├── 1
    ├── README.md
    └── DMBI_Pract_1.ipynb
├── A 3
    └── README.md
├── README.md
└── weather-prediction-model-precision-comparison.ipynb


/1/README.md:
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1 | 
2 | 


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/A 3/README.md:
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1 | 
2 | 


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/README.md:
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1 | # Weather-Forecasting-Using-ML
2 | The aim of this project to see to do the prediction of the weather using the different types of machine learning model like Decision Tree Regressor, Random Forest Regressor and Extreme Gradient Boosting.
3 | 


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/weather-prediction-model-precision-comparison.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "e1f99117",
  6 |    "metadata": {
  7 |     "papermill": {
  8 |      "duration": 0.018583,
  9 |      "end_time": "2022-01-17T18:28:23.555057",
 10 |      "exception": false,
 11 |      "start_time": "2022-01-17T18:28:23.536474",
 12 |      "status": "completed"
 13 |     },
 14 |     "tags": []
 15 |    },
 16 |    "source": [
 17 |     "# **Weather Forecasting**\n",
 18 |     "\n",
 19 |     "Importing some important utilities!"
 20 |    ]
 21 |   },
 22 |   {
 23 |    "cell_type": "code",
 24 |    "execution_count": 1,
 25 |    "id": "7f3b5a08",
 26 |    "metadata": {
 27 |     "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
 28 |     "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
 29 |     "execution": {
 30 |      "iopub.execute_input": "2022-01-17T18:28:23.593738Z",
 31 |      "iopub.status.busy": "2022-01-17T18:28:23.592438Z",
 32 |      "iopub.status.idle": "2022-01-17T18:28:24.680688Z",
 33 |      "shell.execute_reply": "2022-01-17T18:28:24.679970Z",
 34 |      "shell.execute_reply.started": "2022-01-17T18:26:27.782932Z"
 35 |     },
 36 |     "papermill": {
 37 |      "duration": 1.10893,
 38 |      "end_time": "2022-01-17T18:28:24.680910",
 39 |      "exception": false,
 40 |      "start_time": "2022-01-17T18:28:23.571980",
 41 |      "status": "completed"
 42 |     },
 43 |     "tags": []
 44 |    },
 45 |    "outputs": [
 46 |     {
 47 |      "name": "stdout",
 48 |      "output_type": "stream",
 49 |      "text": [
 50 |       "/kaggle/input/weather-prediction/seattle-weather.csv\n"
 51 |      ]
 52 |     }
 53 |    ],
 54 |    "source": [
 55 |     "import numpy as np \n",
 56 |     "import pandas as pd \n",
 57 |     "import matplotlib.pyplot as plt\n",
 58 |     "import seaborn as sns\n",
 59 |     "\n",
 60 |     "\n",
 61 |     "import os\n",
 62 |     "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
 63 |     "    for filename in filenames:\n",
 64 |     "        print(os.path.join(dirname, filename))"
 65 |    ]
 66 |   },
 67 |   {
 68 |    "cell_type": "code",
 69 |    "execution_count": 2,
 70 |    "id": "a0a15ca8",
 71 |    "metadata": {
 72 |     "execution": {
 73 |      "iopub.execute_input": "2022-01-17T18:28:24.720626Z",
 74 |      "iopub.status.busy": "2022-01-17T18:28:24.719926Z",
 75 |      "iopub.status.idle": "2022-01-17T18:28:24.738484Z",
 76 |      "shell.execute_reply": "2022-01-17T18:28:24.737856Z",
 77 |      "shell.execute_reply.started": "2022-01-17T18:26:28.020077Z"
 78 |     },
 79 |     "papermill": {
 80 |      "duration": 0.040304,
 81 |      "end_time": "2022-01-17T18:28:24.738646",
 82 |      "exception": false,
 83 |      "start_time": "2022-01-17T18:28:24.698342",
 84 |      "status": "completed"
 85 |     },
 86 |     "tags": []
 87 |    },
 88 |    "outputs": [],
 89 |    "source": [
 90 |     "df=pd.read_csv(\"../input/weather-prediction/seattle-weather.csv\")"
 91 |    ]
 92 |   },
 93 |   {
 94 |    "cell_type": "markdown",
 95 |    "id": "22850560",
 96 |    "metadata": {
 97 |     "papermill": {
 98 |      "duration": 0.0164,
 99 |      "end_time": "2022-01-17T18:28:24.771982",
100 |      "exception": false,
101 |      "start_time": "2022-01-17T18:28:24.755582",
102 |      "status": "completed"
103 |     },
104 |     "tags": []
105 |    },
106 |    "source": [
107 |     "Checking whether the dataset got imported to the notebook."
108 |    ]
109 |   },
110 |   {
111 |    "cell_type": "code",
112 |    "execution_count": 3,
113 |    "id": "8338073a",
114 |    "metadata": {
115 |     "execution": {
116 |      "iopub.execute_input": "2022-01-17T18:28:24.809039Z",
117 |      "iopub.status.busy": "2022-01-17T18:28:24.808356Z",
118 |      "iopub.status.idle": "2022-01-17T18:28:24.815963Z",
119 |      "shell.execute_reply": "2022-01-17T18:28:24.816486Z",
120 |      "shell.execute_reply.started": "2022-01-17T18:26:28.431264Z"
121 |     },
122 |     "papermill": {
123 |      "duration": 0.027906,
124 |      "end_time": "2022-01-17T18:28:24.816651",
125 |      "exception": false,
126 |      "start_time": "2022-01-17T18:28:24.788745",
127 |      "status": "completed"
128 |     },
129 |     "tags": []
130 |    },
131 |    "outputs": [
132 |     {
133 |      "data": {
134 |       "text/plain": [
135 |        "(1461, 6)"
136 |       ]
137 |      },
138 |      "execution_count": 3,
139 |      "metadata": {},
140 |      "output_type": "execute_result"
141 |     }
142 |    ],
143 |    "source": [
144 |     "df.shape"
145 |    ]
146 |   },
147 |   {
148 |    "cell_type": "code",
149 |    "execution_count": 4,
150 |    "id": "3b296fa7",
151 |    "metadata": {
152 |     "execution": {
153 |      "iopub.execute_input": "2022-01-17T18:28:24.854271Z",
154 |      "iopub.status.busy": "2022-01-17T18:28:24.853574Z",
155 |      "iopub.status.idle": "2022-01-17T18:28:24.873794Z",
156 |      "shell.execute_reply": "2022-01-17T18:28:24.874328Z",
157 |      "shell.execute_reply.started": "2022-01-17T18:26:28.655704Z"
158 |     },
159 |     "papermill": {
160 |      "duration": 0.040555,
161 |      "end_time": "2022-01-17T18:28:24.874504",
162 |      "exception": false,
163 |      "start_time": "2022-01-17T18:28:24.833949",
164 |      "status": "completed"
165 |     },
166 |     "tags": []
167 |    },
168 |    "outputs": [
169 |     {
170 |      "data": {
171 |       "text/html": [
172 |        "<div>\n",
173 |        "<style scoped>\n",
174 |        "    .dataframe tbody tr th:only-of-type {\n",
175 |        "        vertical-align: middle;\n",
176 |        "    }\n",
177 |        "\n",
178 |        "    .dataframe tbody tr th {\n",
179 |        "        vertical-align: top;\n",
180 |        "    }\n",
181 |        "\n",
182 |        "    .dataframe thead th {\n",
183 |        "        text-align: right;\n",
184 |        "    }\n",
185 |        "</style>\n",
186 |        "<table border=\"1\" class=\"dataframe\">\n",
187 |        "  <thead>\n",
188 |        "    <tr style=\"text-align: right;\">\n",
189 |        "      <th></th>\n",
190 |        "      <th>date</th>\n",
191 |        "      <th>precipitation</th>\n",
192 |        "      <th>temp_max</th>\n",
193 |        "      <th>temp_min</th>\n",
194 |        "      <th>wind</th>\n",
195 |        "      <th>weather</th>\n",
196 |        "    </tr>\n",
197 |        "  </thead>\n",
198 |        "  <tbody>\n",
199 |        "    <tr>\n",
200 |        "      <th>0</th>\n",
201 |        "      <td>2012-01-01</td>\n",
202 |        "      <td>0.0</td>\n",
203 |        "      <td>12.8</td>\n",
204 |        "      <td>5.0</td>\n",
205 |        "      <td>4.7</td>\n",
206 |        "      <td>drizzle</td>\n",
207 |        "    </tr>\n",
208 |        "    <tr>\n",
209 |        "      <th>1</th>\n",
210 |        "      <td>2012-01-02</td>\n",
211 |        "      <td>10.9</td>\n",
212 |        "      <td>10.6</td>\n",
213 |        "      <td>2.8</td>\n",
214 |        "      <td>4.5</td>\n",
215 |        "      <td>rain</td>\n",
216 |        "    </tr>\n",
217 |        "    <tr>\n",
218 |        "      <th>2</th>\n",
219 |        "      <td>2012-01-03</td>\n",
220 |        "      <td>0.8</td>\n",
221 |        "      <td>11.7</td>\n",
222 |        "      <td>7.2</td>\n",
223 |        "      <td>2.3</td>\n",
224 |        "      <td>rain</td>\n",
225 |        "    </tr>\n",
226 |        "    <tr>\n",
227 |        "      <th>3</th>\n",
228 |        "      <td>2012-01-04</td>\n",
229 |        "      <td>20.3</td>\n",
230 |        "      <td>12.2</td>\n",
231 |        "      <td>5.6</td>\n",
232 |        "      <td>4.7</td>\n",
233 |        "      <td>rain</td>\n",
234 |        "    </tr>\n",
235 |        "    <tr>\n",
236 |        "      <th>4</th>\n",
237 |        "      <td>2012-01-05</td>\n",
238 |        "      <td>1.3</td>\n",
239 |        "      <td>8.9</td>\n",
240 |        "      <td>2.8</td>\n",
241 |        "      <td>6.1</td>\n",
242 |        "      <td>rain</td>\n",
243 |        "    </tr>\n",
244 |        "  </tbody>\n",
245 |        "</table>\n",
246 |        "</div>"
247 |       ],
248 |       "text/plain": [
249 |        "         date  precipitation  temp_max  temp_min  wind  weather\n",
250 |        "0  2012-01-01            0.0      12.8       5.0   4.7  drizzle\n",
251 |        "1  2012-01-02           10.9      10.6       2.8   4.5     rain\n",
252 |        "2  2012-01-03            0.8      11.7       7.2   2.3     rain\n",
253 |        "3  2012-01-04           20.3      12.2       5.6   4.7     rain\n",
