <td>Low</td>\n",
106 |        "    </tr>\n",
107 |        "    <tr>\n",
108 |        "      <th>8</th>\n",
109 |        "      <td>0.72</td>\n",
110 |        "      <td>0.25</td>\n",
111 |        "      <td>Low</td>\n",
112 |        "    </tr>\n",
113 |        "    <tr>\n",
114 |        "      <th>9</th>\n",
115 |        "      <td>0.20</td>\n",
116 |        "      <td>0.85</td>\n",
117 |        "      <td>High</td>\n",
118 |        "    </tr>\n",
119 |        "    <tr>\n",
120 |        "      <th>10</th>\n",
121 |        "      <td>0.30</td>\n",
122 |        "      <td>0.81</td>\n",
123 |        "      <td>High</td>\n",
124 |        "    </tr>\n",
125 |        "    <tr>\n",
126 |        "      <th>11</th>\n",
127 |        "      <td>0.41</td>\n",
128 |        "      <td>0.30</td>\n",
129 |        "      <td>Low</td>\n",
130 |        "    </tr>\n",
131 |        "    <tr>\n",
132 |        "      <th>12</th>\n",
133 |        "      <td>0.78</td>\n",
134 |        "      <td>0.34</td>\n",
135 |        "      <td>High</td>\n",
136 |        "    </tr>\n",
137 |        "    <tr>\n",
138 |        "      <th>13</th>\n",
139 |        "      <td>0.15</td>\n",
140 |        "      <td>0.90</td>\n",
141 |        "      <td>High</td>\n",
142 |        "    </tr>\n",
143 |        "    <tr>\n",
144 |        "      <th>14</th>\n",
145 |        "      <td>0.30</td>\n",
146 |        "      <td>0.60</td>\n",
147 |        "      <td>High</td>\n",
148 |        "    </tr>\n",
149 |        "    <tr>\n",
150 |        "      <th>15</th>\n",
151 |        "      <td>0.35</td>\n",
152 |        "      <td>0.80</td>\n",
153 |        "      <td>High</td>\n",
154 |        "    </tr>\n",
155 |        "    <tr>\n",
156 |        "      <th>16</th>\n",
157 |        "      <td>0.01</td>\n",
158 |        "      <td>0.05</td>\n",
159 |        "      <td>Low</td>\n",
160 |        "    </tr>\n",
161 |        "    <tr>\n",
162 |        "      <th>17</th>\n",
163 |        "      <td>0.08</td>\n",
164 |        "      <td>0.33</td>\n",
165 |        "      <td>Low</td>\n",
166 |        "    </tr>\n",
167 |        "    <tr>\n",
168 |        "      <th>18</th>\n",
169 |        "      <td>0.27</td>\n",
170 |        "      <td>0.29</td>\n",
171 |        "      <td>Low</td>\n",
172 |        "    </tr>\n",
173 |        "    <tr>\n",
174 |        "      <th>19</th>\n",
175 |        "      <td>0.49</td>\n",
176 |        "      <td>0.56</td>\n",
177 |        "      <td>High</td>\n",
178 |        "    </tr>\n",
179 |        "  </tbody>\n",
180 |        "</table>\n",
181 |        "</div>"
182 |       ],
183 |       "text/plain": [
184 |        "    repetition_time  study_time knowledge_level\n",
185 |        "0              0.00        0.00             Low\n",
186 |        "1              0.24        0.90            High\n",
187 |        "2              0.25        0.33             Low\n",
188 |        "3              0.65        0.30            High\n",
189 |        "4              0.98        0.24             Low\n",
190 |        "5              0.10        0.66            High\n",
191 |        "6              0.29        0.56            High\n",
192 |        "7              0.40        0.01             Low\n",
193 |        "8              0.72        0.25             Low\n",
194 |        "9              0.20        0.85            High\n",
195 |        "10             0.30        0.81            High\n",
196 |        "11             0.41        0.30             Low\n",
197 |        "12             0.78        0.34            High\n",
198 |        "13             0.15        0.90            High\n",
199 |        "14             0.30        0.60            High\n",
200 |        "15             0.35        0.80            High\n",
201 |        "16             0.01        0.05             Low\n",
202 |        "17             0.08        0.33             Low\n",
203 |        "18             0.27        0.29             Low\n",
204 |        "19             0.49        0.56            High"
205 |       ]
206 |      },
207 |      "execution_count": 3,
208 |      "metadata": {},
209 |      "output_type": "execute_result"
210 |     }
211 |    ],
212 |    "source": [
213 |     "data.head(20)"
214 |    ]
215 |   },
216 |   {
217 |    "cell_type": "code",
218 |    "execution_count": 4,
219 |    "id": "b9ac4664",
220 |    "metadata": {},
221 |    "outputs": [
222 |     {
223 |      "name": "stdout",
224 |      "output_type": "stream",
225 |      "text": [
226 |       "<class 'pandas.core.frame.DataFrame'>\n",
227 |       "RangeIndex: 258 entries, 0 to 257\n",
228 |       "Data columns (total 3 columns):\n",
229 |       " #   Column           Non-Null Count  Dtype  \n",
230 |       "---  ------           --------------  -----  \n",
231 |       " 0   repetition_time  258 non-null    float64\n",
232 |       " 1   study_time       258 non-null    float64\n",
233 |       " 2   knowledge_level  258 non-null    object \n",
234 |       "dtypes: float64(2), object(1)\n",
235 |       "memory usage: 6.2+ KB\n"
236 |      ]
237 |     }
238 |    ],
239 |    "source": [
240 |     "#exploring data a bit\n",
241 |     "data.info()"
242 |    ]
243 |   },
244 |   {
245 |    "cell_type": "code",
246 |    "execution_count": 5,
247 |    "id": "3e7492c6",
248 |    "metadata": {},
249 |    "outputs": [
250 |     {
251 |      "data": {
252 |       "text/html": [
253 |        "<div>\n",
254 |        "<style scoped>\n",
255 |        "    .dataframe tbody tr th:only-of-type {\n",
256 |        "        vertical-align: middle;\n",
257 |        "    }\n",
258 |        "\n",
259 |        "    .dataframe tbody tr th {\n",
260 |        "        vertical-align: top;\n",
261 |        "    }\n",
262 |        "\n",
263 |        "    .dataframe thead th {\n",
264 |        "        text-align: right;\n",
265 |        "    }\n",
266 |        "</style>\n",
267 |        "<table border=\"1\" class=\"dataframe\">\n",
268 |        "  <thead>\n",
269 |        "    <tr style=\"text-align: right;\">\n",
270 |        "      <th></th>\n",
271 |        "      <th>repetition_time</th>\n",
272 |        "      <th>study_time</th>\n",
273 |        "    </tr>\n",
274 |        "  </thead>\n",
275 |        "  <tbody>\n",
276 |        "    <tr>\n",
277 |        "      <th>count</th>\n",
278 |        "      <td>258.000000</td>\n",
279 |        "      <td>258.000000</td>\n",
280 |        "    </tr>\n",
281 |        "    <tr>\n",
282 |        "      <th>mean</th>\n",
283 |        "      <td>0.432713</td>\n",
284 |        "      <td>0.458539</td>\n",
285 |        "    </tr>\n",
286 |        "    <tr>\n",
287 |        "      <th>std</th>\n",
288 |        "      <td>0.248108</td>\n",
289 |        "      <td>0.255211</td>\n",
290 |        "    </tr>\n",
291 |        "    <tr>\n",
292 |        "      <th>min</th>\n",
293 |        "      <td>0.000000</td>\n",
294 |        "      <td>0.000000</td>\n",
295 |        "    </tr>\n",
296 |        "    <tr>\n",
297 |        "      <th>25%</th>\n",
298 |        "      <td>0.250000</td>\n",
299 |        "      <td>0.250000</td>\n",
300 |        "    </tr>\n",
301 |        "    <tr>\n",
302 |        "      <th>50%</th>\n",
303 |        "      <td>0.330000</td>\n",
304 |        "      <td>0.500000</td>\n",
305 |        "    </tr>\n",
306 |        "    <tr>\n",
307 |        "      <th>75%</th>\n",
308 |        "      <td>0.647500</td>\n",
309 |        "      <td>0.660000</td>\n",
310 |        "    </tr>\n",
311 |        "    <tr>\n",
312 |        "      <th>max</th>\n",
313 |        "      <td>0.990000</td>\n",
314 |        "      <td>0.930000</td>\n",
315 |        "    </tr>\n",
316 |        "  </tbody>\n",
317 |        "</table>\n",
318 |        "</div>"
319 |       ],
320 |       "text/plain": [
321 |        "       repetition_time  study_time\n",
