├── ProjectReport.pdf
├── ReportSummary.pdf
├── README.md
├── LICENSE
├── .gitignore
├── GradientBoosting.ipynb
└── SVR.ipynb


/ProjectReport.pdf:
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https://raw.githubusercontent.com/pavini11/SkyLens/HEAD/ProjectReport.pdf


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/ReportSummary.pdf:
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https://raw.githubusercontent.com/pavini11/SkyLens/HEAD/ReportSummary.pdf


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/README.md:
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1 | # Time-series-forecasting-of-Air-Quality-Prediction
2 | AQI Prediction using LSTM, MLR, SVR, Random Forest, Gradient Boosting
3 | 


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/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2021 Pavini Jain
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/.gitignore:
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  1 | # Byte-compiled / optimized / DLL files
  2 | __pycache__/
  3 | *.py[cod]
  4 | *$py.class
  5 | 
  6 | # C extensions
  7 | *.so
  8 | 
  9 | # Distribution / packaging
 10 | .Python
 11 | build/
 12 | develop-eggs/
 13 | dist/
 14 | downloads/
 15 | eggs/
 16 | .eggs/
 17 | lib/
 18 | lib64/
 19 | parts/
 20 | sdist/
 21 | var/
 22 | wheels/
 23 | pip-wheel-metadata/
 24 | share/python-wheels/
 25 | *.egg-info/
 26 | .installed.cfg
 27 | *.egg
 28 | MANIFEST
 29 | 
 30 | # PyInstaller
 31 | #  Usually these files are written by a python script from a template
 32 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 33 | *.manifest
 34 | *.spec
 35 | 
 36 | # Installer logs
 37 | pip-log.txt
 38 | pip-delete-this-directory.txt
 39 | 
 40 | # Unit test / coverage reports
 41 | htmlcov/
 42 | .tox/
 43 | .nox/
 44 | .coverage
 45 | .coverage.*
 46 | .cache
 47 | nosetests.xml
 48 | coverage.xml
 49 | *.cover
 50 | *.py,cover
 51 | .hypothesis/
 52 | .pytest_cache/
 53 | 
 54 | # Translations
 55 | *.mo
 56 | *.pot
 57 | 
 58 | # Django stuff:
 59 | *.log
 60 | local_settings.py
 61 | db.sqlite3
 62 | db.sqlite3-journal
 63 | 
 64 | # Flask stuff:
 65 | instance/
 66 | .webassets-cache
 67 | 
 68 | # Scrapy stuff:
 69 | .scrapy
 70 | 
 71 | # Sphinx documentation
 72 | docs/_build/
 73 | 
 74 | # PyBuilder
 75 | target/
 76 | 
 77 | # Jupyter Notebook
 78 | .ipynb_checkpoints
 79 | 
 80 | # IPython
 81 | profile_default/
 82 | ipython_config.py
 83 | 
 84 | # pyenv
 85 | .python-version
 86 | 
 87 | # pipenv
 88 | #   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
 89 | #   However, in case of collaboration, if having platform-specific dependencies or dependencies
 90 | #   having no cross-platform support, pipenv may install dependencies that don't work, or not
 91 | #   install all needed dependencies.
 92 | #Pipfile.lock
 93 | 
 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
 95 | __pypackages__/
 96 | 
 97 | # Celery stuff
 98 | celerybeat-schedule
 99 | celerybeat.pid
100 | 
101 | # SageMath parsed files
102 | *.sage.py
103 | 
104 | # Environments
105 | .env
106 | .venv
107 | env/
108 | venv/
109 | ENV/
110 | env.bak/
111 | venv.bak/
112 | 
113 | # Spyder project settings
114 | .spyderproject
115 | .spyproject
116 | 
117 | # Rope project settings
118 | .ropeproject
119 | 
120 | # mkdocs documentation
121 | /site
122 | 
123 | # mypy
124 | .mypy_cache/
125 | .dmypy.json
126 | dmypy.json
127 | 
128 | # Pyre type checker
129 | .pyre/
130 | 


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/GradientBoosting.ipynb:
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   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 2,
   6 |    "metadata": {},
   7 |    "outputs": [],
   8 |    "source": [
   9 |     "import pandas as pd\n",
  10 |     "import numpy as np\n",
  11 |     "import matplotlib.pyplot as plt\n",
  12 |     "import seaborn as sns\n",
  13 |     "import matplotlib.pyplot as plt\n",
  14 |     "\n",
  15 |     "import warnings\n",
  16 |     "warnings.filterwarnings(\"ignore\")\n",
  17 |     "warnings.simplefilter(action='ignore', category=FutureWarning)"
  18 |    ]
  19 |   },
  20 |   {
  21 |    "cell_type": "code",
  22 |    "execution_count": 3,
  23 |    "metadata": {},
  24 |    "outputs": [],
  25 |    "source": [
  26 |     "df_x = pd.read_csv(\"train_x.csv\")\n",
  27 |     "df_y = pd.read_csv(\"train_y.csv\")\n",
  28 |     "df_x1 = pd.read_csv(\"test_x.csv\")\n",
  29 |     "df_y1 = pd.read_csv(\"test_y.csv\")"
  30 |    ]
  31 |   },
  32 |   {
  33 |    "cell_type": "code",
  34 |    "execution_count": 4,
  35 |    "metadata": {},
  36 |    "outputs": [
  37 |     {
  38 |      "data": {
  39 |       "text/html": [
  40 |        "<div>\n",
  41 |        "<style scoped>\n",
  42 |        "    .dataframe tbody tr th:only-of-type {\n",
  43 |        "        vertical-align: middle;\n",
  44 |        "    }\n",
  45 |        "\n",
  46 |        "    .dataframe tbody tr th {\n",
  47 |        "        vertical-align: top;\n",
  48 |        "    }\n",
  49 |        "\n",
  50 |        "    .dataframe thead th {\n",
  51 |        "        text-align: right;\n",
  52 |        "    }\n",
  53 |        "</style>\n",
  54 |        "<table border=\"1\" class=\"dataframe\">\n",
  55 |        "  <thead>\n",
  56 |        "    <tr style=\"text-align: right;\">\n",
  57 |        "      <th></th>\n",
  58 |        "      <th>Date</th>\n",
  59 |        "      <th>PM2.5</th>\n",
  60 |        "      <th>PM10</th>\n",
  61 |        "      <th>NO2</th>\n",
  62 |        "      <th>NOx</th>\n",
  63 |        "      <th>CO</th>\n",
  64 |        "      <th>SO2</th>\n",
  65 |        "      <th>O3</th>\n",
  66 |        "      <th>temp</th>\n",
  67 |        "      <th>max_temp</th>\n",
  68 |        "      <th>min_temp</th>\n",
  69 |        "      <th>humid</th>\n",
  70 |        "      <th>visible</th>\n",
  71 |        "      <th>wind</th>\n",
  72 |        "    </tr>\n",
  73 |        "  </thead>\n",
  74 |        "  <tbody>\n",
  75 |        "    <tr>\n",
  76 |        "      <th>0</th>\n",
  77 |        "      <td>2016-01-01</td>\n",
  78 |        "      <td>56.40</td>\n",
  79 |        "      <td>95.08</td>\n",
  80 |        "      <td>51.73</td>\n",
  81 |        "      <td>34.31</td>\n",
  82 |        "      <td>0.69</td>\n",
  83 |        "      <td>7.44</td>\n",
  84 |        "      <td>55.96</td>\n",
  85 |        "      <td>20.1</td>\n",
  86 |        "      <td>28.4</td>\n",
  87 |        "      <td>15.7</td>\n",
  88 |        "      <td>54.0</td>\n",
  89 |        "      <td>6.0</td>\n",
  90 |        "      <td>3.0</td>\n",
  91 |        "    </tr>\n",
  92 |        "    <tr>\n",
  93 |        "      <th>1</th>\n",
  94 |        "      <td>2016-01-02</td>\n",
  95 |        "      <td>53.69</td>\n",
  96 |        "      <td>93.92</td>\n",
  97 |        "      <td>73.09</td>\n",
  98 |        "      <td>54.77</td>\n",
  99 |        "      <td>0.76</td>\n",
 100 |        "      <td>8.67</td>\n",
 101 |        "      <td>34.06</td>\n",
 102 |        "      <td>20.8</td>\n",
 103 |        "      <td>28.3</td>\n",
 104 |        "      <td>12.9</td>\n",
 105 |        "      <td>45.0</td>\n",
 106 |        "      <td>6.3</td>\n",
 107 |        "      <td>1.1</td>\n",
 108 |        "    </tr>\n",
 109 |        "    <tr>\n",
 110 |        "      <th>2</th>\n",
 111 |        "      <td>2016-01-03</td>\n",
 112 |        "      <td>62.35</td>\n",
 113 |        "      <td>99.34</td>\n",
 114 |        "      <td>77.77</td>\n",
 115 |        "      <td>55.60</td>\n",
 116 |        "      <td>0.96</td>\n",
 117 |        "      <td>9.10</td>\n",
 118 |        "      <td>47.62</td>\n",
 119 |        "      <td>21.4</td>\n",
 120 |        "      <td>29.0</td>\n",
 121 |        "      <td>13.7</td>\n",
 122 |        "      <td>45.0</td>\n",
 123 |        "      <td>6.3</td>\n",
 124 |        "      <td>0.4</td>\n",
 125 |        "    </tr>\n",
 126 |        "    <tr>\n",
 127 |        "      <th>3</th>\n",
 128 |        "      <td>2016-01-04</td>\n",
 129 |        "      <td>64.96</td>\n",
 130 |        "      <td>104.62</td>\n",
 131 |        "      <td>77.07</td>\n",
 132 |        "      <td>50.81</td>\n",
 133 |        "      <td>0.89</td>\n",
 134 |        "      <td>8.87</td>\n",
 135 |        "      <td>50.62</td>\n",
 136 |        "      <td>21.5</td>\n",
 137 |        "      <td>29.0</td>\n",
 138 |        "      <td>15.2</td>\n",
 139 |        "      <td>47.0</td>\n",
 140 |        "      <td>6.9</td>\n",
 141 |        "      <td>2.2</td>\n",
 142 |        "    </tr>\n",
 143 |        "    <tr>\n",
 144 |        "      <th>4</th>\n",
 145 |        "      <td>2016-01-05</td>\n",
 146 |        "      <td>70.15</td>\n",
 147 |        "      <td>105.12</td>\n",
 148 |        "      <td>79.43</td>\n",
 149 |        "      <td>56.05</td>\n",
 150 |        "      <td>0.85</td>\n",
 151 |        "      <td>9.41</td>\n",
 152 |        "      <td>36.94</td>\n",
 153 |        "      <td>20.9</td>\n",
 154 |        "      <td>28.4</td>\n",
 155 |        "      <td>14.7</td>\n",
 156 |        "      <td>51.0</td>\n",
 157 |        "      <td>6.3</td>\n",
 158 |        "      <td>1.1</td>\n",
 159 |        "    </tr>\n",
 160 |        "  </tbody>\n",
 161 |        "</table>\n",
 162 |        "</div>"
 163 |       ],
 164 |       "text/plain": [
 165 |        "         Date  PM2.5    PM10    NO2    NOx    CO   SO2     O3  temp  max_temp  \\\n",
 166 |        "0  2016-01-01  56.40   95.08  51.73  34.31  0.69  7.44  55.96  20.1      28.4   \n",
 167 |        "1  2016-01-02  53.69   93.92  73.09  54.77  0.76  8.67  34.06  20.8      28.3   \n",
 168 |        "2  2016-01-03  62.35   99.34  77.77  55.60  0.96  9.10  47.62  21.4      29.0   \n",
 169 |        "3  2016-01-04  64.96  104.62  77.07  50.81  0.89  8.87  50.62  21.5      29.0   \n",
 170 |        "4  2016-01-05  70.15  105.12  79.43  56.05  0.85  9.41  36.94  20.9      28.4   \n",
 171 |        "\n",
 172 |        "   min_temp  humid  visible  wind  \n",
 173 |        "0      15.7   54.0      6.0   3.0  \n",
 174 |        "1      12.9   45.0      6.3   1.1  \n",
 175 |        "2      13.7   45.0      6.3   0.4  \n",
 176 |        "3      15.2   47.0      6.9   2.2  \n",
 177 |        "4      14.7   51.0      6.3   1.1  "
 178 |       ]
 179 |      },
 180 |      "execution_count": 4,
 181 |      "metadata": {},
 182 |      "output_type": "execute_result"
 183 |     }
 184 |    ],
 185 |    "source": [
 186 |     "df_x.head(5)"
 187 |    ]
 188 |   },
 189 |   {
 190 |    "cell_type": "code",
 191 |    "execution_count": 5,
 192 |    "metadata": {},
 193 |    "outputs": [
 194 |     {
 195 |      "data": {
 196 |       "text/html": [
 197 |        "<div>\n",
 198 |        "<style scoped>\n",
 199 |        "    .dataframe tbody tr th:only-of-type {\n",
 200 |        "        vertical-align: middle;\n",
 201 |        "    }\n",
 202 |        "\n",
 203 |        "    .dataframe tbody tr th {\n",
 204 |        "        vertical-align: top;\n",
 205 |        "    }\n",
 206 |        "\n",
 207 |        "    .dataframe thead th {\n",
 208 |        "        text-align: right;\n",
 209 |        "    }\n",
 210 |        "</style>\n",
 211 |        "<table border=\"1\" class=\"dataframe\">\n",
 212 |        "  <thead>\n",
 213 |        "    <tr style=\"text-align: right;\">\n",
 214 |        "      <th></th>\n",
 215 |        "      <th>Date</th>\n",
 216 |        "      <th>PM2.5</th>\n",
 217 |        "      <th>PM10</th>\n",
 218 |        "      <th>NO2</th>\n",
 219 |        "      <th>NOx</th>\n",
 220 |        "      <th>CO</th>\n",
 221 |        "      <th>SO2</th>\n",
 222 |        "      <th>O3</th>\n",
 223 |        "      <th>temp</th>\n",
 224 |        "      <th>max_temp</th>\n",
 225 |        "      <th>min_temp</th>\n",
 226 |        "      <th>humid</th>\n",
 227 |        "      <th>visible</th>\n",
 228 |        "      <th>wind</th>\n",
 229 |        "    </tr>\n",
 230 |        "  </thead>\n",
 231 |        "  <tbody>\n",
 232 |        "    <tr>\n",
 233 |        "      <th>0</th>\n",
 234 |        "      <td>01-01-2020</td>\n",
 235 |        "      <td>30.71</td>\n",
 236 |        "      <td>70.80</td>\n",
 237 |        "      <td>27.31</td>\n",
 238 |        "      <td>26.74</td>\n",
 239 |        "      <td>1.07</td>\n",
 240 |        "      <td>8.56</td>\n",
 241 |        "      <td>37.39</td>\n",
 242 |        "      <td>22.0</td>\n",
 243 |        "      <td>27.2</td>\n",
 244 |        "      <td>19.2</td>\n",
 245 |        "      <td>75</td>\n",
 246 |        "      <td>6.9</td>\n",
 247 |        "      <td>3.3</td>\n",
 248 |        "    </tr>\n",
 249 |        "    <tr>\n",
 250 |        "      <th>1</th>\n",
 251 |        "      <td>02-01-2020</td>\n",
 252 |        "      <td>25.78</td>\n",
 253 |        "      <td>62.73</td>\n",
 254 |        "      <td>28.05</td>\n",
 255 |        "      <td>30.18</td>\n",
 256 |        "      <td>1.16</td>\n",
 257 |        "      <td>7.52</td>\n",
 258 |        "      <td>33.11</td>\n",
 259 |        "      <td>22.2</td>\n",
 260 |        "      <td>26.0</td>\n",
 261 |        "      <td>19.4</td>\n",
 262 |        "      <td>77</td>\n",
 263 |        "      <td>5.5</td>\n",
 264 |        "      <td>2.0</td>\n",
 265 |        "    </tr>\n",
 266 |        "    <tr>\n",
 267 |        "      <th>2</th>\n",
 268 |        "      <td>03-01-2020</td>\n",
 269 |        "      <td>29.66</td>\n",
 270 |        "      <td>69.62</td>\n",
 271 |        "      <td>26.26</td>\n",
 272 |        "      <td>26.92</td>\n",
 273 |        "      <td>0.99</td>\n",
 274 |        "      <td>6.40</td>\n",
 275 |        "      <td>32.98</td>\n",
 276 |        "      <td>23.4</td>\n",
 277 |        "      <td>28.4</td>\n",
 278 |        "      <td>19.4</td>\n",
 279 |        "      <td>68</td>\n",
 280 |        "      <td>6.9</td>\n",
 281 |        "      <td>3.5</td>\n",
 282 |        "    </tr>\n",
 283 |        "    <tr>\n",
 284 |        "      <th>3</th>\n",
 285 |        "      <td>04-01-2020</td>\n",
 286 |        "      <td>52.62</td>\n",
 287 |        "      <td>106.81</td>\n",
 288 |        "      <td>31.56</td>\n",
 289 |        "      <td>41.89</td>\n",
 290 |        "      <td>1.39</td>\n",
 291 |        "      <td>6.94</td>\n",
 292 |        "      <td>37.35</td>\n",
 293 |        "      <td>23.6</td>\n",
 294 |        "      <td>30.5</td>\n",
 295 |        "      <td>19.0</td>\n",
 296 |        "      <td>63</td>\n",
 297 |        "      <td>6.3</td>\n",
 298 |        "      <td>1.3</td>\n",
 299 |        "    </tr>\n",
 300 |        "    <tr>\n",
 301 |        "      <th>4</th>\n",
 302 |        "      <td>05-01-2020</td>\n",
 303 |        "      <td>52.64</td>\n",
 304 |        "      <td>97.94</td>\n",
 305 |        "      <td>29.10</td>\n",
 306 |        "      <td>29.52</td>\n",
 307 |        "      <td>1.01</td>\n",
 308 |        "      <td>7.01</td>\n",
 309 |        "      <td>45.10</td>\n",
 310 |        "      <td>24.2</td>\n",
