tree_reggression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "844a8351",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tqdm in /home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages (4.64.0)\r\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "!pip install tqdm\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0e95cff5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Timestamp  Value\n",
      "0  2001-01-11     32\n",
      "1  2001-02-11     39\n",
      "2  2001-03-11     41\n",
      "3  2001-04-11     47\n",
      "4  2001-04-12     23\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('1003-zeya-svobodny.csv', sep=';')\n",
    "print(df.head())\n",
    "prediction = {}\n",
    "for i in range(100):\n",
    "    prediction['day' + str(i)] = []\n",
    "prediction['to_predict'] = []\n",
    "day_couner = 0\n",
    "counter = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "037abff9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊  | 7050/7150 [00:02<00:00, 2490.55it/s]\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>day0</th>\n",
       "      <th>day1</th>\n",
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       "      <th>day9</th>\n",
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       "      <th>day91</th>\n",
       "      <th>day92</th>\n",
       "      <th>day93</th>\n",
       "      <th>day94</th>\n",
       "      <th>day95</th>\n",
       "      <th>day96</th>\n",
       "      <th>day97</th>\n",
       "      <th>day98</th>\n",
       "      <th>day99</th>\n",
       "      <th>to_predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>32</td>\n",
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       "      <td>81</td>\n",
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       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>96</td>\n",
       "      <td>98</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>96</td>\n",
       "      <td>98</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>-12</td>\n",
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       "      <td>102</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>-12</td>\n",
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       "      <td>98</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "      <td>84</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   day0  day1  day2  day3  day4  day5  day6  day7  day8  day9  ...  day91  \\\n",
       "0    32    39    41    47    23    28   -12   -18     7    28  ...     75   \n",
       "1    39    41    47    23    28   -12   -18     7    28    31  ...     81   \n",
       "2    41    47    23    28   -12   -18     7    28    31    -8  ...     82   \n",
       "3    47    23    28   -12   -18     7    28    31    -8    32  ...     98   \n",
       "4    23    28   -12   -18     7    28    31    -8    32    45  ...    104   \n",
       "\n",
       "   day92  day93  day94  day95  day96  day97  day98  day99  to_predict  \n",
       "0     81     82     98    104     89     80     82     96          98  \n",
       "1     82     98    104     89     80     82     96     98         102  \n",
       "2     98    104     89     80     82     96     98    102         102  \n",
       "3    104     89     80     82     96     98    102    102          84  \n",
       "4     89     80     82     96     98    102    102     84          78  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in tqdm(range(len(df['Value']))):\n",
    "    if i + 100 >= len(df['Value']):\n",
    "        break\n",
    "    counter = 0\n",
    "    for j in range(i, i + 100):\n",
    "        prediction['day' + str(counter)].append(df['Value'][j].astype(int))\n",
    "        counter += 1\n",
    "    prediction['to_predict'].append(df['Value'][i + 100].astype(int))\n",
    "prediction = pd.DataFrame(prediction)\n",
    "prediction.head()\n",
    "         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4fe9d229",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e7700252",
   "metadata": {},
   "outputs": [
    {
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       "      <th>day0</th>\n",
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       "      <td>101</td>\n",
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       "      <td>185</td>\n",
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       "      <td>73</td>\n",
       "      <td>75</td>\n",
       "      <td>66</td>\n",
       "      <td>75</td>\n",
       "      <td>126</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>221</td>\n",
       "      <td>256</td>\n",
       "      <td>230</td>\n",
       "      <td>181</td>\n",
       "      <td>143</td>\n",
       "      <td>118</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>103</td>\n",
       "      <td>101</td>\n",
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       "      <td>21</td>\n",
       "      <td>27</td>\n",
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       "      <td>29</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
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       "      <td>59</td>\n",
       "      <td>67</td>\n",
       "      <td>76</td>\n",
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       "      <td>75</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>56</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>67</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>87</td>\n",
       "      <td>92</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>103</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   day0  day1  day2  day3  day4  day5  day6  day7  day8  day9  ...  day91  \\\n",
       "0    32    39    41    47    23    28   -12   -18     7    28  ...     75   \n",
       "1    98   102   102    84    78    87    90    92    95   100  ...     94   \n",
       "2   184   178   161   145   133   127   126   152   147   124  ...     62   \n",
       "3   221   256   230   181   143   118   109   109   103   101  ...     21   \n",
       "4    76    75    60    50    50    56    70    70    67    70  ...     78   \n",
       "\n",
       "   day92  day93  day94  day95  day96  day97  day98  day99  to_predict  \n",
       "0     81     82     98    104     89     80     82     96          98  \n",
       "1    101    121    141    165    185    190    193    191         184  \n",
       "2     71     71     68     73     75     66     75    126         221  \n",
       "3     27     27     29     47     47     47     59     67          76  \n",
