3.1.1. Decission Tree Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "id": "protecting-tuner",
   "metadata": {},
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    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '/Volumes/GoogleDrive/.shortcut-targets-by-id/1TlzU2Kb_sVqH6O3SZ_gaC4kqBXYYltvK/All_women Data Sience/_functions_')\n",
    "\n",
    "from functions_plot_EDA import *\n",
    "from fx_MLRegression import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "entitled-assembly",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>turbine</th>\n",
       "      <th>time</th>\n",
       "      <th>wind_avg_ms</th>\n",
       "      <th>wind_max_ms</th>\n",
       "      <th>wind_min_ms</th>\n",
       "      <th>rot_speed_avg_1m</th>\n",
       "      <th>rot_speed_max_1m</th>\n",
       "      <th>rot_speed_min_1m</th>\n",
       "      <th>power_avg_kw</th>\n",
       "      <th>power_max_kw</th>\n",
       "      <th>power_min_kw</th>\n",
       "      <th>power_wind_kw</th>\n",
       "      <th>power_tech_kw</th>\n",
       "      <th>power_forcemaj_kw</th>\n",
       "      <th>power_ext_kw</th>\n",
       "      <th>tot_en_prod_kwh</th>\n",
       "      <th>en_prod_kwh</th>\n",
       "      <th>blade_angle</th>\n",
       "      <th>op_hours</th>\n",
       "      <th>nacelle_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-01-01 00:10:00</td>\n",
       "      <td>6.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>9.39</td>\n",
       "      <td>9.63</td>\n",
       "      <td>9.06</td>\n",
       "      <td>638</td>\n",
       "      <td>689</td>\n",
       "      <td>544</td>\n",
       "      <td>638</td>\n",
       "      <td>638</td>\n",
       "      <td>638</td>\n",
       "      <td>638</td>\n",
       "      <td>24353178</td>\n",
       "      <td>104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1180 days 14:40:00</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-01-01 00:20:00</td>\n",
       "      <td>6.2</td>\n",
       "      <td>7.4</td>\n",
       "      <td>5.5</td>\n",
       "      <td>9.53</td>\n",
       "      <td>9.79</td>\n",
       "      <td>9.17</td>\n",
       "      <td>666</td>\n",
       "      <td>721</td>\n",
       "      <td>593</td>\n",
       "      <td>666</td>\n",
       "      <td>666</td>\n",
       "      <td>666</td>\n",
       "      <td>666</td>\n",
       "      <td>24353286</td>\n",
       "      <td>108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1180 days 14:50:00</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-01-01 00:30:00</td>\n",
       "      <td>6.2</td>\n",
       "      <td>7.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>9.17</td>\n",
       "      <td>9.54</td>\n",
       "      <td>8.81</td>\n",
       "      <td>594</td>\n",
       "      <td>670</td>\n",
       "      <td>499</td>\n",
       "      <td>596</td>\n",
       "      <td>596</td>\n",
       "      <td>596</td>\n",
       "      <td>596</td>\n",
       "      <td>24353381</td>\n",
       "      <td>95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1180 days 15:00:00</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-01-01 00:40:00</td>\n",
       "      <td>6.1</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.61</td>\n",
       "      <td>9.04</td>\n",
       "      <td>8.28</td>\n",
       "      <td>480</td>\n",
       "      <td>567</td>\n",
       "      <td>412</td>\n",
       "      <td>482</td>\n",
       "      <td>482</td>\n",
       "      <td>482</td>\n",
       "      <td>482</td>\n",
       "      <td>24353458</td>\n",
       "      <td>77</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1180 days 15:10:00</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-01-01 00:50:00</td>\n",
       "      <td>5.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>4.7</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.80</td>\n",
       "      <td>8.17</td>\n",
       "      <td>454</td>\n",
       "      <td>518</td>\n",
       "      <td>390</td>\n",
       "      <td>455</td>\n",
       "      <td>455</td>\n",
       "      <td>455</td>\n",
       "      <td>455</td>\n",
       "      <td>24353531</td>\n",
       "      <td>73</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1180 days 15:20:00</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>52252</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-12-31 23:10:00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.68</td>\n",
       "      <td>15.39</td>\n",
       "      <td>13.86</td>\n",
       "      <td>3069</td>\n",
       "      <td>3131</td>\n",
       "      <td>3001</td>\n",
       "      <td>3070</td>\n",
       "      <td>3070</td>\n",
       "      <td>3070</td>\n",
       "      <td>3070</td>\n",
       "      <td>31893213</td>\n",