254 |        "4  2012-01-05            1.3       8.9       2.8   6.1     rain"
255 |       ]
256 |      },
257 |      "execution_count": 4,
258 |      "metadata": {},
259 |      "output_type": "execute_result"
260 |     }
261 |    ],
262 |    "source": [
263 |     "df.head()"
264 |    ]
265 |   },
266 |   {
267 |    "cell_type": "code",
268 |    "execution_count": 5,
269 |    "id": "9e31c1ae",
270 |    "metadata": {
271 |     "execution": {
272 |      "iopub.execute_input": "2022-01-17T18:28:24.915262Z",
273 |      "iopub.status.busy": "2022-01-17T18:28:24.914568Z",
274 |      "iopub.status.idle": "2022-01-17T18:28:24.945122Z",
275 |      "shell.execute_reply": "2022-01-17T18:28:24.945644Z",
276 |      "shell.execute_reply.started": "2022-01-17T18:26:28.863043Z"
277 |     },
278 |     "papermill": {
279 |      "duration": 0.052648,
280 |      "end_time": "2022-01-17T18:28:24.945844",
281 |      "exception": false,
282 |      "start_time": "2022-01-17T18:28:24.893196",
283 |      "status": "completed"
284 |     },
285 |     "tags": []
286 |    },
287 |    "outputs": [
288 |     {
289 |      "data": {
290 |       "text/html": [
291 |        "<div>\n",
292 |        "<style scoped>\n",
293 |        "    .dataframe tbody tr th:only-of-type {\n",
294 |        "        vertical-align: middle;\n",
295 |        "    }\n",
296 |        "\n",
297 |        "    .dataframe tbody tr th {\n",
298 |        "        vertical-align: top;\n",
299 |        "    }\n",
300 |        "\n",
301 |        "    .dataframe thead th {\n",
302 |        "        text-align: right;\n",
303 |        "    }\n",
304 |        "</style>\n",
305 |        "<table border=\"1\" class=\"dataframe\">\n",
306 |        "  <thead>\n",
307 |        "    <tr style=\"text-align: right;\">\n",
308 |        "      <th></th>\n",
309 |        "      <th>precipitation</th>\n",
310 |        "      <th>temp_max</th>\n",
311 |        "      <th>temp_min</th>\n",
312 |        "      <th>wind</th>\n",
313 |        "    </tr>\n",
314 |        "  </thead>\n",
315 |        "  <tbody>\n",
316 |        "    <tr>\n",
317 |        "      <th>count</th>\n",
318 |        "      <td>1461.000000</td>\n",
319 |        "      <td>1461.000000</td>\n",
320 |        "      <td>1461.000000</td>\n",
321 |        "      <td>1461.000000</td>\n",
322 |        "    </tr>\n",
323 |        "    <tr>\n",
324 |        "      <th>mean</th>\n",
325 |        "      <td>3.029432</td>\n",
326 |        "      <td>16.439083</td>\n",
327 |        "      <td>8.234771</td>\n",
328 |        "      <td>3.241136</td>\n",
329 |        "    </tr>\n",
330 |        "    <tr>\n",
331 |        "      <th>std</th>\n",
332 |        "      <td>6.680194</td>\n",
333 |        "      <td>7.349758</td>\n",
334 |        "      <td>5.023004</td>\n",
335 |        "      <td>1.437825</td>\n",
336 |        "    </tr>\n",
337 |        "    <tr>\n",
338 |        "      <th>min</th>\n",
339 |        "      <td>0.000000</td>\n",
340 |        "      <td>-1.600000</td>\n",
341 |        "      <td>-7.100000</td>\n",
342 |        "      <td>0.400000</td>\n",
343 |        "    </tr>\n",
344 |        "    <tr>\n",
345 |        "      <th>25%</th>\n",
346 |        "      <td>0.000000</td>\n",
347 |        "      <td>10.600000</td>\n",
348 |        "      <td>4.400000</td>\n",
349 |        "      <td>2.200000</td>\n",
350 |        "    </tr>\n",
351 |        "    <tr>\n",
352 |        "      <th>50%</th>\n",
353 |        "      <td>0.000000</td>\n",
354 |        "      <td>15.600000</td>\n",
355 |        "      <td>8.300000</td>\n",
356 |        "      <td>3.000000</td>\n",
357 |        "    </tr>\n",
358 |        "    <tr>\n",
359 |        "      <th>75%</th>\n",
360 |        "      <td>2.800000</td>\n",
361 |        "      <td>22.200000</td>\n",
362 |        "      <td>12.200000</td>\n",
363 |        "      <td>4.000000</td>\n",
364 |        "    </tr>\n",
365 |        "    <tr>\n",
366 |        "      <th>max</th>\n",
367 |        "      <td>55.900000</td>\n",
368 |        "      <td>35.600000</td>\n",
369 |        "      <td>18.300000</td>\n",
370 |        "      <td>9.500000</td>\n",
371 |        "    </tr>\n",
372 |        "  </tbody>\n",
373 |        "</table>\n",
374 |        "</div>"
375 |       ],
376 |       "text/plain": [
377 |        "       precipitation     temp_max     temp_min         wind\n",
378 |        "count    1461.000000  1461.000000  1461.000000  1461.000000\n",
379 |        "mean        3.029432    16.439083     8.234771     3.241136\n",
380 |        "std         6.680194     7.349758     5.023004     1.437825\n",
381 |        "min         0.000000    -1.600000    -7.100000     0.400000\n",
382 |        "25%         0.000000    10.600000     4.400000     2.200000\n",
383 |        "50%         0.000000    15.600000     8.300000     3.000000\n",
384 |        "75%         2.800000    22.200000    12.200000     4.000000\n",
385 |        "max        55.900000    35.600000    18.300000     9.500000"
386 |       ]
387 |      },
388 |      "execution_count": 5,
389 |      "metadata": {},
390 |      "output_type": "execute_result"
391 |     }
392 |    ],
393 |    "source": [
394 |     "df.describe()"
395 |    ]
396 |   },
397 |   {
398 |    "cell_type": "code",
399 |    "execution_count": 6,
400 |    "id": "e9199357",
401 |    "metadata": {
402 |     "execution": {
403 |      "iopub.execute_input": "2022-01-17T18:28:24.986316Z",
404 |      "iopub.status.busy": "2022-01-17T18:28:24.985541Z",
405 |      "iopub.status.idle": "2022-01-17T18:28:25.001327Z",
406 |      "shell.execute_reply": "2022-01-17T18:28:25.001911Z",
407 |      "shell.execute_reply.started": "2022-01-17T18:26:29.075592Z"
408 |     },
409 |     "papermill": {
410 |      "duration": 0.037571,
411 |      "end_time": "2022-01-17T18:28:25.002095",
412 |      "exception": false,
413 |      "start_time": "2022-01-17T18:28:24.964524",
414 |      "status": "completed"
415 |     },
416 |     "tags": []
417 |    },
418 |    "outputs": [
419 |     {
420 |      "name": "stdout",
421 |      "output_type": "stream",
422 |      "text": [
423 |       "<class 'pandas.core.frame.DataFrame'>\n",
424 |       "RangeIndex: 1461 entries, 0 to 1460\n",
425 |       "Data columns (total 6 columns):\n",
426 |       " #   Column         Non-Null Count  Dtype  \n",
427 |       "---  ------         --------------  -----  \n",
428 |       " 0   date           1461 non-null   object \n",
429 |       " 1   precipitation  1461 non-null   float64\n",
430 |       " 2   temp_max       1461 non-null   float64\n",
431 |       " 3   temp_min       1461 non-null   float64\n",
432 |       " 4   wind           1461 non-null   float64\n",
433 |       " 5   weather        1461 non-null   object \n",
434 |       "dtypes: float64(4), object(2)\n",
435 |       "memory usage: 68.6+ KB\n"
436 |      ]
437 |     }
438 |    ],
439 |    "source": [
440 |     "df.info()"
441 |    ]
442 |   },
443 |   {
444 |    "cell_type": "code",
445 |    "execution_count": 7,
446 |    "id": "7170b5b1",
447 |    "metadata": {
448 |     "execution": {
449 |      "iopub.execute_input": "2022-01-17T18:28:25.042985Z",
450 |      "iopub.status.busy": "2022-01-17T18:28:25.042234Z",
451 |      "iopub.status.idle": "2022-01-17T18:28:25.050362Z",
452 |      "shell.execute_reply": "2022-01-17T18:28:25.051172Z",
453 |      "shell.execute_reply.started": "2022-01-17T18:26:29.270323Z"
454 |     },
455 |     "papermill": {
456 |      "duration": 0.030259,
457 |      "end_time": "2022-01-17T18:28:25.051393",
458 |      "exception": false,
459 |      "start_time": "2022-01-17T18:28:25.021134",
460 |      "status": "completed"
461 |     },
462 |     "tags": []
463 |    },
464 |    "outputs": [
465 |     {
466 |      "data": {
467 |       "text/plain": [
468 |        "date             0\n",
469 |        "precipitation    0\n",
470 |        "temp_max         0\n",
471 |        "temp_min         0\n",
472 |        "wind             0\n",
473 |        "weather          0\n",
474 |        "dtype: int64"
475 |       ]
476 |      },
477 |      "execution_count": 7,
478 |      "metadata": {},
479 |      "output_type": "execute_result"
480 |     }
481 |    ],
482 |    "source": [
483 |     "df.isnull().sum()"
484 |    ]
485 |   },
486 |   {
487 |    "cell_type": "markdown",
488 |    "id": "c5033ab6",
489 |    "metadata": {
490 |     "papermill": {
491 |      "duration": 0.01838,
492 |      "end_time": "2022-01-17T18:28:25.089311",
493 |      "exception": false,
494 |      "start_time": "2022-01-17T18:28:25.070931",
495 |      "status": "completed"
496 |     },
497 |     "tags": []
498 |    },
499 |    "source": [
500 |     "There are no null values in the data set."
501 |    ]
502 |   },
503 |   {
504 |    "cell_type": "markdown",
505 |    "id": "d7abbe40",
506 |    "metadata": {
507 |     "papermill": {
508 |      "duration": 0.018787,
509 |      "end_time": "2022-01-17T18:28:25.127355",
510 |      "exception": false,
511 |      "start_time": "2022-01-17T18:28:25.108568",
512 |      "status": "completed"
513 |     },
514 |     "tags": []
515 |    },
516 |    "source": [
517 |     "# Data Pre-Processing:\n",
518 |     "The column weather contains the data value in the string form and we need to predict the weather data so for that we need to process the data."