       "4     75     75     87     92     95     93     93    103          97  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "03f50866",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e7a34_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_e7a34_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
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       "      <th id=\"T_e7a34_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_e7a34_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "      <td id=\"T_e7a34_row0_col1\" class=\"data row0 col1\" >1744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_e7a34_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_e7a34_row1_col1\" class=\"data row1 col1\" >to_predict</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_e7a34_row2_col0\" class=\"data row2 col0\" >Original Data</td>\n",
       "      <td id=\"T_e7a34_row2_col1\" class=\"data row2 col1\" >(7050, 101)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_e7a34_row3_col0\" class=\"data row3 col0\" >Missing Values</td>\n",
       "      <td id=\"T_e7a34_row3_col1\" class=\"data row3 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_e7a34_row4_col0\" class=\"data row4 col0\" >Numeric Features</td>\n",
       "      <td id=\"T_e7a34_row4_col1\" class=\"data row4 col1\" >100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_e7a34_row5_col0\" class=\"data row5 col0\" >Categorical Features</td>\n",
       "      <td id=\"T_e7a34_row5_col1\" class=\"data row5 col1\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_e7a34_row6_col0\" class=\"data row6 col0\" >Ordinal Features</td>\n",
       "      <td id=\"T_e7a34_row6_col1\" class=\"data row6 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_e7a34_row7_col0\" class=\"data row7 col0\" >High Cardinality Features</td>\n",
       "      <td id=\"T_e7a34_row7_col1\" class=\"data row7 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_e7a34_row8_col0\" class=\"data row8 col0\" >High Cardinality Method</td>\n",
       "      <td id=\"T_e7a34_row8_col1\" class=\"data row8 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_e7a34_row9_col0\" class=\"data row9 col0\" >Transformed Train Set</td>\n",
       "      <td id=\"T_e7a34_row9_col1\" class=\"data row9 col1\" >(4934, 100)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_e7a34_row10_col0\" class=\"data row10 col0\" >Transformed Test Set</td>\n",
       "      <td id=\"T_e7a34_row10_col1\" class=\"data row10 col1\" >(2116, 100)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_e7a34_row11_col0\" class=\"data row11 col0\" >Shuffle Train-Test</td>\n",
       "      <td id=\"T_e7a34_row11_col1\" class=\"data row11 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_e7a34_row12_col0\" class=\"data row12 col0\" >Stratify Train-Test</td>\n",
       "      <td id=\"T_e7a34_row12_col1\" class=\"data row12 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_e7a34_row13_col0\" class=\"data row13 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_e7a34_row13_col1\" class=\"data row13 col1\" >KFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_e7a34_row14_col0\" class=\"data row14 col0\" >Fold Number</td>\n",
       "      <td id=\"T_e7a34_row14_col1\" class=\"data row14 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_e7a34_row15_col0\" class=\"data row15 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_e7a34_row15_col1\" class=\"data row15 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_e7a34_row16_col0\" class=\"data row16 col0\" >Use GPU</td>\n",
       "      <td id=\"T_e7a34_row16_col1\" class=\"data row16 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_e7a34_row17_col0\" class=\"data row17 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_e7a34_row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_e7a34_row18_col0\" class=\"data row18 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_e7a34_row18_col1\" class=\"data row18 col1\" >reg-default-name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_e7a34_row19_col0\" class=\"data row19 col0\" >USI</td>\n",
       "      <td id=\"T_e7a34_row19_col1\" class=\"data row19 col1\" >5746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_e7a34_row20_col0\" class=\"data row20 col0\" >Imputation Type</td>\n",
       "      <td id=\"T_e7a34_row20_col1\" class=\"data row20 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_e7a34_row21_col0\" class=\"data row21 col0\" >Iterative Imputation Iteration</td>\n",
       "      <td id=\"T_e7a34_row21_col1\" class=\"data row21 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "      <td id=\"T_e7a34_row22_col0\" class=\"data row22 col0\" >Numeric Imputer</td>\n",
       "      <td id=\"T_e7a34_row22_col1\" class=\"data row22 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "      <td id=\"T_e7a34_row23_col0\" class=\"data row23 col0\" >Iterative Imputation Numeric Model</td>\n",
       "      <td id=\"T_e7a34_row23_col1\" class=\"data row23 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "      <td id=\"T_e7a34_row24_col0\" class=\"data row24 col0\" >Categorical Imputer</td>\n",
       "      <td id=\"T_e7a34_row24_col1\" class=\"data row24 col1\" >constant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "      <td id=\"T_e7a34_row25_col0\" class=\"data row25 col0\" >Iterative Imputation Categorical Model</td>\n",
       "      <td id=\"T_e7a34_row25_col1\" class=\"data row25 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "      <td id=\"T_e7a34_row26_col0\" class=\"data row26 col0\" >Unknown Categoricals Handling</td>\n",
       "      <td id=\"T_e7a34_row26_col1\" class=\"data row26 col1\" >least_frequent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "      <td id=\"T_e7a34_row27_col0\" class=\"data row27 col0\" >Normalize</td>\n",
       "      <td id=\"T_e7a34_row27_col1\" class=\"data row27 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "      <td id=\"T_e7a34_row28_col0\" class=\"data row28 col0\" >Normalize Method</td>\n",
       "      <td id=\"T_e7a34_row28_col1\" class=\"data row28 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "      <td id=\"T_e7a34_row29_col0\" class=\"data row29 col0\" >Transformation</td>\n",
       "      <td id=\"T_e7a34_row29_col1\" class=\"data row29 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "      <td id=\"T_e7a34_row30_col0\" class=\"data row30 col0\" >Transformation Method</td>\n",
       "      <td id=\"T_e7a34_row30_col1\" class=\"data row30 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "      <td id=\"T_e7a34_row31_col0\" class=\"data row31 col0\" >PCA</td>\n",
       "      <td id=\"T_e7a34_row31_col1\" class=\"data row31 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "      <td id=\"T_e7a34_row32_col0\" class=\"data row32 col0\" >PCA Method</td>\n",