       "      <td>510</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1515 days 04:42:00</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52253</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-12-31 23:20:00</td>\n",
       "      <td>13.5</td>\n",
       "      <td>17.3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>14.70</td>\n",
       "      <td>15.67</td>\n",
       "      <td>14.12</td>\n",
       "      <td>3068</td>\n",
       "      <td>3119</td>\n",
       "      <td>2899</td>\n",
       "      <td>3070</td>\n",
       "      <td>3070</td>\n",
       "      <td>3070</td>\n",
       "      <td>3070</td>\n",
       "      <td>31893723</td>\n",
       "      <td>510</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1515 days 04:52:00</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52254</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-12-31 23:30:00</td>\n",
       "      <td>14.3</td>\n",
       "      <td>19.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.69</td>\n",
       "      <td>15.78</td>\n",
       "      <td>14.07</td>\n",
       "      <td>3070</td>\n",
       "      <td>3121</td>\n",
       "      <td>3014</td>\n",
       "      <td>3070</td>\n",
       "      <td>3070</td>\n",
       "      <td>3070</td>\n",
       "      <td>3070</td>\n",
       "      <td>31894234</td>\n",
       "      <td>511</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1515 days 05:02:00</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52255</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-12-31 23:40:00</td>\n",
       "      <td>13.8</td>\n",
       "      <td>19.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>14.69</td>\n",
       "      <td>15.33</td>\n",
       "      <td>13.95</td>\n",
       "      <td>3069</td>\n",
       "      <td>3114</td>\n",
       "      <td>3016</td>\n",
       "      <td>3070</td>\n",
       "      <td>3070</td>\n",
       "      <td>3070</td>\n",
       "      <td>3070</td>\n",
       "      <td>31894744</td>\n",
       "      <td>510</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1515 days 05:12:00</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52256</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-12-31 23:50:00</td>\n",
       "      <td>14.2</td>\n",
       "      <td>19.4</td>\n",
       "      <td>7.9</td>\n",
       "      <td>14.67</td>\n",
       "      <td>15.50</td>\n",
       "      <td>14.02</td>\n",
       "      <td>3068</td>\n",
       "      <td>3108</td>\n",
       "      <td>2866</td>\n",
       "      <td>3070</td>\n",
       "      <td>3070</td>\n",
       "      <td>3070</td>\n",
       "      <td>3070</td>\n",
       "      <td>31895254</td>\n",
       "      <td>510</td>\n",
       "      <td>10.9</td>\n",
       "      <td>1515 days 05:22:00</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>52257 rows × 20 columns</p>\n",
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      ],
      "text/plain": [
       "      turbine                time  wind_avg_ms  wind_max_ms  wind_min_ms  \\\n",
       "0         WT1 2017-01-01 00:10:00          6.2          7.2          5.5   \n",
       "1         WT1 2017-01-01 00:20:00          6.2          7.4          5.5   \n",
       "2         WT1 2017-01-01 00:30:00          6.2          7.3          5.3   \n",
       "3         WT1 2017-01-01 00:40:00          6.1          7.3          4.9   \n",
       "4         WT1 2017-01-01 00:50:00          5.8          7.7          4.7   \n",
       "...       ...                 ...          ...          ...          ...   \n",
       "52252     WT1 2017-12-31 23:10:00         14.0         19.5         10.0   \n",
       "52253     WT1 2017-12-31 23:20:00         13.5         17.3          9.2   \n",
       "52254     WT1 2017-12-31 23:30:00         14.3         19.5          8.0   \n",
       "52255     WT1 2017-12-31 23:40:00         13.8         19.4          8.7   \n",
       "52256     WT1 2017-12-31 23:50:00         14.2         19.4          7.9   \n",
       "\n",
       "       rot_speed_avg_1m  rot_speed_max_1m  rot_speed_min_1m  power_avg_kw  \\\n",
       "0                  9.39              9.63              9.06           638   \n",
       "1                  9.53              9.79              9.17           666   \n",
       "2                  9.17              9.54              8.81           594   \n",
       "3                  8.61              9.04              8.28           480   \n",
       "4                  8.48              8.80              8.17           454   \n",
       "...                 ...               ...               ...           ...   \n",
       "52252             14.68             15.39             13.86          3069   \n",
       "52253             14.70             15.67             14.12          3068   \n",