519 |    ]
520 |   },
521 |   {
522 |    "cell_type": "code",
523 |    "execution_count": 8,
524 |    "id": "c9c58d80",
525 |    "metadata": {
526 |     "execution": {
527 |      "iopub.execute_input": "2022-01-17T18:28:25.169436Z",
528 |      "iopub.status.busy": "2022-01-17T18:28:25.168474Z",
529 |      "iopub.status.idle": "2022-01-17T18:28:25.313363Z",
530 |      "shell.execute_reply": "2022-01-17T18:28:25.312694Z",
531 |      "shell.execute_reply.started": "2022-01-17T18:26:29.864666Z"
532 |     },
533 |     "papermill": {
534 |      "duration": 0.167064,
535 |      "end_time": "2022-01-17T18:28:25.313519",
536 |      "exception": false,
537 |      "start_time": "2022-01-17T18:28:25.146455",
538 |      "status": "completed"
539 |     },
540 |     "tags": []
541 |    },
542 |    "outputs": [],
543 |    "source": [
544 |     "from sklearn.preprocessing import LabelEncoder\n",
545 |     "\n",
546 |     "df['weather']=LabelEncoder().fit_transform(df['weather'])"
547 |    ]
548 |   },
549 |   {
550 |    "cell_type": "markdown",
551 |    "id": "d0e6fb39",
552 |    "metadata": {
553 |     "papermill": {
554 |      "duration": 0.018578,
555 |      "end_time": "2022-01-17T18:28:25.352428",
556 |      "exception": false,
557 |      "start_time": "2022-01-17T18:28:25.333850",
558 |      "status": "completed"
559 |     },
560 |     "tags": []
561 |    },
562 |    "source": [
563 |     "# Data Visualization:"
564 |    ]
565 |   },
566 |   {
567 |    "cell_type": "code",
568 |    "execution_count": 9,
569 |    "id": "6838e1f5",
570 |    "metadata": {
571 |     "execution": {
572 |      "iopub.execute_input": "2022-01-17T18:28:25.399815Z",
573 |      "iopub.status.busy": "2022-01-17T18:28:25.399116Z",
574 |      "iopub.status.idle": "2022-01-17T18:28:25.720028Z",
575 |      "shell.execute_reply": "2022-01-17T18:28:25.719453Z",
576 |      "shell.execute_reply.started": "2022-01-17T18:26:30.270184Z"
577 |     },
578 |     "papermill": {
579 |      "duration": 0.348987,
580 |      "end_time": "2022-01-17T18:28:25.720179",
581 |      "exception": false,
582 |      "start_time": "2022-01-17T18:28:25.371192",
583 |      "status": "completed"
584 |     },
585 |     "tags": []
586 |    },
587 |    "outputs": [
588 |     {
589 |      "data": {
590 |       "text/plain": [
591 |        "<AxesSubplot:>"
592 |       ]
593 |      },
594 |      "execution_count": 9,
595 |      "metadata": {},
596 |      "output_type": "execute_result"
597 |     },
598 |     {
599 |      "data": {
600 |       "image/png": 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r1bADJcuI2A04Gnhq+ZoAMjO3aC40SVLbjHWyBE4G3g5cCjzcXDiSpDYb92rYezLzW41GIknSiBo0WZ4XER8BvgY8MLkyMy9rJCpJUiuNezXs5HOSduxZlzT8pBdJUruM9Qg+mblH04FIktpvLEfwiYgDM/NzEfGOlW3PzOOaCUuSpNHRr2S5Tvlz3aYDkSS138Q4VsNm5mfKn8esmnAkSW021m2WEbE5cCgwv/c1mblvM2FJktpo3HvDfp1iYIKzgI4230qSqmp6UIKI2Af4BDAHOCkzPzjNfi8DzgB2yswlVc87aLL8Q2YeX/VkkiQ9WhExBzgB2Bu4BVgcEYsy89op+60LHAZcUte5B02Wn4iIo4Dv4KAEkqRpNFwNuzOwLDOvB4iILwL7AddO2e/9wIeAd9d14kGT5bOA11AMQjBZDeugBJKkFVTtDRsRC4AFPasWZubCcn4T4OaebbfwyKA5k6//c2DTzPxmRKzyZPlyYIvMfLCuE0uSuqdqb9gyMS7su+NKRMRqwHHAQZWCWInVBtzvamCDuk8uSeqWzGpTH7cCm/YszyvXTVoX2BY4PyJuBHYBFkVE71Ctj8qgJcsNgJ9GxGJWbLP01hFJ0qqyGNiqvJ3xVuBVwKsnN2bmPcDGk8sRcT7wrlXZG/aoqieSJHVfkyP4ZObyiDgEOIfi1pFTMvOaiHgfsCQzFzV17kEHUv/BTNsj4qLM3LWekCRJbdX0CD6ZeTZw9pR1R06z7+51nXfQkmU/c2s6jiSpxZoelGC2DNrBp5+OXh5JkuorWUqS1NmnjtRVsvyTqxMRCyJiSUQs+eXvb6vpNJKkUZYZlaZRNXDJMiKeRDHUUAKLM/OXPZtfM3X/3htLd9tkT6tpJWkMjHXJMiLeCPwYeCmwP3BxRBw8uT0zr24mPElSm2TFaVQNWrJ8N7BDZt4FEBEbARcCpzQVmCRJo2LQZHkXcG/P8r3lOkmS/qir1bCDJstlwCURcSZFSXk/YGlEvAMgM49rKD5JUouMciedKgZNlj8vp0lnlj/XrTccSVKbTfTfpZUGHe7umKYDkSS1X/7pnYSdMFCyLB9v8l7gqb2vycztGopLkqSRMWg17OcpesReRXdL2ZKkiiZG+f6PCgZNlr9q8tEnkqRumBjnaljgqIg4CTiXFR/+/LVGopIktdJYt1kCrweeAazBI9WwCZgsJUmdN2iy3Ckzt240EklS63W1U8ugTx25MCK2aTQSSVLrJVFpGlWDlix3Aa6IiBso2iwDSG8dkST16mrJctBkuU+jUUiSOqGryXKgatjMvAnYFNiznL9v0NdKktR2g47gcxSwI7A1cCpFr9jPAbs1F5okqW1Gud2xikGrYf8O2AG4DCAzb4sIB1GXJK1gopu5cuBk+WBmZkQkQESs02BMkqSW6uoIPoO2O345Ij4DbBARbwK+B5zYXFiSpDbKitOoGrRk+XjgDOC3FO2WRwJ7NRWUJEmjZNBkuXdmHg58d3JFRHwMOLyRqCRJrdTVW0dmTJYR8Y/AW4AtImJpz6Z1gR81GZgkqX0mopttlv1Kll8AvgUcCxzRs/7ezPx1Y1FJklpplNsdq5gxWWbmPcA9wAGrJhxJkkbPoG2WkiT1NZZtlpIkDWPcByWQJKmvrg5KYLKUJNWmqx18fHKIJEl9rJKS5b8u32hVnKa1tn7xsbMdwsiaN3fj2Q5hZJ1/5UmzHcJI2/PZb5rtEEbWexs8tm2WkiT1YW9YSZL66GqbpclSklSbrlbD2sFHkqQ+LFlKkmpjm6UkSX10NVlaDStJqk1GtamfiNgnIq6LiGURccRKtr8jIq6NiKURcW5EPLWO92WylCS1QkTMAU4AXgRsAxwQEdtM2e1yYMfM3A44A/hwHec2WUqSajNRcepjZ2BZZl6fmQ8CXwT2690hM8/LzPvKxYuBeVXfE9hmKUmqUcNtlpsAN/cs3wI8d4b93wB8q44TmywlSbWpOihBRCwAFvSsWpiZCx/FcQ4EdgT+qmJIgMlSklSjqoMSlIlxuuR4K7Bpz/K8ct0KImIviiFw/yozH6gWUcE2S0lSWywGtoqIzSNiTeBVwKLeHSJiB+AzwL6ZeUddJ7ZkKUmqTZNtlpm5PCIOAc4B5gCnZOY1EfE+YElmLgI+AjwW+EpEAPwiM/etem6TpSSpNk0PSpCZZwNnT1l3ZM/8Xk2c12QpSapNV586YpulJEl9WLKUJNWmq4/oMllKkmrT1YHUTZaSpNp0tc3SZClJqs1ER9OlHXwkSerDkqUkqTZj22YZEVcxQzV0+cwwSZI6Wgk7WMnyxeXPfyp/nl7+/Pv6w5EktdnYliwz8yaAiNg7M3fo2XRERFwGHNFUcJKkdunqfZbDdPCJiNitZ+Evhny9JEmtNEwHnzcAp0TE+kAAdwMHNxKVJKmVunrryMDJMjMvBZ5dJksy857GopIktVI3U+UQyTIiHgO8DJgPrF4+J4zMfF8jkUmSWmdsO/j0OBO4B7gUeKCZcCRJGj3DJMt5mblPY5FIklqvq22Ww/RmvTAinjXozhGxICKWRMSSs+6//lGEJklqm6w4japhSpZ/CRwUETdQVMMGkNON4JOZC4GFAOc/8eWjfA0kSTWxzRJe1FgUkqRO6Go17CBjw66Xmb8F7l0F8UiSNHIGKVl+gWJ82EspqpR7BzNKYIsG4pIktVA3y5WDjQ07OZD6j4AfAD/MzJ82GpUkqZW62mY5TG/Yk4EnA5+MiOsj4oyIOKyhuCRJLZQV/42qYYa7Oy8iLgB2AvYA3gxsC3yiodgkSS3T1ZLlMMPdnQusA1wE/BDYKTPvaCowSZJGxTDVsEuBBylKk9sB20bEWo1EJUlqpQmy0jSqhqmGfTtARKwLHAScCjwJeEwjkUmSWmd00101w1TDHgI8D3gOcCNwCkV1rCRJwBgPStBjLnAccGlmLm8oHkmSRs4w1bAfbTIQSVL7jX1vWEmS+hnleyWrMFlKkmpjyVKSpD66WrIc5j5LSZLGkiVLSVJtrIaVJKmPiexmNazJUpJUm26mSpOlJKlGXR3Bxw4+kiT1YclSklSbrt46YrKUJNXG3rCSJPVhm6UkSbMsIvaJiOsiYllEHLGS7Y+JiC+V2y+JiPl1nNdkKUmqTVb8N5OImAOcALwI2AY4ICK2mbLbG4C7M/NpwMeBD9XxvkyWkqTaTFSc+tgZWJaZ12fmg8AXgf2m7LMf8Nly/gzgBRERVd4TmCwlSTXKzEpTRCyIiCU904Kew28C3NyzfEu5jpXtk5nLgXuAjaq+Lzv4SJJqU7WDT2YuBBbWE019LFlKktriVmDTnuV55bqV7hMRqwPrA3dVPbHJUpJUm4bbLBcDW0XE5hGxJvAqYNGUfRYBryvn9we+n1l9dPdVUg27190XrorTtNZ3NtxttkMYWRetvsZshzCy9nz2m2Y7hJH2/StPnO0QxlKTI/hk5vKIOAQ4B5gDnJKZ10TE+4AlmbkIOBk4PSKWAb+mSKiV2WYpSapN04MSZObZwNlT1h3ZM/8H4OV1n9dkKUmqTQ01niPJNktJkvqwZClJqo0DqUuS1IeP6JIkqQ+fOiJJ0piyZClJqk1Xe8OaLCVJtelqNazJUpJUGzv4SJLUx0RHq2Ht4CNJUh+WLCVJtelmuXLAkmVEzImI85oORpLUbhNkpWlUDVSyzMyHI2IiItbPzHuaDkqS1E6jnPCqGKYa9nfAVRHxXeD3kysz8621RyVJaiXvs4SvlZMkSWNl4GSZmZ+NiLWAzTLzugZjkiS1VFerYQe+dSQiXgJcAXy7XN4+IhY1FJckqYWy4r9RNcx9lkcDOwO/AcjMK4Atao9IktRamVlpGlXDJMuHVtITtqvP+ZQk6Y+G6eBzTUS8GpgTEVsBbwUubCYsSVIbjX2bJXAo8EzgAeA/gd8Cb2sgJklSS3W1GnaY3rD3Ae8tJ0mS/kRXS5YDJ8uIeDrwLmB+7+syc8/6w5IktdEo92itYpg2y68AnwZOAh5uJhxJkkbPMMlyeWZ+qrFIJEmt19XnWfZNlhHxuHL2rIh4C/BfFJ18AMjMXzcUmySpZca5GvZSikeURbn87p5tiQMTSJJKY1uyzMzNASJibmb+oXdbRMxtKjBJUvt0tWQ5zH2WKxuAwEEJJEmdN0ib5ZOATYC1ImIHHqmOXQ9Ye4bXLQAWAMSc9VlttXWqRytJGmljWw0L/DVwEDAPOK5n/b3Av0z3osxcCCwEWH3NTbp59SRJK+hqNewgbZafBT4bES/LzK+ugpgkSS01ziVLADLzqxHxtxTjw87tWf++JgKTJGlUDDPc3acp2ij3oBjFZ3/gxw3FJUlqoa5Www7TG/YvMvO1wN2ZeQywK/D0ZsKSJLVR5kSlaVQNM9zd/eXP+yLiKcBdwJPrD0mS1FZj/9QR4BsRsQHwEeAyitF7TmoiKElSO43yMymrGKaDz/vL2a9GxDeAuZl5TzNhSZI0OgZus4yItSPif0fEiZn5APCEiHhxg7FJklpmgqw0japhOvicSvG0kV3L5VuBD9QekSSptTKz0jSqhmmz3DIzXxkRBwBk5n0REf1eJEkaH10dlGCYkuWDEbEWRcceImJLep5rKUnSbIqIx0XEdyPiZ+XPDVeyz/YRcVFEXBMRSyPilYMce5hkeRTwbWDTiPg8cC7wniFeL0nquKz4r6IjgHMzcyuKHHXESva5D3htZj4T2Af4t/JOjxkNUw37OuCbwBnA9cBhmXnnEK+XJHXcLLc77gfsXs5/FjgfOLx3h8z875752yLiDuDxwG9mOvAwyfJk4HnA3sCWwOURcUFmfmKIY0iSOmyWe7Q+MTNvL+d/CTxxpp0jYmdgTeDn/Q48zH2W50XEBcBOFOPDvpliUHWTpSQJqF6y7H0Wcmlh+cjHye3fA560kpe+d0ocGRHTBhMRTwZOB16XA4yzN8xA6ucC6wAXAT8EdsrMOwZ9vSRJ/fQ+C3ma7XtNty0i/icinpyZt5fJcKU5KiLWo2hWfG9mXjxIXMN08FkKPAhsC2wHbFv2jpUkCShuHakyVbSIon8N5c8zp+4QEWsC/wX8R2aeMeiBB06Wmfn2zHw+8FKKQdRPpU+DqCRpvMzyoAQfBPaOiJ8Be5XLRMSOETE5lvkrgOcDB0XEFeW0fb8DD1MNewhFB5/nADcCp1BUx0qSBMxuB5/MvAt4wUrWLwHeWM5/DvjcsMcepjfsXOA44NLMXD7siSRJ3TfKQ9ZVMUxv2I82GYgkSaNqmJKlJEkz6urYsCZLSVJtahiybiSZLCVJtelqyXKY+ywlSRpLliwlSbUZ+96wkiT1Y5ulJEl9WLKUJKmPriZLO/hIktSHJUtJUm26Wa6E6GqReSYRsaD3YaJ6hNdmZl6f6XltZub1abdxrYZd0H+XseW1mZnXZ3pem5l5fVpsXJOlJEkDM1lKktTHuCZL2w2m57WZmddnel6bmXl9WmwsO/hIkjSMcS1ZSpI0sE4ly4jYMSKO77PPUyLijHJ++4j4mwGOu8J+EbFvRBxRPeLBRcQGEfGWVXlOqZ+IODsiNhhi//kRcXWDIY2kiDgoIp7Ss3xjRGw8mzFpOCOdLCNizjD7Z+aSzHxrn31uy8z9y8Xtgb7Jcup+mbkoMz84TGw12AAwWc5g1L9QRMSbI+K1sx1HnTLzbzLzN7MdRwscBDyl306DiAgHk5kNmTkrEzAf+CnweeAnwBnA2sCNwIeAy4BXAS8ELiqXvwI8tnz9TsCFwJXAj4F1gd2Bb5TbjwZOL1/7M+BNPee9GlgT+AXwK+AK4JXAzuX+l5fH3nqa/Q4C/m/P8b4PLAXOBTYr158GHF8e53pg/4rX64vA/WUMHwHeDSwuz3vMlGt6GvDf5bXdC/hReQ12nunaTHPe3YEfAGeW7+ODwN+X1/wqYMtyv5cAl5TX7nvAE8v1nwCOLOf/GrgAWK3B36mrZ+t3uotT+Xv21nL+48D3y/k9y9+vG4GNy2v/E+BE4BrgO8Ba5b7Pofg7vbL83R35/6MB3vd0n0tHln+XV1N06Algf+B3wHXl3+9a5XU7pnz9VcAzytevA5xS/n1dDuxXrj8IWETxWfOD2b4+4zjN5i/jfIqRkXYrl08B3lX+Er2nXLdx+eG6Trl8ePnLuGb5wb1TuX49iqH7dmfFZHll+Yu5MXAzxTe7P36g0pP0eo9Tzu8FfHWa/f64DJwFvK6cPxj4ejl/WvlHtBqwDbCshus1GfcLe/4QVwO+ATy/3Gc58Kxy/aXldQ1gv57YVnptpjnv7sBvgCcDjwFu5ZHkfBjwb+X8hjzSYeyNwMfK+bUpPjz3oPiw2LLB36lR/0JxNPCucv58ii+FPy7jeN5s/S32uaa7AF8p539YxrsGcBTwD6yYLJcD25f7fhk4sJxfCjy/nG9LspzpfR/OSj6XyvnH9RzjdOAlPf/fO/ZsuxE4tJx/C3BSOf9/eq7bBuXvxjoUnzm39B7fadVOs10Ne3Nm/qic/xzwl+X8l8qfu1Akmh9FxBXA64CnUpT4bs/MxQCZ+dvMXL6S45+Zmfdn5p3AeRQlx5msD3ylbFP5OPDMAd7DrsAXyvnTe94DFMlpIjOvBZ44wLEG9cJyupzim+kzgK3KbTdk5lWZOUGRpM7N4i/vKooPtEnDXJvFmXl7Zj4A/Jyi1MCUY84DzomIqyiS1DMBMvM+4E3Adym+YPz8Ub/r/o4Afp6Z25fn24rifW0PPCcinl/u9zTgYxTX7RnAqyn+394F/EvP8bajKEnsChzZ2+a0Es8G3gz8GfAa4OmZuTNwEnDoNK9ZvdznbRQfwqPoUoprtx7wAMWXhx2B51EkkV43ZOYVPa+bX7ZnbpCZF5TrT2884nrM9L7vZ+WfSwB7RMQl5d/Bnsz8GfK1nnPNL+dfCBxRHvd8YC6wWbntu5n566pvTI/ObNd9T71vZXL59+XPoPgFOaB3p4h4VsXjT+f9wHmZ+XcRMZ/il7WKB3rmo+KxegVwbGZ+ZoWVRcy955zoWZ5gxf/vYa7NIMf8JHBcZi6KiN0pSlGTngXcRU1tNgPq/UIB8FiK5PkLyi8UABHxxy8U5Qfc/J5jnJmZ9wP3R8TkF4qvT3O+xZl5e3nMqV8o9pjmNSv7sBwpmflQRNxAUbK5kKKUuAfFF46fTNm99/fkYYqai1bq875vYOWfS3OBf6coQd4cEUdTJLvpTF6vh3nk7yiAl2XmdVOO/Vwe+VzULJjtkuVmEbFrOf9q4P9N2X4xsFtEPA0gItaJiKdTVOc9OSJ2KtevO02j934RMTciNqKoKls8Zfu9FG2dk9anqGaE4o9kuv16XUjRtgpF1dvUb9t16Y3hHODgiHgsQERsEhFPGPJ4/a7NsHqv3esmV0bEU4F3AjsALyr/6FeFyS8U25fT0zLz5HLbbH2hmO41D8+wzyj4IUWp+4Jy/s3A5WWNxYyy6Pzzm4iYrHH5+6aCbMBK3zfTfy5NJsY7y7/N/XuONdNnSK9zgEMjIspj71DHG1F1s50srwP+KSJ+QtHm9anejZn5K4qk9Z8RsZSiKuQZmfkgRUebT0bElRRVbiv7BreUoorxYuD9mXnblO3nAdtExBUR8Urgw8CxEXE5K354Td2v16HA68v4XkPRjle7zLyLotrnamBviqrfi8rS0BkM9ofYq9+1GdbRFFXYlwJ3ApR/8CdTtNPdBrwBOKn8Bt6EUf9C0VY/pGizvigz/wf4A8N9KXw9cEJZtVhnDUvTVvq+Z/hc+g1FB6erKX7/en9fTgM+XX6GzFTifj9F2+jSstbj/bW+Iz1qszaCT1ll+I3M3Lah4x8N/C4zP9rE8dusy9cmIr5A0db4LYoOEW8sN/0OOJCiFPfH37uIOK1cPqP3d7K8RltQVN1uDHw4M0+c5py7U3wheHG5fH65vKR3W+91n7LPxsCSzJxf24WQVCuT5Rjy2vTnNZLUy7FhBfyx09TUnooPZOaqamMcKSZLSb1MltKA/EIhjS+TpSRJfcx2b1hJkkaeyVKSpD5MlpIk9WGylCSpD5OlJEl9/H98nIBwQyAZ+QAAAABJRU5ErkJggg==\n",
601 |       "text/plain": [
602 |        "<Figure size 576x432 with 2 Axes>"
603 |       ]
604 |      },
605 |      "metadata": {
606 |       "needs_background": "light"
607 |      },
608 |      "output_type": "display_data"
609 |     }
610 |    ],
611 |    "source": [
612 |     "fig, axes=plt.subplots(figsize=(8, 6))\n",
613 |     "sns.heatmap(df.corr(), ax=axes)"
614 |    ]
615 |   },
616 |   {
617 |    "cell_type": "markdown",
618 |    "id": "b54b4bc5",
619 |    "metadata": {
620 |     "papermill": {
621 |      "duration": 0.019975,
622 |      "end_time": "2022-01-17T18:28:25.760945",
623 |      "exception": false,
624 |      "start_time": "2022-01-17T18:28:25.740970",
625 |      "status": "completed"
626 |     },
627 |     "tags": []
628 |    },
629 |    "source": [
630 |     "We can observe that wind and preception are weakly co-related.temp_max and wind are negatively correlated means they move in the opposite direction."
631 |    ]
632 |   },
633 |   {
634 |    "cell_type": "markdown",
635 |    "id": "6199b721",
636 |    "metadata": {
637 |     "papermill": {
638 |      "duration": 0.020226,
639 |      "end_time": "2022-01-17T18:28:25.801083",
640 |      "exception": false,
641 |      "start_time": "2022-01-17T18:28:25.780857",
642 |      "status": "completed"
643 |     },
644 |     "tags": []
645 |    },
646 |    "source": [
647 |     "# Machine Learning Model for Prediction of Weather..."