       "      <td id=\"T_e7a34_row32_col1\" class=\"data row32 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "      <td id=\"T_e7a34_row33_col0\" class=\"data row33 col0\" >PCA Components</td>\n",
       "      <td id=\"T_e7a34_row33_col1\" class=\"data row33 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "      <td id=\"T_e7a34_row34_col0\" class=\"data row34 col0\" >Ignore Low Variance</td>\n",
       "      <td id=\"T_e7a34_row34_col1\" class=\"data row34 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "      <td id=\"T_e7a34_row35_col0\" class=\"data row35 col0\" >Combine Rare Levels</td>\n",
       "      <td id=\"T_e7a34_row35_col1\" class=\"data row35 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "      <td id=\"T_e7a34_row36_col0\" class=\"data row36 col0\" >Rare Level Threshold</td>\n",
       "      <td id=\"T_e7a34_row36_col1\" class=\"data row36 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "      <td id=\"T_e7a34_row37_col0\" class=\"data row37 col0\" >Numeric Binning</td>\n",
       "      <td id=\"T_e7a34_row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "      <td id=\"T_e7a34_row38_col0\" class=\"data row38 col0\" >Remove Outliers</td>\n",
       "      <td id=\"T_e7a34_row38_col1\" class=\"data row38 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "      <td id=\"T_e7a34_row39_col0\" class=\"data row39 col0\" >Outliers Threshold</td>\n",
       "      <td id=\"T_e7a34_row39_col1\" class=\"data row39 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "      <td id=\"T_e7a34_row40_col0\" class=\"data row40 col0\" >Remove Multicollinearity</td>\n",
       "      <td id=\"T_e7a34_row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "      <td id=\"T_e7a34_row41_col0\" class=\"data row41 col0\" >Multicollinearity Threshold</td>\n",
       "      <td id=\"T_e7a34_row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "      <td id=\"T_e7a34_row42_col0\" class=\"data row42 col0\" >Remove Perfect Collinearity</td>\n",
       "      <td id=\"T_e7a34_row42_col1\" class=\"data row42 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "      <td id=\"T_e7a34_row43_col0\" class=\"data row43 col0\" >Clustering</td>\n",
       "      <td id=\"T_e7a34_row43_col1\" class=\"data row43 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "      <td id=\"T_e7a34_row44_col0\" class=\"data row44 col0\" >Clustering Iteration</td>\n",
       "      <td id=\"T_e7a34_row44_col1\" class=\"data row44 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "      <td id=\"T_e7a34_row45_col0\" class=\"data row45 col0\" >Polynomial Features</td>\n",
       "      <td id=\"T_e7a34_row45_col1\" class=\"data row45 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "      <td id=\"T_e7a34_row46_col0\" class=\"data row46 col0\" >Polynomial Degree</td>\n",
       "      <td id=\"T_e7a34_row46_col1\" class=\"data row46 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "      <td id=\"T_e7a34_row47_col0\" class=\"data row47 col0\" >Trignometry Features</td>\n",
       "      <td id=\"T_e7a34_row47_col1\" class=\"data row47 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "      <td id=\"T_e7a34_row48_col0\" class=\"data row48 col0\" >Polynomial Threshold</td>\n",
       "      <td id=\"T_e7a34_row48_col1\" class=\"data row48 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "      <td id=\"T_e7a34_row49_col0\" class=\"data row49 col0\" >Group Features</td>\n",
       "      <td id=\"T_e7a34_row49_col1\" class=\"data row49 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "      <td id=\"T_e7a34_row50_col0\" class=\"data row50 col0\" >Feature Selection</td>\n",
       "      <td id=\"T_e7a34_row50_col1\" class=\"data row50 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "      <td id=\"T_e7a34_row51_col0\" class=\"data row51 col0\" >Feature Selection Method</td>\n",
       "      <td id=\"T_e7a34_row51_col1\" class=\"data row51 col1\" >classic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "      <td id=\"T_e7a34_row52_col0\" class=\"data row52 col0\" >Features Selection Threshold</td>\n",
       "      <td id=\"T_e7a34_row52_col1\" class=\"data row52 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "      <td id=\"T_e7a34_row53_col0\" class=\"data row53 col0\" >Feature Interaction</td>\n",
       "      <td id=\"T_e7a34_row53_col1\" class=\"data row53 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "      <td id=\"T_e7a34_row54_col0\" class=\"data row54 col0\" >Feature Ratio</td>\n",
       "      <td id=\"T_e7a34_row54_col1\" class=\"data row54 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "      <td id=\"T_e7a34_row55_col0\" class=\"data row55 col0\" >Interaction Threshold</td>\n",
       "      <td id=\"T_e7a34_row55_col1\" class=\"data row55 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
       "      <td id=\"T_e7a34_row56_col0\" class=\"data row56 col0\" >Transform Target</td>\n",
       "      <td id=\"T_e7a34_row56_col1\" class=\"data row56 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a34_level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
       "      <td id=\"T_e7a34_row57_col0\" class=\"data row57 col0\" >Transform Target Method</td>\n",
       "      <td id=\"T_e7a34_row57_col1\" class=\"data row57 col1\" >box-cox</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f0fd46c5b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.regression import *\n",
    "s = setup(prediction, target = 'to_predict')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "22ad88a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_e80a5 th {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_e80a5_row0_col0, #T_e80a5_row0_col5, #T_e80a5_row0_col6, #T_e80a5_row1_col0, #T_e80a5_row1_col1, #T_e80a5_row1_col2, #T_e80a5_row1_col3, #T_e80a5_row1_col4, #T_e80a5_row1_col5, #T_e80a5_row1_col6, #T_e80a5_row2_col0, #T_e80a5_row2_col1, #T_e80a5_row2_col2, #T_e80a5_row2_col3, #T_e80a5_row2_col4, #T_e80a5_row2_col5, #T_e80a5_row2_col6, #T_e80a5_row3_col0, #T_e80a5_row3_col1, #T_e80a5_row3_col2, #T_e80a5_row3_col3, #T_e80a5_row3_col4, #T_e80a5_row3_col5, #T_e80a5_row3_col6, #T_e80a5_row4_col0, #T_e80a5_row4_col1, #T_e80a5_row4_col2, #T_e80a5_row4_col3, #T_e80a5_row4_col4, #T_e80a5_row5_col0, #T_e80a5_row5_col1, #T_e80a5_row5_col2, #T_e80a5_row5_col3, #T_e80a5_row5_col4, #T_e80a5_row5_col5, #T_e80a5_row5_col6, #T_e80a5_row6_col0, #T_e80a5_row6_col1, #T_e80a5_row6_col2, #T_e80a5_row6_col3, #T_e80a5_row6_col4, #T_e80a5_row6_col5, #T_e80a5_row6_col6, #T_e80a5_row7_col0, #T_e80a5_row7_col1, #T_e80a5_row7_col2, #T_e80a5_row7_col3, #T_e80a5_row7_col4, #T_e80a5_row7_col5, #T_e80a5_row7_col