       "52254             14.69             15.78             14.07          3070   \n",
       "52255             14.69             15.33             13.95          3069   \n",
       "52256             14.67             15.50             14.02          3068   \n",
       "\n",
       "       power_max_kw  power_min_kw  power_wind_kw  power_tech_kw  \\\n",
       "0               689           544            638            638   \n",
       "1               721           593            666            666   \n",
       "2               670           499            596            596   \n",
       "3               567           412            482            482   \n",
       "4               518           390            455            455   \n",
       "...             ...           ...            ...            ...   \n",
       "52252          3131          3001           3070           3070   \n",
       "52253          3119          2899           3070           3070   \n",
       "52254          3121          3014           3070           3070   \n",
       "52255          3114          3016           3070           3070   \n",
       "52256          3108          2866           3070           3070   \n",
       "\n",
       "       power_forcemaj_kw  power_ext_kw  tot_en_prod_kwh  en_prod_kwh  \\\n",
       "0                    638           638         24353178          104   \n",
       "1                    666           666         24353286          108   \n",
       "2                    596           596         24353381           95   \n",
       "3                    482           482         24353458           77   \n",
       "4                    455           455         24353531           73   \n",
       "...                  ...           ...              ...          ...   \n",
       "52252               3070          3070         31893213          510   \n",
       "52253               3070          3070         31893723          510   \n",
       "52254               3070          3070         31894234          511   \n",
       "52255               3070          3070         31894744          510   \n",
       "52256               3070          3070         31895254          510   \n",
       "\n",
       "       blade_angle            op_hours  nacelle_pos  \n",
       "0              1.0  1180 days 14:40:00          240  \n",
       "1              1.0  1180 days 14:50:00          242  \n",
       "2              1.0  1180 days 15:00:00          243  \n",
       "3              1.0  1180 days 15:10:00          237  \n",
       "4              1.0  1180 days 15:20:00          225  \n",
       "...            ...                 ...          ...  \n",
       "52252         11.7  1515 days 04:42:00          204  \n",
       "52253          8.9  1515 days 04:52:00          209  \n",
       "52254         11.0  1515 days 05:02:00          210  \n",
       "52255          9.3  1515 days 05:12:00          210  \n",
       "52256         10.9  1515 days 05:22:00          206  \n",
       "\n",
       "[52257 rows x 20 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wt = pd.read_csv('../_datasets/3.1._WT_2017_wt1.csv',parse_dates =[1], delimiter=',', skipinitialspace = True, decimal ='.')\n",
    "df_wt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fatal-grace",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>windspeed_gr</th>\n",
       "      <th>direct_ground</th>\n",
       "      <th>atmo_press_hpa</th>\n",
       "      <th>air_t</th>\n",
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       "      <th>0</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
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       "      <td>230</td>\n",
       "      <td>982.1</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>-5.5</td>\n",
       "      <td>85.3</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>2017</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01 00:10:00</td>\n",
       "      <td>3.1</td>\n",
       "      <td>230</td>\n",
       "      <td>981.9</td>\n",
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       "      <td>-5.6</td>\n",
       "      <td>85.8</td>\n",
       "      <td>-5.9</td>\n",
       "      <td>2017</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01 00:20:00</td>\n",
       "      <td>2.7</td>\n",
       "      <td>230</td>\n",
       "      <td>981.7</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>86.2</td>\n",
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       "      <th>3</th>\n",
       "      <td>2017-01-01 00:30:00</td>\n",
       "      <td>2.9</td>\n",
       "      <td>220</td>\n",
       "      <td>981.6</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>85.8</td>\n",
       "      <td>-6.1</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-01 00:40:00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>230</td>\n",