322 |        "count       258.000000  258.000000\n",
323 |        "mean          0.432713    0.458539\n",
324 |        "std           0.248108    0.255211\n",
325 |        "min           0.000000    0.000000\n",
326 |        "25%           0.250000    0.250000\n",
327 |        "50%           0.330000    0.500000\n",
328 |        "75%           0.647500    0.660000\n",
329 |        "max           0.990000    0.930000"
330 |       ]
331 |      },
332 |      "execution_count": 5,
333 |      "metadata": {},
334 |      "output_type": "execute_result"
335 |     }
336 |    ],
337 |    "source": [
338 |     "data.describe()"
339 |    ]
340 |   },
341 |   {
342 |    "cell_type": "code",
343 |    "execution_count": 7,
344 |    "id": "9aa1bc54",
345 |    "metadata": {},
346 |    "outputs": [
347 |     {
348 |      "data": {
349 |       "text/plain": [
350 |        "High    151\n",
351 |        "Low     107\n",
352 |        "Name: knowledge_level, dtype: int64"
353 |       ]
354 |      },
355 |      "execution_count": 7,
356 |      "metadata": {},
357 |      "output_type": "execute_result"
358 |     }
359 |    ],
360 |    "source": [
361 |     "data['knowledge_level'].value_counts()"
362 |    ]
363 |   },
364 |   {
365 |    "cell_type": "code",
366 |    "execution_count": 8,
367 |    "id": "693ee341",
368 |    "metadata": {},
369 |    "outputs": [],
370 |    "source": [
371 |     "#splitting data and setup\n",
372 |     "x = data.drop(data.columns[[2]], axis = 1)\n",
373 |     "y=data['knowledge_level']"
374 |    ]
375 |   },
376 |   {
377 |    "cell_type": "code",
378 |    "execution_count": 10,
379 |    "id": "6b3ff4f5",
380 |    "metadata": {},
381 |    "outputs": [
382 |     {
383 |      "data": {
384 |       "text/html": [
385 |        "<div>\n",
386 |        "<style scoped>\n",
387 |        "    .dataframe tbody tr th:only-of-type {\n",
388 |        "        vertical-align: middle;\n",
389 |        "    }\n",
390 |        "\n",
391 |        "    .dataframe tbody tr th {\n",
392 |        "        vertical-align: top;\n",
393 |        "    }\n",
394 |        "\n",
395 |        "    .dataframe thead th {\n",
396 |        "        text-align: right;\n",
397 |        "    }\n",
398 |        "</style>\n",
399 |        "<table border=\"1\" class=\"dataframe\">\n",
400 |        "  <thead>\n",
401 |        "    <tr style=\"text-align: right;\">\n",
402 |        "      <th></th>\n",
403 |        "      <th>repetition_time</th>\n",
404 |        "      <th>study_time</th>\n",
405 |        "    </tr>\n",
406 |        "  </thead>\n",
407 |        "  <tbody>\n",
408 |        "    <tr>\n",
409 |        "      <th>0</th>\n",
410 |        "      <td>0.00</td>\n",
411 |        "      <td>0.00</td>\n",
412 |        "    </tr>\n",
413 |        "    <tr>\n",
414 |        "      <th>1</th>\n",
415 |        "      <td>0.24</td>\n",
416 |        "      <td>0.90</td>\n",
417 |        "    </tr>\n",
418 |        "    <tr>\n",
419 |        "      <th>2</th>\n",
420 |        "      <td>0.25</td>\n",
421 |        "      <td>0.33</td>\n",
422 |        "    </tr>\n",
423 |        "    <tr>\n",
424 |        "      <th>3</th>\n",
425 |        "      <td>0.65</td>\n",
426 |        "      <td>0.30</td>\n",
427 |        "    </tr>\n",
428 |        "    <tr>\n",
429 |        "      <th>4</th>\n",
430 |        "      <td>0.98</td>\n",
431 |        "      <td>0.24</td>\n",
432 |        "    </tr>\n",
433 |        "  </tbody>\n",
434 |        "</table>\n",
435 |        "</div>"
436 |       ],
437 |       "text/plain": [
438 |        "   repetition_time  study_time\n",
439 |        "0             0.00        0.00\n",
440 |        "1             0.24        0.90\n",
441 |        "2             0.25        0.33\n",
442 |        "3             0.65        0.30\n",
443 |        "4             0.98        0.24"
444 |       ]
445 |      },
446 |      "execution_count": 10,
447 |      "metadata": {},
448 |      "output_type": "execute_result"
449 |     }
450 |    ],
451 |    "source": [
452 |     "x.head()"
453 |    ]
454 |   },
455 |   {
456 |    "cell_type": "code",
457 |    "execution_count": 11,
458 |    "id": "9495f882",
459 |    "metadata": {},
460 |    "outputs": [],
461 |    "source": [
462 |     "#encoding data\n",
463 |     "y = pd.get_dummies(data,columns = ['knowledge_level'])"
464 |    ]
465 |   },
466 |   {
467 |    "cell_type": "code",
468 |    "execution_count": 12,
469 |    "id": "04424408",
470 |    "metadata": {},
471 |    "outputs": [
472 |     {
473 |      "data": {
474 |       "text/html": [
475 |        "<div>\n",
476 |        "<style scoped>\n",
477 |        "    .dataframe tbody tr th:only-of-type {\n",
478 |        "        vertical-align: middle;\n",
479 |        "    }\n",
480 |        "\n",
481 |        "    .dataframe tbody tr th {\n",
482 |        "        vertical-align: top;\n",
483 |        "    }\n",
484 |        "\n",
485 |        "    .dataframe thead th {\n",
486 |        "        text-align: right;\n",
487 |        "    }\n",
488 |        "</style>\n",
489 |        "<table border=\"1\" class=\"dataframe\">\n",
490 |        "  <thead>\n",
491 |        "    <tr style=\"text-align: right;\">\n",
492 |        "      <th></th>\n",
493 |        "      <th>repetition_time</th>\n",
494 |        "      <th>study_time</th>\n",
495 |        "      <th>knowledge_level_High</th>\n",
496 |        "      <th>knowledge_level_Low</th>\n",
497 |        "    </tr>\n",
498 |        "  </thead>\n",
499 |        "  <tbody>\n",
500 |        "    <tr>\n",
501 |        "      <th>0</th>\n",
502 |        "      <td>0.00</td>\n",
503 |        "      <td>0.00</td>\n",
504 |        "      <td>0</td>\n",
505 |        "      <td>1</td>\n",
506 |        "    </tr>\n",
507 |        "    <tr>\n",
508 |        "      <th>1</th>\n",
509 |        "      <td>0.24</td>\n",
510 |        "      <td>0.90</td>\n",
511 |        "      <td>1</td>\n",
512 |        "      <td>0</td>\n",
513 |        "    </tr>\n",
514 |        "    <tr>\n",
515 |        "      <th>2</th>\n",
516 |        "      <td>0.25</td>\n",
517 |        "      <td>0.33</td>\n",
518 |        "      <td>0</td>\n",
519 |        "      <td>1</td>\n",
520 |        "    </tr>\n",
521 |        "    <tr>\n",
522 |        "      <th>3</th>\n",
523 |        "      <td>0.65</td>\n",
524 |        "      <td>0.30</td>\n",
525 |        "      <td>1</td>\n",
526 |        "      <td>0</td>\n",
527 |        "    </tr>\n",
528 |        "    <tr>\n",
529 |        "      <th>4</th>\n",
530 |        "      <td>0.98</td>\n",
531 |        "      <td>0.24</td>\n",
532 |        "      <td>0</td>\n",
533 |        "      <td>1</td>\n",
534 |        "    </tr>\n",
535 |        "  </tbody>\n",
536 |        "</table>\n",
537 |        "</div>"
538 |       ],
539 |       "text/plain": [
540 |        "   repetition_time  study_time  knowledge_level_High  knowledge_level_Low\n",
541 |        "0             0.00        0.00                     0                    1\n",
542 |        "1             0.24        0.90                     1                    0\n",
543 |        "2             0.25        0.33                     0                    1\n",
544 |        "3             0.65        0.30                     1                    0\n",
545 |        "4             0.98        0.24                     0                    1"
546 |       ]
547 |      },
548 |      "execution_count": 12,
549 |      "metadata": {},
550 |      "output_type": "execute_result"
551 |     }
552 |    ],
553 |    "source": [
554 |     "y.head()"
555 |    ]
556 |   },
557 |   {
558 |    "cell_type": "code",
559 |    "execution_count": 15,
560 |    "id": "fcac0cd7",
561 |    "metadata": {},
562 |    "outputs": [],
563 |    "source": [
564 |     "#splitting data\n",
565 |     "from sklearn.model_selection import train_test_split\n",