 311 |        "      <td>30.5</td>\n",
 312 |        "      <td>20.4</td>\n",
 313 |        "      <td>63</td>\n",
 314 |        "      <td>6.3</td>\n",
 315 |        "      <td>1.3</td>\n",
 316 |        "    </tr>\n",
 317 |        "  </tbody>\n",
 318 |        "</table>\n",
 319 |        "</div>"
 320 |       ],
 321 |       "text/plain": [
 322 |        "         Date  PM2.5    PM10    NO2    NOx    CO   SO2     O3  temp  max_temp  \\\n",
 323 |        "0  01-01-2020  30.71   70.80  27.31  26.74  1.07  8.56  37.39  22.0      27.2   \n",
 324 |        "1  02-01-2020  25.78   62.73  28.05  30.18  1.16  7.52  33.11  22.2      26.0   \n",
 325 |        "2  03-01-2020  29.66   69.62  26.26  26.92  0.99  6.40  32.98  23.4      28.4   \n",
 326 |        "3  04-01-2020  52.62  106.81  31.56  41.89  1.39  6.94  37.35  23.6      30.5   \n",
 327 |        "4  05-01-2020  52.64   97.94  29.10  29.52  1.01  7.01  45.10  24.2      30.5   \n",
 328 |        "\n",
 329 |        "   min_temp  humid  visible  wind  \n",
 330 |        "0      19.2     75      6.9   3.3  \n",
 331 |        "1      19.4     77      5.5   2.0  \n",
 332 |        "2      19.4     68      6.9   3.5  \n",
 333 |        "3      19.0     63      6.3   1.3  \n",
 334 |        "4      20.4     63      6.3   1.3  "
 335 |       ]
 336 |      },
 337 |      "execution_count": 5,
 338 |      "metadata": {},
 339 |      "output_type": "execute_result"
 340 |     }
 341 |    ],
 342 |    "source": [
 343 |     "df_x1.head(5)"
 344 |    ]
 345 |   },
 346 |   {
 347 |    "cell_type": "code",
 348 |    "execution_count": 6,
 349 |    "metadata": {},
 350 |    "outputs": [
 351 |     {
 352 |      "data": {
 353 |       "text/html": [
 354 |        "<div>\n",
 355 |        "<style scoped>\n",
 356 |        "    .dataframe tbody tr th:only-of-type {\n",
 357 |        "        vertical-align: middle;\n",
 358 |        "    }\n",
 359 |        "\n",
 360 |        "    .dataframe tbody tr th {\n",
 361 |        "        vertical-align: top;\n",
 362 |        "    }\n",
 363 |        "\n",
 364 |        "    .dataframe thead th {\n",
 365 |        "        text-align: right;\n",
 366 |        "    }\n",
 367 |        "</style>\n",
 368 |        "<table border=\"1\" class=\"dataframe\">\n",
 369 |        "  <thead>\n",
 370 |        "    <tr style=\"text-align: right;\">\n",
 371 |        "      <th></th>\n",
 372 |        "      <th>Date</th>\n",
 373 |        "      <th>AQI</th>\n",
 374 |        "    </tr>\n",
 375 |        "  </thead>\n",
 376 |        "  <tbody>\n",
 377 |        "    <tr>\n",
 378 |        "      <th>0</th>\n",
 379 |        "      <td>2016-01-01</td>\n",
 380 |        "      <td>101.0</td>\n",
 381 |        "    </tr>\n",
 382 |        "    <tr>\n",
 383 |        "      <th>1</th>\n",
 384 |        "      <td>2016-01-02</td>\n",
 385 |        "      <td>92.0</td>\n",
 386 |        "    </tr>\n",
 387 |        "    <tr>\n",
 388 |        "      <th>2</th>\n",
 389 |        "      <td>2016-01-03</td>\n",
 390 |        "      <td>108.0</td>\n",
 391 |        "    </tr>\n",
 392 |        "    <tr>\n",
 393 |        "      <th>3</th>\n",
 394 |        "      <td>2016-01-04</td>\n",
 395 |        "      <td>109.0</td>\n",
 396 |        "    </tr>\n",
 397 |        "    <tr>\n",
 398 |        "      <th>4</th>\n",
 399 |        "      <td>2016-01-05</td>\n",
 400 |        "      <td>114.0</td>\n",
 401 |        "    </tr>\n",
 402 |        "  </tbody>\n",
 403 |        "</table>\n",
 404 |        "</div>"
 405 |       ],
 406 |       "text/plain": [
 407 |        "         Date    AQI\n",
 408 |        "0  2016-01-01  101.0\n",
 409 |        "1  2016-01-02   92.0\n",
 410 |        "2  2016-01-03  108.0\n",
 411 |        "3  2016-01-04  109.0\n",
 412 |        "4  2016-01-05  114.0"
 413 |       ]
 414 |      },
 415 |      "execution_count": 6,
 416 |      "metadata": {},
 417 |      "output_type": "execute_result"
 418 |     }
 419 |    ],
 420 |    "source": [
 421 |     "df_y.head(5)"
 422 |    ]
 423 |   },
 424 |   {
 425 |    "cell_type": "code",
 426 |    "execution_count": 7,
 427 |    "metadata": {},
 428 |    "outputs": [
 429 |     {
 430 |      "data": {
 431 |       "text/html": [
 432 |        "<div>\n",
 433 |        "<style scoped>\n",
 434 |        "    .dataframe tbody tr th:only-of-type {\n",
 435 |        "        vertical-align: middle;\n",
 436 |        "    }\n",
 437 |        "\n",
 438 |        "    .dataframe tbody tr th {\n",
 439 |        "        vertical-align: top;\n",
 440 |        "    }\n",
 441 |        "\n",
 442 |        "    .dataframe thead th {\n",
 443 |        "        text-align: right;\n",
 444 |        "    }\n",
 445 |        "</style>\n",
 446 |        "<table border=\"1\" class=\"dataframe\">\n",
 447 |        "  <thead>\n",
 448 |        "    <tr style=\"text-align: right;\">\n",
 449 |        "      <th></th>\n",
 450 |        "      <th>Date</th>\n",
 451 |        "      <th>AQI</th>\n",
 452 |        "    </tr>\n",
 453 |        "  </thead>\n",
 454 |        "  <tbody>\n",
 455 |        "    <tr>\n",
 456 |        "      <th>0</th>\n",
 457 |        "      <td>2020-01-01</td>\n",
 458 |        "      <td>82.0</td>\n",
 459 |        "    </tr>\n",
 460 |        "    <tr>\n",
 461 |        "      <th>1</th>\n",
 462 |        "      <td>2020-01-02</td>\n",
 463 |        "      <td>81.0</td>\n",
 464 |        "    </tr>\n",
 465 |        "    <tr>\n",
 466 |        "      <th>2</th>\n",
 467 |        "      <td>2020-01-03</td>\n",
 468 |        "      <td>85.0</td>\n",
 469 |        "    </tr>\n",
 470 |        "    <tr>\n",
 471 |        "      <th>3</th>\n",
 472 |        "      <td>2020-01-04</td>\n",
 473 |        "      <td>95.0</td>\n",
 474 |        "    </tr>\n",
 475 |        "    <tr>\n",
 476 |        "      <th>4</th>\n",
 477 |        "      <td>2020-01-05</td>\n",
 478 |        "      <td>118.0</td>\n",
 479 |        "    </tr>\n",
 480 |        "  </tbody>\n",
 481 |        "</table>\n",
 482 |        "</div>"
 483 |       ],
 484 |       "text/plain": [
 485 |        "         Date    AQI\n",
 486 |        "0  2020-01-01   82.0\n",
 487 |        "1  2020-01-02   81.0\n",
 488 |        "2  2020-01-03   85.0\n",
 489 |        "3  2020-01-04   95.0\n",
 490 |        "4  2020-01-05  118.0"
 491 |       ]
 492 |      },
 493 |      "execution_count": 7,
 494 |      "metadata": {},
 495 |      "output_type": "execute_result"
 496 |     }
 497 |    ],
 498 |    "source": [
 499 |     "df_y1.head(5)"
 500 |    ]
 501 |   },
 502 |   {
 503 |    "cell_type": "code",
 504 |    "execution_count": 8,
 505 |    "metadata": {},
 506 |    "outputs": [
 507 |     {
 508 |      "name": "stdout",
 509 |      "output_type": "stream",
 510 |      "text": [
 511 |       "<class 'pandas.core.frame.DataFrame'>\n",
 512 |       "RangeIndex: 1441 entries, 0 to 1440\n",
 513 |       "Data columns (total 14 columns):\n",
 514 |       "Date        1441 non-null object\n",
 515 |       "PM2.5       1441 non-null float64\n",
 516 |       "PM10        1441 non-null float64\n",
 517 |       "NO2         1441 non-null float64\n",
 518 |       "NOx         1441 non-null float64\n",
 519 |       "CO          1441 non-null float64\n",
 520 |       "SO2         1441 non-null float64\n",
 521 |       "O3          1441 non-null float64\n",
 522 |       "temp        1441 non-null float64\n",
 523 |       "max_temp    1441 non-null float64\n",
 524 |       "min_temp    1441 non-null float64\n",
 525 |       "humid       1441 non-null float64\n",
 526 |       "visible     1441 non-null float64\n",
 527 |       "wind        1441 non-null float64\n",
 528 |       "dtypes: float64(13), object(1)\n",
 529 |       "memory usage: 157.7+ KB\n"
 530 |      ]
 531 |     }
 532 |    ],
 533 |    "source": [
 534 |     "df_x.info()"
 535 |    ]
 536 |   },
 537 |   {
 538 |    "cell_type": "code",
 539 |    "execution_count": 9,
 540 |    "metadata": {},
 541 |    "outputs": [
 542 |     {
 543 |      "name": "stdout",
 544 |      "output_type": "stream",
 545 |      "text": [
 546 |       "<class 'pandas.core.frame.DataFrame'>\n",
 547 |       "RangeIndex: 1441 entries, 0 to 1440\n",
 548 |       "Data columns (total 2 columns):\n",
 549 |       "Date    1441 non-null object\n",
 550 |       "AQI     1441 non-null float64\n",
 551 |       "dtypes: float64(1), object(1)\n",
 552 |       "memory usage: 22.6+ KB\n"
 553 |      ]
 554 |     }
 555 |    ],
 556 |    "source": [
 557 |     "df_y.info()"
 558 |    ]
 559 |   },
 560 |   {
 561 |    "cell_type": "code",
 562 |    "execution_count": 10,
 563 |    "metadata": {},
 564 |    "outputs": [
 565 |     {
 566 |      "name": "stdout",
 567 |      "output_type": "stream",
 568 |      "text": [
 569 |       "<class 'pandas.core.frame.DataFrame'>\n",
 570 |       "RangeIndex: 182 entries, 0 to 181\n",
 571 |       "Data columns (total 14 columns):\n",
 572 |       "Date        182 non-null object\n",
 573 |       "PM2.5       182 non-null float64\n",
 574 |       "PM10        182 non-null float64\n",
 575 |       "NO2         182 non-null float64\n",
 576 |       "NOx         182 non-null float64\n",
 577 |       "CO          182 non-null float64\n",
 578 |       "SO2         182 non-null float64\n",
 579 |       "O3          182 non-null float64\n",
 580 |       "temp        182 non-null float64\n",
 581 |       "max_temp    182 non-null float64\n",
 582 |       "min_temp    182 non-null float64\n",
 583 |       "humid       182 non-null int64\n",
 584 |       "visible     182 non-null float64\n",
 585 |       "wind        182 non-null float64\n",
 586 |       "dtypes: float64(12), int64(1), object(1)\n",
 587 |       "memory usage: 20.0+ KB\n"
 588 |      ]
 589 |     }
 590 |    ],
 591 |    "source": [
 592 |     "df_x1.info()"
 593 |    ]
 594 |   },
 595 |   {
 596 |    "cell_type": "code",
 597 |    "execution_count": 11,
 598 |    "metadata": {},
 599 |    "outputs": [
 600 |     {
 601 |      "name": "stdout",
 602 |      "output_type": "stream",
 603 |      "text": [
 604 |       "<class 'pandas.core.frame.DataFrame'>\n",
 605 |       "RangeIndex: 182 entries, 0 to 181\n",
 606 |       "Data columns (total 2 columns):\n",
 607 |       "Date    182 non-null object\n",
 608 |       "AQI     182 non-null float64\n",
 609 |       "dtypes: float64(1), object(1)\n",
 610 |       "memory usage: 2.9+ KB\n"
 611 |      ]
 612 |     }
 613 |    ],
 614 |    "source": [
 615 |     "df_y1.info()"
 616 |    ]
 617 |   },
 618 |   {
 619 |    "cell_type": "code",
 620 |    "execution_count": 12,
 621 |    "metadata": {},
 622 |    "outputs": [],
 623 |    "source": [
 624 |     "df_x[\"Date\"] = pd.to_datetime(df_x[\"Date\"])\n",
 625 |     "df_x1[\"Date\"] = pd.to_datetime(df_x[\"Date\"])"
 626 |    ]
 627 |   },
 628 |   {
 629 |    "cell_type": "code",
 630 |    "execution_count": 13,
 631 |    "metadata": {},
 632 |    "outputs": [
 633 |     {
 634 |      "name": "stdout",
 635 |      "output_type": "stream",
 636 |      "text": [
 637 |       "<class 'pandas.core.frame.DataFrame'>\n",
 638 |       "RangeIndex: 1441 entries, 0 to 1440\n",
 639 |       "Data columns (total 14 columns):\n",
 640 |       "Date        1441 non-null datetime64[ns]\n",
 641 |       "PM2.5       1441 non-null float64\n",
 642 |       "PM10        1441 non-null float64\n",
 643 |       "NO2         1441 non-null float64\n",
 644 |       "NOx         1441 non-null float64\n",
 645 |       "CO          1441 non-null float64\n",
 646 |       "SO2         1441 non-null float64\n",
 647 |       "O3          1441 non-null float64\n",
 648 |       "temp        1441 non-null float64\n",
 649 |       "max_temp    1441 non-null float64\n",
 650 |       "min_temp    1441 non-null float64\n",
 651 |       "humid       1441 non-null float64\n",
 652 |       "visible     1441 non-null float64\n",
 653 |       "wind        1441 non-null float64\n",
 654 |       "dtypes: datetime64[ns](1), float64(13)\n",
 655 |       "memory usage: 157.7 KB\n"
 656 |      ]
 657 |     }
 658 |    ],
 659 |    "source": [
 660 |     "df_x.info()"
 661 |    ]
 662 |   },
 663 |   {
 664 |    "cell_type": "code",
 665 |    "execution_count": 14,
 666 |    "metadata": {},
 667 |    "outputs": [
 668 |     {
 669 |      "name": "stdout",
 670 |      "output_type": "stream",
 671 |      "text": [
 672 |       "<class 'pandas.core.frame.DataFrame'>\n",
 673 |       "RangeIndex: 182 entries, 0 to 181\n",
 674 |       "Data columns (total 14 columns):\n",
 675 |       "Date        182 non-null datetime64[ns]\n",
 676 |       "PM2.5       182 non-null float64\n",
 677 |       "PM10        182 non-null float64\n",
 678 |       "NO2         182 non-null float64\n",
 679 |       "NOx         182 non-null float64\n",
 680 |       "CO          182 non-null float64\n",
 681 |       "SO2         182 non-null float64\n",
 682 |       "O3          182 non-null float64\n",
 683 |       "temp        182 non-null float64\n",
 684 |       "max_temp    182 non-null float64\n",
 685 |       "min_temp    182 non-null float64\n",
 686 |       "humid       182 non-null int64\n",
 687 |       "visible     182 non-null float64\n",
 688 |       "wind        182 non-null float64\n",
 689 |       "dtypes: datetime64[ns](1), float64(12), int64(1)\n",
 690 |       "memory usage: 20.0 KB\n"
 691 |      ]
 692 |     }
 693 |    ],
 694 |    "source": [
 695 |     "df_x1.info()"
 696 |    ]
 697 |   },
 698 |   {
 699 |    "cell_type": "code",
 700 |    "execution_count": 15,
 701 |    "metadata": {},
 702 |    "outputs": [
 703 |     {
 704 |      "data": {
 705 |       "text/plain": [
 706 |        "<matplotlib.axes._subplots.AxesSubplot at 0x2414bee1358>"
 707 |       ]
 708 |      },
 709 |      "execution_count": 15,
 710 |      "metadata": {},
 711 |      "output_type": "execute_result"
 712 |     },
 713 |     {
 714 |      "data": {
 715 |       "image/png": 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\n",
 716 |       "text/plain": [
 717 |        "<Figure size 432x288 with 1 Axes>"
 718 |       ]
 719 |      },
 720 |      "metadata": {
 721 |       "needs_background": "light"
 722 |      },
 723 |      "output_type": "display_data"
 724 |     }
 725 |    ],
 726 |    "source": [
 727 |     "sns.heatmap(df_x.isnull(),cbar=False, yticklabels=False)"
 728 |    ]
 729 |   },
 730 |   {
 731 |    "cell_type": "code",
 732 |    "execution_count": 16,
 733 |    "metadata": {},
 734 |    "outputs": [
 735 |     {
 736 |      "data": {
 737 |       "text/plain": [
 738 |        "<matplotlib.axes._subplots.AxesSubplot at 0x2414c2341d0>"
 739 |       ]
 740 |      },
 741 |      "execution_count": 16,
 742 |      "metadata": {},
 743 |      "output_type": "execute_result"
 744 |     },
 745 |     {
 746 |      "data": {
 747 |       "image/png": 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\n",
 748 |       "text/plain": [
 749 |        "<Figure size 432x288 with 1 Axes>"
 750 |       ]
 751 |      },
 752 |      "metadata": {
 753 |       "needs_background": "light"
 754 |      },