       "      <td>981.5</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-5.7</td>\n",
       "      <td>83.7</td>\n",
       "      <td>-6.4</td>\n",
       "      <td>2017</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50964</th>\n",
       "      <td>2017-12-31 23:10:00</td>\n",
       "      <td>7.9</td>\n",
       "      <td>220</td>\n",
       "      <td>957.3</td>\n",
       "      <td>10.3</td>\n",
       "      <td>8.2</td>\n",
       "      <td>73.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50965</th>\n",
       "      <td>2017-12-31 23:20:00</td>\n",
       "      <td>8.2</td>\n",
       "      <td>220</td>\n",
       "      <td>957.3</td>\n",
       "      <td>10.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>74.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50966</th>\n",
       "      <td>2017-12-31 23:30:00</td>\n",
       "      <td>7.9</td>\n",
       "      <td>220</td>\n",
       "      <td>957.3</td>\n",
       "      <td>10.3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>72.5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50967</th>\n",
       "      <td>2017-12-31 23:40:00</td>\n",
       "      <td>7.9</td>\n",
       "      <td>220</td>\n",
       "      <td>957.2</td>\n",
       "      <td>10.3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>72.2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50968</th>\n",
       "      <td>2017-12-31 23:50:00</td>\n",
       "      <td>7.2</td>\n",
       "      <td>220</td>\n",
       "      <td>957.1</td>\n",
       "      <td>10.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>71.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50969 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     time  windspeed_gr  direct_ground  atmo_press_hpa  air_t  \\\n",
       "0     2017-01-01 00:00:00           3.4            230           982.1   -3.9   \n",
       "1     2017-01-01 00:10:00           3.1            230           981.9   -3.9   \n",
       "2     2017-01-01 00:20:00           2.7            230           981.7   -4.1   \n",
       "3     2017-01-01 00:30:00           2.9            220           981.6   -4.1   \n",
       "4     2017-01-01 00:40:00           3.5            230           981.5   -4.0   \n",
       "...                   ...           ...            ...             ...    ...   \n",
       "50964 2017-12-31 23:10:00           7.9            220           957.3   10.3   \n",
       "50965 2017-12-31 23:20:00           8.2            220           957.3   10.1   \n",
       "50966 2017-12-31 23:30:00           7.9            220           957.3   10.3   \n",
       "50967 2017-12-31 23:40:00           7.9            220           957.2   10.3   \n",
       "50968 2017-12-31 23:50:00           7.2            220           957.1   10.5   \n",
       "\n",
       "       tm_5cm  rel_hum_2m  dew_t_2m  year  \n",
       "0        -5.5        85.3      -6.0  2017  \n",
       "1        -5.6        85.8      -5.9  2017  \n",
       "2        -5.8        86.2      -6.1  2017  \n",
       "3        -5.8        85.8      -6.1  2017  \n",
       "4        -5.7        83.7      -6.4  2017  \n",
       "...       ...         ...       ...   ...  \n",
       "50964     8.2        73.3       5.7  2017  \n",
       "50965     8.7        74.5       5.8  2017  \n",
       "50966     8.5        72.5       5.6  2017  \n",
       "50967     8.7        72.2       5.5  2017  \n",
       "50968     9.5        71.1       5.5  2017  \n",
       "\n",
       "[50969 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weather = pd.read_csv('../_datasets/3.1._completeweather_2017.csv',parse_dates =[0], delimiter=',', skipinitialspace = True, decimal ='.')\n",
    "df_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "abstract-apollo",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>turbine</th>\n",
       "      <th>time</th>\n",
       "      <th>wind_avg_ms</th>\n",
       "      <th>wind_max_ms</th>\n",
       "      <th>wind_min_ms</th>\n",
       "      <th>rot_speed_avg_1m</th>\n",
       "      <th>rot_speed_max_1m</th>\n",
       "      <th>rot_speed_min_1m</th>\n",
       "      <th>power_avg_kw</th>\n",
       "      <th>power_max_kw</th>\n",
       "      <th>...</th>\n",
       "      <th>op_hours</th>\n",
       "      <th>nacelle_pos</th>\n",
       "      <th>windspeed_gr</th>\n",
       "      <th>direct_ground</th>\n",
       "      <th>atmo_press_hpa</th>\n",
       "      <th>air_t</th>\n",
       "      <th>tm_5cm</th>\n",
       "      <th>rel_hum_2m</th>\n",
       "      <th>dew_t_2m</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-01-01 00:10:00</td>\n",
       "      <td>6.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>9.39</td>\n",