566 |     "xtrain,xtest,ytrain,ytest = train_test_split(x,y['knowledge_level_High'],test_size=0.25, random_state=0 )"
567 |    ]
568 |   },
569 |   {
570 |    "cell_type": "code",
571 |    "execution_count": 16,
572 |    "id": "3c9aa28d",
573 |    "metadata": {},
574 |    "outputs": [],
575 |    "source": [
576 |     "#scaling\n",
577 |     "from sklearn.preprocessing import StandardScaler\n",
578 |     "sc = StandardScaler()\n",
579 |     "xtrain = sc.fit_transform(xtrain)\n",
580 |     "xtest = sc.fit_transform(xtest)"
581 |    ]
582 |   },
583 |   {
584 |    "cell_type": "code",
585 |    "execution_count": 17,
586 |    "id": "12f194f5",
587 |    "metadata": {},
588 |    "outputs": [
589 |     {
590 |      "data": {
591 |       "text/plain": [
592 |        "GaussianNB()"
593 |       ]
594 |      },
595 |      "execution_count": 17,
596 |      "metadata": {},
597 |      "output_type": "execute_result"
598 |     }
599 |    ],
600 |    "source": [
601 |     "#training\n",
602 |     "from sklearn.naive_bayes import GaussianNB\n",
603 |     "nvb = GaussianNB()\n",
604 |     "nvb.fit(xtrain,ytrain)"
605 |    ]
606 |   },
607 |   {
608 |    "cell_type": "code",
609 |    "execution_count": 18,
610 |    "id": "b21580bd",
611 |    "metadata": {},
612 |    "outputs": [],
613 |    "source": [
614 |     "y_p = nvb.predict(xtest)"
615 |    ]
616 |   },
617 |   {
618 |    "cell_type": "code",
619 |    "execution_count": 19,
620 |    "id": "fd57d426",
621 |    "metadata": {},
622 |    "outputs": [],
623 |    "source": [
624 |     "#Results\n",
625 |     "from sklearn.metrics import confusion_matrix\n",
626 |     "cm = confusion_matrix(ytest,y_p)"
627 |    ]
628 |   },
629 |   {
630 |    "cell_type": "code",
631 |    "execution_count": 20,
632 |    "id": "ed6fb178",
633 |    "metadata": {},
634 |    "outputs": [
635 |     {
636 |      "data": {
637 |       "text/plain": [
638 |        "array([[26,  3],\n",
639 |        "       [ 4, 32]], dtype=int64)"
640 |       ]
641 |      },
642 |      "execution_count": 20,
643 |      "metadata": {},
644 |      "output_type": "execute_result"
645 |     }
646 |    ],
647 |    "source": [
648 |     "cm"
649 |    ]
650 |   },
651 |   {
652 |    "cell_type": "code",
653 |    "execution_count": 23,
654 |    "id": "c061eade",
655 |    "metadata": {},
656 |    "outputs": [
657 |     {
658 |      "name": "stderr",
659 |      "output_type": "stream",
660 |      "text": [
661 |       "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
662 |       "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
663 |      ]
664 |     },
665 |     {
666 |      "data": {
667 |       "image/png": 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\n",
668 |       "text/plain": [
669 |        "<Figure size 432x288 with 1 Axes>"
670 |       ]
671 |      },
672 |      "metadata": {
673 |       "needs_background": "light"
674 |      },
675 |      "output_type": "display_data"
676 |     }
677 |    ],
678 |    "source": [
679 |     "#visualising model prediction\n",
680 |     "from matplotlib.colors import ListedColormap\n",
681 |     "X_set, y_set = xtest, ytest\n",
682 |     "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
683 |     "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
684 |     "plt.contourf(X1, X2, nvb.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
685 |     "             alpha = 0.75, cmap = ListedColormap(('white', 'grey')))\n",
686 |     "plt.xlim(X1.min(), X1.max())\n",
687 |     "plt.ylim(X2.min(), X2.max())\n",
688 |     "for i, j in enumerate(np.unique(y_set)):\n",
689 |     "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
690 |     "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
691 |     "plt.title('Knowledge Level')\n",
692 |     "plt.xlabel('Repitition Time')\n",
693 |     "plt.ylabel('Study Time')\n",
694 |     "plt.legend()\n",
695 |     "plt.show()\n",
696 |     "\n",
697 |     "    \n",
698 |     "                   "
699 |    ]
700 |   },
701 |   {
702 |    "cell_type": "code",
703 |    "execution_count": null,
704 |    "id": "d53397f1",
705 |    "metadata": {},
706 |    "outputs": [],
707 |    "source": []
708 |   }
709 |  ],
710 |  "metadata": {
711 |   "kernelspec": {
712 |    "display_name": "Python 3",
713 |    "language": "python",
714 |    "name": "python3"
715 |   },
716 |   "language_info": {
717 |    "codemirror_mode": {
718 |     "name": "ipython",
719 |     "version": 3
720 |    },
721 |    "file_extension": ".py",
722 |    "mimetype": "text/x-python",
723 |    "name": "python",
724 |    "nbconvert_exporter": "python",
725 |    "pygments_lexer": "ipython3",
726 |    "version": "3.8.8"
727 |   }
728 |  },
729 |  "nbformat": 4,
730 |  "nbformat_minor": 5
731 | }
732 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # Machine-Learning-Collection
2 | It contains machine learning codes that I made and some of files for data source are from geeksforgeeks courses and Kraggle.com
3 | 
4 | 
5 | 
6 | 


--------------------------------------------------------------------------------
/Traffic-Light-Detection-CNN/traffic_cnn.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "908e1a66",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import tensorflow as tf\n",
 11 |     "from keras.preprocessing.image import ImageDataGenerator\n",
 12 |     "import numpy as np\n",
 13 |     "import pandas as pd\n",
 14 |     "import matplotlib.pyplot as plt"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "code",
 19 |    "execution_count": 2,
 20 |    "id": "3abce3f8",
 21 |    "metadata": {},
 22 |    "outputs": [
 23 |     {
 24 |      "name": "stdout",
 25 |      "output_type": "stream",
 26 |      "text": [
 27 |       "Found 2600 images belonging to 1 classes.\n"
 28 |      ]
 29 |     }
 30 |    ],
 31 |    "source": [
 32 |     "#Preprocessing the Training set\n",
 33 |     "\n",
 34 |     "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
 35 |     "                                   shear_range = 0.2,\n",
 36 |     "                                   zoom_range = 0.2,\n",
 37 |     "                                   horizontal_flip = True)\n",
 38 |     "training_set = train_datagen.flow_from_directory(r\"L:\\project - traffic-light\\train_dataset\",\n",
 39 |     "                                                 target_size = (64, 64),\n",
 40 |     "                                                 batch_size = 32,\n",
 41 |     "                                                 class_mode = 'binary')\n"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "code",
 46 |    "execution_count": 3,
 47 |    "id": "d7b66b17",
 48 |    "metadata": {},
 49 |    "outputs": [
 50 |     {
 51 |      "name": "stdout",
 52 |      "output_type": "stream",
 53 |      "text": [
 54 |       "Found 400 images belonging to 1 classes.\n"
 55 |      ]
 56 |     }
 57 |    ],
 58 |    "source": [
 59 |     "#Preprocessing the Test set\n",
 60 |     "\n",
 61 |     "test_datagen = ImageDataGenerator(rescale = 1./255)\n",
 62 |     "test_set = test_datagen.flow_from_directory(r\"L:\\project - traffic-light\\test_dataset\",\n",
 63 |     "                                            target_size = (64, 64),\n",
 64 |     "                                            batch_size = 32,\n",
 65 |     "                                            class_mode = 'binary')"
 66 |    ]
 67 |   },
 68 |   {
 69 |    "cell_type": "code",
 70 |    "execution_count": 4,
 71 |    "id": "7ffed715",
 72 |    "metadata": {},
 73 |    "outputs": [],
 74 |    "source": [
 75 |     "#starting CNN process\n",
 76 |     "cnn = tf.keras.models.Sequential()"
 77 |    ]
 78 |   },
 79 |   {
 80 |    "cell_type": "code",
 81 |    "execution_count": 5,
 82 |    "id": "ee48cbed",
 83 |    "metadata": {},
 84 |    "outputs": [],