 755 |      "output_type": "display_data"
 756 |     }
 757 |    ],
 758 |    "source": [
 759 |     "sns.heatmap(df_x1.isnull(),cbar=False, yticklabels=False)"
 760 |    ]
 761 |   },
 762 |   {
 763 |    "cell_type": "code",
 764 |    "execution_count": 17,
 765 |    "metadata": {},
 766 |    "outputs": [],
 767 |    "source": [
 768 |     "x_train = df_x.drop(['Date'], axis = 1)\n",
 769 |     "y_train = df_y[\"AQI\"]\n",
 770 |     "x_test = df_x1.drop(['Date'], axis = 1)\n",
 771 |     "y_test = df_y1[\"AQI\"]"
 772 |    ]
 773 |   },
 774 |   {
 775 |    "cell_type": "code",
 776 |    "execution_count": 18,
 777 |    "metadata": {},
 778 |    "outputs": [
 779 |     {
 780 |      "data": {
 781 |       "text/html": [
 782 |        "<div>\n",
 783 |        "<style scoped>\n",
 784 |        "    .dataframe tbody tr th:only-of-type {\n",
 785 |        "        vertical-align: middle;\n",
 786 |        "    }\n",
 787 |        "\n",
 788 |        "    .dataframe tbody tr th {\n",
 789 |        "        vertical-align: top;\n",
 790 |        "    }\n",
 791 |        "\n",
 792 |        "    .dataframe thead th {\n",
 793 |        "        text-align: right;\n",
 794 |        "    }\n",
 795 |        "</style>\n",
 796 |        "<table border=\"1\" class=\"dataframe\">\n",
 797 |        "  <thead>\n",
 798 |        "    <tr style=\"text-align: right;\">\n",
 799 |        "      <th></th>\n",
 800 |        "      <th>PM2.5</th>\n",
 801 |        "      <th>PM10</th>\n",
 802 |        "      <th>NO2</th>\n",
 803 |        "      <th>NOx</th>\n",
 804 |        "      <th>CO</th>\n",
 805 |        "      <th>SO2</th>\n",
 806 |        "      <th>O3</th>\n",
 807 |        "      <th>temp</th>\n",
 808 |        "      <th>max_temp</th>\n",
 809 |        "      <th>min_temp</th>\n",
 810 |        "      <th>humid</th>\n",
 811 |        "      <th>visible</th>\n",
 812 |        "      <th>wind</th>\n",
 813 |        "    </tr>\n",
 814 |        "  </thead>\n",
 815 |        "  <tbody>\n",
 816 |        "    <tr>\n",
 817 |        "      <th>0</th>\n",
 818 |        "      <td>56.40</td>\n",
 819 |        "      <td>95.08</td>\n",
 820 |        "      <td>51.73</td>\n",
 821 |        "      <td>34.31</td>\n",
 822 |        "      <td>0.69</td>\n",
 823 |        "      <td>7.44</td>\n",
 824 |        "      <td>55.96</td>\n",
 825 |        "      <td>20.1</td>\n",
 826 |        "      <td>28.4</td>\n",
 827 |        "      <td>15.7</td>\n",
 828 |        "      <td>54.0</td>\n",
 829 |        "      <td>6.0</td>\n",
 830 |        "      <td>3.0</td>\n",
 831 |        "    </tr>\n",
 832 |        "    <tr>\n",
 833 |        "      <th>1</th>\n",
 834 |        "      <td>53.69</td>\n",
 835 |        "      <td>93.92</td>\n",
 836 |        "      <td>73.09</td>\n",
 837 |        "      <td>54.77</td>\n",
 838 |        "      <td>0.76</td>\n",
 839 |        "      <td>8.67</td>\n",
 840 |        "      <td>34.06</td>\n",
 841 |        "      <td>20.8</td>\n",
 842 |        "      <td>28.3</td>\n",
 843 |        "      <td>12.9</td>\n",
 844 |        "      <td>45.0</td>\n",
 845 |        "      <td>6.3</td>\n",
 846 |        "      <td>1.1</td>\n",
 847 |        "    </tr>\n",
 848 |        "    <tr>\n",
 849 |        "      <th>2</th>\n",
 850 |        "      <td>62.35</td>\n",
 851 |        "      <td>99.34</td>\n",
 852 |        "      <td>77.77</td>\n",
 853 |        "      <td>55.60</td>\n",
 854 |        "      <td>0.96</td>\n",
 855 |        "      <td>9.10</td>\n",
 856 |        "      <td>47.62</td>\n",
 857 |        "      <td>21.4</td>\n",
 858 |        "      <td>29.0</td>\n",
 859 |        "      <td>13.7</td>\n",
 860 |        "      <td>45.0</td>\n",
 861 |        "      <td>6.3</td>\n",
 862 |        "      <td>0.4</td>\n",
 863 |        "    </tr>\n",
 864 |        "    <tr>\n",
 865 |        "      <th>3</th>\n",
 866 |        "      <td>64.96</td>\n",
 867 |        "      <td>104.62</td>\n",
 868 |        "      <td>77.07</td>\n",
 869 |        "      <td>50.81</td>\n",
 870 |        "      <td>0.89</td>\n",
 871 |        "      <td>8.87</td>\n",
 872 |        "      <td>50.62</td>\n",
 873 |        "      <td>21.5</td>\n",
 874 |        "      <td>29.0</td>\n",
 875 |        "      <td>15.2</td>\n",
 876 |        "      <td>47.0</td>\n",
 877 |        "      <td>6.9</td>\n",
 878 |        "      <td>2.2</td>\n",
 879 |        "    </tr>\n",
 880 |        "    <tr>\n",
 881 |        "      <th>4</th>\n",
 882 |        "      <td>70.15</td>\n",
 883 |        "      <td>105.12</td>\n",
 884 |        "      <td>79.43</td>\n",
 885 |        "      <td>56.05</td>\n",
 886 |        "      <td>0.85</td>\n",
 887 |        "      <td>9.41</td>\n",
 888 |        "      <td>36.94</td>\n",
 889 |        "      <td>20.9</td>\n",
 890 |        "      <td>28.4</td>\n",
 891 |        "      <td>14.7</td>\n",
 892 |        "      <td>51.0</td>\n",
 893 |        "      <td>6.3</td>\n",
 894 |        "      <td>1.1</td>\n",
 895 |        "    </tr>\n",
 896 |        "  </tbody>\n",
 897 |        "</table>\n",
 898 |        "</div>"
 899 |       ],
 900 |       "text/plain": [
 901 |        "   PM2.5    PM10    NO2    NOx    CO   SO2     O3  temp  max_temp  min_temp  \\\n",
 902 |        "0  56.40   95.08  51.73  34.31  0.69  7.44  55.96  20.1      28.4      15.7   \n",
 903 |        "1  53.69   93.92  73.09  54.77  0.76  8.67  34.06  20.8      28.3      12.9   \n",
 904 |        "2  62.35   99.34  77.77  55.60  0.96  9.10  47.62  21.4      29.0      13.7   \n",
 905 |        "3  64.96  104.62  77.07  50.81  0.89  8.87  50.62  21.5      29.0      15.2   \n",
 906 |        "4  70.15  105.12  79.43  56.05  0.85  9.41  36.94  20.9      28.4      14.7   \n",
 907 |        "\n",
 908 |        "   humid  visible  wind  \n",
 909 |        "0   54.0      6.0   3.0  \n",
 910 |        "1   45.0      6.3   1.1  \n",
 911 |        "2   45.0      6.3   0.4  \n",
 912 |        "3   47.0      6.9   2.2  \n",
 913 |        "4   51.0      6.3   1.1  "
 914 |       ]
 915 |      },
 916 |      "execution_count": 18,
 917 |      "metadata": {},
 918 |      "output_type": "execute_result"
 919 |     }
 920 |    ],
 921 |    "source": [
 922 |     "x_train.head(5)"
 923 |    ]
 924 |   },
 925 |   {
 926 |    "cell_type": "code",
 927 |    "execution_count": 19,
 928 |    "metadata": {},
 929 |    "outputs": [
 930 |     {
 931 |      "data": {
 932 |       "text/html": [
 933 |        "<div>\n",
 934 |        "<style scoped>\n",
 935 |        "    .dataframe tbody tr th:only-of-type {\n",
 936 |        "        vertical-align: middle;\n",
 937 |        "    }\n",
 938 |        "\n",
 939 |        "    .dataframe tbody tr th {\n",
 940 |        "        vertical-align: top;\n",
 941 |        "    }\n",
 942 |        "\n",
 943 |        "    .dataframe thead th {\n",
 944 |        "        text-align: right;\n",
 945 |        "    }\n",
 946 |        "</style>\n",
 947 |        "<table border=\"1\" class=\"dataframe\">\n",
 948 |        "  <thead>\n",
 949 |        "    <tr style=\"text-align: right;\">\n",
 950 |        "      <th></th>\n",
 951 |        "      <th>PM2.5</th>\n",
 952 |        "      <th>PM10</th>\n",
 953 |        "      <th>NO2</th>\n",
 954 |        "      <th>NOx</th>\n",
 955 |        "      <th>CO</th>\n",
 956 |        "      <th>SO2</th>\n",
 957 |        "      <th>O3</th>\n",
 958 |        "      <th>temp</th>\n",
 959 |        "      <th>max_temp</th>\n",
 960 |        "      <th>min_temp</th>\n",
 961 |        "      <th>humid</th>\n",
 962 |        "      <th>visible</th>\n",
 963 |        "      <th>wind</th>\n",
 964 |        "    </tr>\n",
 965 |        "  </thead>\n",
 966 |        "  <tbody>\n",
 967 |        "    <tr>\n",
 968 |        "      <th>0</th>\n",
 969 |        "      <td>30.71</td>\n",
 970 |        "      <td>70.80</td>\n",
 971 |        "      <td>27.31</td>\n",
 972 |        "      <td>26.74</td>\n",
 973 |        "      <td>1.07</td>\n",
 974 |        "      <td>8.56</td>\n",
 975 |        "      <td>37.39</td>\n",
 976 |        "      <td>22.0</td>\n",
 977 |        "      <td>27.2</td>\n",
 978 |        "      <td>19.2</td>\n",
 979 |        "      <td>75</td>\n",
 980 |        "      <td>6.9</td>\n",
 981 |        "      <td>3.3</td>\n",
 982 |        "    </tr>\n",
 983 |        "    <tr>\n",
 984 |        "      <th>1</th>\n",
 985 |        "      <td>25.78</td>\n",
 986 |        "      <td>62.73</td>\n",
 987 |        "      <td>28.05</td>\n",
 988 |        "      <td>30.18</td>\n",
 989 |        "      <td>1.16</td>\n",
 990 |        "      <td>7.52</td>\n",
 991 |        "      <td>33.11</td>\n",
 992 |        "      <td>22.2</td>\n",
 993 |        "      <td>26.0</td>\n",
 994 |        "      <td>19.4</td>\n",
 995 |        "      <td>77</td>\n",
 996 |        "      <td>5.5</td>\n",
 997 |        "      <td>2.0</td>\n",
 998 |        "    </tr>\n",
 999 |        "    <tr>\n",
1000 |        "      <th>2</th>\n",
1001 |        "      <td>29.66</td>\n",
1002 |        "      <td>69.62</td>\n",
1003 |        "      <td>26.26</td>\n",
1004 |        "      <td>26.92</td>\n",
1005 |        "      <td>0.99</td>\n",
1006 |        "      <td>6.40</td>\n",
1007 |        "      <td>32.98</td>\n",
1008 |        "      <td>23.4</td>\n",
1009 |        "      <td>28.4</td>\n",
1010 |        "      <td>19.4</td>\n",
1011 |        "      <td>68</td>\n",
1012 |        "      <td>6.9</td>\n",
1013 |        "      <td>3.5</td>\n",
1014 |        "    </tr>\n",
1015 |        "    <tr>\n",
1016 |        "      <th>3</th>\n",
1017 |        "      <td>52.62</td>\n",
1018 |        "      <td>106.81</td>\n",
1019 |        "      <td>31.56</td>\n",
1020 |        "      <td>41.89</td>\n",
1021 |        "      <td>1.39</td>\n",
1022 |        "      <td>6.94</td>\n",
1023 |        "      <td>37.35</td>\n",
1024 |        "      <td>23.6</td>\n",
1025 |        "      <td>30.5</td>\n",
1026 |        "      <td>19.0</td>\n",
1027 |        "      <td>63</td>\n",
1028 |        "      <td>6.3</td>\n",
1029 |        "      <td>1.3</td>\n",
1030 |        "    </tr>\n",
1031 |        "    <tr>\n",
1032 |        "      <th>4</th>\n",
1033 |        "      <td>52.64</td>\n",
1034 |        "      <td>97.94</td>\n",
1035 |        "      <td>29.10</td>\n",
1036 |        "      <td>29.52</td>\n",
1037 |        "      <td>1.01</td>\n",
1038 |        "      <td>7.01</td>\n",
1039 |        "      <td>45.10</td>\n",
1040 |        "      <td>24.2</td>\n",
1041 |        "      <td>30.5</td>\n",
1042 |        "      <td>20.4</td>\n",
1043 |        "      <td>63</td>\n",
1044 |        "      <td>6.3</td>\n",
1045 |        "      <td>1.3</td>\n",
1046 |        "    </tr>\n",
1047 |        "  </tbody>\n",
1048 |        "</table>\n",
1049 |        "</div>"
1050 |       ],
1051 |       "text/plain": [
1052 |        "   PM2.5    PM10    NO2    NOx    CO   SO2     O3  temp  max_temp  min_temp  \\\n",
1053 |        "0  30.71   70.80  27.31  26.74  1.07  8.56  37.39  22.0      27.2      19.2   \n",
1054 |        "1  25.78   62.73  28.05  30.18  1.16  7.52  33.11  22.2      26.0      19.4   \n",
1055 |        "2  29.66   69.62  26.26  26.92  0.99  6.40  32.98  23.4      28.4      19.4   \n",
1056 |        "3  52.62  106.81  31.56  41.89  1.39  6.94  37.35  23.6      30.5      19.0   \n",
1057 |        "4  52.64   97.94  29.10  29.52  1.01  7.01  45.10  24.2      30.5      20.4   \n",
1058 |        "\n",
1059 |        "   humid  visible  wind  \n",
1060 |        "0     75      6.9   3.3  \n",
1061 |        "1     77      5.5   2.0  \n",
1062 |        "2     68      6.9   3.5  \n",
1063 |        "3     63      6.3   1.3  \n",
1064 |        "4     63      6.3   1.3  "
1065 |       ]
1066 |      },
1067 |      "execution_count": 19,
1068 |      "metadata": {},
1069 |      "output_type": "execute_result"
1070 |     }
1071 |    ],
1072 |    "source": [
1073 |     "x_test.head(5)"
1074 |    ]
1075 |   },
1076 |   {
1077 |    "cell_type": "code",
1078 |    "execution_count": 20,
1079 |    "metadata": {},
1080 |    "outputs": [
1081 |     {
1082 |      "data": {
1083 |       "text/plain": [
1084 |        "0    101.0\n",
1085 |        "1     92.0\n",
1086 |        "2    108.0\n",
1087 |        "3    109.0\n",
1088 |        "4    114.0\n",
1089 |        "Name: AQI, dtype: float64"
1090 |       ]
1091 |      },
1092 |      "execution_count": 20,
1093 |      "metadata": {},
1094 |      "output_type": "execute_result"
1095 |     }
1096 |    ],
1097 |    "source": [
1098 |     "y_train.head(5)"
1099 |    ]
1100 |   },
1101 |   {
1102 |    "cell_type": "code",
1103 |    "execution_count": 21,
1104 |    "metadata": {},
1105 |    "outputs": [
1106 |     {
1107 |      "data": {
1108 |       "text/plain": [
1109 |        "0     82.0\n",
1110 |        "1     81.0\n",
1111 |        "2     85.0\n",
1112 |        "3     95.0\n",
1113 |        "4    118.0\n",
1114 |        "Name: AQI, dtype: float64"
1115 |       ]
1116 |      },
1117 |      "execution_count": 21,
1118 |      "metadata": {},
1119 |      "output_type": "execute_result"
1120 |     }
1121 |    ],
1122 |    "source": [
1123 |     "y_test.head(5)"
1124 |    ]
1125 |   },
1126 |   {
1127 |    "cell_type": "code",
1128 |    "execution_count": 22,
1129 |    "metadata": {},
1130 |    "outputs": [],
1131 |    "source": [
1132 |     "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
1133 |     "sc_x = StandardScaler()\n",
1134 |     "sc_y = StandardScaler()\n",
1135 |     "x_train = sc_x.fit_transform(x_train)\n",
1136 |     "x_test = sc_x.transform(x_test)\n",
1137 |     "y_train = sc_y.fit_transform(np.array(y_train).reshape(-1,1))\n",
1138 |     "y_test = sc_y.transform(np.array(y_test).reshape(-1,1))"
1139 |    ]
1140 |   },
1141 |   {
1142 |    "cell_type": "code",
1143 |    "execution_count": 23,
1144 |    "metadata": {},
1145 |    "outputs": [
1146 |     {
1147 |      "data": {
1148 |       "text/plain": [
1149 |        "array([[-0.27610501],\n",
1150 |        "       [-0.30227705],\n",
1151 |        "       [-0.1975889 ],\n",
1152 |        "       [ 0.06413148],\n",
1153 |        "       [ 0.66608835]])"
1154 |       ]
1155 |      },
1156 |      "execution_count": 23,
1157 |      "metadata": {},
1158 |      "output_type": "execute_result"
1159 |     }
1160 |    ],
1161 |    "source": [
1162 |     "y_test[:5]"
1163 |    ]
1164 |   },
1165 |   {
1166 |    "cell_type": "code",
1167 |    "execution_count": 24,
1168 |    "metadata": {},
1169 |    "outputs": [
1170 |     {
1171 |      "data": {
1172 |       "text/plain": [
1173 |        "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
1174 |        "                          learning_rate=0.1, loss='ls', max_depth=3,\n",
1175 |        "                          max_features=None, max_leaf_nodes=None,\n",
1176 |        "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
1177 |        "                          min_samples_leaf=1, min_samples_split=2,\n",
1178 |        "                          min_weight_fraction_leaf=0.0, n_estimators=100,\n",