       "      <td>9.63</td>\n",
       "      <td>9.06</td>\n",
       "      <td>638</td>\n",
       "      <td>689</td>\n",
       "      <td>...</td>\n",
       "      <td>1180 days 14:40:00</td>\n",
       "      <td>240</td>\n",
       "      <td>3.1</td>\n",
       "      <td>230.0</td>\n",
       "      <td>981.9</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>85.8</td>\n",
       "      <td>-5.9</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-01-01 00:20:00</td>\n",
       "      <td>6.2</td>\n",
       "      <td>7.4</td>\n",
       "      <td>5.5</td>\n",
       "      <td>9.53</td>\n",
       "      <td>9.79</td>\n",
       "      <td>9.17</td>\n",
       "      <td>666</td>\n",
       "      <td>721</td>\n",
       "      <td>...</td>\n",
       "      <td>1180 days 14:50:00</td>\n",
       "      <td>242</td>\n",
       "      <td>2.7</td>\n",
       "      <td>230.0</td>\n",
       "      <td>981.7</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>86.2</td>\n",
       "      <td>-6.1</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-01-01 00:30:00</td>\n",
       "      <td>6.2</td>\n",
       "      <td>7.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>9.17</td>\n",
       "      <td>9.54</td>\n",
       "      <td>8.81</td>\n",
       "      <td>594</td>\n",
       "      <td>670</td>\n",
       "      <td>...</td>\n",
       "      <td>1180 days 15:00:00</td>\n",
       "      <td>243</td>\n",
       "      <td>2.9</td>\n",
       "      <td>220.0</td>\n",
       "      <td>981.6</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>85.8</td>\n",
       "      <td>-6.1</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-01-01 00:40:00</td>\n",
       "      <td>6.1</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.61</td>\n",
       "      <td>9.04</td>\n",
       "      <td>8.28</td>\n",
       "      <td>480</td>\n",
       "      <td>567</td>\n",
       "      <td>...</td>\n",
       "      <td>1180 days 15:10:00</td>\n",
       "      <td>237</td>\n",
       "      <td>3.5</td>\n",
       "      <td>230.0</td>\n",
       "      <td>981.5</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-5.7</td>\n",
       "      <td>83.7</td>\n",
       "      <td>-6.4</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-01-01 00:50:00</td>\n",
       "      <td>5.8</td>\n",
       "      <td>7.7</td>\n",
       "      <td>4.7</td>\n",
       "      <td>8.48</td>\n",
       "      <td>8.80</td>\n",
       "      <td>8.17</td>\n",
       "      <td>454</td>\n",
       "      <td>518</td>\n",
       "      <td>...</td>\n",
       "      <td>1180 days 15:20:00</td>\n",
       "      <td>225</td>\n",
       "      <td>3.8</td>\n",
       "      <td>230.0</td>\n",
       "      <td>981.5</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>80.5</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52252</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-12-31 23:10:00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.68</td>\n",
       "      <td>15.39</td>\n",
       "      <td>13.86</td>\n",
       "      <td>3069</td>\n",
       "      <td>3131</td>\n",
       "      <td>...</td>\n",
       "      <td>1515 days 04:42:00</td>\n",
       "      <td>204</td>\n",
       "      <td>7.9</td>\n",
       "      <td>220.0</td>\n",
       "      <td>957.3</td>\n",
       "      <td>10.3</td>\n",
       "      <td>8.2</td>\n",
       "      <td>73.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52253</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-12-31 23:20:00</td>\n",
       "      <td>13.5</td>\n",
       "      <td>17.3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>14.70</td>\n",
       "      <td>15.67</td>\n",
       "      <td>14.12</td>\n",
       "      <td>3068</td>\n",
       "      <td>3119</td>\n",
       "      <td>...</td>\n",
       "      <td>1515 days 04:52:00</td>\n",
       "      <td>209</td>\n",
       "      <td>8.2</td>\n",
       "      <td>220.0</td>\n",
       "      <td>957.3</td>\n",
       "      <td>10.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>74.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52254</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-12-31 23:30:00</td>\n",
       "      <td>14.3</td>\n",
       "      <td>19.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.69</td>\n",
       "      <td>15.78</td>\n",
       "      <td>14.07</td>\n",
       "      <td>3070</td>\n",
       "      <td>3121</td>\n",
       "      <td>...</td>\n",
       "      <td>1515 days 05:02:00</td>\n",
       "      <td>210</td>\n",
       "      <td>7.9</td>\n",
       "      <td>220.0</td>\n",
       "      <td>957.3</td>\n",
       "      <td>10.3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>72.5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52255</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-12-31 23:40:00</td>\n",