 85 |    "source": [
 86 |     "#Convolution\n",
 87 |     "cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu', input_shape=[64, 64, 3]))"
 88 |    ]
 89 |   },
 90 |   {
 91 |    "cell_type": "code",
 92 |    "execution_count": 6,
 93 |    "id": "4d426e93",
 94 |    "metadata": {},
 95 |    "outputs": [],
 96 |    "source": [
 97 |     "#Pooling\n",
 98 |     "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))"
 99 |    ]
100 |   },
101 |   {
102 |    "cell_type": "code",
103 |    "execution_count": 7,
104 |    "id": "3d32d4cc",
105 |    "metadata": {},
106 |    "outputs": [],
107 |    "source": [
108 |     "#second convolutional layer\n",
109 |     "cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu'))\n",
110 |     "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))"
111 |    ]
112 |   },
113 |   {
114 |    "cell_type": "code",
115 |    "execution_count": 8,
116 |    "id": "bda02cfa",
117 |    "metadata": {},
118 |    "outputs": [],
119 |    "source": [
120 |     "#Flattening\n",
121 |     "cnn.add(tf.keras.layers.Flatten())"
122 |    ]
123 |   },
124 |   {
125 |    "cell_type": "code",
126 |    "execution_count": 9,
127 |    "id": "eed05de7",
128 |    "metadata": {},
129 |    "outputs": [],
130 |    "source": [
131 |     "#Full Connection\n",
132 |     "cnn.add(tf.keras.layers.Dense(units=128, activation='relu'))"
133 |    ]
134 |   },
135 |   {
136 |    "cell_type": "code",
137 |    "execution_count": 10,
138 |    "id": "58b17c93",
139 |    "metadata": {},
140 |    "outputs": [],
141 |    "source": [
142 |     "#Output Layer\n",
143 |     "cnn.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))"
144 |    ]
145 |   },
146 |   {
147 |    "cell_type": "code",
148 |    "execution_count": 11,
149 |    "id": "950662cd",
150 |    "metadata": {},
151 |    "outputs": [],
152 |    "source": [
153 |     "#Training the CNN"
154 |    ]
155 |   },
156 |   {
157 |    "cell_type": "code",
158 |    "execution_count": 12,
159 |    "id": "32091fb8",
160 |    "metadata": {},
161 |    "outputs": [],
162 |    "source": [
163 |     "#Compiling the CNN\n",
164 |     "cnn.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
165 |    ]
166 |   },
167 |   {
168 |    "cell_type": "code",
169 |    "execution_count": 13,
170 |    "id": "e84b7991",
171 |    "metadata": {},
172 |    "outputs": [
173 |     {
174 |      "name": "stdout",
175 |      "output_type": "stream",
176 |      "text": [
177 |       "Epoch 1/3\n",
178 |       "82/82 [==============================] - 270s 3s/step - loss: 0.0123 - accuracy: 0.9877 - val_loss: 1.2561e-17 - val_accuracy: 1.0000\n",
179 |       "Epoch 2/3\n",
180 |       "82/82 [==============================] - 287s 4s/step - loss: 1.1705e-16 - accuracy: 1.0000 - val_loss: 1.2272e-17 - val_accuracy: 1.0000\n",
181 |       "Epoch 3/3\n",
182 |       "82/82 [==============================] - 312s 4s/step - loss: 6.2950e-17 - accuracy: 1.0000 - val_loss: 1.2272e-17 - val_accuracy: 1.0000\n"
183 |      ]
184 |     }
185 |    ],
186 |    "source": [
187 |     "#Training the CNN on the Training set and evaluating it on the Test se\n",
188 |     "model = cnn.fit(x = training_set, validation_data = test_set, epochs = 3)\n"
189 |    ]
190 |   },
191 |   {
192 |    "cell_type": "code",
193 |    "execution_count": 14,
194 |    "id": "547bf9f9",
195 |    "metadata": {},
196 |    "outputs": [
197 |     {
198 |      "name": "stdout",
199 |      "output_type": "stream",
200 |      "text": [
201 |       "Model: \"sequential\"\n",
202 |       "_________________________________________________________________\n",
203 |       " Layer (type)                Output Shape              Param #   \n",
204 |       "=================================================================\n",
205 |       " conv2d (Conv2D)             (None, 62, 62, 32)        896       \n",
206 |       "                                                                 \n",
207 |       " max_pooling2d (MaxPooling2D  (None, 31, 31, 32)       0         \n",
208 |       " )                                                               \n",
209 |       "                                                                 \n",
210 |       " conv2d_1 (Conv2D)           (None, 29, 29, 32)        9248      \n",
211 |       "                                                                 \n",
212 |       " max_pooling2d_1 (MaxPooling  (None, 14, 14, 32)       0         \n",
213 |       " 2D)                                                             \n",
214 |       "                                                                 \n",
215 |       " flatten (Flatten)           (None, 6272)              0         \n",
216 |       "                                                                 \n",
217 |       " dense (Dense)               (None, 128)               802944    \n",
218 |       "                                                                 \n",
219 |       " dense_1 (Dense)             (None, 1)                 129       \n",
220 |       "                                                                 \n",
221 |       "=================================================================\n",
222 |       "Total params: 813,217\n",
223 |       "Trainable params: 813,217\n",
224 |       "Non-trainable params: 0\n",
225 |       "_________________________________________________________________\n"
226 |      ]
227 |     }
228 |    ],
229 |    "source": [
230 |     "cnn.summary()"
231 |    ]
232 |   },
233 |   {
234 |    "cell_type": "code",
235 |    "execution_count": null,
236 |    "id": "21da903f",
237 |    "metadata": {},
238 |    "outputs": [],
239 |    "source": []
240 |   },
241 |   {
242 |    "cell_type": "code",
243 |    "execution_count": null,
244 |    "id": "4bc02657",
245 |    "metadata": {},
246 |    "outputs": [],
247 |    "source": []
248 |   }
249 |  ],
250 |  "metadata": {
251 |   "kernelspec": {
252 |    "display_name": "Python 3",
253 |    "language": "python",
254 |    "name": "python3"
255 |   },
256 |   "language_info": {
257 |    "codemirror_mode": {
258 |     "name": "ipython",
259 |     "version": 3
260 |    },
261 |    "file_extension": ".py",
262 |    "mimetype": "text/x-python",
263 |    "name": "python",
264 |    "nbconvert_exporter": "python",
265 |    "pygments_lexer": "ipython3",
266 |    "version": "3.8.8"
267 |   }
268 |  },
269 |  "nbformat": 4,
270 |  "nbformat_minor": 5
271 | }
272 | 


--------------------------------------------------------------------------------
/googlebrains-vpp-ver1.ipynb:
--------------------------------------------------------------------------------
1 | {"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"Reference Notebooks:\n","metadata":{}},{"cell_type":"markdown","source":"## Import libraries","metadata":{"papermill":{"duration":0.016249,"end_time":"2021-10-02T10:01:32.887198","exception":false,"start_time":"2021-10-02T10:01:32.870949","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import gc\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import KFold\nfrom sklearn.preprocessing import RobustScaler\nfrom sklearn.metrics import mean_absolute_error\n\nimport tensorflow as tf\nfrom tensorflow.keras.utils import plot_model\nfrom tensorflow.keras.models import Model, load_model\nfrom tensorflow.keras.callbacks import EarlyStopping\nfrom tensorflow.keras.callbacks import ModelCheckpoint\nfrom tensorflow.keras.callbacks import ReduceLROnPlateau\nfrom tensorflow.keras.layers import Bidirectional, LSTM, Conv1D\nfrom tensorflow.keras.layers import Dense, Dropout, Input\nfrom tensorflow.keras.layers import