1179 |        "                          n_iter_no_change=None, presort='auto',\n",
1180 |        "                          random_state=None, subsample=1.0, tol=0.0001,\n",
1181 |        "                          validation_fraction=0.1, verbose=0, warm_start=False)"
1182 |       ]
1183 |      },
1184 |      "execution_count": 24,
1185 |      "metadata": {},
1186 |      "output_type": "execute_result"
1187 |     }
1188 |    ],
1189 |    "source": [
1190 |     "from sklearn.ensemble import GradientBoostingRegressor\n",
1191 |     "regressor = GradientBoostingRegressor()\n",
1192 |     "regressor.fit(x_train,y_train)"
1193 |    ]
1194 |   },
1195 |   {
1196 |    "cell_type": "code",
1197 |    "execution_count": 25,
1198 |    "metadata": {},
1199 |    "outputs": [
1200 |     {
1201 |      "data": {
1202 |       "text/plain": [
1203 |        "array([-0.30664803,  0.02744575, -0.45522541,  0.56821673,  0.39152823,\n",
1204 |        "       -0.33634286, -0.45985512, -0.13761617,  0.21430141,  0.06167805,\n",
1205 |        "       -0.43960067, -0.05112361,  0.40469433,  0.91172871,  0.24935004,\n",
1206 |        "        0.34019051,  0.25525236, -0.59217263, -0.56262186, -0.01497731,\n",
1207 |        "        0.0695675 , -0.02144429,  0.04325328,  0.15946079,  0.18446   ,\n",
1208 |        "        0.25582022,  0.30949316,  0.40278977,  0.4255181 ,  0.2840163 ,\n",
1209 |        "        0.42057773,  0.45941366, -0.24938813, -0.25418846, -0.42233104,\n",
1210 |        "        0.11668701,  0.33066576,  0.29736599,  0.32064904,  0.40746019,\n",
1211 |        "       -0.16741748, -0.11139299, -0.30030267,  0.00940919,  0.52902124,\n",
1212 |        "        0.71358506,  0.4200336 ,  0.26898954,  0.4043111 ,  0.51775921,\n",
1213 |        "        0.24915816, -0.20381793, -0.32190889,  0.13897562, -0.13482573,\n",
1214 |        "        0.45736285,  0.2023384 ,  0.32740856,  0.14504189,  0.24373536,\n",
1215 |        "        0.4673738 ,  0.65162784,  1.76219074,  0.63494524,  0.62830402,\n",
1216 |        "        0.30860789,  0.10367325, -0.05551396,  0.20358276,  0.30723921,\n",
1217 |        "        0.29236797,  0.1460285 , -0.06520416, -0.35593281,  0.07230334,\n",
1218 |        "       -0.43199656, -0.50446128, -0.31805938,  0.44619972,  0.50666258,\n",
1219 |        "        0.37658415, -0.03478484,  0.09924211,  0.2197565 , -0.86057172,\n",
1220 |        "       -0.94029625, -0.77387695, -0.65316783, -0.22228412,  0.1078362 ,\n",
1221 |        "       -0.12004117, -0.17820506, -0.07598379, -0.11422471, -0.05717854,\n",
1222 |        "       -0.63267181, -0.18355135, -0.35640078, -0.47804469,  0.18869273,\n",
1223 |        "       -0.64542504, -0.57419083, -0.55506317, -0.49390715, -0.42547427,\n",
1224 |        "       -0.48798456, -0.5072359 , -0.29127774, -0.49157359, -0.85665164,\n",
1225 |        "       -0.93764569, -0.76543852, -0.69058052, -0.60870061, -0.25151876,\n",
1226 |        "       -0.89037835, -0.64424282, -0.74117626, -0.55070149, -0.79329866,\n",
1227 |        "       -0.66968387, -0.56791011, -0.56984256, -0.48223239, -0.30810351,\n",
1228 |        "       -0.14926282, -0.44770745, -0.47163955, -0.72383692, -0.79947907,\n",
1229 |        "       -0.54709498, -0.4843061 , -0.86389296, -0.90695394, -0.61251399,\n",
1230 |        "       -0.81859382, -0.39373544, -0.30160905, -0.25753693, -0.82518774,\n",
1231 |        "       -0.91156371, -0.83574485, -0.63531245, -0.46670179, -0.58265605,\n",
1232 |        "       -0.63542767, -0.51736277, -0.46767405, -0.77992992, -0.80571348,\n",
1233 |        "       -0.8893096 , -0.7368831 , -1.12757017, -1.29664219, -1.33817791,\n",
1234 |        "       -1.12177791, -0.85475676, -0.83829278, -1.06097844, -0.87068831,\n",
1235 |        "       -0.83739172, -0.78359487, -1.05402323, -1.15873018, -0.97677208,\n",
1236 |        "       -1.1738632 , -1.08756149, -0.87845   , -1.05953758, -0.89726162,\n",
1237 |        "       -0.92808345, -1.07917673, -1.09615972, -1.04304939, -0.91874159,\n",
1238 |        "       -0.85788747, -0.96075367, -1.11091323, -1.15244895, -1.11325572,\n",
1239 |        "       -1.02376198, -1.1516053 ])"
1240 |       ]
1241 |      },
1242 |      "execution_count": 25,
1243 |      "metadata": {},
1244 |      "output_type": "execute_result"
1245 |     }
1246 |    ],
1247 |    "source": [
1248 |     "y_prediction =  regressor.predict(x_test)\n",
1249 |     "y_prediction "
1250 |    ]
1251 |   },
1252 |   {
1253 |    "cell_type": "code",
1254 |    "execution_count": 26,
1255 |    "metadata": {},
1256 |    "outputs": [
1257 |     {
1258 |      "name": "stdout",
1259 |      "output_type": "stream",
1260 |      "text": [
1261 |       "R2 SCORE is 0.770856636180251\n",
1262 |       "mean_sqrd_error is  0.058365602156151296\n",
1263 |       "Root mean squared error of is 0.24158973934368838\n",
1264 |       "Mean Absolute error is 0.1872874239791087\n"
1265 |      ]
1266 |     }
1267 |    ],
1268 |    "source": [
1269 |     "from sklearn.metrics import r2_score\n",
1270 |     "from sklearn.metrics import mean_squared_error\n",
1271 |     "from sklearn.metrics import mean_absolute_error\n",
1272 |     "score = r2_score(y_test,y_prediction)\n",
1273 |     "mean_error = mean_squared_error(y_test,y_prediction)\n",
1274 |     "mae = mean_absolute_error(y_test,y_prediction)\n",
1275 |     "print(\"R2 SCORE is\", score)\n",
1276 |     "print(\"mean_sqrd_error is \", mean_error)\n",
1277 |     "print(\"Root mean squared error of is\",np.sqrt(mean_error))\n",
1278 |     "print(\"Mean Absolute error is\", mae)"
1279 |    ]
1280 |   },
1281 |   {
1282 |    "cell_type": "code",
1283 |    "execution_count": 27,
1284 |    "metadata": {},
1285 |    "outputs": [
1286 |     {
1287 |      "data": {
1288 |       "text/plain": [
1289 |        "array([[ 82.],\n",
1290 |        "       [ 81.],\n",
1291 |        "       [ 85.],\n",
1292 |        "       [ 95.],\n",
1293 |        "       [118.]])"
1294 |       ]
1295 |      },
1296 |      "execution_count": 27,
1297 |      "metadata": {},
1298 |      "output_type": "execute_result"
1299 |     }
1300 |    ],
1301 |    "source": [
1302 |     "y_test = sc_y.inverse_transform(y_test)\n",
1303 |     "y_test[:5]"
1304 |    ]
1305 |   },
1306 |   {
1307 |    "cell_type": "code",
1308 |    "execution_count": 28,
1309 |    "metadata": {},
1310 |    "outputs": [
1311 |     {
1312 |      "data": {
1313 |       "text/plain": [
1314 |        "array([ 80.83299016,  93.59828519,  75.15603902, 114.26045103,\n",
1315 |        "       107.50940979])"
1316 |       ]
1317 |      },
1318 |      "execution_count": 28,
1319 |      "metadata": {},
1320 |      "output_type": "execute_result"
1321 |     }
1322 |    ],
1323 |    "source": [
1324 |     "y_prediction = sc_y.inverse_transform(y_prediction)\n",
1325 |     "y_prediction[:5]"
1326 |    ]
1327 |   },
1328 |   {
1329 |    "cell_type": "code",
1330 |    "execution_count": 29,
1331 |    "metadata": {},
1332 |    "outputs": [
1333 |     {
1334 |      "data": {
1335 |       "image/png": 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\n",
1336 |       "text/plain": [
1337 |        "<Figure size 432x288 with 1 Axes>"
1338 |       ]
1339 |      },
1340 |      "metadata": {
1341 |       "needs_background": "light"
1342 |      },
1343 |      "output_type": "display_data"
1344 |     }
1345 |    ],
1346 |    "source": [
1347 |     "df1 = pd.DataFrame({'Actual': y_test[100:120].flatten(), 'Predicted': y_prediction[100:120].flatten()})\n",
1348 |     "df1.plot(kind='line')\n",
1349 |     "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
1350 |     "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
1351 |     "plt.show()"
1352 |    ]
1353 |   },
1354 |   {
1355 |    "cell_type": "code",
1356 |    "execution_count": 30,
1357 |    "metadata": {},
1358 |    "outputs": [
1359 |     {
1360 |      "data": {
1361 |       "text/html": [
1362 |        "<div>\n",
1363 |        "<style scoped>\n",
1364 |        "    .dataframe tbody tr th:only-of-type {\n",
1365 |        "        vertical-align: middle;\n",
1366 |        "    }\n",
1367 |        "\n",
1368 |        "    .dataframe tbody tr th {\n",
1369 |        "        vertical-align: top;\n",
1370 |        "    }\n",
1371 |        "\n",
1372 |        "    .dataframe thead th {\n",
1373 |        "        text-align: right;\n",
1374 |        "    }\n",
1375 |        "</style>\n",
1376 |        "<table border=\"1\" class=\"dataframe\">\n",
1377 |        "  <thead>\n",
1378 |        "    <tr style=\"text-align: right;\">\n",
1379 |        "      <th></th>\n",
1380 |        "      <th>ACTUAL AQI</th>\n",
1381 |        "      <th>PREDICTED AQI</th>\n",
1382 |        "      <th>DIFFERNCE</th>\n",
1383 |        "      <th>PERCENTAGE ERROR</th>\n",
1384 |        "    </tr>\n",
1385 |        "  </thead>\n",
1386 |        "  <tbody>\n",
1387 |        "    <tr>\n",
1388 |        "      <th>0</th>\n",
1389 |        "      <td>82.0</td>\n",
1390 |        "      <td>80.832990</td>\n",
1391 |        "      <td>1.167010</td>\n",
1392 |        "      <td>1.423183</td>\n",
1393 |        "    </tr>\n",
1394 |        "    <tr>\n",
1395 |        "      <th>1</th>\n",
1396 |        "      <td>81.0</td>\n",
1397 |        "      <td>93.598285</td>\n",
1398 |        "      <td>-12.598285</td>\n",
1399 |        "      <td>15.553439</td>\n",
1400 |        "    </tr>\n",
1401 |        "    <tr>\n",
1402 |        "      <th>2</th>\n",
1403 |        "      <td>85.0</td>\n",
1404 |        "      <td>75.156039</td>\n",
1405 |        "      <td>9.843961</td>\n",
1406 |        "      <td>11.581131</td>\n",
1407 |        "    </tr>\n",
1408 |        "    <tr>\n",
1409 |        "      <th>3</th>\n",
1410 |        "      <td>95.0</td>\n",
1411 |        "      <td>114.260451</td>\n",
1412 |        "      <td>-19.260451</td>\n",
1413 |        "      <td>20.274159</td>\n",
1414 |        "    </tr>\n",
1415 |        "    <tr>\n",
1416 |        "      <th>4</th>\n",
1417 |        "      <td>118.0</td>\n",
1418 |        "      <td>107.509410</td>\n",
1419 |        "      <td>10.490590</td>\n",
1420 |        "      <td>8.890331</td>\n",
1421 |        "    </tr>\n",
1422 |        "    <tr>\n",
1423 |        "      <th>5</th>\n",
1424 |        "      <td>81.0</td>\n",
1425 |        "      <td>79.698389</td>\n",
1426 |        "      <td>1.301611</td>\n",
1427 |        "      <td>1.606927</td>\n",
1428 |        "    </tr>\n",
1429 |        "    <tr>\n",
1430 |        "      <th>6</th>\n",
1431 |        "      <td>75.0</td>\n",
1432 |        "      <td>74.979144</td>\n",
1433 |        "      <td>0.020856</td>\n",
1434 |        "      <td>0.027808</td>\n",
1435 |        "    </tr>\n",
1436 |        "    <tr>\n",
1437 |        "      <th>7</th>\n",
1438 |        "      <td>93.0</td>\n",
1439 |        "      <td>87.291481</td>\n",
1440 |        "      <td>5.708519</td>\n",
1441 |        "      <td>6.138193</td>\n",
1442 |        "    </tr>\n",
1443 |        "    <tr>\n",
1444 |        "      <th>8</th>\n",
1445 |        "      <td>101.0</td>\n",
1446 |        "      <td>100.737800</td>\n",
1447 |        "      <td>0.262200</td>\n",
1448 |        "      <td>0.259604</td>\n",
1449 |        "    </tr>\n",
1450 |        "    <tr>\n",
1451 |        "      <th>9</th>\n",
1452 |        "      <td>94.0</td>\n",
1453 |        "      <td>94.906258</td>\n",
1454 |        "      <td>-0.906258</td>\n",
1455 |        "      <td>0.964104</td>\n",
1456 |        "    </tr>\n",
1457 |        "    <tr>\n",
1458 |        "      <th>10</th>\n",
1459 |        "      <td>78.0</td>\n",
1460 |        "      <td>75.753040</td>\n",
1461 |        "      <td>2.246960</td>\n",
1462 |        "      <td>2.880717</td>\n",
1463 |        "    </tr>\n",
1464 |        "    <tr>\n",
1465 |        "      <th>11</th>\n",
1466 |        "      <td>79.0</td>\n",
1467 |        "      <td>90.596251</td>\n",
1468 |        "      <td>-11.596251</td>\n",
1469 |        "      <td>14.678798</td>\n",
1470 |        "    </tr>\n",
1471 |        "    <tr>\n",
1472 |        "      <th>12</th>\n",
1473 |        "      <td>106.0</td>\n",
1474 |        "      <td>108.012470</td>\n",
1475 |        "      <td>-2.012470</td>\n",
1476 |        "      <td>1.898556</td>\n",
1477 |        "    </tr>\n",
1478 |        "    <tr>\n",
1479 |        "      <th>13</th>\n",
1480 |        "      <td>121.0</td>\n",
1481 |        "      <td>127.385603</td>\n",
1482 |        "      <td>-6.385603</td>\n",
1483 |        "      <td>5.277358</td>\n",
1484 |        "    </tr>\n",
1485 |        "    <tr>\n",
1486 |        "      <th>14</th>\n",
1487 |        "      <td>111.0</td>\n",
1488 |        "      <td>102.076964</td>\n",
1489 |        "      <td>8.923036</td>\n",
1490 |        "      <td>8.038772</td>\n",
1491 |        "    </tr>\n",
1492 |        "  </tbody>\n",
1493 |        "</table>\n",
1494 |        "</div>"
1495 |       ],
1496 |       "text/plain": [
1497 |        "    ACTUAL AQI  PREDICTED AQI  DIFFERNCE  PERCENTAGE ERROR\n",
1498 |        "0         82.0      80.832990   1.167010          1.423183\n",
1499 |        "1         81.0      93.598285 -12.598285         15.553439\n",
1500 |        "2         85.0      75.156039   9.843961         11.581131\n",
1501 |        "3         95.0     114.260451 -19.260451         20.274159\n",
1502 |        "4        118.0     107.509410  10.490590          8.890331\n",
1503 |        "5         81.0      79.698389   1.301611          1.606927\n",
1504 |        "6         75.0      74.979144   0.020856          0.027808\n",
1505 |        "7         93.0      87.291481   5.708519          6.138193\n",
1506 |        "8        101.0     100.737800   0.262200          0.259604\n",
1507 |        "9         94.0      94.906258  -0.906258          0.964104\n",
1508 |        "10        78.0      75.753040   2.246960          2.880717\n",
1509 |        "11        79.0      90.596251 -11.596251         14.678798\n",
1510 |        "12       106.0     108.012470  -2.012470          1.898556\n",
1511 |        "13       121.0     127.385603  -6.385603          5.277358\n",
1512 |        "14       111.0     102.076964   8.923036          8.038772"
1513 |       ]
1514 |      },
1515 |      "execution_count": 30,
1516 |      "metadata": {},
1517 |      "output_type": "execute_result"
1518 |     }
1519 |    ],
1520 |    "source": [
1521 |     "result = pd.DataFrame()\n",
1522 |     "result[\"ACTUAL AQI\"] = y_test.flatten()\n",
1523 |     "result[\"PREDICTED AQI\"] = y_prediction\n",
1524 |     "result[\"DIFFERNCE\"] = result[\"ACTUAL AQI\"] - result[\"PREDICTED AQI\"]\n",
1525 |     "result[\"PERCENTAGE ERROR\"] = ( abs(result[\"ACTUAL AQI\"] - result[\"PREDICTED AQI\"] ) / result[\"ACTUAL AQI\"] ) * 100\n",
1526 |     "result.head(15)"
1527 |    ]
1528 |   },
1529 |   {
1530 |    "cell_type": "code",
1531 |    "execution_count": null,
1532 |    "metadata": {},
1533 |    "outputs": [],
1534 |    "source": []
1535 |   }
1536 |  ],
1537 |  "metadata": {
1538 |   "kernelspec": {
1539 |    "display_name": "Python 3",
1540 |    "language": "python",