       "      <td>13.8</td>\n",
       "      <td>19.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>14.69</td>\n",
       "      <td>15.33</td>\n",
       "      <td>13.95</td>\n",
       "      <td>3069</td>\n",
       "      <td>3114</td>\n",
       "      <td>...</td>\n",
       "      <td>1515 days 05:12:00</td>\n",
       "      <td>210</td>\n",
       "      <td>7.9</td>\n",
       "      <td>220.0</td>\n",
       "      <td>957.2</td>\n",
       "      <td>10.3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>72.2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52256</th>\n",
       "      <td>WT1</td>\n",
       "      <td>2017-12-31 23:50:00</td>\n",
       "      <td>14.2</td>\n",
       "      <td>19.4</td>\n",
       "      <td>7.9</td>\n",
       "      <td>14.67</td>\n",
       "      <td>15.50</td>\n",
       "      <td>14.02</td>\n",
       "      <td>3068</td>\n",
       "      <td>3108</td>\n",
       "      <td>...</td>\n",
       "      <td>1515 days 05:22:00</td>\n",
       "      <td>206</td>\n",
       "      <td>7.2</td>\n",
       "      <td>220.0</td>\n",
       "      <td>957.1</td>\n",
       "      <td>10.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>71.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2017.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>52257 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      turbine                time  wind_avg_ms  wind_max_ms  wind_min_ms  \\\n",
       "0         WT1 2017-01-01 00:10:00          6.2          7.2          5.5   \n",
       "1         WT1 2017-01-01 00:20:00          6.2          7.4          5.5   \n",
       "2         WT1 2017-01-01 00:30:00          6.2          7.3          5.3   \n",
       "3         WT1 2017-01-01 00:40:00          6.1          7.3          4.9   \n",
       "4         WT1 2017-01-01 00:50:00          5.8          7.7          4.7   \n",
       "...       ...                 ...          ...          ...          ...   \n",
       "52252     WT1 2017-12-31 23:10:00         14.0         19.5         10.0   \n",
       "52253     WT1 2017-12-31 23:20:00         13.5         17.3          9.2   \n",
       "52254     WT1 2017-12-31 23:30:00         14.3         19.5          8.0   \n",
       "52255     WT1 2017-12-31 23:40:00         13.8         19.4          8.7   \n",
       "52256     WT1 2017-12-31 23:50:00         14.2         19.4          7.9   \n",
       "\n",
       "       rot_speed_avg_1m  rot_speed_max_1m  rot_speed_min_1m  power_avg_kw  \\\n",
       "0                  9.39              9.63              9.06           638   \n",
       "1                  9.53              9.79              9.17           666   \n",
       "2                  9.17              9.54              8.81           594   \n",
       "3                  8.61              9.04              8.28           480   \n",
       "4                  8.48              8.80              8.17           454   \n",
       "...                 ...               ...               ...           ...   \n",
       "52252             14.68             15.39             13.86          3069   \n",
       "52253             14.70             15.67             14.12          3068   \n",
       "52254             14.69             15.78             14.07          3070   \n",
       "52255             14.69             15.33             13.95          3069   \n",
       "52256             14.67             15.50             14.02          3068   \n",
       "\n",
       "       power_max_kw  ...            op_hours  nacelle_pos  windspeed_gr  \\\n",
       "0               689  ...  1180 days 14:40:00          240           3.1   \n",
       "1               721  ...  1180 days 14:50:00          242           2.7   \n",
       "2               670  ...  1180 days 15:00:00          243           2.9   \n",
       "3               567  ...  1180 days 15:10:00          237           3.5   \n",
       "4               518  ...  1180 days 15:20:00          225           3.8   \n",
       "...             ...  ...                 ...          ...           ...   \n",
       "52252          3131  ...  1515 days 04:42:00          204           7.9   \n",
       "52253          3119  ...  1515 days 04:52:00          209           8.2   \n",
       "52254          3121  ...  1515 days 05:02:00          210           7.9   \n",
       "52255          3114  ...  1515 days 05:12:00          210           7.9   \n",
       "52256          3108  ...  1515 days 05:22:00          206           7.2   \n",
       "\n",
       "       direct_ground  atmo_press_hpa  air_t  tm_5cm  rel_hum_2m dew_t_2m  \\\n",
       "0              230.0           981.9   -3.9    -5.6        85.8     -5.9   \n",