6, #T_e80a5_row8_col0, #T_e80a5_row8_col1, #T_e80a5_row8_col2, #T_e80a5_row8_col3, #T_e80a5_row8_col4, #T_e80a5_row8_col5, #T_e80a5_row8_col6, #T_e80a5_row9_col0, #T_e80a5_row9_col1, #T_e80a5_row9_col2, #T_e80a5_row9_col3, #T_e80a5_row9_col4, #T_e80a5_row9_col5, #T_e80a5_row9_col6, #T_e80a5_row10_col0, #T_e80a5_row10_col1, #T_e80a5_row10_col2, #T_e80a5_row10_col3, #T_e80a5_row10_col4, #T_e80a5_row10_col5, #T_e80a5_row10_col6, #T_e80a5_row11_col0, #T_e80a5_row11_col1, #T_e80a5_row11_col2, #T_e80a5_row11_col3, #T_e80a5_row11_col4, #T_e80a5_row11_col5, #T_e80a5_row11_col6, #T_e80a5_row12_col0, #T_e80a5_row12_col1, #T_e80a5_row12_col2, #T_e80a5_row12_col3, #T_e80a5_row12_col4, #T_e80a5_row12_col5, #T_e80a5_row12_col6, #T_e80a5_row13_col0, #T_e80a5_row13_col1, #T_e80a5_row13_col2, #T_e80a5_row13_col3, #T_e80a5_row13_col4, #T_e80a5_row13_col5, #T_e80a5_row13_col6, #T_e80a5_row14_col0, #T_e80a5_row14_col1, #T_e80a5_row14_col2, #T_e80a5_row14_col3, #T_e80a5_row14_col4, #T_e80a5_row14_col5, #T_e80a5_row14_col6, #T_e80a5_row15_col0, #T_e80a5_row15_col1, #T_e80a5_row15_col2, #T_e80a5_row15_col3, #T_e80a5_row15_col4, #T_e80a5_row15_col5, #T_e80a5_row15_col6, #T_e80a5_row16_col0, #T_e80a5_row16_col1, #T_e80a5_row16_col2, #T_e80a5_row16_col3, #T_e80a5_row16_col4, #T_e80a5_row16_col5, #T_e80a5_row16_col6, #T_e80a5_row17_col0, #T_e80a5_row17_col1, #T_e80a5_row17_col2, #T_e80a5_row17_col3, #T_e80a5_row17_col4, #T_e80a5_row17_col5, #T_e80a5_row17_col6 {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_e80a5_row0_col1, #T_e80a5_row0_col2, #T_e80a5_row0_col3, #T_e80a5_row0_col4, #T_e80a5_row4_col5, #T_e80a5_row4_col6 {\n",
       "  text-align: left;\n",
       "  background-color: yellow;\n",
       "}\n",
       "#T_e80a5_row0_col7, #T_e80a5_row1_col7, #T_e80a5_row2_col7, #T_e80a5_row3_col7, #T_e80a5_row5_col7, #T_e80a5_row6_col7, #T_e80a5_row7_col7, #T_e80a5_row8_col7, #T_e80a5_row9_col7, #T_e80a5_row10_col7, #T_e80a5_row11_col7, #T_e80a5_row12_col7, #T_e80a5_row13_col7, #T_e80a5_row14_col7, #T_e80a5_row15_col7, #T_e80a5_row16_col7, #T_e80a5_row17_col7 {\n",
       "  text-align: left;\n",
       "  background-color: lightgrey;\n",
       "}\n",
       "#T_e80a5_row4_col7 {\n",
       "  text-align: left;\n",
       "  background-color: yellow;\n",
       "  background-color: lightgrey;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_e80a5\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e80a5_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_e80a5_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_e80a5_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_e80a5_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_e80a5_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_e80a5_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_e80a5_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "      <th id=\"T_e80a5_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e80a5_level0_row0\" class=\"row_heading level0 row0\" >et</th>\n",
       "      <td id=\"T_e80a5_row0_col0\" class=\"data row0 col0\" >Extra Trees Regressor</td>\n",
       "      <td id=\"T_e80a5_row0_col1\" class=\"data row0 col1\" >10.9024</td>\n",
       "      <td id=\"T_e80a5_row0_col2\" class=\"data row0 col2\" >359.8850</td>\n",
       "      <td id=\"T_e80a5_row0_col3\" class=\"data row0 col3\" >18.6874</td>\n",
       "      <td id=\"T_e80a5_row0_col4\" class=\"data row0 col4\" >0.9637</td>\n",
       "      <td id=\"T_e80a5_row0_col5\" class=\"data row0 col5\" >0.2114</td>\n",
       "      <td id=\"T_e80a5_row0_col6\" class=\"data row0 col6\" >0.1136</td>\n",
       "      <td id=\"T_e80a5_row0_col7\" class=\"data row0 col7\" >0.5750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e80a5_level0_row1\" class=\"row_heading level0 row1\" >lightgbm</th>\n",
       "      <td id=\"T_e80a5_row1_col0\" class=\"data row1 col0\" >Light Gradient Boosting Machine</td>\n",
       "      <td id=\"T_e80a5_row1_col1\" class=\"data row1 col1\" >11.2236</td>\n",
       "      <td id=\"T_e80a5_row1_col2\" class=\"data row1 col2\" >380.2312</td>\n",
       "      <td id=\"T_e80a5_row1_col3\" class=\"data row1 col3\" >19.1702</td>\n",
       "      <td id=\"T_e80a5_row1_col4\" class=\"data row1 col4\" >0.9617</td>\n",
       "      <td id=\"T_e80a5_row1_col5\" class=\"data row1 col5\" >0.2197</td>\n",
       "      <td id=\"T_e80a5_row1_col6\" class=\"data row1 col6\" >0.1229</td>\n",
       "      <td id=\"T_e80a5_row1_col7\" class=\"data row1 col7\" >0.2870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e80a5_level0_row2\" class=\"row_heading level0 row2\" >gbr</th>\n",
       "      <td id=\"T_e80a5_row2_col0\" class=\"data row2 col0\" >Gradient Boosting Regressor</td>\n",
       "      <td id=\"T_e80a5_row2_col1\" class=\"data row2 col1\" >11.5043</td>\n",
       "      <td id=\"T_e80a5_row2_col2\" class=\"data row2 col2\" >385.5695</td>\n",
       "      <td id=\"T_e80a5_row2_col3\" class=\"data row2 col3\" >19.3421</td>\n",
       "      <td id=\"T_e80a5_row2_col4\" class=\"data row2 col4\" >0.9611</td>\n",
       "      <td id=\"T_e80a5_row2_col5\" class=\"data row2 col5\" >0.2217</td>\n",
       "      <td id=\"T_e80a5_row2_col6\" class=\"data row2 col6\" >0.1251</td>\n",
       "      <td id=\"T_e80a5_row2_col7\" class=\"data row2 col7\" >0.8260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e80a5_level0_row3\" class=\"row_heading level0 row3\" >rf</th>\n",
       "      <td id=\"T_e80a5_row3_col0\" class=\"data row3 col0\" >Random Forest Regressor</td>\n",
       "      <td id=\"T_e80a5_row3_col1\" class=\"data row3 col1\" >11.2502</td>\n",
       "      <td id=\"T_e80a5_row3_col2\" class=\"data row3 col2\" >391.9751</td>\n",
       "      <td id=\"T_e80a5_row3_col3\" class=\"data row3 col3\" >19.4655</td>\n",
       "      <td id=\"T_e80a5_row3_col4\" class=\"data row3 col4\" >0.9606</td>\n",
       "      <td id=\"T_e80a5_row3_col5\" class=\"data row3 col5\" >0.2193</td>\n",
       "      <td id=\"T_e80a5_row3_col6\" class=\"data row3 col6\" >0.1155</td>\n",
       "      <td id=\"T_e80a5_row3_col7\" class=\"data row3 col7\" >1.7750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e80a5_level0_row4\" class=\"row_heading level0 row4\" >omp</th>\n",
       "      <td id=\"T_e80a5_row4_col0\" class=\"data row4 col0\" >Orthogonal Matching Pursuit</td>\n",
       "      <td id=\"T_e80a5_row4_col1\" class=\"data row4 col1\" >11.0242</td>\n",
       "      <td id=\"T_e80a5_row4_col2\" class=\"data row4 col2\" >402.3896</td>\n",
       "      <td id=\"T_e80a5_row4_col3\" class=\"data row4 col3\" >19.6306</td>\n",
       "      <td id=\"T_e80a5_row4_col4\" class=\"data row4 col4\" >0.9596</td>\n",