1541 |    "name": "python3"
1542 |   },
1543 |   "language_info": {
1544 |    "codemirror_mode": {
1545 |     "name": "ipython",
1546 |     "version": 3
1547 |    },
1548 |    "file_extension": ".py",
1549 |    "mimetype": "text/x-python",
1550 |    "name": "python",
1551 |    "nbconvert_exporter": "python",
1552 |    "pygments_lexer": "ipython3",
1553 |    "version": "3.7.3"
1554 |   }
1555 |  },
1556 |  "nbformat": 4,
1557 |  "nbformat_minor": 2
1558 | }
1559 | 


--------------------------------------------------------------------------------
/SVR.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 1,
   6 |    "metadata": {},
   7 |    "outputs": [],
   8 |    "source": [
   9 |     "import pandas as pd\n",
  10 |     "import numpy as np\n",
  11 |     "import matplotlib.pyplot as plt\n",
  12 |     "import seaborn as sns\n",
  13 |     "import matplotlib.pyplot as plt\n",
  14 |     "\n",
  15 |     "import warnings\n",
  16 |     "warnings.filterwarnings(\"ignore\")\n",
  17 |     "warnings.simplefilter(action='ignore', category=FutureWarning)"
  18 |    ]
  19 |   },
  20 |   {
  21 |    "cell_type": "code",
  22 |    "execution_count": 2,
  23 |    "metadata": {},
  24 |    "outputs": [],
  25 |    "source": [
  26 |     "df_x = pd.read_csv(\"train_x.csv\")\n",
  27 |     "df_y = pd.read_csv(\"train_y.csv\")\n",
  28 |     "df_x1 = pd.read_csv(\"test_x.csv\")\n",
  29 |     "df_y1 = pd.read_csv(\"test_y.csv\")"
  30 |    ]
  31 |   },
  32 |   {
  33 |    "cell_type": "code",
  34 |    "execution_count": 3,
  35 |    "metadata": {},
  36 |    "outputs": [
  37 |     {
  38 |      "data": {
  39 |       "text/html": [
  40 |        "<div>\n",
  41 |        "<style scoped>\n",
  42 |        "    .dataframe tbody tr th:only-of-type {\n",
  43 |        "        vertical-align: middle;\n",
  44 |        "    }\n",
  45 |        "\n",
  46 |        "    .dataframe tbody tr th {\n",
  47 |        "        vertical-align: top;\n",
  48 |        "    }\n",
  49 |        "\n",
  50 |        "    .dataframe thead th {\n",
  51 |        "        text-align: right;\n",
  52 |        "    }\n",
  53 |        "</style>\n",
  54 |        "<table border=\"1\" class=\"dataframe\">\n",
  55 |        "  <thead>\n",
  56 |        "    <tr style=\"text-align: right;\">\n",
  57 |        "      <th></th>\n",
  58 |        "      <th>Date</th>\n",
  59 |        "      <th>PM2.5</th>\n",
  60 |        "      <th>PM10</th>\n",
  61 |        "      <th>NO2</th>\n",
  62 |        "      <th>NOx</th>\n",
  63 |        "      <th>CO</th>\n",
  64 |        "      <th>SO2</th>\n",
  65 |        "      <th>O3</th>\n",
  66 |        "      <th>temp</th>\n",
  67 |        "      <th>max_temp</th>\n",
  68 |        "      <th>min_temp</th>\n",
  69 |        "      <th>humid</th>\n",
  70 |        "      <th>visible</th>\n",
  71 |        "      <th>wind</th>\n",
  72 |        "    </tr>\n",
  73 |        "  </thead>\n",
  74 |        "  <tbody>\n",
  75 |        "    <tr>\n",
  76 |        "      <th>0</th>\n",
  77 |        "      <td>2016-01-01</td>\n",
  78 |        "      <td>56.40</td>\n",
  79 |        "      <td>95.08</td>\n",
  80 |        "      <td>51.73</td>\n",
  81 |        "      <td>34.31</td>\n",
  82 |        "      <td>0.69</td>\n",
  83 |        "      <td>7.44</td>\n",
  84 |        "      <td>55.96</td>\n",
  85 |        "      <td>20.1</td>\n",
  86 |        "      <td>28.4</td>\n",
  87 |        "      <td>15.7</td>\n",
  88 |        "      <td>54.0</td>\n",
  89 |        "      <td>6.0</td>\n",
  90 |        "      <td>3.0</td>\n",
  91 |        "    </tr>\n",
  92 |        "    <tr>\n",
  93 |        "      <th>1</th>\n",
  94 |        "      <td>2016-01-02</td>\n",
  95 |        "      <td>53.69</td>\n",
  96 |        "      <td>93.92</td>\n",
  97 |        "      <td>73.09</td>\n",
  98 |        "      <td>54.77</td>\n",
  99 |        "      <td>0.76</td>\n",
 100 |        "      <td>8.67</td>\n",
 101 |        "      <td>34.06</td>\n",
 102 |        "      <td>20.8</td>\n",
 103 |        "      <td>28.3</td>\n",
 104 |        "      <td>12.9</td>\n",
 105 |        "      <td>45.0</td>\n",
 106 |        "      <td>6.3</td>\n",
 107 |        "      <td>1.1</td>\n",
 108 |        "    </tr>\n",
 109 |        "    <tr>\n",
 110 |        "      <th>2</th>\n",
 111 |        "      <td>2016-01-03</td>\n",
 112 |        "      <td>62.35</td>\n",
 113 |        "      <td>99.34</td>\n",
 114 |        "      <td>77.77</td>\n",
 115 |        "      <td>55.60</td>\n",
 116 |        "      <td>0.96</td>\n",
 117 |        "      <td>9.10</td>\n",
 118 |        "      <td>47.62</td>\n",
 119 |        "      <td>21.4</td>\n",
 120 |        "      <td>29.0</td>\n",
 121 |        "      <td>13.7</td>\n",
 122 |        "      <td>45.0</td>\n",
 123 |        "      <td>6.3</td>\n",
 124 |        "      <td>0.4</td>\n",
 125 |        "    </tr>\n",
 126 |        "    <tr>\n",
 127 |        "      <th>3</th>\n",
 128 |        "      <td>2016-01-04</td>\n",
 129 |        "      <td>64.96</td>\n",
 130 |        "      <td>104.62</td>\n",
 131 |        "      <td>77.07</td>\n",
 132 |        "      <td>50.81</td>\n",
 133 |        "      <td>0.89</td>\n",
 134 |        "      <td>8.87</td>\n",
 135 |        "      <td>50.62</td>\n",
 136 |        "      <td>21.5</td>\n",
 137 |        "      <td>29.0</td>\n",
 138 |        "      <td>15.2</td>\n",
 139 |        "      <td>47.0</td>\n",
 140 |        "      <td>6.9</td>\n",
 141 |        "      <td>2.2</td>\n",
 142 |        "    </tr>\n",
 143 |        "    <tr>\n",
 144 |        "      <th>4</th>\n",
 145 |        "      <td>2016-01-05</td>\n",
 146 |        "      <td>70.15</td>\n",
 147 |        "      <td>105.12</td>\n",
 148 |        "      <td>79.43</td>\n",
 149 |        "      <td>56.05</td>\n",
 150 |        "      <td>0.85</td>\n",
 151 |        "      <td>9.41</td>\n",
 152 |        "      <td>36.94</td>\n",
 153 |        "      <td>20.9</td>\n",
 154 |        "      <td>28.4</td>\n",
 155 |        "      <td>14.7</td>\n",
 156 |        "      <td>51.0</td>\n",
 157 |        "      <td>6.3</td>\n",
 158 |        "      <td>1.1</td>\n",
 159 |        "    </tr>\n",
 160 |        "  </tbody>\n",
 161 |        "</table>\n",
 162 |        "</div>"
 163 |       ],
 164 |       "text/plain": [
 165 |        "         Date  PM2.5    PM10    NO2    NOx    CO   SO2     O3  temp  max_temp  \\\n",
 166 |        "0  2016-01-01  56.40   95.08  51.73  34.31  0.69  7.44  55.96  20.1      28.4   \n",
 167 |        "1  2016-01-02  53.69   93.92  73.09  54.77  0.76  8.67  34.06  20.8      28.3   \n",
 168 |        "2  2016-01-03  62.35   99.34  77.77  55.60  0.96  9.10  47.62  21.4      29.0   \n",
 169 |        "3  2016-01-04  64.96  104.62  77.07  50.81  0.89  8.87  50.62  21.5      29.0   \n",
 170 |        "4  2016-01-05  70.15  105.12  79.43  56.05  0.85  9.41  36.94  20.9      28.4   \n",
 171 |        "\n",
 172 |        "   min_temp  humid  visible  wind  \n",
 173 |        "0      15.7   54.0      6.0   3.0  \n",
 174 |        "1      12.9   45.0      6.3   1.1  \n",
 175 |        "2      13.7   45.0      6.3   0.4  \n",
 176 |        "3      15.2   47.0      6.9   2.2  \n",
 177 |        "4      14.7   51.0      6.3   1.1  "
 178 |       ]
 179 |      },
 180 |      "execution_count": 3,
 181 |      "metadata": {},
 182 |      "output_type": "execute_result"
 183 |     }
 184 |    ],
 185 |    "source": [
 186 |     "df_x.head(5)"
 187 |    ]
 188 |   },
 189 |   {
 190 |    "cell_type": "code",
 191 |    "execution_count": 4,
 192 |    "metadata": {},
 193 |    "outputs": [
 194 |     {
 195 |      "data": {
 196 |       "text/html": [
 197 |        "<div>\n",
 198 |        "<style scoped>\n",
 199 |        "    .dataframe tbody tr th:only-of-type {\n",
 200 |        "        vertical-align: middle;\n",
 201 |        "    }\n",
 202 |        "\n",
 203 |        "    .dataframe tbody tr th {\n",
 204 |        "        vertical-align: top;\n",
 205 |        "    }\n",
 206 |        "\n",
 207 |        "    .dataframe thead th {\n",
 208 |        "        text-align: right;\n",
 209 |        "    }\n",
 210 |        "</style>\n",
 211 |        "<table border=\"1\" class=\"dataframe\">\n",
 212 |        "  <thead>\n",
 213 |        "    <tr style=\"text-align: right;\">\n",
 214 |        "      <th></th>\n",
 215 |        "      <th>Date</th>\n",
 216 |        "      <th>PM2.5</th>\n",
 217 |        "      <th>PM10</th>\n",
 218 |        "      <th>NO2</th>\n",
 219 |        "      <th>NOx</th>\n",
 220 |        "      <th>CO</th>\n",
 221 |        "      <th>SO2</th>\n",
 222 |        "      <th>O3</th>\n",
 223 |        "      <th>temp</th>\n",
 224 |        "      <th>max_temp</th>\n",
 225 |        "      <th>min_temp</th>\n",
 226 |        "      <th>humid</th>\n",
 227 |        "      <th>visible</th>\n",
 228 |        "      <th>wind</th>\n",
 229 |        "    </tr>\n",
 230 |        "  </thead>\n",
 231 |        "  <tbody>\n",
 232 |        "    <tr>\n",
 233 |        "      <th>0</th>\n",
 234 |        "      <td>01-01-2020</td>\n",
 235 |        "      <td>30.71</td>\n",
 236 |        "      <td>70.80</td>\n",
 237 |        "      <td>27.31</td>\n",
 238 |        "      <td>26.74</td>\n",
 239 |        "      <td>1.07</td>\n",
 240 |        "      <td>8.56</td>\n",
 241 |        "      <td>37.39</td>\n",
 242 |        "      <td>22.0</td>\n",
 243 |        "      <td>27.2</td>\n",
 244 |        "      <td>19.2</td>\n",
 245 |        "      <td>75</td>\n",
 246 |        "      <td>6.9</td>\n",
 247 |        "      <td>3.3</td>\n",
 248 |        "    </tr>\n",
 249 |        "    <tr>\n",
 250 |        "      <th>1</th>\n",
 251 |        "      <td>02-01-2020</td>\n",
 252 |        "      <td>25.78</td>\n",
 253 |        "      <td>62.73</td>\n",
 254 |        "      <td>28.05</td>\n",
 255 |        "      <td>30.18</td>\n",
 256 |        "      <td>1.16</td>\n",
 257 |        "      <td>7.52</td>\n",
 258 |        "      <td>33.11</td>\n",
 259 |        "      <td>22.2</td>\n",
 260 |        "      <td>26.0</td>\n",
 261 |        "      <td>19.4</td>\n",
 262 |        "      <td>77</td>\n",
 263 |        "      <td>5.5</td>\n",
 264 |        "      <td>2.0</td>\n",
 265 |        "    </tr>\n",
 266 |        "    <tr>\n",
 267 |        "      <th>2</th>\n",
 268 |        "      <td>03-01-2020</td>\n",
 269 |        "      <td>29.66</td>\n",
 270 |        "      <td>69.62</td>\n",
 271 |        "      <td>26.26</td>\n",
 272 |        "      <td>26.92</td>\n",
 273 |        "      <td>0.99</td>\n",
 274 |        "      <td>6.40</td>\n",
 275 |        "      <td>32.98</td>\n",
 276 |        "      <td>23.4</td>\n",
 277 |        "      <td>28.4</td>\n",
 278 |        "      <td>19.4</td>\n",
 279 |        "      <td>68</td>\n",
 280 |        "      <td>6.9</td>\n",
 281 |        "      <td>3.5</td>\n",
 282 |        "    </tr>\n",
 283 |        "    <tr>\n",
 284 |        "      <th>3</th>\n",
 285 |        "      <td>04-01-2020</td>\n",
 286 |        "      <td>52.62</td>\n",
 287 |        "      <td>106.81</td>\n",
 288 |        "      <td>31.56</td>\n",
 289 |        "      <td>41.89</td>\n",
 290 |        "      <td>1.39</td>\n",
 291 |        "      <td>6.94</td>\n",
 292 |        "      <td>37.35</td>\n",
 293 |        "      <td>23.6</td>\n",
 294 |        "      <td>30.5</td>\n",
 295 |        "      <td>19.0</td>\n",
 296 |        "      <td>63</td>\n",
 297 |        "      <td>6.3</td>\n",
 298 |        "      <td>1.3</td>\n",
 299 |        "    </tr>\n",
 300 |        "    <tr>\n",
 301 |        "      <th>4</th>\n",
 302 |        "      <td>05-01-2020</td>\n",
 303 |        "      <td>52.64</td>\n",
 304 |        "      <td>97.94</td>\n",
 305 |        "      <td>29.10</td>\n",
 306 |        "      <td>29.52</td>\n",
 307 |        "      <td>1.01</td>\n",
 308 |        "      <td>7.01</td>\n",
 309 |        "      <td>45.10</td>\n",
 310 |        "      <td>24.2</td>\n",
 311 |        "      <td>30.5</td>\n",
 312 |        "      <td>20.4</td>\n",
 313 |        "      <td>63</td>\n",
 314 |        "      <td>6.3</td>\n",
 315 |        "      <td>1.3</td>\n",
 316 |        "    </tr>\n",
 317 |        "  </tbody>\n",
 318 |        "</table>\n",
 319 |        "</div>"
 320 |       ],
 321 |       "text/plain": [
 322 |        "         Date  PM2.5    PM10    NO2    NOx    CO   SO2     O3  temp  max_temp  \\\n",
 323 |        "0  01-01-2020  30.71   70.80  27.31  26.74  1.07  8.56  37.39  22.0      27.2   \n",
 324 |        "1  02-01-2020  25.78   62.73  28.05  30.18  1.16  7.52  33.11  22.2      26.0   \n",
 325 |        "2  03-01-2020  29.66   69.62  26.26  26.92  0.99  6.40  32.98  23.4      28.4   \n",
 326 |        "3  04-01-2020  52.62  106.81  31.56  41.89  1.39  6.94  37.35  23.6      30.5   \n",
 327 |        "4  05-01-2020  52.64   97.94  29.10  29.52  1.01  7.01  45.10  24.2      30.5   \n",
 328 |        "\n",
 329 |        "   min_temp  humid  visible  wind  \n",
 330 |        "0      19.2     75      6.9   3.3  \n",
 331 |        "1      19.4     77      5.5   2.0  \n",
 332 |        "2      19.4     68      6.9   3.5  \n",
 333 |        "3      19.0     63      6.3   1.3  \n",
 334 |        "4      20.4     63      6.3   1.3  "
 335 |       ]
 336 |      },
 337 |      "execution_count": 4,
 338 |      "metadata": {},
 339 |      "output_type": "execute_result"
 340 |     }
 341 |    ],
 342 |    "source": [
 343 |     "df_x1.head(5)"
 344 |    ]
 345 |   },
 346 |   {
 347 |    "cell_type": "code",
 348 |    "execution_count": 5,
 349 |    "metadata": {},
 350 |    "outputs": [
 351 |     {
 352 |      "data": {
 353 |       "text/html": [
 354 |        "<div>\n",
 355 |        "<style scoped>\n",
 356 |        "    .dataframe tbody tr th:only-of-type {\n",
 357 |        "        vertical-align: middle;\n",
 358 |        "    }\n",
 359 |        "\n",
 360 |        "    .dataframe tbody tr th {\n",
 361 |        "        vertical-align: top;\n",
 362 |        "    }\n",
 363 |        "\n",
 364 |        "    .dataframe thead th {\n",
 365 |        "        text-align: right;\n",
 366 |        "    }\n",
 367 |        "</style>\n",
 368 |        "<table border=\"1\" class=\"dataframe\">\n",
 369 |        "  <thead>\n",
 370 |        "    <tr style=\"text-align: right;\">\n",
 371 |        "      <th></th>\n",
 372 |        "      <th>Date</th>\n",
 373 |        "      <th>AQI</th>\n",
 374 |        "    </tr>\n",
 375 |        "  </thead>\n",
 376 |        "  <tbody>\n",
 377 |        "    <tr>\n",
 378 |        "      <th>0</th>\n",
 379 |        "      <td>2016-01-01</td>\n",
 380 |        "      <td>101.0</td>\n",
 381 |        "    </tr>\n",
 382 |        "    <tr>\n",
 383 |        "      <th>1</th>\n",
 384 |        "      <td>2016-01-02</td>\n",
 385 |        "      <td>92.0</td>\n",
 386 |        "    </tr>\n",
 387 |        "    <tr>\n",
 388 |        "      <th>2</th>\n",
 389 |        "      <td>2016-01-03</td>\n",
 390 |        "      <td>108.0</td>\n",