       "1              230.0           981.7   -4.1    -5.8        86.2     -6.1   \n",
       "2              220.0           981.6   -4.1    -5.8        85.8     -6.1   \n",
       "3              230.0           981.5   -4.0    -5.7        83.7     -6.4   \n",
       "4              230.0           981.5   -3.9    -5.6        80.5     -6.8   \n",
       "...              ...             ...    ...     ...         ...      ...   \n",
       "52252          220.0           957.3   10.3     8.2        73.3      5.7   \n",
       "52253          220.0           957.3   10.1     8.7        74.5      5.8   \n",
       "52254          220.0           957.3   10.3     8.5        72.5      5.6   \n",
       "52255          220.0           957.2   10.3     8.7        72.2      5.5   \n",
       "52256          220.0           957.1   10.5     9.5        71.1      5.5   \n",
       "\n",
       "         year  \n",
       "0      2017.0  \n",
       "1      2017.0  \n",
       "2      2017.0  \n",
       "3      2017.0  \n",
       "4      2017.0  \n",
       "...       ...  \n",
       "52252  2017.0  \n",
       "52253  2017.0  \n",
       "52254  2017.0  \n",
       "52255  2017.0  \n",
       "52256  2017.0  \n",
       "\n",
       "[52257 rows x 28 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df_wt,df_weather,on='time', how='left')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "shaped-bobby",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 50657 entries, 0 to 52256\n",
      "Data columns (total 28 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   turbine            50657 non-null  object        \n",
      " 1   time               50657 non-null  datetime64[ns]\n",
      " 2   wind_avg_ms        50657 non-null  float64       \n",
      " 3   wind_max_ms        50657 non-null  float64       \n",
      " 4   wind_min_ms        50657 non-null  float64       \n",
      " 5   rot_speed_avg_1m   50657 non-null  float64       \n",
      " 6   rot_speed_max_1m   50657 non-null  float64       \n",
      " 7   rot_speed_min_1m   50657 non-null  float64       \n",
      " 8   power_avg_kw       50657 non-null  int64         \n",
      " 9   power_max_kw       50657 non-null  int64         \n",
      " 10  power_min_kw       50657 non-null  int64         \n",
      " 11  power_wind_kw      50657 non-null  int64         \n",
      " 12  power_tech_kw      50657 non-null  int64         \n",
      " 13  power_forcemaj_kw  50657 non-null  int64         \n",
      " 14  power_ext_kw       50657 non-null  int64         \n",
      " 15  tot_en_prod_kwh    50657 non-null  int64         \n",
      " 16  en_prod_kwh        50657 non-null  int64         \n",
      " 17  blade_angle        50657 non-null  float64       \n",
      " 18  op_hours           50657 non-null  object        \n",
      " 19  nacelle_pos        50657 non-null  int64         \n",
      " 20  windspeed_gr       50657 non-null  float64       \n",
      " 21  direct_ground      50657 non-null  float64       \n",
      " 22  atmo_press_hpa     50657 non-null  float64       \n",
      " 23  air_t              50657 non-null  float64       \n",
      " 24  tm_5cm             50657 non-null  float64       \n",
      " 25  rel_hum_2m         50657 non-null  float64       \n",
      " 26  dew_t_2m           50657 non-null  float64       \n",
      " 27  year               50657 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(15), int64(10), object(2)\n",
      "memory usage: 11.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.dropna(how='any',inplace=True)\n",
    "df.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fresh-poster",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('../_datasets/3.1.combined_WT1_weather.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gorgeous-starter",
   "metadata": {},
   "source": [
    "# Decission Tree Regression\n",
    "##  Define the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "relative-baking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['turbine', 'time', 'wind_avg_ms', 'wind_max_ms', 'wind_min_ms',\n",
       "       'rot_speed_avg_1m', 'rot_speed_max_1m', 'rot_speed_min_1m',\n",
       "       'power_avg_kw', 'power_max_kw', 'power_min_kw', 'power_wind_kw',\n",
       "       'power_tech_kw', 'power_forcemaj_kw', 'power_ext_kw', 'tot_en_prod_kwh',\n",
       "       'en_prod_kwh', 'blade_angle', 'op_hours', 'nacelle_pos', 'windspeed_gr',\n",
       "       'direct_ground', 'atmo_press_hpa', 'air_t', 'tm_5cm', 'rel_hum_2m',\n",
       "       'dew_t_2m', 'year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "growing-representation",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "features = ['windspeed_gr','atmo_press_hpa', 'air_t','rel_hum_2m']\n",
    "target = ['en_prod_kwh']\n",
    "X = df[features]\n",