648 |    ]
649 |   },
650 |   {
651 |    "cell_type": "code",
652 |    "execution_count": 10,
653 |    "id": "e03df9e9",
654 |    "metadata": {
655 |     "execution": {
656 |      "iopub.execute_input": "2022-01-17T18:28:25.849813Z",
657 |      "iopub.status.busy": "2022-01-17T18:28:25.849110Z",
658 |      "iopub.status.idle": "2022-01-17T18:28:25.909728Z",
659 |      "shell.execute_reply": "2022-01-17T18:28:25.910319Z",
660 |      "shell.execute_reply.started": "2022-01-17T18:26:30.871953Z"
661 |     },
662 |     "papermill": {
663 |      "duration": 0.087338,
664 |      "end_time": "2022-01-17T18:28:25.910505",
665 |      "exception": false,
666 |      "start_time": "2022-01-17T18:28:25.823167",
667 |      "status": "completed"
668 |     },
669 |     "tags": []
670 |    },
671 |    "outputs": [],
672 |    "source": [
673 |     "from sklearn.model_selection import train_test_split\n",
674 |     "features=[\"precipitation\", \"temp_max\", \"temp_min\", \"wind\"]\n",
675 |     "X=df[features]\n",
676 |     "y=df.weather\n",
677 |     "train_X, test_X, train_y, test_y = train_test_split(X, y,random_state = 0)"
678 |    ]
679 |   },
680 |   {
681 |    "cell_type": "markdown",
682 |    "id": "3412f643",
683 |    "metadata": {
684 |     "papermill": {
685 |      "duration": 0.020598,
686 |      "end_time": "2022-01-17T18:28:25.952976",
687 |      "exception": false,
688 |      "start_time": "2022-01-17T18:28:25.932378",
689 |      "status": "completed"
690 |     },
691 |     "tags": []
692 |    },
693 |    "source": [
694 |     "# Decision Tree Regressor:"
695 |    ]
696 |   },
697 |   {
698 |    "cell_type": "code",
699 |    "execution_count": 11,
700 |    "id": "b6ada6af",
701 |    "metadata": {
702 |     "execution": {
703 |      "iopub.execute_input": "2022-01-17T18:28:25.999213Z",
704 |      "iopub.status.busy": "2022-01-17T18:28:25.998361Z",
705 |      "iopub.status.idle": "2022-01-17T18:28:26.183378Z",
706 |      "shell.execute_reply": "2022-01-17T18:28:26.183960Z",
707 |      "shell.execute_reply.started": "2022-01-17T18:27:52.558428Z"
708 |     },
709 |     "papermill": {
710 |      "duration": 0.211343,
711 |      "end_time": "2022-01-17T18:28:26.184155",
712 |      "exception": false,
713 |      "start_time": "2022-01-17T18:28:25.972812",
714 |      "status": "completed"
715 |     },
716 |     "tags": []
717 |    },
718 |    "outputs": [
719 |     {
720 |      "name": "stdout",
721 |      "output_type": "stream",
722 |      "text": [
723 |       "Mean Absolute Error: 0.713115\n"
724 |      ]
725 |     }
726 |    ],
727 |    "source": [
728 |     "from sklearn.tree import DecisionTreeRegressor\n",
729 |     "from sklearn.metrics import mean_absolute_error\n",
730 |     "\n",
731 |     "model1=DecisionTreeRegressor(random_state=1)\n",
732 |     "model1.fit(train_X, train_y)\n",
733 |     "pred1=model1.predict(test_X)\n",
734 |     "print(\"Mean Absolute Error: %f\" %(mean_absolute_error(test_y, pred1)))"
735 |    ]
736 |   },
737 |   {
738 |    "cell_type": "markdown",
739 |    "id": "8cdfc40b",
740 |    "metadata": {
741 |     "papermill": {
742 |      "duration": 0.020228,
743 |      "end_time": "2022-01-17T18:28:26.224737",
744 |      "exception": false,
745 |      "start_time": "2022-01-17T18:28:26.204509",
746 |      "status": "completed"
747 |     },
748 |     "tags": []
749 |    },
750 |    "source": [
751 |     "# Random Forest Regressor:"
752 |    ]
753 |   },
754 |   {
755 |    "cell_type": "code",
756 |    "execution_count": 12,
757 |    "id": "8762a4b2",
758 |    "metadata": {
759 |     "execution": {
760 |      "iopub.execute_input": "2022-01-17T18:28:26.271563Z",
761 |      "iopub.status.busy": "2022-01-17T18:28:26.269644Z",
762 |      "iopub.status.idle": "2022-01-17T18:28:26.603478Z",
763 |      "shell.execute_reply": "2022-01-17T18:28:26.602952Z",
764 |      "shell.execute_reply.started": "2022-01-17T18:27:54.071643Z"
765 |     },
766 |     "papermill": {
767 |      "duration": 0.357538,
768 |      "end_time": "2022-01-17T18:28:26.603623",
769 |      "exception": false,
770 |      "start_time": "2022-01-17T18:28:26.246085",
771 |      "status": "completed"
772 |     },
773 |     "tags": []
774 |    },
775 |    "outputs": [
776 |     {
777 |      "name": "stdout",
778 |      "output_type": "stream",
779 |      "text": [
780 |       "Mean Absolute Error: 0.694026\n"
781 |      ]
782 |     }
783 |    ],
784 |    "source": [
785 |     "from sklearn.ensemble import RandomForestRegressor\n",
786 |     "\n",
787 |     "model2=RandomForestRegressor(random_state=1)\n",
788 |     "model2.fit(train_X, train_y)\n",
789 |     "pred2=model2.predict(test_X)\n",
790 |     "print(\"Mean Absolute Error: %f\" %(mean_absolute_error(test_y, pred2)))\n",
791 |     "    "
792 |    ]
793 |   },
794 |   {
795 |    "cell_type": "markdown",
796 |    "id": "491907bf",
797 |    "metadata": {
798 |     "papermill": {
799 |      "duration": 0.020355,
800 |      "end_time": "2022-01-17T18:28:26.644903",
801 |      "exception": false,
802 |      "start_time": "2022-01-17T18:28:26.624548",
803 |      "status": "completed"
804 |     },
805 |     "tags": []
806 |    },
807 |    "source": [
808 |     "# Extreme Gradient Regressor:"
809 |    ]
810 |   },
811 |   {
812 |    "cell_type": "code",
813 |    "execution_count": 13,
814 |    "id": "6b4aa12e",
815 |    "metadata": {
816 |     "execution": {
817 |      "iopub.execute_input": "2022-01-17T18:28:26.693123Z",
818 |      "iopub.status.busy": "2022-01-17T18:28:26.692193Z",
819 |      "iopub.status.idle": "2022-01-17T18:28:27.094277Z",
820 |      "shell.execute_reply": "2022-01-17T18:28:27.094978Z",
821 |      "shell.execute_reply.started": "2022-01-17T18:26:36.390565Z"
822 |     },
823 |     "papermill": {
824 |      "duration": 0.429633,
825 |      "end_time": "2022-01-17T18:28:27.095173",
826 |      "exception": false,
827 |      "start_time": "2022-01-17T18:28:26.665540",
828 |      "status": "completed"
829 |     },
830 |     "tags": []
831 |    },
832 |    "outputs": [
833 |     {
834 |      "name": "stdout",
835 |      "output_type": "stream",
836 |      "text": [
837 |       "Mean Absolute Error: 0.682803\n"
838 |      ]
839 |     }
840 |    ],
841 |    "source": [
842 |     "from xgboost import XGBRegressor\n",
843 |     "\n",
844 |     "model3= XGBRegressor(n_estimators=100, learning_rate=0.04)\n",
845 |     "model3.fit(train_X, train_y)\n",
846 |     "pred3=model3.predict(test_X)\n",
847 |     "print(\"Mean Absolute Error: %f\" %(mean_absolute_error(test_y, pred3)))"
848 |    ]
849 |   },
850 |   {
851 |    "cell_type": "markdown",
852 |    "id": "3854c2ff",
853 |    "metadata": {
854 |     "papermill": {
855 |      "duration": 0.020554,
856 |      "end_time": "2022-01-17T18:28:27.138278",
857 |      "exception": false,
858 |      "start_time": "2022-01-17T18:28:27.117724",
859 |      "status": "completed"
860 |     },
861 |     "tags": []
862 |    },
863 |    "source": [
864 |     "The least mean absolute error we got is of the XGB Regressor Model. "
865 |    ]
866 |   }
867 |  ],
868 |  "metadata": {
869 |   "kernelspec": {
870 |    "display_name": "Python 3",
871 |    "language": "python",
872 |    "name": "python3"
873 |   },
874 |   "language_info": {
875 |    "codemirror_mode": {
876 |     "name": "ipython",
877 |     "version": 3
878 |    },
879 |    "file_extension": ".py",
880 |    "mimetype": "text/x-python",
881 |    "name": "python",
882 |    "nbconvert_exporter": "python",
883 |    "pygments_lexer": "ipython3",
884 |    "version": "3.7.12"
885 |   },
886 |   "papermill": {
887 |    "default_parameters": {},
888 |    "duration": 15.101768,
889 |    "end_time": "2022-01-17T18:28:27.871289",
890 |    "environment_variables": {},
891 |    "exception": null,
892 |    "input_path": "__notebook__.ipynb",
893 |    "output_path": "__notebook__.ipynb",
894 |    "parameters": {},
895 |    "start_time": "2022-01-17T18:28:12.769521",
896 |    "version": "2.3.3"
897 |   }
898 |  },
899 |  "nbformat": 4,
900 |  "nbformat_minor": 5
901 | }
902 | 


--------------------------------------------------------------------------------
/1/DMBI_Pract_1.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "provenance": []
  7 |     },
  8 |     "kernelspec": {
  9 |       "name": "python3",
 10 |       "display_name": "Python 3"
 11 |     },
 12 |     "language_info": {
 13 |       "name": "python"
 14 |     }
 15 |   },
 16 |   "cells": [
 17 |     {
 18 |       "cell_type": "code",
 19 |       "execution_count": null,
 20 |       "metadata": {
 21 |         "colab": {
 22 |           "base_uri": "https://localhost:8080/",
 23 |           "height": 73
 24 |         },
 25 |         "id": "bojUzwbBRO3g",
 26 |         "outputId": "9a689748-863b-4e97-af9b-f2da9de416ae"
 27 |       },
 28 |       "outputs": [
 29 |         {
 30 |           "output_type": "display_data",
 31 |           "data": {
 32 |             "text/plain": [
 33 |               "<IPython.core.display.HTML object>"
 34 |             ],
 35 |             "text/html": [
 36 |               "\n",
 37 |               "     <input type=\"file\" id=\"files-7f65937c-5de6-459c-b6d1-68f1f114a7b7\" name=\"files[]\" multiple disabled\n",
 38 |               "        style=\"border:none\" />\n",
 39 |               "     <output id=\"result-7f65937c-5de6-459c-b6d1-68f1f114a7b7\">\n",
 40 |               "      Upload widget is only available when the cell has been executed in the\n",
 41 |               "      current browser session. Please rerun this cell to enable.\n",
 42 |               "      </output>\n",
 43 |               "      <script>// Copyright 2017 Google LLC\n",
 44 |               "//\n",
 45 |               "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
 46 |               "// you may not use this file except in compliance with the License.\n",
 47 |               "// You may obtain a copy of the License at\n",
 48 |               "//\n",
 49 |               "//      http://www.apache.org/licenses/LICENSE-2.0\n",
 50 |               "//\n",
 51 |               "// Unless required by applicable law or agreed to in writing, software\n",
 52 |               "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
 53 |               "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
 54 |               "// See the License for the specific language governing permissions and\n",
 55 |               "// limitations under the License.\n",
 56 |               "\n",
 57 |               "/**\n",
 58 |               " * @fileoverview Helpers for google.colab Python module.\n",
 59 |               " */\n",
 60 |               "(function(scope) {\n",
 61 |               "function span(text, styleAttributes = {}) {\n",
 62 |               "  const element = document.createElement('span');\n",
 63 |               "  element.textContent = text;\n",
 64 |               "  for (const key of Object.keys(styleAttributes)) {\n",
 65 |               "    element.style[key] = styleAttributes[key];\n",
 66 |               "  }\n",
 67 |               "  return element;\n",
 68 |               "}\n",
 69 |               "\n",
 70 |               "// Max number of bytes which will be uploaded at a time.\n",
 71 |               "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
 72 |               "\n",
 73 |               "function _uploadFiles(inputId, outputId) {\n",
 74 |               "  const steps = uploadFilesStep(inputId, outputId);\n",
 75 |               "  const outputElement = document.getElementById(outputId);\n",
 76 |               "  // Cache steps on the outputElement to make it available for the next call\n",