 391 |        "    </tr>\n",
 392 |        "    <tr>\n",
 393 |        "      <th>3</th>\n",
 394 |        "      <td>2016-01-04</td>\n",
 395 |        "      <td>109.0</td>\n",
 396 |        "    </tr>\n",
 397 |        "    <tr>\n",
 398 |        "      <th>4</th>\n",
 399 |        "      <td>2016-01-05</td>\n",
 400 |        "      <td>114.0</td>\n",
 401 |        "    </tr>\n",
 402 |        "  </tbody>\n",
 403 |        "</table>\n",
 404 |        "</div>"
 405 |       ],
 406 |       "text/plain": [
 407 |        "         Date    AQI\n",
 408 |        "0  2016-01-01  101.0\n",
 409 |        "1  2016-01-02   92.0\n",
 410 |        "2  2016-01-03  108.0\n",
 411 |        "3  2016-01-04  109.0\n",
 412 |        "4  2016-01-05  114.0"
 413 |       ]
 414 |      },
 415 |      "execution_count": 5,
 416 |      "metadata": {},
 417 |      "output_type": "execute_result"
 418 |     }
 419 |    ],
 420 |    "source": [
 421 |     "df_y.head(5)"
 422 |    ]
 423 |   },
 424 |   {
 425 |    "cell_type": "code",
 426 |    "execution_count": 6,
 427 |    "metadata": {},
 428 |    "outputs": [
 429 |     {
 430 |      "data": {
 431 |       "text/html": [
 432 |        "<div>\n",
 433 |        "<style scoped>\n",
 434 |        "    .dataframe tbody tr th:only-of-type {\n",
 435 |        "        vertical-align: middle;\n",
 436 |        "    }\n",
 437 |        "\n",
 438 |        "    .dataframe tbody tr th {\n",
 439 |        "        vertical-align: top;\n",
 440 |        "    }\n",
 441 |        "\n",
 442 |        "    .dataframe thead th {\n",
 443 |        "        text-align: right;\n",
 444 |        "    }\n",
 445 |        "</style>\n",
 446 |        "<table border=\"1\" class=\"dataframe\">\n",
 447 |        "  <thead>\n",
 448 |        "    <tr style=\"text-align: right;\">\n",
 449 |        "      <th></th>\n",
 450 |        "      <th>Date</th>\n",
 451 |        "      <th>AQI</th>\n",
 452 |        "    </tr>\n",
 453 |        "  </thead>\n",
 454 |        "  <tbody>\n",
 455 |        "    <tr>\n",
 456 |        "      <th>0</th>\n",
 457 |        "      <td>2020-01-01</td>\n",
 458 |        "      <td>82.0</td>\n",
 459 |        "    </tr>\n",
 460 |        "    <tr>\n",
 461 |        "      <th>1</th>\n",
 462 |        "      <td>2020-01-02</td>\n",
 463 |        "      <td>81.0</td>\n",
 464 |        "    </tr>\n",
 465 |        "    <tr>\n",
 466 |        "      <th>2</th>\n",
 467 |        "      <td>2020-01-03</td>\n",
 468 |        "      <td>85.0</td>\n",
 469 |        "    </tr>\n",
 470 |        "    <tr>\n",
 471 |        "      <th>3</th>\n",
 472 |        "      <td>2020-01-04</td>\n",
 473 |        "      <td>95.0</td>\n",
 474 |        "    </tr>\n",
 475 |        "    <tr>\n",
 476 |        "      <th>4</th>\n",
 477 |        "      <td>2020-01-05</td>\n",
 478 |        "      <td>118.0</td>\n",
 479 |        "    </tr>\n",
 480 |        "  </tbody>\n",
 481 |        "</table>\n",
 482 |        "</div>"
 483 |       ],
 484 |       "text/plain": [
 485 |        "         Date    AQI\n",
 486 |        "0  2020-01-01   82.0\n",
 487 |        "1  2020-01-02   81.0\n",
 488 |        "2  2020-01-03   85.0\n",
 489 |        "3  2020-01-04   95.0\n",
 490 |        "4  2020-01-05  118.0"
 491 |       ]
 492 |      },
 493 |      "execution_count": 6,
 494 |      "metadata": {},
 495 |      "output_type": "execute_result"
 496 |     }
 497 |    ],
 498 |    "source": [
 499 |     "df_y1.head(5)"
 500 |    ]
 501 |   },
 502 |   {
 503 |    "cell_type": "code",
 504 |    "execution_count": 7,
 505 |    "metadata": {},
 506 |    "outputs": [
 507 |     {
 508 |      "name": "stdout",
 509 |      "output_type": "stream",
 510 |      "text": [
 511 |       "<class 'pandas.core.frame.DataFrame'>\n",
 512 |       "RangeIndex: 1441 entries, 0 to 1440\n",
 513 |       "Data columns (total 14 columns):\n",
 514 |       "Date        1441 non-null object\n",
 515 |       "PM2.5       1441 non-null float64\n",
 516 |       "PM10        1441 non-null float64\n",
 517 |       "NO2         1441 non-null float64\n",
 518 |       "NOx         1441 non-null float64\n",
 519 |       "CO          1441 non-null float64\n",
 520 |       "SO2         1441 non-null float64\n",
 521 |       "O3          1441 non-null float64\n",
 522 |       "temp        1441 non-null float64\n",
 523 |       "max_temp    1441 non-null float64\n",
 524 |       "min_temp    1441 non-null float64\n",
 525 |       "humid       1441 non-null float64\n",
 526 |       "visible     1441 non-null float64\n",
 527 |       "wind        1441 non-null float64\n",
 528 |       "dtypes: float64(13), object(1)\n",
 529 |       "memory usage: 157.7+ KB\n"
 530 |      ]
 531 |     }
 532 |    ],
 533 |    "source": [
 534 |     "df_x.info()"
 535 |    ]
 536 |   },
 537 |   {
 538 |    "cell_type": "code",
 539 |    "execution_count": 8,
 540 |    "metadata": {},
 541 |    "outputs": [
 542 |     {
 543 |      "name": "stdout",
 544 |      "output_type": "stream",
 545 |      "text": [
 546 |       "<class 'pandas.core.frame.DataFrame'>\n",
 547 |       "RangeIndex: 1441 entries, 0 to 1440\n",
 548 |       "Data columns (total 2 columns):\n",
 549 |       "Date    1441 non-null object\n",
 550 |       "AQI     1441 non-null float64\n",
 551 |       "dtypes: float64(1), object(1)\n",
 552 |       "memory usage: 22.6+ KB\n"
 553 |      ]
 554 |     }
 555 |    ],
 556 |    "source": [
 557 |     "df_y.info()"
 558 |    ]
 559 |   },
 560 |   {
 561 |    "cell_type": "code",
 562 |    "execution_count": 9,
 563 |    "metadata": {},
 564 |    "outputs": [
 565 |     {
 566 |      "name": "stdout",
 567 |      "output_type": "stream",
 568 |      "text": [
 569 |       "<class 'pandas.core.frame.DataFrame'>\n",
 570 |       "RangeIndex: 182 entries, 0 to 181\n",
 571 |       "Data columns (total 14 columns):\n",
 572 |       "Date        182 non-null object\n",
 573 |       "PM2.5       182 non-null float64\n",
 574 |       "PM10        182 non-null float64\n",
 575 |       "NO2         182 non-null float64\n",
 576 |       "NOx         182 non-null float64\n",
 577 |       "CO          182 non-null float64\n",
 578 |       "SO2         182 non-null float64\n",
 579 |       "O3          182 non-null float64\n",
 580 |       "temp        182 non-null float64\n",
 581 |       "max_temp    182 non-null float64\n",
 582 |       "min_temp    182 non-null float64\n",
 583 |       "humid       182 non-null int64\n",
 584 |       "visible     182 non-null float64\n",
 585 |       "wind        182 non-null float64\n",
 586 |       "dtypes: float64(12), int64(1), object(1)\n",
 587 |       "memory usage: 20.0+ KB\n"
 588 |      ]
 589 |     }
 590 |    ],
 591 |    "source": [
 592 |     "df_x1.info()"
 593 |    ]
 594 |   },
 595 |   {
 596 |    "cell_type": "code",
 597 |    "execution_count": 10,
 598 |    "metadata": {},
 599 |    "outputs": [
 600 |     {
 601 |      "name": "stdout",
 602 |      "output_type": "stream",
 603 |      "text": [
 604 |       "<class 'pandas.core.frame.DataFrame'>\n",
 605 |       "RangeIndex: 182 entries, 0 to 181\n",
 606 |       "Data columns (total 2 columns):\n",
 607 |       "Date    182 non-null object\n",
 608 |       "AQI     182 non-null float64\n",
 609 |       "dtypes: float64(1), object(1)\n",
 610 |       "memory usage: 2.9+ KB\n"
 611 |      ]
 612 |     }
 613 |    ],
 614 |    "source": [
 615 |     "df_y1.info()"
 616 |    ]
 617 |   },
 618 |   {
 619 |    "cell_type": "code",
 620 |    "execution_count": 11,
 621 |    "metadata": {},
 622 |    "outputs": [],
 623 |    "source": [
 624 |     "df_x[\"Date\"] = pd.to_datetime(df_x[\"Date\"])\n",
 625 |     "df_x1[\"Date\"] = pd.to_datetime(df_x[\"Date\"])"
 626 |    ]
 627 |   },
 628 |   {
 629 |    "cell_type": "code",
 630 |    "execution_count": 12,
 631 |    "metadata": {},
 632 |    "outputs": [
 633 |     {
 634 |      "name": "stdout",
 635 |      "output_type": "stream",
 636 |      "text": [
 637 |       "<class 'pandas.core.frame.DataFrame'>\n",
 638 |       "RangeIndex: 1441 entries, 0 to 1440\n",
 639 |       "Data columns (total 14 columns):\n",
 640 |       "Date        1441 non-null datetime64[ns]\n",
 641 |       "PM2.5       1441 non-null float64\n",
 642 |       "PM10        1441 non-null float64\n",
 643 |       "NO2         1441 non-null float64\n",
 644 |       "NOx         1441 non-null float64\n",
 645 |       "CO          1441 non-null float64\n",
 646 |       "SO2         1441 non-null float64\n",
 647 |       "O3          1441 non-null float64\n",
 648 |       "temp        1441 non-null float64\n",
 649 |       "max_temp    1441 non-null float64\n",
 650 |       "min_temp    1441 non-null float64\n",
 651 |       "humid       1441 non-null float64\n",
 652 |       "visible     1441 non-null float64\n",
 653 |       "wind        1441 non-null float64\n",
 654 |       "dtypes: datetime64[ns](1), float64(13)\n",
 655 |       "memory usage: 157.7 KB\n"
 656 |      ]
 657 |     }
 658 |    ],
 659 |    "source": [
 660 |     "df_x.info()"
 661 |    ]
 662 |   },
 663 |   {
 664 |    "cell_type": "code",
 665 |    "execution_count": 13,
 666 |    "metadata": {},
 667 |    "outputs": [
 668 |     {
 669 |      "name": "stdout",
 670 |      "output_type": "stream",
 671 |      "text": [
 672 |       "<class 'pandas.core.frame.DataFrame'>\n",
 673 |       "RangeIndex: 182 entries, 0 to 181\n",
 674 |       "Data columns (total 14 columns):\n",
 675 |       "Date        182 non-null datetime64[ns]\n",
 676 |       "PM2.5       182 non-null float64\n",
 677 |       "PM10        182 non-null float64\n",
 678 |       "NO2         182 non-null float64\n",
 679 |       "NOx         182 non-null float64\n",
 680 |       "CO          182 non-null float64\n",
 681 |       "SO2         182 non-null float64\n",
 682 |       "O3          182 non-null float64\n",
 683 |       "temp        182 non-null float64\n",
 684 |       "max_temp    182 non-null float64\n",
 685 |       "min_temp    182 non-null float64\n",
 686 |       "humid       182 non-null int64\n",
 687 |       "visible     182 non-null float64\n",
 688 |       "wind        182 non-null float64\n",
 689 |       "dtypes: datetime64[ns](1), float64(12), int64(1)\n",
 690 |       "memory usage: 20.0 KB\n"
 691 |      ]
 692 |     }
 693 |    ],
 694 |    "source": [
 695 |     "df_x1.info()"
 696 |    ]
 697 |   },
 698 |   {
 699 |    "cell_type": "code",
 700 |    "execution_count": 14,
 701 |    "metadata": {},
 702 |    "outputs": [
 703 |     {
 704 |      "data": {
 705 |       "text/plain": [
 706 |        "<matplotlib.axes._subplots.AxesSubplot at 0x18ef365fac8>"
 707 |       ]
 708 |      },
 709 |      "execution_count": 14,
 710 |      "metadata": {},
 711 |      "output_type": "execute_result"
 712 |     },
 713 |     {
 714 |      "data": {
 715 |       "image/png": 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B8yrV/gIwf+jr+cBJFequ0/133nQfNa69xp3uJZm53XDHf0Scn5mP71n3cuBJOTQ/LiK2oay8mpeZ6/asP2NfXFbYlEPT6+bRXk+ZcL45Jdx3pTT935SVN6UZl4j4KHBMZn5vDLU/SVkAcSZTuy5qTxn7BrANZUHN8HlqbEpzIGXq3k0sWaWa2WOGQSzZTOfelNbLRd3XjwHOz54D+BHxpczcdSmzJHpd+0CNFWl/jrKL02UR8S7K4FqN5XLvpHRZLBgcyMyFUVZF/WeF+o+jrEQ7CbiQSstPB6Is+V2azMy3THL9MTseeEqWZcX3pexmdjCwLWWPiueuyIu7C3YAXjyOfj/KZjHXUrp0aq6iGzXjAo+eXglsnpk1VpAOHFWx1p1k5q7dp+dRWo7nZmbNcYAqd7rzKcsI51KaE/emTND/Yf/LG59u+tBTKbsUbQN8mdI86bXaaqj+v09zeE3K6r11M7PX/gvjrj9OEXF5Zj68+/wDlFkAb+q+viwzt12R17e8xtrvt+Qca+aY9zKOqVuz/h1lb4xeW7N2dU8DnpcVtmFtLSJ2ovxSfSKlxXEpJYB7L/qqtZ/uegA5zaBUj5rNmv/d6pPnU/oZ35yZx9Wq3dVfm7Ipzb6UjUWOzsxfryz1a4uIK4BtM/P2biDngMz81uC1HNP8yHGIiB0ogfWJ7udgrcy8tkLdx1EWLayVmQ+MiIdT9rl9ed/aI+fZHziA0mX3oCgbKH04M3euUPsUyorDrzO162LWA6URcV5m7hARNzN1Y63qszu6G7PtKd1fLwVuycwtZv5Tyzbr7oWICErT5CDKX3hORNwOHJcVlsox5uY/LA7bXSiBuwllE40a030G9edRmlh7AZ8CHpkjU7wmuf4YnUTZKOk3lBkG5wKD5cfV1riPWze/dTvgocAnKAOEJ1L2qOjrWOAfgdMAMvPyiHhShbqjDqTsP3Jhd55roiwvr+FU6uytvVhm7tD9t8Zc96WKiHMoLccLKO/P7WvdyPTp030F5c21/eA3e5Td7j8UEYdlZt+NkDdgSfP/BdRv/n+K8jSBM4EjMrPW5sqD+u+mbG79EWDrrLCXQ8v645SZR3Zv6g2Bs3NJc2sOpW93ZbE78Ai6KXuZ+fNKC1/o6v0sYsq9xjg2YhrX1qxk5qdq1JlORDwIuD4zb4uyNeg2wKcz86ZKp1hImYK5FeVG4KaIuCAzb+lbuM8y4EuBp452kndNrLOz4t4L42j+R9kXdrivbPCNqNJM6erfRlnxVr0ZNO76WrbBIp3BfNRurvEFNQbSIuJk4D3A+ynTJA8BtsvM5/WtPXKed1FmF7yI8gvv5ZTNh3qvDByaATBFjRkAEXEZpZWxCWV63WmUzer/qW/tkfOsRZkG+yrK44Du1bdmnzvd1aYblczMG6Ps8tTbOJv/mTnWJyGv7PW1XD4fEccD9+n6RvehrKKs4aWUZ6RtRJledzYlEGt7LWUsYBFlr9szst7WrNsNfb46ZYP0XnuaDLmjGxPYHTg2M4/rbgSriIiDKINoj6KsvD2BqXvMzFqf0J1pBUvv1S0Nmv+rU97Ym1OaEidkecKDtLzWozw36w+Uft03ArU2enpoZu41fCDKznK9dqebxsHdiPzioI1KW7Nm5m9HDh3brTSdabrj8vprlE23XkzZcB1Kn3ota1BaGt+pnQt9uhf+xtTm+eKXKM/X6vUNaND8/xxlTfu5wDOA6zLz0D41dc8y3TLXqLQ72FJqV19Wu5TzVNmaNSKG686h3Pm+bDBdsGftLSk3TRdk5klR9gXeMzPf0bf2uPXZ8KbGxiQz1R9383nL7LYojIiPU1a2SMsUES+jNPU3i4jhHfXWpuedaDdV7PGU54sNbz+6DnU2AxqcZzBAvenI9My1KUvKaziaJTdLtwM/oXQx9NatAjxk6OtrKU8Nn3g1VqSNRYPm/+KnB3R9QxVL627uM5Rur7dT+kQHbs7+S5jnUh5cuiolAAf+QN2Vei22Zn0Gd376wvOAWU8pjYjPZ+a/RMQiph9AHvsexH1VWRwxDuNu/o90jwSlD+fPOPqvCRAR82da2RYRx2Xm2KfXddOkZrV9a5SnL9xEmVJX5ekLEbFhZv6iawVcxMjTl2uuBhyXib3TZczN/3F3j0h9LEd41FiAsTxm2tB+WTbOzKdXuxIgMwdPo16bsofH7yiPqT85Kz17cNwmedrRlOb/irwQ6R6sT1P4/CgPI6guM4/IsnfugcADKCscvzqOc9U2yXe6D4+IwaPFA1ij+9rmvzTBhvpbVwX2jrIhe+1d2AZ+DfySMvhXa/nyWE1s6Nr8l2bUauR3NufZddn/Sz/dDJI9WTJXev8cw77G4zCxoSvdk0XE6pl568ix+w2tAu29eGE5vfCu/oFGg1nzKY97uqzBuaqa2NkL0j1Z10TfPzMXdF8/B3h7Zj6k8nn2oDwwYH3KXa3dd2Nm6EoTqBuAOgH4BmWgaF1gv8y8vvJ5fkh5rt73a9bV0hm60oSKiGcD/0VZsPCkHMPTWCLi25nZavqZsE9Xmkjd3PQHUfaJfQhwekS8PzM/UPlUl3QLkU5l6tMdqm3mr6kMXWkyXUHpTkjg2oh4LGXXq9rWoazEfNrQsaTiE1Q0ld0LktSQd7rSBOoeEPl2YEuGluLWeOpCV/8/MvNdEXEc0z/dYdYPj9TMDF1pMn2C8uDXYyhPo92bugsiBrMVLqHSM9G0fOxekCZQRHwnMx8VEYuGNn46NzOfWPk82wOvY+r2iyvFFokrK+90pcl0a0TMAa7pntd1A+PZW+BE4NWUZ6TdMYb6GuGdrjSBujvQ7wP3Ad5CmWXwrsy8sPJ5zsvMHWrW1MwMXWkCRcR2wOspewwMnjdYvdkfETtTnrZ9Ds7TbcLuBWky/Tdtmv17A1tQgn1wHufpjpGhK02mGzPztGX/b709fDBQpzYMXWkyHR4RH2P8zf4FEbHlyrIX7d2BfbrSBIqIEynN/isZavZn5j6Vz/N9yh4P1zK+pztoiKErTaDh+bljPs/86Y6vDE/VXVnZvSBNpibNfsO1Pe90pQlks//uy9CVJpDN/rsvQ1eSGpqzoi9Aku5JDF1JasjQlaSGDF1JasjQlaSG/h+WB7E69hZlGwAAAABJRU5ErkJggg==\n",
 716 |       "text/plain": [
 717 |        "<Figure size 432x288 with 1 Axes>"
 718 |       ]
 719 |      },
 720 |      "metadata": {
 721 |       "needs_background": "light"
 722 |      },
 723 |      "output_type": "display_data"
 724 |     }
 725 |    ],
 726 |    "source": [
 727 |     "sns.heatmap(df_x.isnull(),cbar=False, yticklabels=False)"
 728 |    ]
 729 |   },
 730 |   {
 731 |    "cell_type": "code",
 732 |    "execution_count": 15,
 733 |    "metadata": {},
 734 |    "outputs": [
 735 |     {
 736 |      "data": {
 737 |       "text/plain": [
 738 |        "<matplotlib.axes._subplots.AxesSubplot at 0x18ef3998940>"
 739 |       ]
 740 |      },
 741 |      "execution_count": 15,
 742 |      "metadata": {},
 743 |      "output_type": "execute_result"
 744 |     },
 745 |     {
 746 |      "data": {
 747 |       "image/png": 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\n",
 748 |       "text/plain": [
 749 |        "<Figure size 432x288 with 1 Axes>"
 750 |       ]
 751 |      },