    "y = df[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "imperial-drill",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_std = sc.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "structured-diabetes",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "located-convention",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=20)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "# Instantiate the model\n",
    "tree = DecisionTreeRegressor(max_depth=20)\n",
    "\n",
    "# Fit the model\n",
    "tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "monthly-tension",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>CV_R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train</th>\n",
       "      <td>11.753441</td>\n",
       "      <td>1008.886764</td>\n",
       "      <td>31.762978</td>\n",
       "      <td>0.957022</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test</th>\n",
       "      <td>64.871826</td>\n",
       "      <td>12981.346932</td>\n",
       "      <td>113.935714</td>\n",
       "      <td>0.460490</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MAE           MSE        RMSE        R2  CV_R2\n",
       "Train  11.753441   1008.886764   31.762978  0.957022   0.44\n",
       "Test   64.871826  12981.346932  113.935714  0.460490    NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtr = Allmetrics(tree,X_train, y_train,X_test, y_test)\n",
    "dtr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "polish-liquid",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "params = {\"splitter\":(\"best\", \"random\"), \n",
    "    \"max_depth\":(list(range(20, 30,2))), \n",
    "    \"min_samples_split\":list(range(2,10,2)),\n",
    "     \"min_samples_leaf\":[1,2,3,4,5,6,7,8,9,10],\n",
    "          \"max_features\":(\"auto\",\"log2\",\"sqrt\",None),\n",
    "         }       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "loved-malawi",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 1600 candidates, totalling 16000 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=DecisionTreeRegressor(max_depth=20), n_jobs=-1,\n",
       "             param_grid={'max_depth': [20, 22, 24, 26, 28],\n",
       "                         'max_features': ('auto', 'log2', 'sqrt', None),\n",
       "                         'min_samples_leaf': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         'min_samples_split': [2, 4, 6, 8],\n",
       "                         'splitter': ('best', 'random')},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_cv = GridSearchCV(tree, params, n_jobs=-1, verbose=1, cv=10)\n",
    "\n",
    "grid_search_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "chief-animal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 24,\n",
       " 'max_features': 'auto',\n",
       " 'min_samples_leaf': 6,\n",
       " 'min_samples_split': 8,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "laden-somalia",
   "metadata": {},
   "outputs": [],
   "source": [
    "tuned_hyper_model= DecisionTreeRegressor(max_depth= 24,\n",
    " max_features= 'auto',\n",
    " min_samples_split= 8,\n",
    " min_samples_leaf= 6,\n",
    " splitter= 'best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "swedish-madonna",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=24, max_features='auto', min_samples_leaf=6,\n",
       "                      min_samples_split=8)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_hyper_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "severe-baseball",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>CV_R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train</th>\n",
       "      <td>44.515427</td>\n",
       "      <td>5029.304667</td>\n",
       "      <td>70.917591</td>\n",
       "      <td>0.785754</td>\n",
       "      <td>0.499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test</th>\n",
       "      <td>67.960153</td>\n",
       "      <td>11555.836300</td>\n",
       "      <td>107.498076</td>\n",
       "      <td>0.519735</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MAE           MSE        RMSE        R2  CV_R2\n",
       "Train  44.515427   5029.304667   70.917591  0.785754  0.499\n",
       "Test   67.960153  11555.836300  107.498076  0.519735    NaN"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtr_tuned = Allmetrics(tuned_hyper_model,X_train, y_train,X_test, y_test)\n",
    "dtr_tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "gorgeous-withdrawal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7ff68c159e50>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_pred=tuned_hyper_model.predict(X_test)\n",
    "plt.scatter(y_test,tuned_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "infrared-polyester",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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