 77 |               "  // to uploadFilesContinue from Python.\n",
 78 |               "  outputElement.steps = steps;\n",
 79 |               "\n",
 80 |               "  return _uploadFilesContinue(outputId);\n",
 81 |               "}\n",
 82 |               "\n",
 83 |               "// This is roughly an async generator (not supported in the browser yet),\n",
 84 |               "// where there are multiple asynchronous steps and the Python side is going\n",
 85 |               "// to poll for completion of each step.\n",
 86 |               "// This uses a Promise to block the python side on completion of each step,\n",
 87 |               "// then passes the result of the previous step as the input to the next step.\n",
 88 |               "function _uploadFilesContinue(outputId) {\n",
 89 |               "  const outputElement = document.getElementById(outputId);\n",
 90 |               "  const steps = outputElement.steps;\n",
 91 |               "\n",
 92 |               "  const next = steps.next(outputElement.lastPromiseValue);\n",
 93 |               "  return Promise.resolve(next.value.promise).then((value) => {\n",
 94 |               "    // Cache the last promise value to make it available to the next\n",
 95 |               "    // step of the generator.\n",
 96 |               "    outputElement.lastPromiseValue = value;\n",
 97 |               "    return next.value.response;\n",
 98 |               "  });\n",
 99 |               "}\n",
100 |               "\n",
101 |               "/**\n",
102 |               " * Generator function which is called between each async step of the upload\n",
103 |               " * process.\n",
104 |               " * @param {string} inputId Element ID of the input file picker element.\n",
105 |               " * @param {string} outputId Element ID of the output display.\n",
106 |               " * @return {!Iterable<!Object>} Iterable of next steps.\n",
107 |               " */\n",
108 |               "function* uploadFilesStep(inputId, outputId) {\n",
109 |               "  const inputElement = document.getElementById(inputId);\n",
110 |               "  inputElement.disabled = false;\n",
111 |               "\n",
112 |               "  const outputElement = document.getElementById(outputId);\n",
113 |               "  outputElement.innerHTML = '';\n",
114 |               "\n",
115 |               "  const pickedPromise = new Promise((resolve) => {\n",
116 |               "    inputElement.addEventListener('change', (e) => {\n",
117 |               "      resolve(e.target.files);\n",
118 |               "    });\n",
119 |               "  });\n",
120 |               "\n",
121 |               "  const cancel = document.createElement('button');\n",
122 |               "  inputElement.parentElement.appendChild(cancel);\n",
123 |               "  cancel.textContent = 'Cancel upload';\n",
124 |               "  const cancelPromise = new Promise((resolve) => {\n",
125 |               "    cancel.onclick = () => {\n",
126 |               "      resolve(null);\n",
127 |               "    };\n",
128 |               "  });\n",
129 |               "\n",
130 |               "  // Wait for the user to pick the files.\n",
131 |               "  const files = yield {\n",
132 |               "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
133 |               "    response: {\n",
134 |               "      action: 'starting',\n",
135 |               "    }\n",
136 |               "  };\n",
137 |               "\n",
138 |               "  cancel.remove();\n",
139 |               "\n",
140 |               "  // Disable the input element since further picks are not allowed.\n",
141 |               "  inputElement.disabled = true;\n",
142 |               "\n",
143 |               "  if (!files) {\n",
144 |               "    return {\n",
145 |               "      response: {\n",
146 |               "        action: 'complete',\n",
147 |               "      }\n",
148 |               "    };\n",
149 |               "  }\n",
150 |               "\n",
151 |               "  for (const file of files) {\n",
152 |               "    const li = document.createElement('li');\n",
153 |               "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
154 |               "    li.append(span(\n",
155 |               "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
156 |               "        `last modified: ${\n",
157 |               "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
158 |               "                                    'n/a'} - `));\n",
159 |               "    const percent = span('0% done');\n",
160 |               "    li.appendChild(percent);\n",
161 |               "\n",
162 |               "    outputElement.appendChild(li);\n",
163 |               "\n",
164 |               "    const fileDataPromise = new Promise((resolve) => {\n",
165 |               "      const reader = new FileReader();\n",
166 |               "      reader.onload = (e) => {\n",
167 |               "        resolve(e.target.result);\n",
168 |               "      };\n",
169 |               "      reader.readAsArrayBuffer(file);\n",
170 |               "    });\n",
171 |               "    // Wait for the data to be ready.\n",
172 |               "    let fileData = yield {\n",
173 |               "      promise: fileDataPromise,\n",
174 |               "      response: {\n",
175 |               "        action: 'continue',\n",
176 |               "      }\n",
177 |               "    };\n",
178 |               "\n",
179 |               "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
180 |               "    let position = 0;\n",
181 |               "    do {\n",
182 |               "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
183 |               "      const chunk = new Uint8Array(fileData, position, length);\n",
184 |               "      position += length;\n",
185 |               "\n",
186 |               "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
187 |               "      yield {\n",
188 |               "        response: {\n",
189 |               "          action: 'append',\n",
190 |               "          file: file.name,\n",
191 |               "          data: base64,\n",
192 |               "        },\n",
193 |               "      };\n",
194 |               "\n",
195 |               "      let percentDone = fileData.byteLength === 0 ?\n",
196 |               "          100 :\n",
197 |               "          Math.round((position / fileData.byteLength) * 100);\n",
198 |               "      percent.textContent = `${percentDone}% done`;\n",
199 |               "\n",
200 |               "    } while (position < fileData.byteLength);\n",
201 |               "  }\n",
202 |               "\n",
203 |               "  // All done.\n",
204 |               "  yield {\n",
205 |               "    response: {\n",
206 |               "      action: 'complete',\n",
207 |               "    }\n",
208 |               "  };\n",
209 |               "}\n",
210 |               "\n",
211 |               "scope.google = scope.google || {};\n",
212 |               "scope.google.colab = scope.google.colab || {};\n",
213 |               "scope.google.colab._files = {\n",
214 |               "  _uploadFiles,\n",
215 |               "  _uploadFilesContinue,\n",
216 |               "};\n",
217 |               "})(self);\n",
218 |               "</script> "
219 |             ]
220 |           },
221 |           "metadata": {}
222 |         },
223 |         {
224 |           "output_type": "stream",
225 |           "name": "stdout",
226 |           "text": [
227 |             "Saving rainfall.csv to rainfall (1).csv\n"
228 |           ]
229 |         }
230 |       ],
231 |       "source": [
232 |         "from google.colab import files\n",
233 |         "uploaded=files.upload()"
234 |       ]
235 |     },
236 |     {
237 |       "cell_type": "code",
238 |       "source": [
239 |         "import pandas as pd\n",
240 |         "import seaborn as sns\n",
241 |         "import numpy as np\n",
242 |         "import matplotlib.pyplot as plt"
243 |       ],
244 |       "metadata": {
245 |         "id": "Y8z_21JyTgzT"
246 |       },
247 |       "execution_count": null,
248 |       "outputs": []
249 |     },
250 |     {
251 |       "cell_type": "code",
252 |       "source": [
253 |         "df=pd.read_csv('rainfall.csv')"
254 |       ],
255 |       "metadata": {
256 |         "id": "naFx0tx-Towz"
257 |       },
258 |       "execution_count": null,
259 |       "outputs": []
260 |     },
261 |     {
262 |       "cell_type": "code",
263 |       "source": [
264 |         "df.head()"
265 |       ],
266 |       "metadata": {
267 |         "colab": {
268 |           "base_uri": "https://localhost:8080/",
269 |           "height": 206
270 |         },
271 |         "id": "DqFRanxxTtOy",
272 |         "outputId": "294768af-b6a4-423a-a357-c57e5f939b3b"
273 |       },
274 |       "execution_count": null,
275 |       "outputs": [
276 |         {
277 |           "output_type": "execute_result",
278 |           "data": {
279 |             "text/plain": [
280 |               "     datetime  temp   dew  humidity  sealevelpressure  winddir  \\\n",
281 |               "0  01-01-2016  28.4  11.9      37.8            1016.4    147.5   \n",
282 |               "1  02-01-2016  26.8  13.0      44.8            1017.2    110.3   \n",
283 |               "2  03-01-2016  25.5  14.6      52.8            1015.7    145.3   \n",
284 |               "3  04-01-2016  26.4  13.1      46.6            1015.3    126.9   \n",
285 |               "4  05-01-2016  27.1  13.5      44.9            1014.4    125.5   \n",
286 |               "\n",
287 |               "   solarradiation  windspeed  precipprob  preciptype  \n",
288 |               "0           216.1       16.6           0           0  \n",
289 |               "1           215.7       16.6           0           0  \n",
290 |               "2           221.1       18.4           0           0  \n",
291 |               "3           216.2       16.6           0           0  \n",
292 |               "4           208.1       16.6           0           0  "
293 |             ],
294 |             "text/html": [
295 |               "\n",
296 |               "  <div id=\"df-3bea082a-5fd2-4ddf-9dcb-2591fe629621\">\n",
297 |               "    <div class=\"colab-df-container\">\n",
298 |               "      <div>\n",
299 |               "<style scoped>\n",
300 |               "    .dataframe tbody tr th:only-of-type {\n",
301 |               "        vertical-align: middle;\n",
302 |               "    }\n",
303 |               "\n",
304 |               "    .dataframe tbody tr th {\n",
305 |               "        vertical-align: top;\n",
306 |               "    }\n",
307 |               "\n",
308 |               "    .dataframe thead th {\n",
309 |               "        text-align: right;\n",
310 |               "    }\n",
311 |               "</style>\n",
312 |               "<table border=\"1\" class=\"dataframe\">\n",
313 |               "  <thead>\n",
314 |               "    <tr style=\"text-align: right;\">\n",
315 |               "      <th></th>\n",
316 |               "      <th>datetime</th>\n",
317 |               "      <th>temp</th>\n",
318 |               "      <th>dew</th>\n",
319 |               "      <th>humidity</th>\n",
320 |               "      <th>sealevelpressure</th>\n",
321 |               "      <th>winddir</th>\n",
322 |               "      <th>solarradiation</th>\n",
323 |               "      <th>windspeed</th>\n",
324 |               "      <th>precipprob</th>\n",
325 |               "      <th>preciptype</th>\n",