 752 |      "metadata": {
 753 |       "needs_background": "light"
 754 |      },
 755 |      "output_type": "display_data"
 756 |     }
 757 |    ],
 758 |    "source": [
 759 |     "sns.heatmap(df_x1.isnull(),cbar=False, yticklabels=False)"
 760 |    ]
 761 |   },
 762 |   {
 763 |    "cell_type": "code",
 764 |    "execution_count": 16,
 765 |    "metadata": {},
 766 |    "outputs": [],
 767 |    "source": [
 768 |     "x_train = df_x.drop(['Date'], axis = 1)\n",
 769 |     "y_train = df_y[\"AQI\"]\n",
 770 |     "x_test = df_x1.drop(['Date'], axis = 1)\n",
 771 |     "y_test = df_y1[\"AQI\"]"
 772 |    ]
 773 |   },
 774 |   {
 775 |    "cell_type": "code",
 776 |    "execution_count": 17,
 777 |    "metadata": {},
 778 |    "outputs": [
 779 |     {
 780 |      "data": {
 781 |       "text/html": [
 782 |        "<div>\n",
 783 |        "<style scoped>\n",
 784 |        "    .dataframe tbody tr th:only-of-type {\n",
 785 |        "        vertical-align: middle;\n",
 786 |        "    }\n",
 787 |        "\n",
 788 |        "    .dataframe tbody tr th {\n",
 789 |        "        vertical-align: top;\n",
 790 |        "    }\n",
 791 |        "\n",
 792 |        "    .dataframe thead th {\n",
 793 |        "        text-align: right;\n",
 794 |        "    }\n",
 795 |        "</style>\n",
 796 |        "<table border=\"1\" class=\"dataframe\">\n",
 797 |        "  <thead>\n",
 798 |        "    <tr style=\"text-align: right;\">\n",
 799 |        "      <th></th>\n",
 800 |        "      <th>PM2.5</th>\n",
 801 |        "      <th>PM10</th>\n",
 802 |        "      <th>NO2</th>\n",
 803 |        "      <th>NOx</th>\n",
 804 |        "      <th>CO</th>\n",
 805 |        "      <th>SO2</th>\n",
 806 |        "      <th>O3</th>\n",
 807 |        "      <th>temp</th>\n",
 808 |        "      <th>max_temp</th>\n",
 809 |        "      <th>min_temp</th>\n",
 810 |        "      <th>humid</th>\n",
 811 |        "      <th>visible</th>\n",
 812 |        "      <th>wind</th>\n",
 813 |        "    </tr>\n",
 814 |        "  </thead>\n",
 815 |        "  <tbody>\n",
 816 |        "    <tr>\n",
 817 |        "      <th>0</th>\n",
 818 |        "      <td>56.40</td>\n",
 819 |        "      <td>95.08</td>\n",
 820 |        "      <td>51.73</td>\n",
 821 |        "      <td>34.31</td>\n",
 822 |        "      <td>0.69</td>\n",
 823 |        "      <td>7.44</td>\n",
 824 |        "      <td>55.96</td>\n",
 825 |        "      <td>20.1</td>\n",
 826 |        "      <td>28.4</td>\n",
 827 |        "      <td>15.7</td>\n",
 828 |        "      <td>54.0</td>\n",
 829 |        "      <td>6.0</td>\n",
 830 |        "      <td>3.0</td>\n",
 831 |        "    </tr>\n",
 832 |        "    <tr>\n",
 833 |        "      <th>1</th>\n",
 834 |        "      <td>53.69</td>\n",
 835 |        "      <td>93.92</td>\n",
 836 |        "      <td>73.09</td>\n",
 837 |        "      <td>54.77</td>\n",
 838 |        "      <td>0.76</td>\n",
 839 |        "      <td>8.67</td>\n",
 840 |        "      <td>34.06</td>\n",
 841 |        "      <td>20.8</td>\n",
 842 |        "      <td>28.3</td>\n",
 843 |        "      <td>12.9</td>\n",
 844 |        "      <td>45.0</td>\n",
 845 |        "      <td>6.3</td>\n",
 846 |        "      <td>1.1</td>\n",
 847 |        "    </tr>\n",
 848 |        "    <tr>\n",
 849 |        "      <th>2</th>\n",
 850 |        "      <td>62.35</td>\n",
 851 |        "      <td>99.34</td>\n",
 852 |        "      <td>77.77</td>\n",
 853 |        "      <td>55.60</td>\n",
 854 |        "      <td>0.96</td>\n",
 855 |        "      <td>9.10</td>\n",
 856 |        "      <td>47.62</td>\n",
 857 |        "      <td>21.4</td>\n",
 858 |        "      <td>29.0</td>\n",
 859 |        "      <td>13.7</td>\n",
 860 |        "      <td>45.0</td>\n",
 861 |        "      <td>6.3</td>\n",
 862 |        "      <td>0.4</td>\n",
 863 |        "    </tr>\n",
 864 |        "    <tr>\n",
 865 |        "      <th>3</th>\n",
 866 |        "      <td>64.96</td>\n",
 867 |        "      <td>104.62</td>\n",
 868 |        "      <td>77.07</td>\n",
 869 |        "      <td>50.81</td>\n",
 870 |        "      <td>0.89</td>\n",
 871 |        "      <td>8.87</td>\n",
 872 |        "      <td>50.62</td>\n",
 873 |        "      <td>21.5</td>\n",
 874 |        "      <td>29.0</td>\n",
 875 |        "      <td>15.2</td>\n",
 876 |        "      <td>47.0</td>\n",
 877 |        "      <td>6.9</td>\n",
 878 |        "      <td>2.2</td>\n",
 879 |        "    </tr>\n",
 880 |        "    <tr>\n",
 881 |        "      <th>4</th>\n",
 882 |        "      <td>70.15</td>\n",
 883 |        "      <td>105.12</td>\n",
 884 |        "      <td>79.43</td>\n",
 885 |        "      <td>56.05</td>\n",
 886 |        "      <td>0.85</td>\n",
 887 |        "      <td>9.41</td>\n",
 888 |        "      <td>36.94</td>\n",
 889 |        "      <td>20.9</td>\n",
 890 |        "      <td>28.4</td>\n",
 891 |        "      <td>14.7</td>\n",
 892 |        "      <td>51.0</td>\n",
 893 |        "      <td>6.3</td>\n",
 894 |        "      <td>1.1</td>\n",
 895 |        "    </tr>\n",
 896 |        "  </tbody>\n",
 897 |        "</table>\n",
 898 |        "</div>"
 899 |       ],
 900 |       "text/plain": [
 901 |        "   PM2.5    PM10    NO2    NOx    CO   SO2     O3  temp  max_temp  min_temp  \\\n",
 902 |        "0  56.40   95.08  51.73  34.31  0.69  7.44  55.96  20.1      28.4      15.7   \n",
 903 |        "1  53.69   93.92  73.09  54.77  0.76  8.67  34.06  20.8      28.3      12.9   \n",
 904 |        "2  62.35   99.34  77.77  55.60  0.96  9.10  47.62  21.4      29.0      13.7   \n",
 905 |        "3  64.96  104.62  77.07  50.81  0.89  8.87  50.62  21.5      29.0      15.2   \n",
 906 |        "4  70.15  105.12  79.43  56.05  0.85  9.41  36.94  20.9      28.4      14.7   \n",
 907 |        "\n",
 908 |        "   humid  visible  wind  \n",
 909 |        "0   54.0      6.0   3.0  \n",
 910 |        "1   45.0      6.3   1.1  \n",
 911 |        "2   45.0      6.3   0.4  \n",
 912 |        "3   47.0      6.9   2.2  \n",
 913 |        "4   51.0      6.3   1.1  "
 914 |       ]
 915 |      },
 916 |      "execution_count": 17,
 917 |      "metadata": {},
 918 |      "output_type": "execute_result"
 919 |     }
 920 |    ],
 921 |    "source": [
 922 |     "x_train.head(5)"
 923 |    ]
 924 |   },
 925 |   {
 926 |    "cell_type": "code",
 927 |    "execution_count": 18,
 928 |    "metadata": {},
 929 |    "outputs": [
 930 |     {
 931 |      "data": {
 932 |       "text/html": [
 933 |        "<div>\n",
 934 |        "<style scoped>\n",
 935 |        "    .dataframe tbody tr th:only-of-type {\n",
 936 |        "        vertical-align: middle;\n",
 937 |        "    }\n",
 938 |        "\n",
 939 |        "    .dataframe tbody tr th {\n",
 940 |        "        vertical-align: top;\n",
 941 |        "    }\n",
 942 |        "\n",
 943 |        "    .dataframe thead th {\n",
 944 |        "        text-align: right;\n",
 945 |        "    }\n",
 946 |        "</style>\n",
 947 |        "<table border=\"1\" class=\"dataframe\">\n",
 948 |        "  <thead>\n",
 949 |        "    <tr style=\"text-align: right;\">\n",
 950 |        "      <th></th>\n",
 951 |        "      <th>PM2.5</th>\n",
 952 |        "      <th>PM10</th>\n",
 953 |        "      <th>NO2</th>\n",
 954 |        "      <th>NOx</th>\n",
 955 |        "      <th>CO</th>\n",
 956 |        "      <th>SO2</th>\n",
 957 |        "      <th>O3</th>\n",
 958 |        "      <th>temp</th>\n",
 959 |        "      <th>max_temp</th>\n",
 960 |        "      <th>min_temp</th>\n",
 961 |        "      <th>humid</th>\n",
 962 |        "      <th>visible</th>\n",
 963 |        "      <th>wind</th>\n",
 964 |        "    </tr>\n",
 965 |        "  </thead>\n",
 966 |        "  <tbody>\n",
 967 |        "    <tr>\n",
 968 |        "      <th>0</th>\n",
 969 |        "      <td>30.71</td>\n",
 970 |        "      <td>70.80</td>\n",
 971 |        "      <td>27.31</td>\n",
 972 |        "      <td>26.74</td>\n",
 973 |        "      <td>1.07</td>\n",
 974 |        "      <td>8.56</td>\n",
 975 |        "      <td>37.39</td>\n",
 976 |        "      <td>22.0</td>\n",
 977 |        "      <td>27.2</td>\n",
 978 |        "      <td>19.2</td>\n",
 979 |        "      <td>75</td>\n",
 980 |        "      <td>6.9</td>\n",
 981 |        "      <td>3.3</td>\n",
 982 |        "    </tr>\n",
 983 |        "    <tr>\n",
 984 |        "      <th>1</th>\n",
 985 |        "      <td>25.78</td>\n",
 986 |        "      <td>62.73</td>\n",
 987 |        "      <td>28.05</td>\n",
 988 |        "      <td>30.18</td>\n",
 989 |        "      <td>1.16</td>\n",
 990 |        "      <td>7.52</td>\n",
 991 |        "      <td>33.11</td>\n",
 992 |        "      <td>22.2</td>\n",
 993 |        "      <td>26.0</td>\n",
 994 |        "      <td>19.4</td>\n",
 995 |        "      <td>77</td>\n",
 996 |        "      <td>5.5</td>\n",
 997 |        "      <td>2.0</td>\n",
 998 |        "    </tr>\n",
 999 |        "    <tr>\n",
1000 |        "      <th>2</th>\n",
1001 |        "      <td>29.66</td>\n",
1002 |        "      <td>69.62</td>\n",
1003 |        "      <td>26.26</td>\n",
1004 |        "      <td>26.92</td>\n",
1005 |        "      <td>0.99</td>\n",
1006 |        "      <td>6.40</td>\n",
1007 |        "      <td>32.98</td>\n",
1008 |        "      <td>23.4</td>\n",
1009 |        "      <td>28.4</td>\n",
1010 |        "      <td>19.4</td>\n",
1011 |        "      <td>68</td>\n",
1012 |        "      <td>6.9</td>\n",
1013 |        "      <td>3.5</td>\n",
1014 |        "    </tr>\n",
1015 |        "    <tr>\n",
1016 |        "      <th>3</th>\n",
1017 |        "      <td>52.62</td>\n",
1018 |        "      <td>106.81</td>\n",
1019 |        "      <td>31.56</td>\n",
1020 |        "      <td>41.89</td>\n",
1021 |        "      <td>1.39</td>\n",
1022 |        "      <td>6.94</td>\n",
1023 |        "      <td>37.35</td>\n",
1024 |        "      <td>23.6</td>\n",
1025 |        "      <td>30.5</td>\n",
1026 |        "      <td>19.0</td>\n",
1027 |        "      <td>63</td>\n",
1028 |        "      <td>6.3</td>\n",
1029 |        "      <td>1.3</td>\n",
1030 |        "    </tr>\n",
1031 |        "    <tr>\n",
1032 |        "      <th>4</th>\n",
1033 |        "      <td>52.64</td>\n",
1034 |        "      <td>97.94</td>\n",
1035 |        "      <td>29.10</td>\n",
1036 |        "      <td>29.52</td>\n",
1037 |        "      <td>1.01</td>\n",
1038 |        "      <td>7.01</td>\n",
1039 |        "      <td>45.10</td>\n",
1040 |        "      <td>24.2</td>\n",
1041 |        "      <td>30.5</td>\n",
1042 |        "      <td>20.4</td>\n",
1043 |        "      <td>63</td>\n",
1044 |        "      <td>6.3</td>\n",
1045 |        "      <td>1.3</td>\n",
1046 |        "    </tr>\n",
1047 |        "  </tbody>\n",
1048 |        "</table>\n",
1049 |        "</div>"
1050 |       ],
1051 |       "text/plain": [
1052 |        "   PM2.5    PM10    NO2    NOx    CO   SO2     O3  temp  max_temp  min_temp  \\\n",
1053 |        "0  30.71   70.80  27.31  26.74  1.07  8.56  37.39  22.0      27.2      19.2   \n",
1054 |        "1  25.78   62.73  28.05  30.18  1.16  7.52  33.11  22.2      26.0      19.4   \n",
1055 |        "2  29.66   69.62  26.26  26.92  0.99  6.40  32.98  23.4      28.4      19.4   \n",
1056 |        "3  52.62  106.81  31.56  41.89  1.39  6.94  37.35  23.6      30.5      19.0   \n",
1057 |        "4  52.64   97.94  29.10  29.52  1.01  7.01  45.10  24.2      30.5      20.4   \n",
1058 |        "\n",
1059 |        "   humid  visible  wind  \n",
1060 |        "0     75      6.9   3.3  \n",
1061 |        "1     77      5.5   2.0  \n",
1062 |        "2     68      6.9   3.5  \n",
1063 |        "3     63      6.3   1.3  \n",
1064 |        "4     63      6.3   1.3  "
1065 |       ]
1066 |      },
1067 |      "execution_count": 18,
1068 |      "metadata": {},
1069 |      "output_type": "execute_result"
1070 |     }
1071 |    ],
1072 |    "source": [
1073 |     "x_test.head(5)"
1074 |    ]
1075 |   },
1076 |   {
1077 |    "cell_type": "code",
1078 |    "execution_count": 19,
1079 |    "metadata": {},
1080 |    "outputs": [
1081 |     {
1082 |      "data": {
1083 |       "text/plain": [
1084 |        "0    101.0\n",
1085 |        "1     92.0\n",
1086 |        "2    108.0\n",
1087 |        "3    109.0\n",
1088 |        "4    114.0\n",
1089 |        "Name: AQI, dtype: float64"
1090 |       ]
1091 |      },
1092 |      "execution_count": 19,
1093 |      "metadata": {},
1094 |      "output_type": "execute_result"
1095 |     }
1096 |    ],
1097 |    "source": [
1098 |     "y_train.head(5)"
1099 |    ]
1100 |   },
1101 |   {
1102 |    "cell_type": "code",
1103 |    "execution_count": 20,
1104 |    "metadata": {},
1105 |    "outputs": [
1106 |     {
1107 |      "data": {
1108 |       "text/plain": [
1109 |        "0     82.0\n",
1110 |        "1     81.0\n",
1111 |        "2     85.0\n",
1112 |        "3     95.0\n",
1113 |        "4    118.0\n",
1114 |        "Name: AQI, dtype: float64"
1115 |       ]
1116 |      },
1117 |      "execution_count": 20,
1118 |      "metadata": {},
1119 |      "output_type": "execute_result"
1120 |     }
1121 |    ],
1122 |    "source": [
1123 |     "y_test.head(5)"
1124 |    ]
1125 |   },
1126 |   {
1127 |    "cell_type": "code",
1128 |    "execution_count": 21,
1129 |    "metadata": {},
1130 |    "outputs": [],
1131 |    "source": [
1132 |     "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
1133 |     "sc_x = StandardScaler()\n",
1134 |     "sc_y = StandardScaler()\n",
1135 |     "x_train = sc_x.fit_transform(x_train)\n",
1136 |     "x_test = sc_x.transform(x_test)\n",
1137 |     "y_train = sc_y.fit_transform(np.array(y_train).reshape(-1,1))\n",
1138 |     "y_test = sc_y.transform(np.array(y_test).reshape(-1,1))"
1139 |    ]
1140 |   },
1141 |   {
1142 |    "cell_type": "code",
1143 |    "execution_count": 22,
1144 |    "metadata": {},
1145 |    "outputs": [
1146 |     {
1147 |      "data": {
1148 |       "text/plain": [
1149 |        "array([[-0.27610501],\n",
1150 |        "       [-0.30227705],\n",
1151 |        "       [-0.1975889 ],\n",
1152 |        "       [ 0.06413148],\n",
1153 |        "       [ 0.66608835]])"
1154 |       ]
1155 |      },
1156 |      "execution_count": 22,
1157 |      "metadata": {},
1158 |      "output_type": "execute_result"
1159 |     }
1160 |    ],
1161 |    "source": [
1162 |     "y_test[:5]"
1163 |    ]
1164 |   },
1165 |   {
1166 |    "cell_type": "code",
1167 |    "execution_count": 23,
1168 |    "metadata": {},
1169 |    "outputs": [
1170 |     {
1171 |      "data": {
1172 |       "text/plain": [
1173 |        "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
1174 |        "    gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,\n",
1175 |        "    tol=0.001, verbose=False)"
1176 |       ]
1177 |      },
1178 |      "execution_count": 23,
1179 |      "metadata": {},
1180 |      "output_type": "execute_result"
1181 |     }
1182 |    ],
1183 |    "source": [
1184 |     "from sklearn.svm import SVR\n",