Concatenate, Add, GRU\n\nnp.random.seed(42)\ntf.random.set_seed(42)","metadata":{"_cell_guid":"3e5a0bd1-3e22-4b2c-a565-68985e55f95e","_uuid":"e331dbcc-0346-4019-9ff6-b890154a878b","collapsed":false,"jupyter":{"outputs_hidden":false},"papermill":{"duration":5.912325,"end_time":"2021-10-02T10:01:38.814556","exception":false,"start_time":"2021-10-02T10:01:32.902231","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-10-21T10:51:31.092445Z","iopub.execute_input":"2021-10-21T10:51:31.093256Z","iopub.status.idle":"2021-10-21T10:51:37.597242Z","shell.execute_reply.started":"2021-10-21T10:51:31.093130Z","shell.execute_reply":"2021-10-21T10:51:37.596298Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"2021-10-21 10:51:32.748629: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/conda/lib\n2021-10-21 10:51:32.748738: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Load source datasets","metadata":{"papermill":{"duration":0.014712,"end_time":"2021-10-02T10:01:38.845382","exception":false,"start_time":"2021-10-02T10:01:38.83067","status":"completed"},"tags":[]}},{"cell_type":"code","source":"train_df = pd.read_csv('../input/ventilator-pressure-prediction/train.csv')\nprint(f\"train_df: {train_df.shape}\")\ntrain_df.head()","metadata":{"_cell_guid":"89eb257e-0ed2-4f8b-94d6-462a5e995eb1","_uuid":"ca71d87d-6594-4f31-906d-0b53cd4c1374","collapsed":false,"jupyter":{"outputs_hidden":false},"papermill":{"duration":10.663035,"end_time":"2021-10-02T10:01:49.523448","exception":false,"start_time":"2021-10-02T10:01:38.860413","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-10-21T10:51:37.599146Z","iopub.execute_input":"2021-10-21T10:51:37.599444Z","iopub.status.idle":"2021-10-21T10:51:46.119928Z","shell.execute_reply.started":"2021-10-21T10:51:37.599406Z","shell.execute_reply":"2021-10-21T10:51:46.118964Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"train_df: (6036000, 8)\n","output_type":"stream"},{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"   id  breath_id   R   C  time_step       u_in  u_out   pressure\n0   1          1  20  50   0.000000   0.083334      0   5.837492\n1   2          1  20  50   0.033652  18.383041      0   5.907794\n2   3          1  20  50   0.067514  22.509278      0   7.876254\n3   4          1  20  50   0.101542  22.808822      0  11.742872\n4   5          1  20  50   0.135756  25.355850      0  12.234987","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>breath_id</th>\n      <th>R</th>\n      <th>C</th>\n      <th>time_step</th>\n      <th>u_in</th>\n      <th>u_out</th>\n      <th>pressure</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>20</td>\n      <td>50</td>\n      <td>0.000000</td>\n      <td>0.083334</td>\n      <td>0</td>\n      <td>5.837492</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n      <td>20</td>\n      <td>50</td>\n      <td>0.033652</td>\n      <td>18.383041</td>\n      <td>0</td>\n      <td>5.907794</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1</td>\n      <td>20</td>\n      <td>50</td>\n      <td>0.067514</td>\n      <td>22.509278</td>\n      <td>0</td>\n      <td>7.876254</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1</td>\n      <td>20</td>\n      <td>50</td>\n      <td>0.101542</td>\n      <td>22.808822</td>\n      <td>0</td>\n      <td>11.742872</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>1</td>\n      <td>20</td>\n      <td>50</td>\n      <td>0.135756</td>\n      <td>25.355850</td>\n      <td>0</td>\n      <td>12.234987</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"test_df = pd.read_csv('../input/ventilator-pressure-prediction/test.csv')\nprint(f\"test_df: {test_df.shape}\")\ntest_df.head()","metadata":{"papermill":{"duration":5.111757,"end_time":"2021-10-02T10:01:54.659801","exception":false,"start_time":"2021-10-02T10:01:49.548044","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-10-21T10:51:46.121283Z","iopub.execute_input":"2021-10-21T10:51:46.121609Z","iopub.status.idle":"2021-10-21T10:51:50.216171Z","shell.execute_reply.started":"2021-10-21T10:51:46.121579Z","shell.execute_reply":"2021-10-21T10:51:50.215312Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"test_df: (4024000, 7)\n","output_type":"stream"},{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"   id  breath_id  R   C  time_step       u_in  u_out\n0   1          0  5  20   0.000000   0.000000      0\n1   2          0  5  20   0.031904   7.515046      0\n2   3          0  5  20   0.063827  14.651675      0\n3   4          0  5  20   0.095751  21.230610      0\n4   5          0  5  20   0.127644  26.320956      0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>breath_id</th>\n      <th>R</th>\n      <th>C</th>\n      <th>time_step</th>\n      <th>u_in</th>\n      <th>u_out</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>5</td>\n      <td>20</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>0</td>\n      <td>5</td>\n      <td>20</td>\n      <td>0.031904</td>\n      <td>7.515046</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>0</td>\n      <td>5</td>\n      <td>20</td>\n      <td>0.063827</td>\n      <td>14.651675</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>0</td>\n      <td>5</td>\n      <td>20</td>\n      <td>0.095751</td>\n      <td>21.230610</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>0</td>\n      <td>5</td>\n      <td>20</td>\n      <td>0.127644</td>\n      <td>26.320956</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Feature Engineering","metadata":{"papermill":{"duration":0.017258,"end_time":"2021-10-02T10:01:54.695134","exception":false,"start_time":"2021-10-02T10:01:54.677876","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def add_features(df):\n    df['cross']= df['u_in'] * df['u_out']\n    df['cross2']= df['time_step'] * df['u_out']\n    df['area'] = df['time_step'] * df['u_in']\n    df['area'] = df.groupby('breath_id')['area'].cumsum()\n    df['time_step_cumsum'] = df.groupby(['breath_id'])['time_step'].cumsum()\n    df['u_in_cumsum'] = (df['u_in']).groupby(df['breath_id']).cumsum()\n    print(\"Step-1...Completed\")\n    \n    df['u_in_lag1'] = df.groupby('breath_id')['u_in'].shift(1)\n    df['u_out_lag1'] = df.groupby('breath_id')['u_out'].shift(1)\n    df['u_in_lag_back1'] = df.groupby('breath_id')['u_in'].shift(-1)\n    df['u_out_lag_back1'] = df.groupby('breath_id')['u_out'].shift(-1)\n    df['u_in_lag2'] = df.groupby('breath_id')['u_in'].shift(2)\n    df['u_out_lag2'] = df.groupby('breath_id')['u_out'].shift(2)\n    df['u_in_lag_back2'] = df.groupby('breath_id')['u_in'].shift(-2)\n    df['u_out_lag_back2'] = df.groupby('breath_id')['u_out'].shift(-2)\n    df['u_in_lag3'] = df.groupby('breath_id')['u_in'].shift(3)\n    df['u_out_lag3'] = df.groupby('breath_id')['u_out'].shift(3)\n    df['u_in_lag_back3'] = df.groupby('breath_id')['u_in'].shift(-3)\n    df['u_out_lag_back3'] = df.groupby('breath_id')['u_out'].shift(-3)\n    df['u_in_lag4'] = df.groupby('breath_id')['u_in'].shift(4)\n    df['u_out_lag4'] = df.groupby('breath_id')['u_out'].shift(4)\n    df['u_in_lag_back4'] = df.groupby('breath_id')['u_in'].shift(-4)\n    df['u_out_lag_back4'] = df.groupby('breath_id')['u_out'].shift(-4)\n    df = df.fillna(0)\n    print(\"Step-2...Completed\")\n    \n    df['breath_id__u_in__max'] = df.groupby(['breath_id'])['u_in'].transform('max')\n    df['breath_id__u_in__mean'] = df.groupby(['breath_id'])['u_in'].transform('mean')\n    df['breath_id__u_in__diffmax'] = df.groupby(['breath_id'])['u_in'].transform('max') - df['u_in']\n    df['breath_id__u_in__diffmean'] = df.groupby(['breath_id'])['u_in'].transform('mean') - df['u_in']\n    print(\"Step-3...Completed\")\n    \n    df['u_in_diff1'] = df['u_in'] - df['u_in_lag1']\n    df['u_out_diff1'] = df['u_out'] - df['u_out_lag1']\n    df['u_in_diff2'] = df['u_in'] - df['u_in_lag2']\n    df['u_out_diff2'] = df['u_out'] - df['u_out_lag2']\n    df['u_in_diff3'] = df['u_in'] - df['u_in_lag3']\n    df['u_out_diff3'] = df['u_out'] - df['u_out_lag3']\n    