326 |               "    </tr>\n",
327 |               "  </thead>\n",
328 |               "  <tbody>\n",
329 |               "    <tr>\n",
330 |               "      <th>0</th>\n",
331 |               "      <td>01-01-2016</td>\n",
332 |               "      <td>28.4</td>\n",
333 |               "      <td>11.9</td>\n",
334 |               "      <td>37.8</td>\n",
335 |               "      <td>1016.4</td>\n",
336 |               "      <td>147.5</td>\n",
337 |               "      <td>216.1</td>\n",
338 |               "      <td>16.6</td>\n",
339 |               "      <td>0</td>\n",
340 |               "      <td>0</td>\n",
341 |               "    </tr>\n",
342 |               "    <tr>\n",
343 |               "      <th>1</th>\n",
344 |               "      <td>02-01-2016</td>\n",
345 |               "      <td>26.8</td>\n",
346 |               "      <td>13.0</td>\n",
347 |               "      <td>44.8</td>\n",
348 |               "      <td>1017.2</td>\n",
349 |               "      <td>110.3</td>\n",
350 |               "      <td>215.7</td>\n",
351 |               "      <td>16.6</td>\n",
352 |               "      <td>0</td>\n",
353 |               "      <td>0</td>\n",
354 |               "    </tr>\n",
355 |               "    <tr>\n",
356 |               "      <th>2</th>\n",
357 |               "      <td>03-01-2016</td>\n",
358 |               "      <td>25.5</td>\n",
359 |               "      <td>14.6</td>\n",
360 |               "      <td>52.8</td>\n",
361 |               "      <td>1015.7</td>\n",
362 |               "      <td>145.3</td>\n",
363 |               "      <td>221.1</td>\n",
364 |               "      <td>18.4</td>\n",
365 |               "      <td>0</td>\n",
366 |               "      <td>0</td>\n",
367 |               "    </tr>\n",
368 |               "    <tr>\n",
369 |               "      <th>3</th>\n",
370 |               "      <td>04-01-2016</td>\n",
371 |               "      <td>26.4</td>\n",
372 |               "      <td>13.1</td>\n",
373 |               "      <td>46.6</td>\n",
374 |               "      <td>1015.3</td>\n",
375 |               "      <td>126.9</td>\n",
376 |               "      <td>216.2</td>\n",
377 |               "      <td>16.6</td>\n",
378 |               "      <td>0</td>\n",
379 |               "      <td>0</td>\n",
380 |               "    </tr>\n",
381 |               "    <tr>\n",
382 |               "      <th>4</th>\n",
383 |               "      <td>05-01-2016</td>\n",
384 |               "      <td>27.1</td>\n",
385 |               "      <td>13.5</td>\n",
386 |               "      <td>44.9</td>\n",
387 |               "      <td>1014.4</td>\n",
388 |               "      <td>125.5</td>\n",
389 |               "      <td>208.1</td>\n",
390 |               "      <td>16.6</td>\n",
391 |               "      <td>0</td>\n",
392 |               "      <td>0</td>\n",
393 |               "    </tr>\n",
394 |               "  </tbody>\n",
395 |               "</table>\n",
396 |               "</div>\n",
397 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3bea082a-5fd2-4ddf-9dcb-2591fe629621')\"\n",
398 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
399 |               "              style=\"display:none;\">\n",
400 |               "        \n",
401 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
402 |               "       width=\"24px\">\n",
403 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
404 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
405 |               "  </svg>\n",
406 |               "      </button>\n",
407 |               "      \n",
408 |               "  <style>\n",
409 |               "    .colab-df-container {\n",
410 |               "      display:flex;\n",
411 |               "      flex-wrap:wrap;\n",
412 |               "      gap: 12px;\n",
413 |               "    }\n",
414 |               "\n",
415 |               "    .colab-df-convert {\n",
416 |               "      background-color: #E8F0FE;\n",
417 |               "      border: none;\n",
418 |               "      border-radius: 50%;\n",
419 |               "      cursor: pointer;\n",
420 |               "      display: none;\n",
421 |               "      fill: #1967D2;\n",
422 |               "      height: 32px;\n",
423 |               "      padding: 0 0 0 0;\n",
424 |               "      width: 32px;\n",
425 |               "    }\n",
426 |               "\n",
427 |               "    .colab-df-convert:hover {\n",
428 |               "      background-color: #E2EBFA;\n",
429 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
430 |               "      fill: #174EA6;\n",
431 |               "    }\n",
432 |               "\n",
433 |               "    [theme=dark] .colab-df-convert {\n",
434 |               "      background-color: #3B4455;\n",
435 |               "      fill: #D2E3FC;\n",
436 |               "    }\n",
437 |               "\n",
438 |               "    [theme=dark] .colab-df-convert:hover {\n",
439 |               "      background-color: #434B5C;\n",
440 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
441 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
442 |               "      fill: #FFFFFF;\n",
443 |               "    }\n",
444 |               "  </style>\n",
445 |               "\n",
446 |               "      <script>\n",
447 |               "        const buttonEl =\n",
448 |               "          document.querySelector('#df-3bea082a-5fd2-4ddf-9dcb-2591fe629621 button.colab-df-convert');\n",
449 |               "        buttonEl.style.display =\n",
450 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
451 |               "\n",
452 |               "        async function convertToInteractive(key) {\n",
453 |               "          const element = document.querySelector('#df-3bea082a-5fd2-4ddf-9dcb-2591fe629621');\n",
454 |               "          const dataTable =\n",
455 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
456 |               "                                                     [key], {});\n",
457 |               "          if (!dataTable) return;\n",
458 |               "\n",
459 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
460 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
461 |               "            + ' to learn more about interactive tables.';\n",
462 |               "          element.innerHTML = '';\n",
463 |               "          dataTable['output_type'] = 'display_data';\n",
464 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
465 |               "          const docLink = document.createElement('div');\n",
466 |               "          docLink.innerHTML = docLinkHtml;\n",
467 |               "          element.appendChild(docLink);\n",
468 |               "        }\n",
469 |               "      </script>\n",
470 |               "    </div>\n",
471 |               "  </div>\n",
472 |               "  "
473 |             ]
474 |           },
475 |           "metadata": {},
476 |           "execution_count": 9
477 |         }
478 |       ]
479 |     },
480 |     {
481 |       "cell_type": "code",
482 |       "source": [
483 |         "sns.boxplot(x=df['temp'])"
484 |       ],
485 |       "metadata": {
486 |         "colab": {
487 |           "base_uri": "https://localhost:8080/",
488 |           "height": 296
489 |         },
490 |         "id": "fiAy3_0gTU-r",
491 |         "outputId": "71aa023c-df4a-4f6d-ece5-5faac7a1f713"
492 |       },
493 |       "execution_count": null,
494 |       "outputs": [
495 |         {
496 |           "output_type": "execute_result",
497 |           "data": {
498 |             "text/plain": [
499 |               "<matplotlib.axes._subplots.AxesSubplot at 0x7f1a16b92f40>"
500 |             ]
501 |           },
502 |           "metadata": {},
503 |           "execution_count": 10
504 |         },
505 |         {
506 |           "output_type": "display_data",
507 |           "data": {
508 |             "text/plain": [
509 |               "<Figure size 432x288 with 1 Axes>"
510 |             ],
511 |             "image/png": 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uQmCC\n"
512 |           },
513 |           "metadata": {
514 |             "needs_background": "light"
515 |           }
516 |         }
517 |       ]
518 |     },
519 |     {
520 |       "cell_type": "code",
521 |       "source": [
522 |         "Q1 = df.temp.quantile(0.25)\n",
523 |         "Q3 = df.temp.quantile(0.75)\n",
524 |         "print(Q1,Q3)"
525 |       ],
526 |       "metadata": {
527 |         "id": "klPgx10KTx8H",
528 |         "colab": {
529 |           "base_uri": "https://localhost:8080/"
530 |         },
531 |         "outputId": "d995e8a7-4a1b-4b7e-d9ff-424bfa48b930"
532 |       },
533 |       "execution_count": null,
534 |       "outputs": [
535 |         {
536 |           "output_type": "stream",
537 |           "name": "stdout",
538 |           "text": [
539 |             "27.2 29.7\n"
540 |           ]
541 |         }
542 |       ]
543 |     },
544 |     {
545 |       "cell_type": "code",
546 |       "source": [
547 |         "IQR=Q3-Q1"
548 |       ],
549 |       "metadata": {
550 |         "id": "riilxh6pUfWl"
551 |       },
552 |       "execution_count": null,
553 |       "outputs": []
554 |     },
555 |     {
556 |       "cell_type": "code",
557 |       "source": [
558 |         "min=Q1-1.5*IQR\n",
559 |         "max=Q3+1.5*IQR\n",
560 |         "l=[min, max]\n",
561 |         "print(l)"
562 |       ],
563 |       "metadata": {
564 |         "colab": {
565 |           "base_uri": "https://localhost:8080/"
566 |         },
567 |         "id": "BqliEnemUmKS",
568 |         "outputId": "0995939c-0944-4dd5-aec3-5f7e0dc821ae"
569 |       },
570 |       "execution_count": null,
571 |       "outputs": [
572 |         {
573 |           "output_type": "stream",
574 |           "name": "stdout",
575 |           "text": [
576 |             "[23.45, 33.45]\n"
577 |           ]
578 |         }
579 |       ]
580 |     },
581 |     {
582 |       "cell_type": "code",
583 |       "source": [
584 |         "df2=df[(df.temp>=min)&(df.temp<max)]"
585 |       ],
586 |       "metadata": {
587 |         "id": "E9fNsfv8UyKD"
588 |       },
589 |       "execution_count": null,
590 |       "outputs": []
591 |     },
592 |     {
593 |       "cell_type": "code",
594 |       "source": [
595 |         "df2.head()"
596 |       ],
597 |       "metadata": {
598 |         "colab": {
599 |           "base_uri": "https://localhost:8080/",
600 |           "height": 206
601 |         },
602 |         "id": "3Vb1xnLxU-Xj",
603 |         "outputId": "438fcefd-b8d2-400f-b850-0f1b6576a4ec"
604 |       },
605 |       "execution_count": null,
606 |       "outputs": [
607 |         {
608 |           "output_type": "execute_result",
609 |           "data": {
610 |             "text/plain": [
611 |               "     datetime  temp   dew  humidity  sealevelpressure  winddir  \\\n",
612 |               "0  01-01-2016  28.4  11.9      37.8            1016.4    147.5   \n",
613 |               "1  02-01-2016  26.8  13.0      44.8            1017.2    110.3   \n",
614 |               "2  03-01-2016  25.5  14.6      52.8            1015.7    145.3   \n",
615 |               "3  04-01-2016  26.4  13.1      46.6            1015.3    126.9   \n",
616 |               "4  05-01-2016  27.1  13.5      44.9            1014.4    125.5   \n",
617 |               "\n",
618 |               "   solarradiation  windspeed  precipprob  preciptype  \n",
619 |               "0           216.1       16.6           0           0  \n",