       "      <td id=\"T_e80a5_row4_col5\" class=\"data row4 col5\" >0.2079</td>\n",
       "      <td id=\"T_e80a5_row4_col6\" class=\"data row4 col6\" >0.1127</td>\n",
       "      <td id=\"T_e80a5_row4_col7\" class=\"data row4 col7\" >0.0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e80a5_level0_row5\" class=\"row_heading level0 row5\" >lasso</th>\n",
       "      <td id=\"T_e80a5_row5_col0\" class=\"data row5 col0\" >Lasso Regression</td>\n",
       "      <td id=\"T_e80a5_row5_col1\" class=\"data row5 col1\" >10.9624</td>\n",
       "      <td id=\"T_e80a5_row5_col2\" class=\"data row5 col2\" >405.4015</td>\n",
       "      <td id=\"T_e80a5_row5_col3\" class=\"data row5 col3\" >19.6412</td>\n",
       "      <td id=\"T_e80a5_row5_col4\" class=\"data row5 col4\" >0.9594</td>\n",
       "      <td id=\"T_e80a5_row5_col5\" class=\"data row5 col5\" >0.2106</td>\n",
       "      <td id=\"T_e80a5_row5_col6\" class=\"data row5 col6\" >0.1143</td>\n",
       "      <td id=\"T_e80a5_row5_col7\" class=\"data row5 col7\" >0.0400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e80a5_level0_row6\" class=\"row_heading level0 row6\" >en</th>\n",
       "      <td id=\"T_e80a5_row6_col0\" class=\"data row6 col0\" >Elastic Net</td>\n",
       "      <td id=\"T_e80a5_row6_col1\" class=\"data row6 col1\" >11.0163</td>\n",
       "      <td id=\"T_e80a5_row6_col2\" class=\"data row6 col2\" >406.8730</td>\n",
       "      <td id=\"T_e80a5_row6_col3\" class=\"data row6 col3\" >19.6792</td>\n",
       "      <td id=\"T_e80a5_row6_col4\" class=\"data row6 col4\" >0.9592</td>\n",
       "      <td id=\"T_e80a5_row6_col5\" class=\"data row6 col5\" >0.2148</td>\n",
       "      <td id=\"T_e80a5_row6_col6\" class=\"data row6 col6\" >0.1148</td>\n",
       "      <td id=\"T_e80a5_row6_col7\" class=\"data row6 col7\" >0.0430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e80a5_level0_row7\" class=\"row_heading level0 row7\" >br</th>\n",
       "      <td id=\"T_e80a5_row7_col0\" class=\"data row7 col0\" >Bayesian Ridge</td>\n",
       "      <td id=\"T_e80a5_row7_col1\" class=\"data row7 col1\" >11.0978</td>\n",
       "      <td id=\"T_e80a5_row7_col2\" class=\"data row7 col2\" >408.1886</td>\n",
       "      <td id=\"T_e80a5_row7_col3\" class=\"data row7 col3\" >19.7254</td>\n",
       "      <td id=\"T_e80a5_row7_col4\" class=\"data row7 col4\" >0.9591</td>\n",
       "      <td id=\"T_e80a5_row7_col5\" class=\"data row7 col5\" >0.2180</td>\n",
       "      <td id=\"T_e80a5_row7_col6\" class=\"data row7 col6\" >0.1155</td>\n",
       "      <td id=\"T_e80a5_row7_col7\" class=\"data row7 col7\" >0.0410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e80a5_level0_row8\" class=\"row_heading level0 row8\" >ridge</th>\n",
       "      <td id=\"T_e80a5_row8_col0\" class=\"data row8 col0\" >Ridge Regression</td>\n",
       "      <td id=\"T_e80a5_row8_col1\" class=\"data row8 col1\" >11.0822</td>\n",
       "      <td id=\"T_e80a5_row8_col2\" class=\"data row8 col2\" >409.3909</td>\n",
       "      <td id=\"T_e80a5_row8_col3\" class=\"data row8 col3\" >19.7408</td>\n",
       "      <td id=\"T_e80a5_row8_col4\" class=\"data row8 col4\" >0.9590</td>\n",
       "      <td id=\"T_e80a5_row8_col5\" class=\"data row8 col5\" >0.2134</td>\n",
       "      <td id=\"T_e80a5_row8_col6\" class=\"data row8 col6\" >0.1155</td>\n",
       "      <td id=\"T_e80a5_row8_col7\" class=\"data row8 col7\" >0.0150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e80a5_level0_row9\" class=\"row_heading level0 row9\" >lr</th>\n",
       "      <td id=\"T_e80a5_row9_col0\" class=\"data row9 col0\" >Linear Regression</td>\n",
       "      <td id=\"T_e80a5_row9_col1\" class=\"data row9 col1\" >11.0822</td>\n",
       "      <td id=\"T_e80a5_row9_col2\" class=\"data row9 col2\" >409.3908</td>\n",
       "      <td id=\"T_e80a5_row9_col3\" class=\"data row9 col3\" >19.7408</td>\n",
       "      <td id=\"T_e80a5_row9_col4\" class=\"data row9 col4\" >0.9590</td>\n",
       "      <td id=\"T_e80a5_row9_col5\" class=\"data row9 col5\" >0.2134</td>\n",
       "      <td id=\"T_e80a5_row9_col6\" class=\"data row9 col6\" >0.1155</td>\n",
       "      <td id=\"T_e80a5_row9_col7\" class=\"data row9 col7\" >0.0190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e80a5_level0_row10\" class=\"row_heading level0 row10\" >huber</th>\n",
       "      <td id=\"T_e80a5_row10_col0\" class=\"data row10 col0\" >Huber Regressor</td>\n",
       "      <td id=\"T_e80a5_row10_col1\" class=\"data row10 col1\" >11.3566</td>\n",
       "      <td id=\"T_e80a5_row10_col2\" class=\"data row10 col2\" >418.1571</td>\n",
       "      <td id=\"T_e80a5_row10_col3\" class=\"data row10 col3\" >20.0562</td>\n",
       "      <td id=\"T_e80a5_row10_col4\" class=\"data row10 col4\" >0.9580</td>\n",
       "      <td id=\"T_e80a5_row10_col5\" class=\"data row10 col5\" >0.2194</td>\n",
       "      <td id=\"T_e80a5_row10_col6\" class=\"data row10 col6\" >0.1130</td>\n",
       "      <td id=\"T_e80a5_row10_col7\" class=\"data row10 col7\" >0.2140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e80a5_level0_row11\" class=\"row_heading level0 row11\" >par</th>\n",
       "      <td id=\"T_e80a5_row11_col0\" class=\"data row11 col0\" >Passive Aggressive Regressor</td>\n",
       "      <td id=\"T_e80a5_row11_col1\" class=\"data row11 col1\" >17.7845</td>\n",
       "      <td id=\"T_e80a5_row11_col2\" class=\"data row11 col2\" >721.8514</td>\n",
       "      <td id=\"T_e80a5_row11_col3\" class=\"data row11 col3\" >26.4435</td>\n",
       "      <td id=\"T_e80a5_row11_col4\" class=\"data row11 col4\" >0.9258</td>\n",
       "      <td id=\"T_e80a5_row11_col5\" class=\"data row11 col5\" >0.2766</td>\n",
       "      <td id=\"T_e80a5_row11_col6\" class=\"data row11 col6\" >0.1920</td>\n",
       "      <td id=\"T_e80a5_row11_col7\" class=\"data row11 col7\" >0.0210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e80a5_level0_row12\" class=\"row_heading level0 row12\" >dt</th>\n",
       "      <td id=\"T_e80a5_row12_col0\" class=\"data row12 col0\" >Decision Tree Regressor</td>\n",
       "      <td id=\"T_e80a5_row12_col1\" class=\"data row12 col1\" >16.1705</td>\n",
       "      <td id=\"T_e80a5_row12_col2\" class=\"data row12 col2\" >766.5394</td>\n",
       "      <td id=\"T_e80a5_row12_col3\" class=\"data row12 col3\" >27.5037</td>\n",
       "      <td id=\"T_e80a5_row12_col4\" class=\"data row12 col4\" >0.9223</td>\n",
       "      <td id=\"T_e80a5_row12_col5\" class=\"data row12 col5\" >0.3166</td>\n",
       "      <td id=\"T_e80a5_row12_col6\" class=\"data row12 col6\" >0.1608</td>\n",