1185 |     "regressor = SVR(kernel = 'rbf')\n",
1186 |     "regressor.fit(x_train,y_train)"
1187 |    ]
1188 |   },
1189 |   {
1190 |    "cell_type": "code",
1191 |    "execution_count": 24,
1192 |    "metadata": {},
1193 |    "outputs": [
1194 |     {
1195 |      "data": {
1196 |       "text/plain": [
1197 |        "array([-0.42595944, -0.32311139, -0.37435607,  0.56882821,  0.35833741,\n",
1198 |        "       -0.4289785 , -0.57286549, -0.26249298,  0.01577009, -0.11099087,\n",
1199 |        "       -0.32003543, -0.21896574,  0.40781822,  0.70554794,  0.22467137,\n",
1200 |        "        0.26272955,  0.05300658, -0.43237994, -0.50516794, -0.15245204,\n",
1201 |        "       -0.16191589, -0.16050723, -0.09468641,  0.01183634,  0.24495599,\n",
1202 |        "        0.43675849,  0.50363451,  0.22383874,  0.36682982,  0.68494588,\n",
1203 |        "        0.37600183,  0.16605308, -0.30409398, -0.25732645, -0.17879819,\n",
1204 |        "       -0.28231346,  0.22057641,  0.25122338,  0.13562446,  0.16869742,\n",
1205 |        "       -0.27649917, -0.19226034, -0.34176244, -0.07255308,  0.79432799,\n",
1206 |        "        0.99324748,  0.03093437,  0.16210712,  0.31583795,  0.47365842,\n",
1207 |        "        0.11191249, -0.22742264, -0.26317195,  0.01138717, -0.16583801,\n",
1208 |        "        0.34501398,  0.20441671,  0.14925122,  0.16647227,  0.23584846,\n",
1209 |        "        0.44561196,  0.74555276,  1.77403237,  0.98925211,  0.6941801 ,\n",
1210 |        "        0.04527876,  0.08692571, -0.15628333, -0.03071387,  0.15375035,\n",
1211 |        "        0.03870278,  0.00487757, -0.18599819, -0.30738972, -0.12022113,\n",
1212 |        "       -0.37615108, -0.56692846, -0.31514682,  0.19595979,  0.30663164,\n",
1213 |        "        0.4771285 , -0.28227281, -0.05911738,  0.04322423, -0.68263281,\n",
1214 |        "       -0.73819769, -0.50290478, -0.46696796, -0.38069815, -0.36181173,\n",
1215 |        "       -0.20645508, -0.36911442, -0.27281806, -0.22542369, -0.37897064,\n",
1216 |        "       -0.38816626, -0.72536073, -0.98441423, -0.7442325 , -0.71863761,\n",
1217 |        "       -0.72144794, -0.74704061, -0.58227917, -0.50226986, -0.33404258,\n",
1218 |        "       -0.48810957, -0.6112883 , -0.42955452, -0.65229843, -0.87307501,\n",
1219 |        "       -1.00287225, -0.82285002, -0.25709449, -0.75710873, -0.60362084,\n",
1220 |        "       -0.26031897, -0.91369875, -0.95760632, -0.81297347, -0.14054648,\n",
1221 |        "       -0.7561288 , -0.61221255, -0.69518032, -0.60207119, -0.37488619,\n",
1222 |        "       -0.20918184, -0.54138463, -0.49764821, -0.84623359, -0.73307276,\n",
1223 |        "       -0.60311798, -0.65324264, -0.86782537, -0.94903969, -0.59868767,\n",
1224 |        "       -0.80292309, -0.57618004, -0.85284508, -0.63479565, -0.51516231,\n",
1225 |        "       -0.51806576, -0.91674212, -0.68377782, -0.57208282, -0.84884395,\n",
1226 |        "       -0.80278251, -0.75762862, -0.58761396, -0.79499592, -0.81003283,\n",
1227 |        "       -0.87723899, -0.83168805, -1.13971526, -1.3723071 , -1.24599784,\n",
1228 |        "       -1.21094141, -0.90016809, -1.01965268, -1.02627238, -1.06362168,\n",
1229 |        "       -1.0106033 , -0.74411236, -0.97876786, -1.04877468, -0.96290783,\n",
1230 |        "       -1.11445661, -1.01896601, -0.86198109, -0.94589339, -0.78238535,\n",
1231 |        "       -0.85986483, -0.96586506, -1.06933763, -0.99446313, -0.97199095,\n",
1232 |        "       -0.98809041, -0.86366581, -0.9923654 , -1.1113921 , -1.06807415,\n",
1233 |        "       -0.08591312, -1.22178744])"
1234 |       ]
1235 |      },
1236 |      "execution_count": 24,
1237 |      "metadata": {},
1238 |      "output_type": "execute_result"
1239 |     }
1240 |    ],
1241 |    "source": [
1242 |     "y_prediction =  regressor.predict(x_test)\n",
1243 |     "y_prediction"
1244 |    ]
1245 |   },
1246 |   {
1247 |    "cell_type": "code",
1248 |    "execution_count": 25,
1249 |    "metadata": {},
1250 |    "outputs": [
1251 |     {
1252 |      "name": "stdout",
1253 |      "output_type": "stream",
1254 |      "text": [
1255 |       "R2 SCORE is 0.8063001007104242\n",
1256 |       "mean_sqrd_error is  0.04933772059187857\n",
1257 |       "Root mean squared error of is 0.22212095937096654\n",
1258 |       "Mean Absolute error is 0.16675010409400015\n"
1259 |      ]
1260 |     }
1261 |    ],
1262 |    "source": [
1263 |     "from sklearn.metrics import r2_score\n",
1264 |     "from sklearn.metrics import mean_squared_error\n",
1265 |     "from sklearn.metrics import mean_absolute_error\n",
1266 |     "score = r2_score(y_test,y_prediction)\n",
1267 |     "mean_error = mean_squared_error(y_test,y_prediction)\n",
1268 |     "mae = mean_absolute_error(y_test,y_prediction)\n",
1269 |     "print(\"R2 SCORE is\", score)\n",
1270 |     "print(\"mean_sqrd_error is \", mean_error)\n",
1271 |     "print(\"Root mean squared error of is\",np.sqrt(mean_error))\n",
1272 |     "print(\"Mean Absolute error is\", mae)"
1273 |    ]
1274 |   },
1275 |   {
1276 |    "cell_type": "code",
1277 |    "execution_count": 26,
1278 |    "metadata": {},
1279 |    "outputs": [
1280 |     {
1281 |      "data": {
1282 |       "text/plain": [
1283 |        "array([[ 82.],\n",
1284 |        "       [ 81.],\n",
1285 |        "       [ 85.],\n",
1286 |        "       [ 95.],\n",
1287 |        "       [118.]])"
1288 |       ]
1289 |      },
1290 |      "execution_count": 26,
1291 |      "metadata": {},
1292 |      "output_type": "execute_result"
1293 |     }
1294 |    ],
1295 |    "source": [
1296 |     "y_test = sc_y.inverse_transform(y_test)\n",
1297 |     "y_test[:5]"
1298 |    ]
1299 |   },
1300 |   {
1301 |    "cell_type": "code",
1302 |    "execution_count": 27,
1303 |    "metadata": {},
1304 |    "outputs": [
1305 |     {
1306 |      "data": {
1307 |       "text/plain": [
1308 |        "array([ 76.27425416,  80.20394657,  78.24595278, 114.28381494,\n",
1309 |        "       106.24123124])"
1310 |       ]
1311 |      },
1312 |      "execution_count": 27,
1313 |      "metadata": {},
1314 |      "output_type": "execute_result"
1315 |     }
1316 |    ],
1317 |    "source": [
1318 |     "y_prediction = sc_y.inverse_transform(y_prediction)\n",
1319 |     "y_prediction[:5]"
1320 |    ]
1321 |   },
1322 |   {
1323 |    "cell_type": "code",
1324 |    "execution_count": 28,
1325 |    "metadata": {},
1326 |    "outputs": [
1327 |     {
1328 |      "data": {
1329 |       "image/png": 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\n",
1330 |       "text/plain": [
1331 |        "<Figure size 432x288 with 1 Axes>"
1332 |       ]
1333 |      },
1334 |      "metadata": {
1335 |       "needs_background": "light"
1336 |      },
1337 |      "output_type": "display_data"
1338 |     }
1339 |    ],
1340 |    "source": [
1341 |     "df1 = pd.DataFrame({'Actual': y_test[100:120].flatten(), 'Predicted': y_prediction[100:120].flatten()})\n",
1342 |     "df1.plot(kind='line')\n",
1343 |     "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
1344 |     "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
1345 |     "plt.show()"
1346 |    ]
1347 |   },
1348 |   {
1349 |    "cell_type": "code",
1350 |    "execution_count": 29,
1351 |    "metadata": {},
1352 |    "outputs": [
1353 |     {
1354 |      "data": {
1355 |       "text/html": [
1356 |        "<div>\n",
1357 |        "<style scoped>\n",
1358 |        "    .dataframe tbody tr th:only-of-type {\n",
1359 |        "        vertical-align: middle;\n",
1360 |        "    }\n",
1361 |        "\n",
1362 |        "    .dataframe tbody tr th {\n",
1363 |        "        vertical-align: top;\n",
1364 |        "    }\n",
1365 |        "\n",
1366 |        "    .dataframe thead th {\n",
1367 |        "        text-align: right;\n",
1368 |        "    }\n",
1369 |        "</style>\n",
1370 |        "<table border=\"1\" class=\"dataframe\">\n",
1371 |        "  <thead>\n",
1372 |        "    <tr style=\"text-align: right;\">\n",
1373 |        "      <th></th>\n",
1374 |        "      <th>ACTUAL AQI</th>\n",
1375 |        "      <th>PREDICTED AQI</th>\n",
1376 |        "      <th>DIFFERENCE</th>\n",
1377 |        "      <th>PERCENTAGE ERROR</th>\n",
1378 |        "    </tr>\n",
1379 |        "  </thead>\n",
1380 |        "  <tbody>\n",
1381 |        "    <tr>\n",
1382 |        "      <th>0</th>\n",
1383 |        "      <td>82.0</td>\n",
1384 |        "      <td>76.274254</td>\n",
1385 |        "      <td>5.725746</td>\n",
1386 |        "      <td>6.982617</td>\n",
1387 |        "    </tr>\n",
1388 |        "    <tr>\n",
1389 |        "      <th>1</th>\n",
1390 |        "      <td>81.0</td>\n",
1391 |        "      <td>80.203947</td>\n",
1392 |        "      <td>0.796053</td>\n",
1393 |        "      <td>0.982782</td>\n",
1394 |        "    </tr>\n",
1395 |        "    <tr>\n",
1396 |        "      <th>2</th>\n",
1397 |        "      <td>85.0</td>\n",
1398 |        "      <td>78.245953</td>\n",
1399 |        "      <td>6.754047</td>\n",
1400 |        "      <td>7.945938</td>\n",
1401 |        "    </tr>\n",
1402 |        "    <tr>\n",
1403 |        "      <th>3</th>\n",
1404 |        "      <td>95.0</td>\n",
1405 |        "      <td>114.283815</td>\n",
1406 |        "      <td>-19.283815</td>\n",
1407 |        "      <td>20.298753</td>\n",
1408 |        "    </tr>\n",
1409 |        "    <tr>\n",
1410 |        "      <th>4</th>\n",
1411 |        "      <td>118.0</td>\n",
1412 |        "      <td>106.241231</td>\n",
1413 |        "      <td>11.758769</td>\n",
1414 |        "      <td>9.965058</td>\n",
1415 |        "    </tr>\n",
1416 |        "    <tr>\n",
1417 |        "      <th>5</th>\n",
1418 |        "      <td>81.0</td>\n",
1419 |        "      <td>76.158900</td>\n",
1420 |        "      <td>4.841100</td>\n",
1421 |        "      <td>5.976667</td>\n",
1422 |        "    </tr>\n",
1423 |        "    <tr>\n",
1424 |        "      <th>6</th>\n",
1425 |        "      <td>75.0</td>\n",
1426 |        "      <td>70.661163</td>\n",
1427 |        "      <td>4.338837</td>\n",
1428 |        "      <td>5.785116</td>\n",
1429 |        "    </tr>\n",
1430 |        "    <tr>\n",
1431 |        "      <th>7</th>\n",
1432 |        "      <td>93.0</td>\n",
1433 |        "      <td>82.520098</td>\n",
1434 |        "      <td>10.479902</td>\n",
1435 |        "      <td>11.268712</td>\n",
1436 |        "    </tr>\n",
1437 |        "    <tr>\n",
1438 |        "      <th>8</th>\n",
1439 |        "      <td>101.0</td>\n",
1440 |        "      <td>93.152173</td>\n",
1441 |        "      <td>7.847827</td>\n",
1442 |        "      <td>7.770126</td>\n",
1443 |        "    </tr>\n",
1444 |        "    <tr>\n",
1445 |        "      <th>9</th>\n",
1446 |        "      <td>94.0</td>\n",
1447 |        "      <td>88.308799</td>\n",
1448 |        "      <td>5.691201</td>\n",
1449 |        "      <td>6.054469</td>\n",
1450 |        "    </tr>\n",
1451 |        "    <tr>\n",
1452 |        "      <th>10</th>\n",
1453 |        "      <td>78.0</td>\n",
1454 |        "      <td>80.321475</td>\n",
1455 |        "      <td>-2.321475</td>\n",
1456 |        "      <td>2.976250</td>\n",
1457 |        "    </tr>\n",
1458 |        "    <tr>\n",
1459 |        "      <th>11</th>\n",
1460 |        "      <td>79.0</td>\n",
1461 |        "      <td>84.183218</td>\n",
1462 |        "      <td>-5.183218</td>\n",
1463 |        "      <td>6.561036</td>\n",
1464 |        "    </tr>\n",
1465 |        "    <tr>\n",
1466 |        "      <th>12</th>\n",
1467 |        "      <td>106.0</td>\n",
1468 |        "      <td>108.131829</td>\n",
1469 |        "      <td>-2.131829</td>\n",
1470 |        "      <td>2.011160</td>\n",
1471 |        "    </tr>\n",
1472 |        "    <tr>\n",
1473 |        "      <th>13</th>\n",
1474 |        "      <td>121.0</td>\n",
1475 |        "      <td>119.507700</td>\n",
1476 |        "      <td>1.492300</td>\n",
1477 |        "      <td>1.233306</td>\n",
1478 |        "    </tr>\n",
1479 |        "    <tr>\n",
1480 |        "      <th>14</th>\n",
1481 |        "      <td>111.0</td>\n",
1482 |        "      <td>101.134023</td>\n",
1483 |        "      <td>9.865977</td>\n",
1484 |        "      <td>8.888267</td>\n",
1485 |        "    </tr>\n",
1486 |        "  </tbody>\n",
1487 |        "</table>\n",
1488 |        "</div>"
1489 |       ],
1490 |       "text/plain": [
1491 |        "    ACTUAL AQI  PREDICTED AQI  DIFFERENCE  PERCENTAGE ERROR\n",
1492 |        "0         82.0      76.274254    5.725746          6.982617\n",
1493 |        "1         81.0      80.203947    0.796053          0.982782\n",
1494 |        "2         85.0      78.245953    6.754047          7.945938\n",
1495 |        "3         95.0     114.283815  -19.283815         20.298753\n",
1496 |        "4        118.0     106.241231   11.758769          9.965058\n",
1497 |        "5         81.0      76.158900    4.841100          5.976667\n",
1498 |        "6         75.0      70.661163    4.338837          5.785116\n",
1499 |        "7         93.0      82.520098   10.479902         11.268712\n",
1500 |        "8        101.0      93.152173    7.847827          7.770126\n",
1501 |        "9         94.0      88.308799    5.691201          6.054469\n",
1502 |        "10        78.0      80.321475   -2.321475          2.976250\n",
1503 |        "11        79.0      84.183218   -5.183218          6.561036\n",
1504 |        "12       106.0     108.131829   -2.131829          2.011160\n",
1505 |        "13       121.0     119.507700    1.492300          1.233306\n",
1506 |        "14       111.0     101.134023    9.865977          8.888267"
1507 |       ]
1508 |      },
1509 |      "execution_count": 29,
1510 |      "metadata": {},
1511 |      "output_type": "execute_result"
1512 |     }
1513 |    ],
1514 |    "source": [
1515 |     "result = pd.DataFrame()\n",
1516 |     "result[\"ACTUAL AQI\"] = y_test.flatten()\n",
1517 |     "result[\"PREDICTED AQI\"] = y_prediction\n",
1518 |     "result[\"DIFFERENCE\"] = result[\"ACTUAL AQI\"] - result[\"PREDICTED AQI\"]\n",
1519 |     "result[\"PERCENTAGE ERROR\"] = ( abs(result[\"ACTUAL AQI\"] - result[\"PREDICTED AQI\"] ) / result[\"ACTUAL AQI\"] ) * 100\n",
1520 |     "result.head(15)"
1521 |    ]
1522 |   },
1523 |   {
1524 |    "cell_type": "code",
1525 |    "execution_count": null,
1526 |    "metadata": {},
1527 |    "outputs": [],
1528 |    "source": []
1529 |   }
1530 |  ],
1531 |  "metadata": {
1532 |   "kernelspec": {
1533 |    "display_name": "Python 3",
1534 |    "language": "python",
1535 |    "name": "python3"
1536 |   },
1537 |   "language_info": {
1538 |    "codemirror_mode": {
1539 |     "name": "ipython",
1540 |     "version": 3
1541 |    },
1542 |    "file_extension": ".py",
1543 |    "mimetype": "text/x-python",
1544 |    "name": "python",
1545 |    "nbconvert_exporter": "python",
1546 |    "pygments_lexer": "ipython3",
1547 |    "version": "3.7.3"
1548 |   }
1549 |  },
1550 |  "nbformat": 4,
1551 |  "nbformat_minor": 2
1552 | }
1553 | 


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