620 |               "1           215.7       16.6           0           0  \n",
621 |               "2           221.1       18.4           0           0  \n",
622 |               "3           216.2       16.6           0           0  \n",
623 |               "4           208.1       16.6           0           0  "
624 |             ],
625 |             "text/html": [
626 |               "\n",
627 |               "  <div id=\"df-13f03d70-a227-4a61-91ff-4c61d21b79f7\">\n",
628 |               "    <div class=\"colab-df-container\">\n",
629 |               "      <div>\n",
630 |               "<style scoped>\n",
631 |               "    .dataframe tbody tr th:only-of-type {\n",
632 |               "        vertical-align: middle;\n",
633 |               "    }\n",
634 |               "\n",
635 |               "    .dataframe tbody tr th {\n",
636 |               "        vertical-align: top;\n",
637 |               "    }\n",
638 |               "\n",
639 |               "    .dataframe thead th {\n",
640 |               "        text-align: right;\n",
641 |               "    }\n",
642 |               "</style>\n",
643 |               "<table border=\"1\" class=\"dataframe\">\n",
644 |               "  <thead>\n",
645 |               "    <tr style=\"text-align: right;\">\n",
646 |               "      <th></th>\n",
647 |               "      <th>datetime</th>\n",
648 |               "      <th>temp</th>\n",
649 |               "      <th>dew</th>\n",
650 |               "      <th>humidity</th>\n",
651 |               "      <th>sealevelpressure</th>\n",
652 |               "      <th>winddir</th>\n",
653 |               "      <th>solarradiation</th>\n",
654 |               "      <th>windspeed</th>\n",
655 |               "      <th>precipprob</th>\n",
656 |               "      <th>preciptype</th>\n",
657 |               "    </tr>\n",
658 |               "  </thead>\n",
659 |               "  <tbody>\n",
660 |               "    <tr>\n",
661 |               "      <th>0</th>\n",
662 |               "      <td>01-01-2016</td>\n",
663 |               "      <td>28.4</td>\n",
664 |               "      <td>11.9</td>\n",
665 |               "      <td>37.8</td>\n",
666 |               "      <td>1016.4</td>\n",
667 |               "      <td>147.5</td>\n",
668 |               "      <td>216.1</td>\n",
669 |               "      <td>16.6</td>\n",
670 |               "      <td>0</td>\n",
671 |               "      <td>0</td>\n",
672 |               "    </tr>\n",
673 |               "    <tr>\n",
674 |               "      <th>1</th>\n",
675 |               "      <td>02-01-2016</td>\n",
676 |               "      <td>26.8</td>\n",
677 |               "      <td>13.0</td>\n",
678 |               "      <td>44.8</td>\n",
679 |               "      <td>1017.2</td>\n",
680 |               "      <td>110.3</td>\n",
681 |               "      <td>215.7</td>\n",
682 |               "      <td>16.6</td>\n",
683 |               "      <td>0</td>\n",
684 |               "      <td>0</td>\n",
685 |               "    </tr>\n",
686 |               "    <tr>\n",
687 |               "      <th>2</th>\n",
688 |               "      <td>03-01-2016</td>\n",
689 |               "      <td>25.5</td>\n",
690 |               "      <td>14.6</td>\n",
691 |               "      <td>52.8</td>\n",
692 |               "      <td>1015.7</td>\n",
693 |               "      <td>145.3</td>\n",
694 |               "      <td>221.1</td>\n",
695 |               "      <td>18.4</td>\n",
696 |               "      <td>0</td>\n",
697 |               "      <td>0</td>\n",
698 |               "    </tr>\n",
699 |               "    <tr>\n",
700 |               "      <th>3</th>\n",
701 |               "      <td>04-01-2016</td>\n",
702 |               "      <td>26.4</td>\n",
703 |               "      <td>13.1</td>\n",
704 |               "      <td>46.6</td>\n",
705 |               "      <td>1015.3</td>\n",
706 |               "      <td>126.9</td>\n",
707 |               "      <td>216.2</td>\n",
708 |               "      <td>16.6</td>\n",
709 |               "      <td>0</td>\n",
710 |               "      <td>0</td>\n",
711 |               "    </tr>\n",
712 |               "    <tr>\n",
713 |               "      <th>4</th>\n",
714 |               "      <td>05-01-2016</td>\n",
715 |               "      <td>27.1</td>\n",
716 |               "      <td>13.5</td>\n",
717 |               "      <td>44.9</td>\n",
718 |               "      <td>1014.4</td>\n",
719 |               "      <td>125.5</td>\n",
720 |               "      <td>208.1</td>\n",
721 |               "      <td>16.6</td>\n",
722 |               "      <td>0</td>\n",
723 |               "      <td>0</td>\n",
724 |               "    </tr>\n",
725 |               "  </tbody>\n",
726 |               "</table>\n",
727 |               "</div>\n",
728 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13f03d70-a227-4a61-91ff-4c61d21b79f7')\"\n",
729 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
730 |               "              style=\"display:none;\">\n",
731 |               "        \n",
732 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
733 |               "       width=\"24px\">\n",
734 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
735 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
736 |               "  </svg>\n",
737 |               "      </button>\n",
738 |               "      \n",
739 |               "  <style>\n",
740 |               "    .colab-df-container {\n",
741 |               "      display:flex;\n",
742 |               "      flex-wrap:wrap;\n",
743 |               "      gap: 12px;\n",
744 |               "    }\n",
745 |               "\n",
746 |               "    .colab-df-convert {\n",
747 |               "      background-color: #E8F0FE;\n",
748 |               "      border: none;\n",
749 |               "      border-radius: 50%;\n",
750 |               "      cursor: pointer;\n",
751 |               "      display: none;\n",
752 |               "      fill: #1967D2;\n",
753 |               "      height: 32px;\n",
754 |               "      padding: 0 0 0 0;\n",
755 |               "      width: 32px;\n",
756 |               "    }\n",
757 |               "\n",
758 |               "    .colab-df-convert:hover {\n",
759 |               "      background-color: #E2EBFA;\n",
760 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
761 |               "      fill: #174EA6;\n",
762 |               "    }\n",
763 |               "\n",
764 |               "    [theme=dark] .colab-df-convert {\n",
765 |               "      background-color: #3B4455;\n",
766 |               "      fill: #D2E3FC;\n",
767 |               "    }\n",
768 |               "\n",
769 |               "    [theme=dark] .colab-df-convert:hover {\n",
770 |               "      background-color: #434B5C;\n",
771 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
772 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
773 |               "      fill: #FFFFFF;\n",
774 |               "    }\n",
775 |               "  </style>\n",
776 |               "\n",
777 |               "      <script>\n",
778 |               "        const buttonEl =\n",
779 |               "          document.querySelector('#df-13f03d70-a227-4a61-91ff-4c61d21b79f7 button.colab-df-convert');\n",
780 |               "        buttonEl.style.display =\n",
781 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
782 |               "\n",
783 |               "        async function convertToInteractive(key) {\n",
784 |               "          const element = document.querySelector('#df-13f03d70-a227-4a61-91ff-4c61d21b79f7');\n",
785 |               "          const dataTable =\n",
786 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
787 |               "                                                     [key], {});\n",
788 |               "          if (!dataTable) return;\n",
789 |               "\n",
790 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
791 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
792 |               "            + ' to learn more about interactive tables.';\n",
793 |               "          element.innerHTML = '';\n",
794 |               "          dataTable['output_type'] = 'display_data';\n",
795 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
796 |               "          const docLink = document.createElement('div');\n",
797 |               "          docLink.innerHTML = docLinkHtml;\n",
798 |               "          element.appendChild(docLink);\n",
799 |               "        }\n",
800 |               "      </script>\n",
801 |               "    </div>\n",
802 |               "  </div>\n",
803 |               "  "
804 |             ]
805 |           },
806 |           "metadata": {},
807 |           "execution_count": 22
808 |         }
809 |       ]
810 |     },
811 |     {
812 |       "cell_type": "code",
813 |       "source": [
814 |         "sns.boxplot(x=df2['temp'])"
815 |       ],
816 |       "metadata": {
817 |         "colab": {
818 |           "base_uri": "https://localhost:8080/",
819 |           "height": 296
820 |         },
821 |         "id": "buPtDIXrU_qT",
822 |         "outputId": "2b8de94f-53a2-4f6b-ab49-c00fdc811dc4"
823 |       },
824 |       "execution_count": null,
825 |       "outputs": [
826 |         {
827 |           "output_type": "execute_result",
828 |           "data": {
829 |             "text/plain": [
830 |               "<matplotlib.axes._subplots.AxesSubplot at 0x7f1a146e72e0>"
831 |             ]
832 |           },
833 |           "metadata": {},
834 |           "execution_count": 23
835 |         },
836 |         {
837 |           "output_type": "display_data",
838 |           "data": {
839 |             "text/plain": [
840 |               "<Figure size 432x288 with 1 Axes>"
841 |             ],
842 |             "image/png": "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\n"
843 |           },
844 |           "metadata": {
845 |             "needs_background": "light"
846 |           }
847 |         }
848 |       ]
849 |     },
850 |     {
851 |       "cell_type": "code",
852 |       "source": [
853 |         "sns.boxplot(data=df2, orient='h')"
854 |       ],
855 |       "metadata": {
856 |         "colab": {
857 |           "base_uri": "https://localhost:8080/",
858 |           "height": 282
859 |         },
860 |         "id": "JncY6hHUVFOz",
861 |         "outputId": "4e8dc5bb-f268-4adf-83f0-b171ce60a003"
862 |       },
863 |       "execution_count": null,
864 |       "outputs": [
865 |         {
866 |           "output_type": "execute_result",
867 |           "data": {
868 |             "text/plain": [
869 |               "<matplotlib.axes._subplots.AxesSubplot at 0x7f1a14765430>"
870 |             ]
871 |           },
872 |           "metadata": {},
873 |           "execution_count": 31
874 |         },
875 |         {
876 |           "output_type": "display_data",
877 |           "data": {
878 |             "text/plain": [
879 |               "<Figure size 432x288 with 1 Axes>"
880 |             ],
881 |             "image/png": 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\n"
882 |           },
883 |           "metadata": {
884 |             "needs_background": "light"
885 |           }
886 |         }
887 |       ]
888 |     },
889 |     {
890 |       "cell_type": "code",
891 |       "source": [],
892 |       "metadata": {
893 |         "id": "ZzUURhC9WiAx"
894 |       },
895 |       "execution_count": null,
896 |       "outputs": []
897 |     }
898 |   ]
899 | }


--------------------------------------------------------------------------------