df['u_in_diff4'] = df['u_in'] - df['u_in_lag4']\n    df['u_out_diff4'] = df['u_out'] - df['u_out_lag4']\n    print(\"Step-4...Completed\")\n    \n    df['one'] = 1\n    df['count'] = (df['one']).groupby(df['breath_id']).cumsum()\n    df['u_in_cummean'] =df['u_in_cumsum'] /df['count']\n    \n    df['breath_id_lag']=df['breath_id'].shift(1).fillna(0)\n    df['breath_id_lag2']=df['breath_id'].shift(2).fillna(0)\n    df['breath_id_lagsame']=np.select([df['breath_id_lag']==df['breath_id']],[1],0)\n    df['breath_id_lag2same']=np.select([df['breath_id_lag2']==df['breath_id']],[1],0)\n    df['breath_id__u_in_lag'] = df['u_in'].shift(1).fillna(0)\n    df['breath_id__u_in_lag'] = df['breath_id__u_in_lag'] * df['breath_id_lagsame']\n    df['breath_id__u_in_lag2'] = df['u_in'].shift(2).fillna(0)\n    df['breath_id__u_in_lag2'] = df['breath_id__u_in_lag2'] * df['breath_id_lag2same']\n    print(\"Step-5...Completed\")\n    \n    df['time_step_diff'] = df.groupby('breath_id')['time_step'].diff().fillna(0)\n    df['ewm_u_in_mean'] = (df\\\n                           .groupby('breath_id')['u_in']\\\n                           .ewm(halflife=9)\\\n                           .mean()\\\n                           .reset_index(level=0,drop=True))\n    df[[\"15_in_sum\",\"15_in_min\",\"15_in_max\",\"15_in_mean\"]] = (df\\\n                                                              .groupby('breath_id')['u_in']\\\n                                                              .rolling(window=15,min_periods=1)\\\n                                                              .agg({\"15_in_sum\":\"sum\",\n                                                                    \"15_in_min\":\"min\",\n                                                                    \"15_in_max\":\"max\",\n                                                                    \"15_in_mean\":\"mean\"})\\\n                                                               .reset_index(level=0,drop=True))\n    print(\"Step-6...Completed\")\n    \n    df['u_in_lagback_diff1'] = df['u_in'] - df['u_in_lag_back1']\n    df['u_out_lagback_diff1'] = df['u_out'] - df['u_out_lag_back1']\n    df['u_in_lagback_diff2'] = df['u_in'] - df['u_in_lag_back2']\n    df['u_out_lagback_diff2'] = df['u_out'] - df['u_out_lag_back2']\n    print(\"Step-7...Completed\")\n    \n    df['R'] = df['R'].astype(str)\n    df['C'] = df['C'].astype(str)\n    df['R__C'] = df[\"R\"].astype(str) + '__' + df[\"C\"].astype(str)\n    df = pd.get_dummies(df)\n    print(\"Step-8...Completed\")\n    \n    return df\n\n\nprint(\"Train data...\\n\")\ntrain = add_features(train_df)\n\nprint(\"\\nTest data...\\n\")\ntest = add_features(test_df)\n\ndel train_df\ndel test_df\ngc.collect()","metadata":{"_cell_guid":"13a36b46-7067-4b29-aad3-7e3b15e8415b","_uuid":"dc41dbf2-f199-4b9d-bbd9-bf6084162b47","collapsed":false,"jupyter":{"outputs_hidden":false},"papermill":{"duration":65.287443,"end_time":"2021-10-02T10:02:59.999123","exception":false,"start_time":"2021-10-02T10:01:54.71168","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-10-21T10:51:50.217553Z","iopub.execute_input":"2021-10-21T10:51:50.218523Z","iopub.status.idle":"2021-10-21T10:53:39.627176Z","shell.execute_reply.started":"2021-10-21T10:51:50.218485Z","shell.execute_reply":"2021-10-21T10:53:39.626257Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Train data...\n\nStep-1...Completed\nStep-2...Completed\nStep-3...Completed\nStep-4...Completed\nStep-5...Completed\nStep-6...Completed\nStep-7...Completed\nStep-8...Completed\n\nTest data...\n\nStep-1...Completed\nStep-2...Completed\nStep-3...Completed\nStep-4...Completed\nStep-5...Completed\nStep-6...Completed\nStep-7...Completed\nStep-8...Completed\n","output_type":"stream"},{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"targets = train[['pressure']].to_numpy().reshape(-1, 80)\n\ntrain.drop(['pressure','id', 'breath_id','one','count',\n            'breath_id_lag','breath_id_lag2','breath_id_lagsame',\n            'breath_id_lag2same'], axis=1, inplace=True)\n\ntest = test.drop(['id', 'breath_id','one','count','breath_id_lag',\n                  'breath_id_lag2','breath_id_lagsame',\n                  'breath_id_lag2same'], axis=1)\n\nprint(f\"train: {train.shape} \\ntest: {test.shape}\")","metadata":{"_cell_guid":"01328860-fa2a-421c-9e5f-ea0048246f98","_uuid":"346bf2c0-96d2-4da5-8837-c0f820294a85","collapsed":false,"jupyter":{"outputs_hidden":false},"papermill":{"duration":6.737804,"end_time":"2021-10-02T10:03:06.757726","exception":false,"start_time":"2021-10-02T10:03:00.019922","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-10-21T10:53:39.629952Z","iopub.execute_input":"2021-10-21T10:53:39.630898Z","iopub.status.idle":"2021-10-21T10:53:44.665608Z","shell.execute_reply.started":"2021-10-21T10:53:39.630851Z","shell.execute_reply":"2021-10-21T10:53:44.664579Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"train: (6036000, 64) \ntest: (4024000, 64)\n","output_type":"stream"}]},{"cell_type":"code","source":"scaler = RobustScaler()\ntrain = scaler.fit_transform(train)\ntest = scaler.transform(test)\n\ntrain = train.reshape(-1, 80, train.shape[-1])\ntest = test.reshape(-1, 80, train.shape[-1])\n\nprint(f\"train: {train.shape} \\ntest: {test.shape} \\ntargets: {targets.shape}\")","metadata":{"papermill":{"duration":16.573662,"end_time":"2021-10-02T10:03:23.35312","exception":false,"start_time":"2021-10-02T10:03:06.779458","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-10-21T10:53:44.666977Z","iopub.execute_input":"2021-10-21T10:53:44.667508Z","iopub.status.idle":"2021-10-21T10:53:57.828243Z","shell.execute_reply.started":"2021-10-21T10:53:44.667463Z","shell.execute_reply":"2021-10-21T10:53:57.827159Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"train: (75450, 80, 64) \ntest: (50300, 80, 64) \ntargets: (75450, 80)\n","output_type":"stream"}]},{"cell_type":"code","source":"pressure = targets.squeeze().reshape(-1,1).astype('float32')\n\nP_MIN = np.min(pressure)\nP_MAX = np.max(pressure)\nP_STEP = (pressure[1] - pressure[0])[0]\nprint('Min pressure: {}'.format(P_MIN))\nprint('Max pressure: {}'.format(P_MAX))\nprint('Pressure step: {}'.format(P_STEP))\nprint('Unique values:  {}'.format(np.unique(pressure).shape[0]))\n\ndel pressure\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2021-10-21T10:53:57.829304Z","iopub.execute_input":"2021-10-21T10:53:57.829552Z","iopub.status.idle":"2021-10-21T10:53:58.365213Z","shell.execute_reply.started":"2021-10-21T10:53:57.829524Z","shell.execute_reply":"2021-10-21T10:53:58.364406Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"Min pressure: -1.8957443237304688\nMax pressure: 64.82099151611328\nPressure step: 0.07030248641967773\nUnique values:  950\n","output_type":"stream"},{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"72"},"metadata":{}}]},{"cell_type":"markdown","source":"## Hardware config","metadata":{"papermill":{"duration":0.02619,"end_time":"2021-10-02T10:03:23.404084","exception":false,"start_time":"2021-10-02T10:03:23.377894","status":"completed"},"tags":[]}},{"cell_type":"code","source":"try:\n    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n    tf.config.experimental_connect_to_cluster(tpu)\n    tf.tpu.experimental.initialize_tpu_system(tpu)\n    tpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)\n    BATCH_SIZE = tpu_strategy.num_replicas_in_sync * 64\n    print(\"Running on TPU:\", tpu.master())\n    print(f\"Batch Size: {BATCH_SIZE}\")\n    \nexcept ValueError:\n    strategy = tf.distribute.get_strategy()\n    BATCH_SIZE = 512\n    print(f\"Running on {strategy.num_replicas_in_sync} replicas\")\n    print(f\"Batch Size: {BATCH_SIZE}\")","metadata":{"papermill":{"duration":5.822056,"end_time":"2021-10-02T10:03:29.255869","exception":false,"start_time":"2021-10-02T10:03:23.433813","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-10-21T10:53:58.366432Z","iopub.execute_input":"2021-10-21T10:53:58.366902Z","iopub.status.idle":"2021-10-21T10:54:04.009906Z","shell.execute_reply.started":"2021-10-21T10:53:58.366869Z","shell.execute_reply":"2021-10-21T10:54:04.008589Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stderr","text":"2021-10-21 10:53:58.374418: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n2021-10-21 10:53:58.377710: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/conda/lib\n2021-10-21 10:53:58.377753: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)\n2021-10-21 10:53:58.377789: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (e3b293513433): /proc/driver/nvidia/version does not exist\n2021-10-21 10:53:58.381038: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2021-10-21 10:53:58.382716: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n2021-10-21 10:53:58.421758: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:301] Initialize GrpcChannelCache for job worker -> {0 -> 10.0.0.2:8470}\n2021-10-21 10:53:58.421844: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:301] Initialize GrpcChannelCache for job localhost -> {0 -> localhost:30470}\n2021-10-21 10:53:58.442591: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:301] Initialize GrpcChannelCache for job worker -> {0 -> 10.0.0.2:8470}\n2021-10-21 10:53:58.442649: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:301] Initialize GrpcChannelCache for job localhost -> {0 -> localhost:30470}\n2021-10-21 10:53:58.444281: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:411] Started server with target: grpc://localhost:30470\n","output_type":"stream"},{"name":"stdout","text":"Running on TPU: grpc://10.0.0.2:8470\nBatch Size: 512\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Keras DNN Model","metadata":{"papermill":{"duration":0.021164,"end_time":"2021-10-02T10:03:29.2981","exception":false,"start_time":"2021-10-02T10:03:29.276936","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def dnn_model1():\n    \n    x_input = Input(shape=(train.shape[-2:]))\n    \n    x1 = Bidirectional(GRU(units=768, return_sequences=True))(x_input)\n    x2 = Bidirectional(GRU(units=512, return_sequences=True))(x1)\n    x3 = Bidirectional(GRU(units=256, return_sequences=True))(x2)\n    \n    z2 = Bidirectional(GRU(units=256, return_sequences=True))(x2)\n    z3 = Bidirectional(GRU(units=128, return_sequences=True))(Add()([x3, z2]))\n    \n    x = Concatenate(axis=2)([x3, z2, z3])\n    x = Bidirectional(GRU(units=192, return_sequences=True))(x)\n    \n    x = Dense(units=240, activation='tanh')(x)\n\n    x_output = Dense(units=1)(x)\n\n    model = Model(inputs=x_input, outputs=x_output, \n                  name='DNN_Model')\n    return model","metadata":{"execution":{"iopub.status.busy":"2021-10-21T10:54:04.011185Z","iopub.execute_input":"2021-10-21T10:54:04.011465Z","iopub.status.idle":"2021-10-21T10:54:04.020622Z","shell.execute_reply.started":"2021-10-21T10:54:04.011434Z","shell.execute_reply":"2021-10-21T10:54:04.019446Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"def dnn_model():\n    \n    x_input = Input(shape=(train.shape[-2:]))\n    \n    x1 = Bidirectional(LSTM(units=768, return_sequences=True))(x_input)\n    x2 = Bidirectional(LSTM(units=512, return_sequences=True))(x1)\n    x3 = Bidirectional(LSTM(units=256, return_sequences=True))(x2)\n    \n    z2 = Bidirectional(GRU(units=256, return_sequences=True))(x2)\n    z3 = Bidirectional(GRU(units=128, return_sequences=True))(Add()([x3, z2]))\n    \n    x = Concatenate(axis=2)([x3, z2, z3])\n    x = Bidirectional(LSTM(units=192, return_sequences=True))(x)\n    \n    x = Dense(units=128, activation='tanh')(x)\n    \n    x_output = Dense(units=1)(x)\n\n    model = Model(inputs=x_input, outputs=x_output, \n                  name='DNN_Model')\n    return model","metadata":{"papermill":{"duration":0.034282,"end_time":"2021-10-02T10:03:29.353571","exception":false,"start_time":"2021-10-02T10:03:29.319289","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-10-21T10:54:04.021963Z","iopub.execute_input":"2021-10-21T10:54:04.022216Z","iopub.status.idle":"2021-10-21T10:54:04.043311Z","shell.execute_reply.started":"2021-10-21T10:54:04.022189Z","shell.execute_reply":"2021-10-21T10:54:04.042481Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"model = dnn_model1()\nmodel.summary()","metadata":{"papermill":{"duration":4.646068,"end_time":"2021-10-02T10:03:34.021825","exception":false,"start_time":"2021-10-02T10:03:29.375757","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-10-21T10:54:04.045173Z","iopub.execute_input":"2021-10-21T10:54:04.045592Z","iopub.status.idle":"2021-10-21T10:54:07.812467Z","shell.execute_reply.started":"2021-10-21T10:54:04.045552Z","shell.execute_reply":"2021-10-21T10:54:07.811111Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Model: \"DNN_Model\"\n__________________________________________________________________________________________________\nLayer (type)                    Output Shape         Param #     Connected to                     \n==================================================================================================\ninput_1 (InputLayer)            [(None, 80, 64)]     0                                            \n__________________________________________________________________________________________________\nbidirectional (Bidirectional)   (None, 80, 1536)     3843072     input_1[0][0]                    \n__________________________________________________________________________________________________\nbidirectional_1 (Bidirectional) (None, 80, 1024)     6297600     bidirectional[0][0]              \n__________________________________________________________________________________________________\nbidirectional_2 (Bidirectional) (None, 80, 512)      1969152     bidirectional_1[0][0]            \n__________________________________________________________________________________________________\nbidirectional_3 (Bidirectional) (None, 80, 512)      1969152     bidirectional_1[0][0]            \n__________________________________________________________________________________________________\nadd (Add)                       (None, 80, 512)      0           bidirectional_2[0][0]            \n                                                                 bidirectional_3[0][0]            \n__________________________________________________________________________________________________\nbidirectional_4 (Bidirectional) (None, 80, 256)      493056      add[0][0]                        \n__________________________________________________________________________________________________\nconcatenate (Concatenate)       (None, 80, 1280)     0           bidirectional_2[0][0]            \n                                                                 bidirectional_3[0][0]            \n                                                                 bidirectional_4[0][0]            \n__________________________________________________________________________________________________\nbidirectional_5 (Bidirectional) (None, 80, 384)      1698048     concatenate[0][0]                \n__________________________________________________________________________________________________\ndense (Dense)                   (None, 80, 240)      92400       bidirectional_5[0][0]            \n__________________________________________________________________________________________________\ndense_1 (Dense)                 (None, 80, 1)        241         dense[0][0]                      \n==================================================================================================\nTotal params: 16,362,721\nTrainable params: 16,362,721\nNon-trainable params: 0\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"model = cnn_model()\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2021-10-21T10:54:07.813698Z","iopub.execute_input":"2021-10-21T10:54:07.813925Z","iopub.status.idle":"2021-10-21T10:54:08.118216Z","shell.execute_reply.started":"2021-10-21T10:54:07.813901Z","shell.execute_reply":"2021-10-21T10:54:08.116843Z"},"trusted":true},"execution_count":12,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_470/149160529.