       "      <td id=\"T_e80a5_row12_col7\" class=\"data row12 col7\" >0.0570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e80a5_level0_row13\" class=\"row_heading level0 row13\" >ada</th>\n",
       "      <td id=\"T_e80a5_row13_col0\" class=\"data row13 col0\" >AdaBoost Regressor</td>\n",
       "      <td id=\"T_e80a5_row13_col1\" class=\"data row13 col1\" >21.9049</td>\n",
       "      <td id=\"T_e80a5_row13_col2\" class=\"data row13 col2\" >863.8214</td>\n",
       "      <td id=\"T_e80a5_row13_col3\" class=\"data row13 col3\" >29.3179</td>\n",
       "      <td id=\"T_e80a5_row13_col4\" class=\"data row13 col4\" >0.9119</td>\n",
       "      <td id=\"T_e80a5_row13_col5\" class=\"data row13 col5\" >0.3684</td>\n",
       "      <td id=\"T_e80a5_row13_col6\" class=\"data row13 col6\" >0.3793</td>\n",
       "      <td id=\"T_e80a5_row13_col7\" class=\"data row13 col7\" >0.3220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e80a5_level0_row14\" class=\"row_heading level0 row14\" >knn</th>\n",
       "      <td id=\"T_e80a5_row14_col0\" class=\"data row14 col0\" >K Neighbors Regressor</td>\n",
       "      <td id=\"T_e80a5_row14_col1\" class=\"data row14 col1\" >19.0701</td>\n",
       "      <td id=\"T_e80a5_row14_col2\" class=\"data row14 col2\" >966.4656</td>\n",
       "      <td id=\"T_e80a5_row14_col3\" class=\"data row14 col3\" >30.9274</td>\n",
       "      <td id=\"T_e80a5_row14_col4\" class=\"data row14 col4\" >0.9021</td>\n",
       "      <td id=\"T_e80a5_row14_col5\" class=\"data row14 col5\" >0.2767</td>\n",
       "      <td id=\"T_e80a5_row14_col6\" class=\"data row14 col6\" >0.1956</td>\n",
       "      <td id=\"T_e80a5_row14_col7\" class=\"data row14 col7\" >0.0470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e80a5_level0_row15\" class=\"row_heading level0 row15\" >lar</th>\n",
       "      <td id=\"T_e80a5_row15_col0\" class=\"data row15 col0\" >Least Angle Regression</td>\n",
       "      <td id=\"T_e80a5_row15_col1\" class=\"data row15 col1\" >16.4952</td>\n",
       "      <td id=\"T_e80a5_row15_col2\" class=\"data row15 col2\" >963.6368</td>\n",
       "      <td id=\"T_e80a5_row15_col3\" class=\"data row15 col3\" >28.1403</td>\n",
       "      <td id=\"T_e80a5_row15_col4\" class=\"data row15 col4\" >0.8982</td>\n",
       "      <td id=\"T_e80a5_row15_col5\" class=\"data row15 col5\" >0.2903</td>\n",
       "      <td id=\"T_e80a5_row15_col6\" class=\"data row15 col6\" >0.1763</td>\n",
       "      <td id=\"T_e80a5_row15_col7\" class=\"data row15 col7\" >0.0190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e80a5_level0_row16\" class=\"row_heading level0 row16\" >llar</th>\n",
       "      <td id=\"T_e80a5_row16_col0\" class=\"data row16 col0\" >Lasso Least Angle Regression</td>\n",
       "      <td id=\"T_e80a5_row16_col1\" class=\"data row16 col1\" >48.3717</td>\n",
       "      <td id=\"T_e80a5_row16_col2\" class=\"data row16 col2\" >4878.4299</td>\n",
       "      <td id=\"T_e80a5_row16_col3\" class=\"data row16 col3\" >69.6823</td>\n",
       "      <td id=\"T_e80a5_row16_col4\" class=\"data row16 col4\" >0.5037</td>\n",
       "      <td id=\"T_e80a5_row16_col5\" class=\"data row16 col5\" >0.5658</td>\n",
       "      <td id=\"T_e80a5_row16_col6\" class=\"data row16 col6\" >0.7968</td>\n",
       "      <td id=\"T_e80a5_row16_col7\" class=\"data row16 col7\" >0.0160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e80a5_level0_row17\" class=\"row_heading level0 row17\" >dummy</th>\n",
       "      <td id=\"T_e80a5_row17_col0\" class=\"data row17 col0\" >Dummy Regressor</td>\n",
       "      <td id=\"T_e80a5_row17_col1\" class=\"data row17 col1\" >68.7681</td>\n",
       "      <td id=\"T_e80a5_row17_col2\" class=\"data row17 col2\" >9819.5150</td>\n",
       "      <td id=\"T_e80a5_row17_col3\" class=\"data row17 col3\" >98.9323</td>\n",
       "      <td id=\"T_e80a5_row17_col4\" class=\"data row17 col4\" >-0.0015</td>\n",
       "      <td id=\"T_e80a5_row17_col5\" class=\"data row17 col5\" >0.7080</td>\n",
       "      <td id=\"T_e80a5_row17_col6\" class=\"data row17 col6\" >1.1411</td>\n",
       "      <td id=\"T_e80a5_row17_col7\" class=\"data row17 col7\" >0.0140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f102df3bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best = compare_models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7d241c5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "855631dea0314afc94d1487872201f38",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "324c6702",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_model(best, plot = 'residuals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3aa1efdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_17d9a_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_17d9a_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_17d9a_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_17d9a_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_17d9a_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_17d9a_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
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       "      <th id=\"T_17d9a_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_17d9a_row0_col0\" class=\"data row0 col0\" >Extra Trees Regressor</td>\n",
       "      <td id=\"T_17d9a_row0_col1\" class=\"data row0 col1\" >10.9768</td>\n",
       "      <td id=\"T_17d9a_row0_col2\" class=\"data row0 col2\" >414.1851</td>\n",
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       "<pandas.io.formats.style.Styler at 0x7f102df70af0>"
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       "      <th></th>\n",
       "      <th>day0</th>\n",
       "      <th>day1</th>\n",
       "      <th>day2</th>\n",
       "      <th>day3</th>\n",
       "      <th>day4</th>\n",
       "      <th>day5</th>\n",
       "      <th>day6</th>\n",
       "      <th>day7</th>\n",
       "      <th>day8</th>\n",
       "      <th>day9</th>\n",
       "      <th>...</th>\n",
       "      <th>day92</th>\n",
       "      <th>day93</th>\n",
       "      <th>day94</th>\n",
       "      <th>day95</th>\n",
       "      <th>day96</th>\n",
       "      <th>day97</th>\n",
       "      <th>day98</th>\n",
       "      <th>day99</th>\n",
       "      <th>to_predict</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>145.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>...</td>\n",
       "      <td>136.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>134</td>\n",
       "      <td>145.21</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>164.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>...</td>\n",
       "      <td>167.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>186</td>\n",
       "      <td>174.55</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>314.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>...</td>\n",
       "      <td>463.0</td>\n",
       "      <td>534.0</td>\n",
       "      <td>575.0</td>\n",
       "      <td>597.0</td>\n",
       "      <td>622.0</td>\n",
       "      <td>644.0</td>\n",
       "      <td>661.0</td>\n",
       "      <td>677.0</td>\n",
       "      <td>693</td>\n",
       "      <td>694.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>184.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>...</td>\n",
       "      <td>131.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>120</td>\n",
       "      <td>120.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>163.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>...</td>\n",
       "      <td>279.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>466</td>\n",
       "      <td>434.27</td>\n",
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       "    <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",
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       "      <th>2111</th>\n",
       "      <td>131.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>...</td>\n",
       "      <td>192.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>234</td>\n",
       "      <td>245.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2112</th>\n",
       "      <td>158.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>...</td>\n",
       "      <td>174.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>150</td>\n",
       "      <td>120.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2113</th>\n",
       "      <td>134.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>...</td>\n",
       "      <td>96.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>119</td>\n",
       "      <td>136.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2114</th>\n",
       "      <td>154.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>...</td>\n",
       "      <td>258.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>141</td>\n",
       "      <td>147.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2115</th>\n",
       "      <td>200.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>...</td>\n",
       "      <td>127.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>140</td>\n",
       "      <td>144.17</td>\n",
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       "<p>2116 rows × 102 columns</p>\n",
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       "       day0   day1   day2   day3   day4   day5   day6   day7   day8   day9  \\\n",
       "0     145.0  141.0  141.0  142.0  136.0  139.0  141.0  160.0  163.0  156.0   \n",
       "1     164.0  168.0  164.0  160.0  152.0  150.0  176.0  194.0  204.0  219.0   \n",
       "2     202.0  212.0  253.0  275.0  245.0  246.0  267.0  306.0  314.0  295.0   \n",
       "3     184.0  189.0  196.0  185.0  179.0  160.0  158.0  146.0  139.0  132.0   \n",
       "4     163.0  176.0  241.0  263.0  231.0  171.0  161.0  168.0  168.0  215.0   \n",
       "...     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "2111  131.0  136.0  146.0  144.0  144.0  144.0  149.0  154.0  157.0  159.0   \n",
       "2112  158.0  152.0  143.0  137.0  132.0  135.0  146.0  141.0  140.0  138.0   \n",
       "2113  134.0  135.0  135.0  136.0  136.0  132.0  134.0  136.0  140.0  143.0   \n",
       "2114  154.0  157.0  159.0  161.0  159.0  155.0  157.0  161.0  165.0  165.0   \n",
       "2115  200.0  223.0  232.0  224.0  209.0  193.0  185.0  174.0  157.0  147.0   \n",
       "\n",
       "      ...  day92  day93  day94  day95  day96  day97  day98  day99  to_predict  \\\n",
       "0     ...  136.0  121.0  121.0  137.0  127.0  119.0  123.0  140.0         134   \n",
       "1     ...  167.0  174.0  188.0  181.0  178.0  174.0  173.0  175.0         186   \n",
       "2     ...  463.0  534.0  575.0  597.0  622.0  644.0  661.0  677.0         693   \n",
       "3     ...  131.0  127.0  127.0  120.0  104.0   99.0  114.0  118.0         120   \n",
       "4     ...  279.0  264.0  287.0  387.0  387.0  402.0  414.0  444.0         466   \n",
       "...   ...    ...    ...    ...    ...    ...    ...    ...    ...         ...   \n",
       "2111  ...  192.0  178.0  167.0  165.0  169.0  198.0  247.0  258.0         234   \n",
       "2112  ...  174.0  210.0  196.0  170.0  163.0  155.0  131.0  122.0         150   \n",
       "2113  ...   96.0  107.0   95.0  108.0  123.0  128.0  135.0  134.0         119   \n",
       "2114  ...  258.0  234.0  218.0  203.0  190.0  172.0  158.0  151.0         141   \n",
       "2115  ...  127.0  127.0  130.0  135.0  138.0  140.0  144.0  144.0         140   \n",
       "\n",
       "       Label  \n",
       "0     145.21  \n",
       "1     174.55  \n",
       "2     694.34  \n",
       "3     120.37  \n",
       "4     434.27  \n",
       "...      ...  \n",
       "2111  245.87  \n",
       "2112  120.34  \n",
       "2113  136.61  \n",
       "2114  147.58  \n",
       "2115  144.17  \n",
       "\n",
       "[2116 rows x 102 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_model(best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "485536c6",
   "metadata": {},
   "outputs": [
    {
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c03c0_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_c03c0_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_c03c0_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
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       "      <th id=\"T_c03c0_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
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       "      <td id=\"T_c03c0_row0_col1\" class=\"data row0 col1\" >3.2946</td>\n",
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       "      <th></th>\n",
       "      <th>day0</th>\n",
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       "      <th>day2</th>\n",
       "      <th>day3</th>\n",
       "      <th>day4</th>\n",
       "      <th>day5</th>\n",
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       "      <th>day7</th>\n",
       "      <th>day8</th>\n",
       "      <th>day9</th>\n",
       "      <th>...</th>\n",
       "      <th>day92</th>\n",
       "      <th>day93</th>\n",
       "      <th>day94</th>\n",
       "      <th>day95</th>\n",
       "      <th>day96</th>\n",
       "      <th>day97</th>\n",
       "      <th>day98</th>\n",
       "      <th>day99</th>\n",
       "      <th>to_predict</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32</td>\n",