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcnn_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'cnn_model' is not defined"],"ename":"NameError","evalue":"name 'cnn_model' is not defined","output_type":"error"}]},{"cell_type":"code","source":"plot_model(\n    model, \n    to_file='Google_Brain_Keras_Model.png', \n    show_shapes=True,\n    show_layer_names=True\n)","metadata":{"papermill":{"duration":1.176472,"end_time":"2021-10-02T10:03:35.22082","exception":false,"start_time":"2021-10-02T10:03:34.044348","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-10-21T10:54:08.119574Z","iopub.status.idle":"2021-10-21T10:54:08.120097Z","shell.execute_reply.started":"2021-10-21T10:54:08.119816Z","shell.execute_reply":"2021-10-21T10:54:08.119834Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"with tpu_strategy.scope():\n    \n    VERBOSE = 0\n    test_preds = []\n    kf = KFold(n_splits=7, shuffle=True, random_state=2021)\n    \n    for fold, (train_idx, test_idx) in enumerate(kf.split(train, targets)):\n        X_train, X_valid = train[train_idx], train[test_idx]\n        y_train, y_valid = targets[train_idx], targets[test_idx]\n        \n        model = dnn_model1()\n        model.compile(optimizer=\"adam\", loss=\"mae\")\n\n        lr = ReduceLROnPlateau(monitor=\"val_loss\", factor=0.75, \n                               patience=10, verbose=VERBOSE)\n        \n        save_locally = tf.saved_model.SaveOptions(experimental_io_device='/job:localhost')\n        chk_point = ModelCheckpoint(f'./Bidirect_LSTM_model_{fold+1}C.h5', options=save_locally, \n                                    monitor='val_loss', verbose=VERBOSE, \n                                    save_best_only=True, mode='min')\n\n        es = EarlyStopping(monitor=\"val_loss\", patience=50, \n                           verbose=VERBOSE, mode=\"min\", \n                           restore_best_weights=True)\n        \n        model.fit(X_train, y_train, \n                  validation_data=(X_valid, y_valid), \n                  epochs=400,\n                  verbose=VERBOSE,\n                  batch_size=BATCH_SIZE, \n                  callbacks=[lr, chk_point, es])\n        \n        load_locally = tf.saved_model.LoadOptions(experimental_io_device='/job:localhost')\n        model = load_model(f'./Bidirect_LSTM_model_{fold+1}C.h5', options=load_locally)\n        \n        y_true = y_valid.squeeze().reshape(-1, 1)\n        y_pred = model.predict(X_valid, batch_size=BATCH_SIZE).squeeze().reshape(-1, 1)\n        score = mean_absolute_error(y_true, y_pred)\n        print(f\"Fold-{fold+1} | OOF Score: {score}\")\n        \n        test_preds.append(model.predict(test, batch_size=BATCH_SIZE).squeeze().reshape(-1, 1).squeeze())","metadata":{"papermill":{"duration":13995.547028,"end_time":"2021-10-02T13:56:50.79368","exception":false,"start_time":"2021-10-02T10:03:35.246652","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-10-21T10:55:47.614251Z","iopub.execute_input":"2021-10-21T10:55:47.614663Z","iopub.status.idle":"2021-10-21T11:02:42.484986Z","shell.execute_reply.started":"2021-10-21T10:55:47.614626Z","shell.execute_reply":"2021-10-21T11:02:42.483621Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stderr","text":"2021-10-21 10:56:11.139431: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 1324462080 exceeds 10% of free system memory.\n","output_type":"stream"},{"name":"stdout","text":"Fold-1 | OOF Score: 0.35343958510227647\n","output_type":"stream"},{"name":"stderr","text":"2021-10-21 11:00:05.736982: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 1030144000 exceeds 10% of free system memory.\n2021-10-21 11:00:29.757281: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 1324462080 exceeds 10% of free system memory.\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_470/2896258413.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     29\u001b[0m                   \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mVERBOSE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m                   \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m                   callbacks=[lr, chk_point, es])\n\u001b[0m\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0mload_locally\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaved_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLoadOptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexperimental_io_device\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'/job:localhost'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1139\u001b[0m               \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1140\u001b[0m               \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1141\u001b[0;31m               return_dict=True)\n\u001b[0m\u001b[1;32m   1142\u001b[0m           \u001b[0mval_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'val_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mval\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval_logs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1143\u001b[0m   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\u001b[0mcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync_executors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2331\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/context.py\u001b[0m in \u001b[0;36msync_executors\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    643\u001b[0m     \"\"\"\n\u001b[1;32m    644\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_context_handle\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 645\u001b[0;31m       \u001b[0mpywrap_tfe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTFE_ContextSyncExecutors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_context_handle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    646\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Context is not initialized.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}]},{"cell_type":"code","source":"for i in range(7):\n    print(test_preds[i][0])\n\nlen(test_preds)","metadata":{"execution":{"iopub.status.busy":"2021-10-21T10:54:08.126662Z","iopub.status.idle":"2021-10-21T10:54:08.126996Z","shell.execute_reply.started":"2021-10-21T10:54:08.126831Z","shell.execute_reply":"2021-10-21T10:54:08.126847Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Create submission file","metadata":{"papermill":{"duration":0.028835,"end_time":"2021-10-02T13:56:50.851574","exception":false,"start_time":"2021-10-02T13:56:50.822739","status":"completed"},"tags":[]}},{"cell_type":"code","source":"submission = pd.read_csv('../input/ventilator-pressure-prediction/sample_submission.csv')\nsubmission[\"pressure\"] = sum(test_preds)/7\nsubmission.to_csv('mean_submission.csv', index=False)","metadata":{"papermill":{"duration":13.347103,"end_time":"2021-10-02T13:57:04.227112","exception":false,"start_time":"2021-10-02T13:56:50.880009","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-10-21T10:54:08.127916Z","iopub.status.idle":"2021-10-21T10:54:08.128205Z","shell.execute_reply.started":"2021-10-21T10:54:08.128059Z","shell.execute_reply":"2021-10-21T10:54:08.128073Z"},"trusted":true},"execution_count":null,"outputs":[]}]}


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