       "      <td>39</td>\n",
       "      <td>41</td>\n",
       "      <td>47</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
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       "      <td>-18</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>81</td>\n",
       "      <td>82</td>\n",
       "      <td>98</td>\n",
       "      <td>104</td>\n",
       "      <td>89</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>96</td>\n",
       "      <td>98</td>\n",
       "      <td>100.52</td>\n",
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       "      <td>39</td>\n",
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       "      <td>31</td>\n",
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       "      <td>82</td>\n",
       "      <td>98</td>\n",
       "      <td>104</td>\n",
       "      <td>89</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>96</td>\n",
       "      <td>98</td>\n",
       "      <td>102</td>\n",
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       "    <tr>\n",
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       "      <td>47</td>\n",
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       "      <td>28</td>\n",
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       "      <td>98</td>\n",
       "      <td>104</td>\n",
       "      <td>89</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>96</td>\n",
       "      <td>98</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "      <td>100.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>-12</td>\n",
       "      <td>-18</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>-8</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>104</td>\n",
       "      <td>89</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>96</td>\n",
       "      <td>98</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "      <td>84</td>\n",
       "      <td>84.00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>-12</td>\n",
       "      <td>-18</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>-8</td>\n",
       "      <td>32</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>89</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>96</td>\n",
       "      <td>98</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "      <td>84</td>\n",
       "      <td>78</td>\n",
       "      <td>78.00</td>\n",
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       "</table>\n",
       "<p>5 rows × 102 columns</p>\n",
       "</div>"
      ],
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       "   day0  day1  day2  day3  day4  day5  day6  day7  day8  day9  ...  day92  \\\n",
       "0    32    39    41    47    23    28   -12   -18     7    28  ...     81   \n",
       "1    39    41    47    23    28   -12   -18     7    28    31  ...     82   \n",
       "2    41    47    23    28   -12   -18     7    28    31    -8  ...     98   \n",
       "3    47    23    28   -12   -18     7    28    31    -8    32  ...    104   \n",
       "4    23    28   -12   -18     7    28    31    -8    32    45  ...     89   \n",
       "\n",
       "   day93  day94  day95  day96  day97  day98  day99  to_predict   Label  \n",
       "0     82     98    104     89     80     82     96          98  100.52  \n",
       "1     98    104     89     80     82     96     98         102  102.00  \n",
       "2    104     89     80     82     96     98    102         102  100.50  \n",
       "3     89     80     82     96     98    102    102          84   84.00  \n",
       "4     80     82     96     98    102    102     84          78   78.00  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute standard_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_coef_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute average_intercept_ was deprecated in version 0.23 and will be removed in 0.25.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/linear_model/_huber.py:296: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/tree/_classes.py:1262: FutureWarning: the n_classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/radmin/anaconda3/envs/yourenvname/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: The outputs_2d_ attribute is deprecated in version 0.22 and will be removed in version 0.24. It is equivalent to n_outputs_ > 1.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "predictions = predict_model(best, data=prediction)\n",
    "predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "77715dca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Successfully Saved\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Pipeline(memory=None,\n",
       "          steps=[('dtypes',\n",
       "                  DataTypes_Auto_infer(categorical_features=[],\n",
       "                                       display_types=True, features_todrop=[],\n",
       "                                       id_columns=[], ml_usecase='regression',\n",
       "                                       numerical_features=[],\n",
       "                                       target='to_predict', time_features=[])),\n",
       "                 ('imputer',\n",
       "                  Simple_Imputer(categorical_strategy='not_available',\n",
       "                                 fill_value_categorical=None,\n",
       "                                 fill_value_numerical=None,\n",
       "                                 numeric_strat...\n",
       "                  ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0,\n",
       "                                      criterion='mse', max_depth=None,\n",
       "                                      max_features='auto', max_leaf_nodes=None,\n",
       "                                      max_samples=None,\n",
       "                                      min_impurity_decrease=0.0,\n",
       "                                      min_impurity_split=None,\n",
       "                                      min_samples_leaf=1, min_samples_split=2,\n",
       "                                      min_weight_fraction_leaf=0.0,\n",
       "                                      n_estimators=100, n_jobs=-1,\n",
       "                                      oob_score=False, random_state=1744,\n",
       "                                      verbose=0, warm_start=False)]],\n",
       "          verbose=False),\n",
       " 'my_best_pipeline.pkl')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_model(best, 'my_best_pipeline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d559519c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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